The objective of this study is to evaluate the effect of constitutive equations on the prediction accuracy for springback in cold stamping with various deformation modes. In this study, two types of yield functions—Hill’48 and Yld2000-2d—were considered to describe yield behavior. Isotropic and kinematic hardening models based on the Yoshida–Uemori model were also adopted to describe hardening behavior. Various material tests (such as uniaxial tension, tension- compression, loading-unloading, and hydraulic bulging tests) were carried out to determine the material parameters of the models. The obtained parameters were implemented in the finite element (FE) simulation to predict springback, and the results were compared with experimental data. U-bending and T-shape drawing were employed to evaluate the springback prediction accuracy. Obviously, the springback prediction accuracy was greatly influenced by constitutive equations. Therefore, it is important to choose appropriate constitutive equations for accurate description of material behaviors in FE simulation.

In the last decades synthetic jet actuators have gained much interest among the flow control techniques due to their short response time, high jet velocity and absence of traditional piping, that matches the requirements of reduced size and low weight. A synthetic jet is generated by the diaphragm oscillation (generally driven by a piezoelectric element) in a relatively small cavity, producing periodic cavity pressure variations associated with cavity volume changes. The pressured air exhausts through an orifice, converting diaphragm electrodynamic energy into jet kinetic energy. This review paper faces the development of various lumped-element models (LEMs) as practical tools to design and manufacture the actuators. LEMs can quickly predict device performances such as the frequency response in terms of diaphragm displacement, cavity pressure and jet velocity, as well as the efficiency of energy conversion of input Joule power into useful kinetic power of air jet. The actuator performance is analyzed also by varying typical geometric parameters such as cavity height and orifice diameter and length, through a suited dimensionless form of the governing equations. A comprehensive and detailed physical modeling aimed to evaluate the device efficiency is introduced, shedding light on the different stages involved in the process. Overall, the influence of the coupling degree of the two oscillators, the diaphragm and the Helmholtz’s one, on the device performance is discussed throughout the paper.

In this paper, an original approach allowing the determination of the iron losses in the electromagnetic devices is presented. This new approach exploits the Loss Surface (LS) hysteresis model and the magnetic flux density waveforms resulting from a generalized nonlinear adaptive magnetic equivalent circuit (MEC) using a mesh-based formulation in two-dimensional (2-D) or quasi three-dimensional (3-D). The model coupling has been applied to a 18-slots/16-poles radial-flux interior permanent-magnet (PM) synchronous machine (PMSM) dedicated to automotive applications, mainly for electric/hybrid/fuel cell vehicles (EVs/HEVs/FCVs). The obtained results have been compared with those made retrospectively in the 2-D transient finite-element (FE) Flux. The influence of the MEC discretization on the iron loss calculation and the electromagnetic performances has been analyzed. The computation time is divided by 3/2 with an error less than 7 %.

Hydrologic studies on rainfall-runoff have been extensively conducted in many regions around the globe to fulfill various desirable needs with a purpose of effective and proper planning and managing water resources for present and future uses, whereas such study is not well drawn much attention to river catchments of Tonle Sap Lake Basin in Cambodia, which may prevail to water insecurity. The Stung Sreng catchment, which is one among them considered to be a significant basin for water resources management in Cambodia, is remarkably increasing under intolerable pressures in water resources development. This study was to apply HEC-HMS (Hydrological Engineering Center-Hydrological Model System) model to predict streamflow of Stung Sangker catchment, located in Tonlesap Lake Basin in Cambodia. The result showed that the calibration was good at monthly basis. The model performance was given by Nash-Sutcliffe Efficiency criteria followed by 0.44 for daily and 0.71 for monthly basis, respectively. Moreover, the Percent Bias (PBIAS) for daily and monthly simulation was 4.13% and 3.56%, indicating a satisfactory model fit. The HEC-HMS conceptual model can be used to simulate flow of Stung Sangke catchment on a continuous time scale particularly monthly basis. The result also indicated that there was a clear seasonal variation in monthly water availability, especially during both wet and dry season.

The relentless challenges posed by the COVID-19 pandemic have placed immense pressure on densely populated regions, such as Hong Kong, particularly in the context of an aging population. The burgeoning demand for healthcare services has necessitated the optimization of resource al-location within healthcare systems. This research presents an innovative analytical queueing model specifically tailored to address the intricate gridlock dynamics prevalent in healthcare systems, with a focused application in the challenging landscape of COVID-19 management. Hong Kong, like numerous other regions, has experienced a demo-graphic transition leading to heightened healthcare demands. Traditional methodologies for scrutinizing gridlock issues in healthcare settings, including simulation models, often prove suboptimal within the context of sophisticated optimization frameworks. This study, however, delves into analytical queueing models, offering a more efficient and effective approach. These models have been underexplored, primarily due to the intricacies involved in modeling gridlock propagation within restricted capacity, a critical concern magnified by the COVID-19 pandemic. The novel Queueing Model introduced in this research incorporates the maintenance of queue capacities and topological aspects as exogenous parameters, rendering it exceptionally adaptable for diverse gridlock-related applications. This adaptability is especially pertinent to healthcare management during the COVID-19 crisis. The model meticulously integrates the concept of gridlock, furnishing a comprehensive comprehension of its origins and repercussions. Validation of this model involves rigorous comparison with established methodologies, exact results, and simulation data, consistently high-lighting its versatility and precision. By applying the model to healthcare management, we scrutinize the dynamics of patient flow within a Hong Kong hospital, placing specific emphasis on the critical issue of bed blockage, further exacerbated by the COVID-19 pandemic. The model's gridlock decomposition unearths the nuanced impact of bed blockage on distinct hospital units, addressing the imperative requirement for quantifying in-patient bed blockage within healthcare systems during a pandemic.

This study examines the activities conducted at the archaeological site of Aptera in Crete, Greece. The research was part of the DIACHRONIC LANDSCAPES International Design Workshop, organized by the CAM (Center for Mediterranean Architecture), TUC (Technological University of Crete School of Architecture), and UNIFE (University of Ferrara Department of Architecture). This article outlines the methods used for data acquisition and processing on a territorial scale, which generated digital outputs necessary for the analysis and design phases of the workshop, as well as for further examination of the results. The collected data, obtained through low-cost aerial photogrammetric surveying and GNSS terrestrial coordinate detection, were integrated in a Structure from Motion workflow that led to the creation and exportation of various digital outputs, such as point clouds, DTM, DSM, orthophotos, and contour lines. An accuracy analysis was performed to evaluate the effectiveness and efficiency of the digital models compared to the implemented surveying strategies, including the Ground Control Point and Quality Check Point marker positioning strategy. The resulting digital models proved to be valuable assets for analysis and design within the workshop and provided insightful prospects for future research and territorial-scale projects.

This paper is referred to a top frontline research in life science, showing that in spite of the large variation of the physiological architecture and behavioral forms of the living systems, the fundamental structure of their informational system is basically the same, allowing the body growth/development by micro to macro-structuration processes, maintaining the same informational configuration and functions like the basic unit of life – the eukaryotic cell. Starting and based on the determination of the special/specific info-operability in living structures and the role of information in structuration and their functionality, there are discussed the informational functions of the eukaryotic/prokaryotic cells, the simplest units of life and is elaborated and presented the Informational Model of the Living Structures, showing that these structures operate on the basis of two main informational systems: one of them defined as the Programmed Informational System, composed by three components, assuring respectively the info-generation, maintenance and the proliferation/reproduction, and another one defined as the Operative Informational System, necessary for the continuous communication with the environment for adaptation, consisting in four main components referred to memory, decision, sentience and info-connection/info-selection. The revealed properties are compared with the Informational System of the Human Body, as a prototype of a living system on the top of the evolutionary scale, showing/demonstrating the compatibility of structuration and functions on the entire scale, from prokaryotic/eukaryotic unit cells to multicellular organisms – plants, animals and human, by means of a continuous adaptation process, which always follows the same line of micro to macro-structuration process based on the same Info-organization and functionality. These models are new and revolutionary in the information and life sciences

(1) Background: donor-derived circulating free DNA (dd-cfDNA) is an innovative biomarker with great potential for the early identification and prevention of graft damage; (2) Methods: Prospective study to analyze dd-cfDNA plasma levels in 30 kidney transplant patients during their post-transplant follow-up (15 days, 3, 6 and 9 months), to determine if the result could be of interest in the identification of possible adverse events, specially allograft rejection. In addition, the aim is also to verify the obtained data on sensitivity, specificity, NPV and PPV; (3) Results: Plasma levels of dd-cfDNA could be considered a novel biomarker of graft rejection after first term post-transplant up to several months before its clinical presentation, mainly in TCMR or ABMR. In this study, it appears to have no positive predictive value for borderline rejection or TCMR IA. Our results show a low NPV of the technique because other pathologies or biological post-transplant complications can also affect dd-cfDNA levels and they must be considered in the interpretation of the result.; (4) Conclusions: dd cfDNA could allow, together with other additional biomarkers in the context of a future score, avoid invasive biopsies in most cases, mainly in the case of suspected TCMR or ABMR.

How Nature discovered genetic coding is a largely ignored question, yet the answer is key to explaining the transition from biochemical building blocks to life. Other, related puzzles also fall inside the aegis enclosing the codes themselves. The peptide bond is unstable with respect to hydrolysis. So, it requires some form of chemical free energy to drive it. Amino acid activation and acyl-transfer are also slow and call for catalysis. All living things must thus also convert free energy and synchronize cellular chemistry. Most importantly, functional proteins occupy only small, isolated regions of sequence space. Nature evolved heritable symbolic data processing to seek out and use those sequences. That system has three parts: a memory of how amino acids behave in solution and inside proteins, a set of code-keys to access that memory, and a scoring function. The code-keys themselves are the genes for cognate pairs of tRNA and aminoacyl-tRNA synthetases, AARS. I outline the surprising links between all these questions and the structural duality of the base-pairing that holds genes together and how those links arise from the experiments.

The study of cell-free DNA (cfDNA) and other peripheral blood components (known as “liquid biopsies”) is promising and has been investigated especially in solid tumors. Nevertheless, it is increasingly showing greater utility in the diagnosis, prognosis, and response to treatment of hematological malignancies; in the future, it could prevent invasive techniques, such as bone marrow (BM) biopsy. Most of the studies about this topic have been focused on B cell lymphoid malignancies; some of them have shown that cfDNA can be used as a novel way for diagnosis and minimal residual monitoring in B cell lymphomas, using techniques such as next-generation sequencing (NGS). In myelodysplastic syndromes, multiple myeloma, or chronic lymphocytic leukemia, liquid biopsies may allow for an interesting genomic representation of the tumor clones affecting different lesions (spatial heterogeneity). In acute leukemias, it can be helpful in the monitoring of early treatment response and the prediction of treatment failure. In chronic lymphocytic leukemia, the evaluation of cfDNA permits the definition of clonal evolution and drug resistance in real-time. However, there are limitations such as the difficulty in obtaining sufficient circulating tumor DNA for achieving a high sensitivity to assess minimal residual disease or the lack of standardization of the method and clinical studies to confirm its prognostic impact. This review focuses on clinical applications of cfDNA on minimal residual disease in hematological malignancies.

Thanks to advanced semiconductor microfabrication technology, chip-scale integration and miniaturization of lab-on-a-chip components, silicon-based optical biosensors have made significant progress for the purpose of point-of-care diagnosis. In this review, we provide an overview of the state-of-the-art in evanescent field biosensing technologies including interferometer, microcavity, photonic crystal, and Bragg grating waveguide-based sensors. Their sensing mechanisms and sensor performances, as well as real biomarkers for label-free detection, are exhibited and compared. We also review the development of chip-level integration for lab-on-a-chip photonic sensing platforms, which consist of the optical sensing device, flow delivery system, optical input and readout equipment. At last, some advanced system-level CMOS-chip packaging examples are presented, indicating the commercialization potential for the low cost, high yield, portable biosensing platform leveraging CMOS processes.

Functional partially linear regression model, contains a functional linear part and a non-parametric part, in which testing the linear relationship between the response and the functional predictor is of fundamental importance. When functional data cannot be approximated with a few principal components, based on a pseudo estimate for the unknown non-parametric component, we develop a U-statistic of order-two in this paper. Under some regularity conditions, we use the martingale central limit theorem to prove that the proposed test statistic is asymptotically normal. The finite sample performance with simulation studies and real data application are assessed to verify the proposed test procedure.

This study delves into the impact of artificial intelligence on students’ learning.The paper pioneers the use of kmeans classification to reduce the dimensionality of features before the classification process, which minimizes redundant information and computational overhead. This step is not commonly found in prior research.Firstly, a random forest model is established with “whether AI assistance was used to complete the paper” as the output, and various features as inputs. The analysis of the predictive accuracy when changing input features reveals the degree of influence of each feature on using AI to complete the paper. After feature selection, the remaining 6 features can still effectively predict the use of AI to complete the paper, indicating the representativeness of the extracted indicators. Subsequently, the PCA-K-means algorithm is employed to classify the use of artificial intelligence. PCA algorithm is used for dimensionality reduction of the extracted features to better characterize students’ use of AI. Three representative variables are selected, and the contribution of each feature is computed based on the factor loading matrix. Finally, the K-means algorithm is used to classify students based on the three dimensional features, showing that students can be effectively classified into 2 categories. The results indicate that the majority of students support the use of AI technology to enhance their abilities, although some students may only intend to use AI to cope with exams and similar tasks.

This article develops duality principles and numerical results for a large class of non-convex variational models. The main results are based on fundamental tools of convex analysis, duality theory and calculus of variations. More specifically the approach is established for a class of non-convex functionals similar as those found in some models in phase transition. Finally, in some sections we present concerning numerical examples and the respective softwares.

Overconsumption of an unbalanced diet and overweight, leading to insulin resistance, dyslipidemia, hepatic steatosis, cardiovascular diseases and obesity, is nowadays a worldwide health issue, with different patterns of metabolic disturbances in the young and adult popula-tions, in both gender. Preclinical models that closely mimic the gradual weight gain that occurs in humans upon consumption of a “western-type” diet (WD) are of great interest. In particular, the diet-induced obesity (DIO) model in the C57BL/6J mouse sub-strain is often used in biomed-ical research, considering that it is prone to metabolic disorders and obesity, and is widely used to generate genetically modified mice. This study aimed to phenotypically characterize the C57BL/6J mouse strain fed a WD-like diet, using high-frequency ultrasound imaging (HFUS) as a complementary tool to longitudinally monitor morphofunctional changes in the liver, heart and kidney associated with DIO. Long term WD feeding increased mice body weight (BW), liver/BW ratio and body condition score (BCS), serum transaminases, glucose and insulin, as well as Homeostasis model assessment (HOMA) indexes, and caused dyslipidemia and insulin resistance. According to the 3Rs, ultrasound imaging has enabled a comprehensive study of metabolic syndrome in mice, in vivo and non-invasive, improving the identification of early stages of diseases.

The heat transfer and fluid mechanics analysis in agitated vessels is usually performed using the Dittus-Boelter correlation. In this study, we propose the analysis of an agitated vessel with a draft tube through the analysis of dimensionless numbers correlations. The heat transfer and fluid mechanics simulation was conducted using ANSYS Fluent software. The simulation used the Sliding Mesh approach and the SST k-ω turbulent model to capture the vessel's fluid flow and heat transfer phenomena. A grid independence study was conducted by varying the number of time steps to obtain an appropriate time step size for the simulation. The convective heat transfer coefficient, as indicated by the mean values of the Nusselt number, was calculated. The local values along the radial coordinate of the heat transfer surface were also determined. These results provide insights into the distribution of heat transfer rates within the vessel, which can help optimize process efficiency and equipment design. The simulation results were analyzed by varying the c and m coefficients in the Nusselt correlation presented as Nu= c Rem Pr1/3. Furthermore, correlations describing the mean Nusselt number at the bottom and wall of the vessel are presented and compared with existing literature, contributing to advancing knowledge in this field.

Prunus armeniaca L. is the native species of cultivated apricot, which is important information for studying the origin, succession pattern, pest control, sustainable development of biodiversity of economic tree species of the genus Apricot, etc. However, with global climate change and increasingly intense human activities, the survival environment of Prunus armeniaca L. is under serious stress, and exploring the potential survival space of Prunus armeniaca L. is an important way to protect the health and development of Prunus armeniaca L.. Exploring the potential living space of Prunus armeniaca L. is an important way to protect the healthy development of Prunus armeniaca L. The Yili River Valley is a typical distribution area of Prunus armeniaca L. . Exploring the potential distribution suitable area of Prunus armeniaca L. in the Yili River Valley and analyzing its future change trend can provide scientific guidance for the protection, development, and utilization of Prunus armeniaca L.. Based on the Geographic Information System software platform combined with the MaxEnt Model, we simulated the potential distribution areas and the degree of adaptation of Prunus armeniaca L. in the Yili River Valley, and predicted the potential distribution changes of Prunus armeniaca L. in the future under different situations. Based on the above methods, the following results were obtained: (1) The MaxEnt Model simulated the geographic distribution of Prunus armeniaca L. with high accuracy and an AUC value of 0.969, and the area of the currently suitable area for Prunus armeniaca L. in the Yili River Valley was about 10407.77 km2, accounting for about 18.45% of the overall land area of the Yili River Valley; the area of the highly suitable area was about 1380.9 km², accounting for about 13.26%, the area of the medium suitable zone is about 2830.15km², and the area of low suitable zone is about 6196.05km². (2) The maximum increase in the total area of the suitable zone under the future low-radiation forcing scenario is 11.49%, and the minimum increase under the future medium-high forcing scenario is 4.94%. (3) The overall migration was in the southwest direction, with the largest migration angle under the SSP245 scenario and the largest migration distance under the SSP585 scenario. Under the background of future climate change, there is a tendency for the potential habitat of Prunus armeniaca L. in the Yili River Valley to expand from low altitude to high altitude, and altitude is the dominant geographic factor influencing the distribution of Prunus armeniaca L.. By simulating and studying the potential geographic distribution of Prunus armeniaca L. in the Yili River Valley under the background of climate change, we can reveal the response mechanism between the geographic distribution of Prunus armeniaca L. and the natural environment, which can provide scientific guidance and data support for the exploitation and protection of Prunus armeniaca L. in the future.

The marine region of the Zhongjieshan Islands is an essential component and location for enhancement and release in Zhoushan Fishing Ground. The scientific assessment of the target species' ecological carrying capacity serves as the foundation for augmentation and release efforts. Based on data from the 2021-2022 survey of biological resources in the waters of Zhongjieshan islands, the Ecopath model was built to analyze the trophic structure, energy flow, and overall characteristics of the ecosystem, as well as the ecological carrying capacity of the main enhancement and releasing species, Larimichthys crocea and Sepiella maindroni. The ecosystem's functional groups had trophic levels ranging from 1 to 3.48, with an average energy transfer efficiency of 8.11% and total flow of 2125.905 t·km-2. The ratio of total primary output to total respiration is 1.713. Zhongjieshan Islands' ecosystem was currently at an embryonic stage. The ecological carrying capacity of Larimichthys crocea and Sepiella maindroni was calculated to be 0.2325 t·km-2 and 0.1617 t·km-2, separately or individually. Larimichthys crocea and Sepiella maindroni showed enormous potential for development and release. The findings offered essential data for ecological studies on the waters of Zhongjieshan Islands, as well as a reference for enhancement and release efforts in this area.

Sick sinus syndrome (SSS) is a disease of sinus rhythm abnormalities. This study aimed to establish an SSS model by injecting 3% NaOH into the jugular vein(JV). Sprague-Dawley rats were randomly divided into sham, 3% NaOH, and 10% NaOH groups. Anesthetized rats underwent electrocardiogram recording before right JV exposure. Rats in the experimental groups received NaOH injections at 0.01 ml/s, while the sham group did not. Model success was evaluated after one hour. Heart rate(HR) and rhythm were documented at 1 hour, 1 week, and 2 weeks post-molding, including HE staining, membrane ion transcription and expression, and calcium ion content test. In the 3% NaOH group, the model establishment rate was 87%, and the mortality rate was 13%, whereas in the 10% NaOH group, they were 57% and 43% respectively (P < 0.01). The HR of the 3% NaOH group decreased at multiple time points post-molding (P < 0.001), presenting as arrhythmias such as sinus bradycardia. The JV sustained less damage, with necrosis of P cells, changes in membrane ion channels, and reduced calcium ion distribution. In conclusion, JV injection of 3% NaOH can establish the SSS model and benefit animals.

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome characterized by multiple risk factors and associated conditions. HFpEF prevalence is rising, and its prognosis remains poor after the first hospitalization. Using a two-hit HFpEF murine model, we aimed at studying cardiac reverse remodelling after stopping the causing stress (Angiotensin II [AngII]) and a high-fat diet [HFD]; MHS) and introducing voluntary exercise. We studied in young male and female C57Bl6/J mice fed or not with an HFD (60% calories from fat) the cardiac response to AngII (1.5 mg/kg/day for 28 days). Then, MHS was stopped, and VE was started for another four weeks (RR). We studied the effects of MHS and RR on the circulatory microRNA (miR) profile (miRNome) and the myocardial miRNome to characterize the cardiac and non-cardiac response of small RNAs. AngII alone and MHS but not the HFD caused cardiac hypertrophy (CH), left ventricular (LV) concentric remodelling and left atrial enlargement in male mice. HFD-induced CH and LV concentric remodelling only in female mice. Four weeks after RR, CH, LV concentric remodelling and atrial enlargement were reversed. We performed bulk circulatory and LV miR sequencing. We did not observe differences linked to biological sex. RR restored normality for circulatory miRNome, whereas LV miRNome remained relatively similar to the one after MHS. Among the 25 most abundant circulatory miRs, ten were modulated by MHS (9 upregulated). In the LV, 8 of the 25 most abundant miR were upregulated by MHS, and 10 were downregulated. MiRNomes from AngII, HFD or MHS shared many common modulated miRs (32), suggesting that the overall response of organs producing the bulk of circulatory small non-coding RNA was similar even for seemingly different stress.

The thermal conditions of transitional (ranging from warm to cold) coldwater streams impact the ranges and resource availabilities for biota inhabiting these lotic systems. With ongoing climate change and increasing land modifications, thermal boundaries may shift, altering thermal transition zones and their biotic communities. The objective of this study was to investigate the condition of trout across three forks of the Whitewater River catchment and to investigate factors influencing fish community composition and distribution. Each fork was characterized into three separate sections: headwater (coolwater), middle (warmwater), and lower (coldwater). Springs were identified throughout each fork, with greatest concentrations in the lower sections of each fork. Using single-pass electrofishing, we sampled 61 sites across the three forks in the Whitewater River system (North = 21 sites, Middle = 19, South = 21), and catch statistics were used to calculate diversity, trout abundance, and trout condition. In general, diversity increased, and trout were healthier but less abundant in middle and headwater sections, whereas diversity decreased slightly, trout condition decreased, and trout abundance increased in lower reaches, with changes differing somewhat among forks. Canonical correlation analysis had strong significant correlations showing simpson diversity and condition increase going upstream with high non trout abundance and catch rates while trout catch rates and reach width decrease. The Whitewater River is a catchment exhibiting transitional temperature-pattern characteristics with generally low fish community diversity and trout conditions that range from thin, normal, and robust. Dominated by a changing landscape (agriculture) and intensifying climate change, we may begin to see stream temperatures increase along with species diversity. Understanding how spring temperature influences species composition and distribution can bring potential stressors to light increasing our understanding of thermal conditions and to help mitigate the negative impacts from land use and climate change.

Epilepsy is known to cause alterations in neural networks. However, many details of these changes remain poorly understood. The objective of this study was to investigate changes in the properties of hippocampal CA1 pyramidal neurons and their synaptic inputs in a rat lithium-pilocarpine model of epilepsy. In the chronic phase of the model, we found a marked loss of pyramidal neurons in the CA1 area. However, the membrane properties of the neurons remained essentially unal-tered. The results of the electrophysiological and morphological studies indicate that the direct pathway from the entorhinal cortex to CA1 neurons is reinforced in epileptic animals, whereas the inputs to them from CA3 are either unaltered or even diminished. In particular, the dendritic spine density in the str. lacunosum moleculare, where the direct pathway from the entorhinal cortex ter-minates, was found to be 2.5 times higher in epileptic rats than in control rats. Furthermore, the summation of responses upon stimulation of the temporoammonic pathway was enhanced by approximately twofold in epileptic rats. This enhancement is believed to be a significant con-tributing factor to the heightened epileptic activity observed in the entorhinal cortex of epileptic rats using an ex vivo 4-aminopyridine model.

Based on the two-flavors Polyakov quark-meson model, we study the bubble dynamics of a first-order phase transition in chiral phase diagram at high density but low temperature in the framework of the homogeneous thermal nucleation theory. Under the mean-field approximation, after obtaining the effective potential with inclusion of the fermionic vacuum term, we build a geometric approach to probe two possibly existing minima, which can actually tunnel through a bounce interpolated between a metastable vacuum to an adjacent true vacuum. By separating our discussions into a weak first-order hadron quark phase transition and strong ones, the bubble profiles, the surface tension, the typical radius of the bounce and the saddle point action in the presence of a nucleation bubble as a function of temperature are numerically calculated in detail as the chemical potentials are fixed at $\mu=306 \mathrm{MeV}$ and $\mu=310 \mathrm{MeV}$. our results show that the surface tension remains a small value even when the density is very high and phase boundaries for hadron and quark matter should be resized properly according to the saddle point action evaluated on the bounce. It is also noticed that the inclusion of the deconfinement phase transition in term of the Polyakov loop does not change chiral phase transition dramatically for light quarks.

We leverage the explainability feature of KAN network and build an explainable language model where certain neurons encode individual words and neuron activation is fully interpretable in terms of the basis of a word. To do that, we propose a continuous word2vec model where a meaning of a word is expressed by a continuous profile of distances of this word from the words in the basis of words which is interpolated. As a result, the whole KAN network can be interpreted as a sequential procedure with word expressions. We then proceed from words to logic programs and develop a clause learning technique based on KAN. A logic program construction process from facts now become fully interpretable. We follow the differentiable inductive logic programming technique, representing a logic program as a matrix learned by KAN. Hence, we obtain an efficient and fully interpretable rule learning approach.

The present study drops the line for the flow characteristics of fly ash-based (FA) geopolymers reinforced with polypropylene (PP) fibers undergoing the extrusion process during three-dimensional printing. Applying to the Herschel-Bulkley rheological model the research is ready with the sound theoretical basis which is used to understand the flow behavior of these materials under various conditions. A combination of experimental and numerical techniques that are based on the Finite Element Method (FEM) in COMSOL has been used in this study. The results of both the experimental and simulation approaches are then compared and the behavior of the material during extrusion is then examined. The experimental results proved PP fiber content has significant effects on rheological properties. The experiments on mixes which are either devoid of fibers or possess a high content of fiber, encountered problems like a high static yield stress and material segregation. Mixes with fibers of moderate content showed smoother extrusion processes, which means that a suitable range of fiber addition exists for the fibers that optimally balance the mechanical properties and extrudability. Numerical simulations typically agreed with experimental data for fiber contents up until it reached a certain level but not above, suggesting the existence of more complex interactions inside the fibers and the necessity for the subsequent model refinements. The investigation outlines a 0.25% and 0.5% fiber content range that leads to enhanced performance without bringing the extrusion process to greater complications and further making the production of proper, architecturally printed structures possible.

(1) Background: in the context of sustainable development goals (SDGs), this work explores the repair direction of future cultivated land protection policies based on the Smith policy implementation model from the historical evolution to ensure that the cultivated land protection policy does not deviate during the implementation process. (2) Methods: this paper uses literature research and inductive deduction methods to sort out the evolution process of Chinese cultivated land protection policy, summarize the successful experience of transferring the focus of cultivated land protection in chronological order, and conclude the repair direction of Chinese cultivated land protection. (3) Results: although the Chinese cultivated land protection policy in the historical evolution has through three significant phases, there are still certain issues in the development trend of Chinese cultivated land protection policy based on regional division of labor for grain transportation and sales. (4) Conclusion: directly utilizing feedback from farmers as stakeholders will contribute to the maturity and improvement of cultivated land protection policies in the specific implementation of cultivated land protection policies.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

The intricate architecture of dendritic arborization is fundamental to the formation and functionality of neural networks, serving as the primary site for synaptic integration and signal propagation. This study presents a pioneering computational model that simulates dendritic growth dynamics, employing exponential and logarithmic scaling over an extended developmental period of 720 days. This model offers a valuable tool for investigating the implications of diverse dendritic growth patterns on neural development, synaptic connectivity, and ultimately, cognitive functions. The computational framework incorporates biologically plausible parameters, allowing for the systematic exploration of dendritic branching patterns and their potential impact on neuronal information processing. By simulating exponential and logarithmic growth scales, the model captures the inherent complexity and diversity of dendritic morphologies observed in various neuronal populations across different brain regions. The findings from this study hold significant implications for our understanding of neural circuit assembly, synaptic integration, and the potential functional consequences of aberrant dendritic growth patterns observed in neurodevelopmental disorders. Furthermore, the model's ability to simulate extended developmental trajectories over a prolonged period of 720 days offers insights into the dynamic interplay between dendritic growth and synaptic pruning, which is crucial for the refinement and optimization of neural networks.

The main challenges in oil platforms are how to define optimal setpoints and decision-making for controlling variables due to many existing dynamical production uncertainties while conceding safe operation and minimizing discharge to sea and atmosphere without killing plantwide inventory. A novel scheduling model to optimize the whole essential units of an oil platform production gathering topside and subsea variables is disclosed. A proper topside and subsea scheduling model enables and creates harmony between optimal production, supply chain, costs, and market dynamics, satisfying demands, environmental and operations constraints leading to stability. The interest of the present research lies in determining the optimal topside and subsea operational settings and decision-making, establishing maximum E&P performance and production control setpoints while predicting and manipulating reservoir lifespan and its revitalization. This objective is attained through a novel multiperiod large-scale model for the planning and operational production scheduling and model predictive control (MPC) in oil platforms complying with sustainability, profitability, platform design capacities, and offloading of oil and gas to supply chains or to pipeline exportation. The present model details heat and mass transfer, PVT, flow profiles along a planning horizon and can be used to any oil platform since comprises all the essential unit operations for oil, gas, CO₂, H₂S and water. The model maximizes the total oil production over any planning horizon. The model is versatile, and decision-making can be either linear or nonlinear but rather a MILP or LP as best choice on optimizing large-scale systems. If the model is applied with auto rescheduling on site within variable hours or minutes, it becomes a real-time optimization schedule approach. Due to its high-velocity performance and robustness, a novel multi-objective function strategy, acting as an LP generic plantwide MPC with industrial scope to maximize production while controlling the process is presented, and as an example is here used to control slug flow to avoid equipment trips and inventory instability, at the same time production is maximized. The output from the decision-making was compared to actual plant data, and the results proved compliance to the design capacity with process safety and sustainability. Comparing to official Brazil's government data for oil, the case study showed superiority to the same size platforms, like FPSOs P-75 and MV32, e.g., more than 101.5% more capacity to produce oil in beginning of campaign after first oil, and 10.5% during campaign. The present work indicates that: 1) the production of millions of barrels is being delayed or left aside; 2) not scheduling production harms the environment and diminish process safety; 3) not scheduling production can create stochastic supply chain deliveries instead of organizing it by deterministic offloading days; 4) scheduling is essential to manipulate, revitalize and monitor reservoir's pressure and content under uncertainty; 5) the oil platform can be automatic optimized by a plantwide MPC reducing human activity/dependency; 6) scheduling gives more transparency to stakeholders and contractors by forecasting business data and capability, and can design, develop or enable new businesses.

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven a promising method for fast SOM content estimation. However, soil physical properties significantly affect the sensitivity of satellite hyperspectral imaging to SOM, leading to poor generalization ability of the estimation model. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on the spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root mean squared height, RMSH), and soil bulk weight (SBW), a soil spectral correction strategy was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPD) exceeding 1.4 in almost all bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, root mean square error (RMSE) of 5.74 g/kg, and RPD of 1.68. The prediction accuracy, R2, RMSE, and RPD of the model after migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction strategy based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. The performance comparison highlighted the advantages of considering soil physical properties in regional-scale SOM prediction.

As a new plant slope protection technology, living stumps can effectively improve slope stability. The current study attemptes to investigate the mechanical characteristics of the root system of living stumps and their reinforcement mechanism on slopes by carrying out a three-dimensional living stump slope modeling test. Using ABS materials, a 3D model of a living elm stump was created by 3D printing and a slope model test was carried out. The reinforcement mechanism of the living stump was studied through a combination of model testing and numerical simulation. The results indicate that, due to the presence of living stumps in the lower and middle parts of the slope, the maximum shear stress area of the soil moved to a deeper direction. The stress state of the soil around the living stump was effectively improved due to the existence of the lateral root system.Living stumps in the lower part of the slope cross the potential sliding surface and transferred the soil shear stress gradually to the root system through root-soil interaction. Moreover, the tap roots and lateral roots of living stumps form a robust spatial structure that bears the shear stress of soil together, thus increasing the stability of the slope.

The article presents investigations of rail vehicle bogie of Y25 type. The Y25 bogie family is one of the most commonly used freight car bogie designs. In addition to several significant advantages characterising this design, it also has several disadvantages observed since the beginning of more than fifty years of operation in several types of cargo vehicles. One of these defects observed in real systems is "unsatisfactory running stability", particularly at long straight tracks. The article used commercial engineering software VI-Rail to create a model of the gondola car (type 412W Eaos) with two Y25 bogies. The car model was tested empty and loaded (maximum permissible load). Motion along straight and curved tracks with different radii values was analysed. The vehicle velocity was changed from a few m/s to the maximum values for which stable solutions of the model existed. For each route, a non-linear critical velocity was determined, defining the maximum operating velocity of the modelled car. The model solutions were recorded, while just one was selected to present the results - the first wheelset lateral displacement ylw. The opinion about the "imperfect running quality" in curved tracks was confirmed. The possible appearance of self-exciting wheelset vibrations in the modelled car's operating velocity range in the laden state was also observed. The research results on the impact of the bogie suspension parameters change on the vehicle model stability are presented. The crucial parameter in the bogie suspension was indicated. Reducing its value by several percent about the nominal value increases the critical velocity of the car to values higher than the maximum operating velocity of the modelled vehicle.

Recent research emphasises the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students' use and intention to use artificial intelligence technology. For this purpose, a sample of 199 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers' intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. The study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.

This paper presents a compartmental model aimed at investigating the pharmacokinetics of medical THC after oral administration. The model was developed to be multi-compartmental, describing the kinetics of THC and its metabolites in bodily tissues. Utilizing the law of mass action, the model was converted into ordinary differential equations (ODEs), illustrating the rate of concentration changes of THC and its metabolites in each compartment. Subsequently, we applied the nonstandard finite difference (NSFD) method to construct numerical solution schemes. These schemes were implemented in the MATLAB program, along with estimated pharmacokinetic rate constants. The results of the investigation demonstrate that the simulation curves depicting the plasma concentration-time profiles of THC and THC-OH closely resemble the actual data samples, indicating that this model accurately describes the pharmacokinetics of medical THC and its metabolites. Moreover, the model exhibits predictive capabilities regarding the pharmacokinetics of THC and its metabolites in various tissues. Consequently, the model serves as a valuable tool for enhancing our understanding of the pharmacokinetics of THC and its metabolites and for guiding adjustments to dosage and administration durations for oral medical THC products.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

Aiming at the development of the application of massive and heterogeneous LEO constellations, the influence of typical perturbation forces on the orbit prediction of LEO satellites at different altitudes is analyzed. Firstly, the main perturbation force model of LEO satellites is introduced. Then experiments for analyzing the influence of orbit prediction on LEO satellites at different altitudes are designed. Finally, the influence of typical perturbation forces on the orbit prediction under different prediction durations by numerical analysis is assessed systematically, and the orbit prediction of LEO satellites with different non-spherical gravitational field orders, ocean tide orders and atmospheric density models were compared and calculated. The research results show that different orders of non-spherical gravitational fields should be considered when the altitude of satellites is different, the reasonable gravitational field orders for LEO satellites at 500km, 1000km, 1500km and 2000km under 1-hour forecast time are 140x140, 80x80, 60x60 and 40x40 orders, the reasonable orders of ocean tide perturbation for 1500km and 2000km satellites are 20x20 orders. Under the 24-hour forecast time, atmospheric resistance has a great influence on the precision of LEO satellites at orbit altitude below 1,000km. The forecast deviation of the LEO satellites orbit at 500km reaches 8751.576m. The difference of orbit precision between different atmospheric density models is in the meter level. The orbit deviation by the Standard Atmosphere 1976 and NRLMSISE 2000 atmospheric models respectively reaches 25.494m. Relevant conclusions provide support for the optimization and selection of dynamical models in high precision orbit determination and fast orbit prediction of LEO satellites.

The company strategic procurement function makes a significant contribution to overall corporate success, and yet remains under-researched in terms of digitalisation and digital maturity. This research adopts an inductive case study approach, using qualitative data from in-depth inter-views with industry practitioners, to develop and apply a digital maturity model for the deployment of strategic procurement analytics. The research presents a snapshot of the digital maturity of the strategic procurement function of a global aircraft manufacturer, and finds that current exploitation of analytics remains constrained by a range of factors, including the need for close compliance with regulatory norms. Thematic analysis of the interview material provides the basis for the development of the maturity model, which - although geared to a specific industry context - is nevertheless of relevance in other business environments. The research thus con-tributes to the existing literature in this field, and will also be of interest to procurement professionals. However, the research clearly has its limitations, not least in that it is based on just one industry case, and cross-industry generalisations from the findings must therefore be treated with caution.

Novolac phenol-formaldehyde resins have gained extensive commercial utilization across diverse applications due to their exceptional mechanical, thermal, and chemical resistance properties. In this investigation, we successfully synthesized a traditional novolac, and a biobased novolac phenol-formaldehyde resin by partially substituting phenol with a bio-oil obtained from the organic phase resulting from the fast pyrolysis of pinewood biomass. Despite extensive investigations into the curing behavior of novolac resins crosslinked with hexamethylenetetramine (HMTA) through numerous model-fitting cure kinetic studies, a comprehensive model for predicting the crosslinking of bio-based phenol-formaldehyde resins in the presence of HMTA has not yet been studied. The primary objective of this research was to compare and apply several commonly used kinetic models to predict the degree of curing and cure rate of the synthesized resins, using DSC dynamic measurements. Our results demonstrated that the autocatalytic model exhibited excellent fitting for the traditional novolac resin and HMTA mixture, while the Kamal model showed better fitting with the experimental data obtained for the bio-based phenol-formaldehyde resin and HMTA system. In addition, the results evidenced a significant drop in the curing temperature by partially replacing phenol with bio-oil. Additionally, the results revealed a notable reduction in curing temperature when phenol was partially substituted with bio-oil. This substitution not only offers environmental benefits but also promotes energy efficiency and enhances production safety. The data collected in this study is pivotal for our subsequent research endeavors, which aim to develop environmentally sustainable materials optimized for use in 3D printing manufacturing processes.

We study epidemic spreading in complex networks by a multiple random walker approach. Each walker performs an independent simple Markovian random walk on a complex undirected (ergodic) random graph where we focus on Barabási-Albert (BA), Erdös-Rényi (ER) and Watts-Strogatz (WS) types. Both, walkers and nodes can be either susceptible (S) or infected and infectious (I) representing their states of health. Susceptible nodes may be infected by visits of infected walkers, and susceptible walkers may be infected by visiting infected nodes. No direct transmission of the disease among walkers (or among nodes) is possible. This model mimics a large class of diseases such as Dengue and Malaria with transmission of the disease via vectors (mosquitos). Infected walkers may die during the time span of their infection introducing an additional compartment D of dead walkers. Infected nodes never die and always recover from their infection after a random finite time. This assumption is based on the observation that infectious vectors (mosquitos) are not ill and do not die from the infection. The infectious time spans of nodes and walkers, and the survival times of infected walkers, are represented by independent random variables. We derive stochastic evolution equations for the mean-field compartmental populations with mortality of walkers and delayed transitions among the compartments. From linear stability analysis, we derive the basic reproduction numbers R M , R 0 with and without mortality, respectively, and prove that R M < R 0 . For R M , R 0 > 1 the healthy state is unstable whereas for zero mortality a stable endemic equilibrium exists (independent of the initial conditions) which we obtained explicitly. We observe that the solutions of the random walk simulations in the considered networks agree well with the mean-field solutions for strongly connected graph topologies, whereas less well for weakly connected structures and for diseases with high mortality. Our model has applications beyond epidemic dynamics, for instance in the kinetics of chemical reactions, the propagation of contaminants, wood fires, among many others.

Asymptotic theories for fractional cointegrations have been extensively studied in the context of time series data, with numerous empirical studies and tests developed. However, most of the previously developed testing procedures for fractional cointegration were primarily designed for time series data. This paper proposes a generalized residual-based test for fractionally cointegrated panels with fixed effects. The test development is based on bivariate panel series with the regressor assumed to be fixed across cross-sectional units. The proposed test procedure accommodates any integration order between [0,1], and it is asymptotically normal under the null hypothesis. Monte Carlo experiments demonstrate that the test exhibits better size and power compared to a similar residual-based test across varying sample sizes.

Pavement crack detection is one of the key links in highway pavement maintenance management. In the current road crack detection, the network model easily ignores the shallow geometric features of cracks and cannot extract the key feature information of cracks. Aiming at the above problems, an EC-YOLOX network model is proposed. The CFPN is constructed to cross-fuse different scale features to solve the feature scale invariance. In the strengthen feature extraction layer, ECANet is fused to enhance the ability to pay attention to key information. The WEIoU loss function is proposed, which assigns different penalty terms to the target box in the vertical and horizontal directions. In terms of data, geometric distortion constraint correction is performed on the single-point perspective pavement image. The experimental data show that the accuracy of EC-YOLOX on the self-built data set reaches 76.34% [email protected], which is 4.45% higher than that of YOLOX. The loss curve of EC-YOLOX is smoother than that of YOLOX, and the minimum training loss value is 2.136, which is 0.648 lower than the minimum loss value 2.784 of YOLOX. Many experiments have verified the effectiveness of EC-YOLOX in improving the detection effect in pavement crack detection.

Compressed carbon dioxide energy storage (CCES) offers several benefits over other existing energy storage systems, including ease of liquefaction, high energy storage density, and environmental friendliness. As a result, the research progress, economic and technological feasibility, and system operation of the CCES system are all discussed in depth in this study. The system evaluation method is summarized and the compressed carbon dioxide storage is analyzed, and the performance optimization direction of the compressed carbon dioxide energy storage technology is discussed. When the overall performance of a transcritical CCES system, a supercritical CCES system and a liquid CCES system are compared, it is discovered that the supercritical CCES system has better thermodynamic characteristics and a simpler system configuration, making it suitable for large-scale development and use. The goal of the CCES system's future development is to create a design with an optimum compression and expansion ratio, a more precise analysis and system model, and multi-field coupling. This review's discussion serves as a guide for the best design and use of the CCES system.

Mangroves offer vital ecological advantages including air and water filtration, coastal and estuarine habitat provision, sediment stabilization, and wave energy absorption. Their intricate root systems play a key role in safeguarding shorelines from tsunamis and erosive storms by dissipating wave energy. Moreover, mangroves shield against boat wakes and wind-waves, bolstering shoreline defense. Wave dissipation is a function of forest width, tree diameter, and forest density. Restoration efforts of juvenile mangroves in Florida’s Indian River Lagoon (IRL) aim to reduce wave energy in areas vulnerable to erosion. Physical model testing of wave dissipation through mangroves is limited due to the complexity in representing the mangrove structure, where prop roots are non-uniform in both diameter and location. Previous studies have quantified wave dissipating effects through use of scaled and parameterized mangrove structures. This study measures the dissipation effects of live mangroves in a wave flume, forced by conditions representative of the IRL. These measurements are used to validate a parameterized dowel model. Error between wave attenuation factors for the live mangrove and dowel system was on average 2.5%. Validation of the modularized dowel system allowed for further parameterized testing to understand forest structure effects, such as sediment stabilization and wave attenuation.

Currently, there is no widely accepted consensus regarding a consistent thermodynamic framework within the special relativity paradigm. However, by postulating that the inverse temperature 4-vector, denoted as β, is future-directed and timelike, intriguing insights emerge. Specifically, it is demonstrated that the q-dependent Tsallis distribution can be conceptualized as a de-Sitterian deformation of the relativistic Maxwell-Jüttner distribution. In this context, the curvature of the de Sitter space-time is characterized by Λ/3, where Λ represents the cosmological constant within the ΛCDM standard model for Cosmology. For a simple gas composed of particles with proper mass m, and within the framework of quantum statistical de Sitterian considerations, the Tsallis parameter q exhibits a dependence on the cosmological constant given by q=1+ℓcΛ/n where ℓc=ℏ/mc is the Compton length of the particle and n is a positive numerical factor. This formulation establishes a novel connection between the Tsallis distribution, quantum statistics, and the cosmological constant, shedding light on the intricate interplay between relativistic thermodynamics and fundamental cosmological parameters.

This study concerns the transport and retention of polydisperse micron-sized (16 ± 6 µm) of microplastics (MPls) in porous media under varying flow rate conditions. Sorption kinetics were modeled using first-order reversible and irreversible kinetic sorption models, with sensitivity analyses providing insights into each sorption parameter's effect. Both numerical modeling and experimental measurements were employed to assess sand filter retention rates. The impact of flow rate on sorption reveals variations in distribution coefficient (Kd), mass transfer coefficient (β), and irreversible sorption rate (K1). Lower flow rates are correlated with higher Kd and β values, indicating an increase in sorption and diminished mass transfer rates. The findings revealed that an increase in Kd resulted in a more gradual sorption process with a decrease in peak concentration, whereas changes in β influenced the rate of sorption and peak concentration to a lesser extent compared to Kd. Lower K1 values are associated with higher peak concentrations and decreased retention efficiency. Retention rates were evaluated by a numerical model and found as 28 ± 1% at a flow rate of 31 ml min⁻¹ and 17 ± 1% at 65 ml min⁻¹. The introduction of MPls into soil environments has been noted to modify transport dynamics into soil. As a result, these alterations effects hydrological characteristics of soil, thereby impacting quality of groundwater and agricultural output. The mean absolute error (MAE) of 6% between modeled and observed retention rates suggests minor discrepancies. This study highlights the importance of examining retention efficiency and the accuracy of numerical models in porous media during MPl transport.

Limited understanding exists regarding the intricate dynamics between the levels of social inhi-bition exhibited by both wives and husbands concerning their perceived family support and de-pressive symptoms, particularly within couples who are parents of children diagnosed with au-tism spectrum disorder (ASD). This study used the actor-partner, interdependence mediation model to analyze data collected from 397 pairs of Chinese parents with children diagnosed with ASD. The findings of the study revealed significant indirect actor effects, indicating that the levels of social inhibition exhibited by both wives and husbands were associated with their own de-pressive symptoms through their respective perceptions of family support. In general, the study did not find significant partner effects, except for some indirect effect (IE) of wives on their hus-bands' depressive symptoms through the wives' perceived social support. This observation sug-gests that cultural norms and role expectations within the family context may influence these dy-namics. These results underscore the potential importance of interventions aimed at addressing social inhibition and enhancing perceived family support to alleviate depressive symptoms in this population. Additionally, interventions targeting fathers should emphasize the importance of their involvement in parenting and collaboration with their partners in caring for children with ASD. Such collaborative efforts may contribute to improved outcomes for both parents and chil-dren within families affected by ASD.

Price markups and firm Tobin’s Q ratios are widely believed to have been increasing in the past several decades. Various models for the calculation of price markups have been developed, but each have methodological drawbacks and some results are poorly reflective of the near past wider macroeconomic experience. This paper defines an approach for the definition and measurement of markup pricing using the measure of Economic Rent and The Capital Asset Pricing Model. The results show limited markup pricing for The UK’s FTSE 100 companies (2018-2023), but that that certain Real Estate, Technology/Media and Financial Services/Equity Investment firms have enjoyed higher price markup levels. An analysis of the business models of these firms is used to qualitatively propose explanations for such markups. Finally, this work offers a formal proof that that expected price markup is equal to Tobin’s Q and finds that the empiric market level of markup is near equivalent to the market Tobin’s Q; differences between the markup and Tobin’s Q at the level of the firm are equally assessed.

Over the last decades, an important number of regional and global Digital Elevation Models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs require to be expressed in the same Coordinate Reference System (CRS), sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention), and with heights relative to the same Vertical Reference System (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements, or disparities, may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect or curvature. Therefore, the control of DEM planimetric accuracy is certainly the most important task to perform at first before any comparison. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy, by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have been already computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been performed in a 11x11 grid with a 0.1-pixel step in both directions. The Copernicus DEM GLO-30 has been interpolated by a “classical” bicubic to achieve these 11x11 displacements. This validation method has been applied in four 10 km x 10 km DEMIX tiles showing different height distributions. Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with with typical errors (e_b) ranging from 12 to 20 % of the pixel size (worst case in Croatia).These errors in displacement retrievals are not constant in the 11x11 grid and the overall error E_b depends on the roughness (altimetry variations) encountered in the different tiles. The second idea of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d=1 of the interpolated point) on the accuracy of the displacement retrieval. By considering E_b as a quality indicator, tests have been performed in the four DEMIX tiles making the bicubic parameter vary between -1.5 and 0.0 by step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four E_b minimal values. This BBC parameter b* is low for flat areas (around -0.95) and higher in mountainous areas (around -0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r=0.717). The logarithmic regression formula estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC.

In steel recycling, the optimization of electric arc furnaces (EAFs) is of central importance in order to increase efficiency and reduce costs. This study focuses on the optimization of electric arcs, which make a significant contribution to the energy consumption of EAFs. A three-phase equivalent circuit integrated with the Cassie-Mayr arc model is used to capture the nonlinear and dynamic characteristics of arcs, including arc breakage and ignition process. A particle swarm optimization technique is applied to real EAF data containing current and voltage measurements to estimate the parameters of the Cassie-Mayr model. A novel Arc Quality Index (AQI) is introduced in the study, which can be used to evaluate arc quality based on deviations from optimal conditions. The AQI provides a qualitative assessment of arc quality, analogous to indices such as arc coverage and arc stability. The study concludes that the AQI serves as an effective operational tool for EAF operators to optimize production and increase the efficiency and sustainability of steel production. The results underline the importance of understanding electric arc dynamics for the development of EAF technology.

In the face of pressing global challenges, coupling the synergy of dance arts, sustainability, and marketing communications emerges as a potent catalyst for advancing sustainable behavior within society. Although prior research has examined the emotional stimulations raised by dance performance, it has not systematically demonstrated the role of traditional dance as a vehicle for sustainability marketing communications. This study fills this gap by using the AIDA model (Attention, Interest, Desire, Action) as a foundation, conceptualizing dance performance elements into seven dimensions, and incorporating emotional engagement and environmental consciousness to foster sustainable behavior. Collaborating with Taiwanese dance troupe, the research produced a performance titled, ‘The New Dragon Palace Chronicle’, which focuses on the themes of destruction and preservation of underwater life, enriched with cultural and sustainability symbolism. The hypothesis were tested on a sample of 315 Taiwanese audience who watching the performance. The results confirmed that the seven dimensions of the dance elements have substantial validity and reliability, significantly drawing the attention of the audience as preliminary stage of awareness. This attention fosters emotional engagement and amplified environmental consciousness and significantly influences interest, desire, and ultimately, sustainable behavior action. The findings underscore the vital role of dance art performance in nurturing actions that contribute to social sustainability.

Human Pose Estimation (HPE) is a technique in computer vision and AI for detecting and tracking human body parts and poses from images or videos. Widely used in augmented reality, animation, fitness applications, and surveillance, HPE methods using monocular cameras are highly versatile due to their applicability in standard video and CCTV footage. These methods have evolved from 2D to 3D pose estimation. However, current 3D HPE methods trained on laboratory-based motion capture data encounter challenges such as limited training data, depth perception ambiguity, left/right switching, and issues with occlusions when applied in real-world environments. This study compares two representative 3D HPE methods by assessing their strengths and weaknesses with real-world videos. Then, we propose data processing techniques to eliminate and correct anomalies like left/right inversion and false detections of joint positions in daily life motions. Finally, we obtain joint angle trajectories using an optimization method based on a 3D humanoid simulator, taking as input the joint coordinate data corrected by applying the proposed human joint data processing technique. The efficacy of the proposed 3D HPE method is verified by applying it to three-dimensional freehand gymnastics exercises and comparing the joint angle trajectories during the motion.

Rapidly and accurately extracting tobacco plant information can facilitate tobacco planting man-agement, precise fertilization and yield prediction. In karst plateau of southern China, tobacco plant identification is affected by large ground undulations, fragmented planting areas, complex and diverse habitats and uneven plant growth. This study took a tobacco planting area in Gui-zhou Province as the research object and used DJI UAVs to collect UAV visible light images. Considering plot fragmentation, plant size, presence of weeds and shadow masking, this area was classified into eight habitats. The datasets of different habitats were constructed to train the U-Net model. The results show that (1) the overall precision, recall, F1-score and IOU of tobacco plant information extraction were 0.68, 0.85, 0.75 and 0.60, respectively. (2) The precision was the highest for the subsurface-fragmented and weed-free habitat and the lowest for the smooth-tectonics and weed-infested habitat. (3) The weed-infested habitat with smaller tobacco plants can cause blurred images, reducing the plant identification accuracy. This study verified the feasibility of the U-Net model for tobacco single-plant identification in complex habitats. Decomposing complex habitats to establish the sample set method is a new attempt to improve crop identification in complex habitats in karst mountainous areas.

Static and functional occlusal relationships are the well-defined objectives of orthodontic therapy. To accomplish it, the jaws must be positioned in centric relation (CR)/ Central occlusion (CO) and appropriately proportioned in three space planes. Orthodontists have well-established techniques when analyzing the skeletal connection between the maxilla and mandible in both sagittal and vertical planes. Although there exist multiple analyses for the skeletal transverse dimension (STD), their inclusion in conventional orthodontic diagnosis is not widely acknowledged. Transverse tooth compensations have been visually represented in prosthodontic literature using a crosshatch arch that passes through the buccal and palatal cusps of maxillary molars, known as the Wilson curve. An inflated Wilson curve has long been seen as a prosthetic maxim, increasing the possibility of working and non-working side interferences. Research has indicated a connection between higher masticatory muscle activity and posterior occlusal contacts or interferences. When skeletal transverse issues are concealed by dental expansion, there is an elevated susceptibility to gingival recession in orthodontic patients who have a small Maxilla or constricted upper jaw. The temporomandibular joints, muscles, periodontal tissue, and airway might all suffer in a susceptible patient with a transverse insufficiency brought on by a narrow maxilla. As for orthodontists, the objective is to create skeletal connections and functional occlusion that are as near ideal as feasible. This will reduce the likelihood of occlusion-related inconsistencies exacerbating the negative consequences on the dentition, periodontium, or joints. An accurate skeletal and dental diagnostic in all three space planes is required. Our goal is to explain all approaches to enable readers to use a transverse skeletal diagnostic in their practice regardless of the technological level at their disposal. The initial step is to ascertain the kind and presence of deficiencies in the maxilla. Since fewer soft tissue alterations are caused by maxilla hypoplasia in the transverse direction, skeletal transverse dimension (STD) assessment is a more significant challenge than vertical or sagittal discrepancy assessment. Model analysis, clinical assessments, radiographic measurements, and occlusograms have all proven successful in producing accurate assessments. Most of the time, surgically assisted maxillary expansion (SARME) is reported to have higher stability than orthopedic maxillary expansion (OME). We examine and discourse on possible research opportunities using the Collaborative Cross (CC) mouse model as a substitute research model.

This paper aims at achieving optimal rotor speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input-output datasets, with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the 5th order nonlinear model of the induction motor, rotor speed is estimated with the optimized EKF algorithm and the used algorithm is tested experimentally.

This paper aims to quantify the influence of hydrogen embrittlement on Wierzbicki and Xue (W-X) model parameters. This model has 4 parameters which have been obtained by tensile tests on smooth specimens, tensile notch specimens, single notch tensile specimens (SENT) and pure shear specimens. Hydrogen embrittlement has been obtained by electrolytic method. Application of the (W-X) model has been done to get the local resistance used to predict failure by the Volumetric Method (VM). An example is given for the case of an embedded pipe submitted to service pressure and lateral seismic displacement. The effect of this displacement is then assimilated to the shearing of the straight section of the pipe. The pipe shows a corrosion defect considered a semi-elliptical crack. The influence of hydrogen embrittlement on local strain resistance and safety factors is discussed.

Bosons are particles that can occupy the same quantum state. Identical classical particles are considered distinguishable, but with bosons this is no longer the case. With bosons 1 and 2 in states α and β, the compound state function, ΨS=(1/2)Ψα(1)Ψβ(2)+Ψβ(1)Ψα(2), does not change when the particles are interchanged, i.e., there is one state where classically there would be two. This leads to boson statistics, which is different from Boltzmann statistics in that it favors putting more particles in the same state. For a sufficiently low temperature, it is possible that all bosons aggregate in the ground state where they effectively form a single megaparticle. We cover boson statistics and show how and why Bose-Einstein condensation can occur. We also show how growing networks like the WWW, the economy, or citation networks in scientific literature can follow boson statistics. Analyses of Bose-Einstein condensation have generally assumed a bath with a temperature, i.e., a thermal equilibrium where random collisions lead to a Gaussian-noise-term. However, many setups in physics involve conversion or transport of energy, i.e., nonequilibrium. Nonequilibrium noise is commonly characterized by the frequent occurrence of large kicks, i.e., outliers. We model this by implementing α-stable noise. No temperature exists in that case. We find that Bose-Einstein condensation is still theoretically possible, but probably much harder to engineer. Social and economic traffic is also often best modeled as nonequilibrium and our results may be significant in these contexts.

Solid-state transformer (SST) is a promising technology for smart grids due to its flexible power control (better reliability) and high efficacy (by decreasing losses) compared with traditional transformers. The SST is designed in tandem with three stages, i.e., the input, isolation, and output stages. As three converter stages are used to design the SST to make a more reliable device in practice, it is highly prone to instabilities. Moreover, the key objective of SST design is to implement a modern power distribution system to make it more intelligent and reliable. The stability issue can be even more complicated by the presence of distributed energy resources in the distribution system. Thus, the stability of SST must be measured prior to /during the design, and to determine, a state-space model is used, which is developed in this paper. Each stage of SST is modeled with its controller, and the system's stability is measured through the controllability and observability test. Further, the conversion from state-space to transfer function model is done, and the stability of SST is shown using frequency and time-domain diagrams. With these, the different plots, namely, the Bode plot, Nyquist plot, Nichols chart, Root locus, pole-zero plot, and Eigen plot, are used to ascertain the relative and absolute stability. Finally, the SST Simulink model is tested and validated through hardwate-in-the-loop simulation, i.e., OPALRT, to show its effectiveness and applicability.

This paper focuses on leveraging a cutting-edge 3D state-space model for a holistic eco-friendly assessment in petrochemical operations. - By applying the ecological carrying capacity theory and the three-dimensional state-space model, this research aims to unleash the next frontier of sustainability in the petrochemical industry. - The proposed methodology will enable advanced and comprehensive eco efficiency evaluation in petrochemical operations. - Through the integration of the ecological carrying capacity theory and advanced modeling techniques, this research seeks to revolutionize sustainability assessments in the petrochemical sector. - The utilization of the cutting-edge 3D state-space model will provide a more advanced and accurate understanding of eco-friendly practices in petrochemical operations. This research presents a unique approach distinct from existing literature, which primarily examines business eco efficiency from behavioral motivation and strategic perspectives. In contrast, this paper introduces a novel three-dimensional state-space model to evaluate the ecological efficiency of petrochemical operations. By combining the ecological carrying index and state space, this model represents the ecological impact of petrochemical operations in a three-dimensional geometric space. It considers petrochemical operations as a significant factor exerting pressure on natural resources and the natural environment. In this three-dimensional state-space model, the three axes represent different dimensions of petrochemical operations. The first axis represents the economic status, which quantitatively describes the financial aspects of the operation. The second axis represents the utilization of resources, capturing the extent to which resources are consumed in the operation. The third axis focuses on the impact of the operation on the environment, allowing for a quantitative assessment of its ecological footprint. By incorporating these dimensions, the model aims to provide a comprehensive and quantitative description of the economic activities of petrochemical operations and their interactions with resources and the environment.The primary focus of this research is to apply the three-dimensional state-space model to calculate the ecological carrying capacity of petrochemical operations. By doing so, it aims to verify the feasibility and applicability of the model in determining the ecological efficiency of these operations. The research also aims to provide theoretical tools that can assist operations in making more informed ecological efficiency judgments. By utilizing this model, petrochemical operations will have a quantitative framework to assess and improve their environmental impact, thus fostering more sustainable practices. The findings of this research demonstrate that the implementation of this method can effectively identify issues related to resources, economy, and environment throughout the development of petrochemical operations. By identifying these problems, it provides managers with a solid foundation for making strategic decisions that promote sustainable development. With this valuable information at their disposal, managers can proactively address challenges, optimize resource utilization, and minimize environmental impacts, thus steering the petrochemical operations towards a more sustainable and responsible path.

This article couples human metabolic factors and heating environmental factors, and uses a 57 node human thermal physiological model to study the heating effect of radiators. It provides a way to objectively calculate and directly quantify the effect of heating equipment on human thermal physiological parameters. As the inlet speed increases, the surface temperature of the human body first increases and then decreases. At a speed of 3m/s, it basically reaches a high value. Continuing to increase the wind speed, as the indoor convection intensity further increases, the surface temperature of the human body will decrease, leading to a decrease in thermal comfort. Compared with floor radiation heating under similar indoor temperature conditions, forced convection radiator heating has similar skin temperatures in various parts of the human body after the indoor design temperature exceeds 20 ℃, and the overall thermal comfort of the two technical solutions is equivalent. Research has shown that the turbulence wind speed of convective radiators should not be too small, with a wind speed range of 2.5m/s to 3m/s. The heating effect of using horizontal air outlet from the lower part of the radiator is better than that of vertical air outlet from the upper part. Convection type radiator heating is more suitable for buildings with rooms that are not too high due to the large indoor temperature gradient.

A new approach for predicting the geometrical characteristics of the mixing behavior of an inclined dense jet for angles ranging from 15° to 85° is proposed in this study. This approach is called group method of data handling (GMDH) which is based on the artificial neural network (ANN) technique. The proposed model was trained and tested using existing experimental data reported in literature. The model was then evaluated using statistical indices as well as compared with analytical models from previous studies. The results of the coefficient of determination (R2) indicate a high accuracy of the proposed model with values of 0.9719 and 0.9513 for training and testing for the dimensionless of the distance from the nozzle to the return point xr/D, and 0.9454 and 0.9565 for training and testing for the dimensionless of the terminal rise height yt/D . Moreover, four previous analytical models were used to evaluate the GMDH model. The results showed the superiority of the proposed model in predicting the geometrical characteristics of the inclined dense jet for all tested angles. Finally, the standard error of estimate (SEE) was applied to demonstrate which model performed the best in terms of getting closer to the actual data. The results illustrate that all fitting lines of the GMDH model performed very well for all geometrical parameter predictions and was the best model with approximately 10% error, which was the lowest value of error among the models. Therefore, this study confirms that the GMDH model can be used to predict the geometrical properties of the inclined negatively buoyant jet with high performance and accuracy.

In hepatocellular carcinoma (HCC), recurrence usually occurs after curative surgical resection. Currently, no approved adjuvant therapy has been shown to reduce recurrence rates. In this report, the in vivo mouse effect of sequential combination treatment with recombinant mouse interferon-alpha (rmIFN-α) and an anti-mouse-PD1 antibody on hepatitis B virus (HBV) clearance was evaluated. A phase I clinical trial was then conducted to assess the safety, tolerability, and inhibitory activity of sequential therapy with ropeginterferon alfa-2b and nivolumab in HCC recurrence in patients who have undergone curative surgery for HBV-related HCC. In animal modeling, HBV suppression was significantly greater with rmIFN-α and anti-PD1 sequential combination treatment than with their treatment alone. In the Phase I study, eleven patients completed the sequential therapy with ropeginterferon alfa-2b every two weeks at the dose of 450 µg for six doses, followed by three doses of nivolumab every two weeks up to 0.75 mg/kg. A notable decrease or clearance of HBV surface antigen was observed in two patients. Dose-limiting toxicity of grade 3 alanine transaminase and aspartate aminotransferase increases was observed in one patient. The maximum tolerated dose was determined. Currently no HCC recurrence has been observed. The treatment modality was well tolerated. The data support further clinical development of sequential combination therapy as a post-surgery prophylactic measure against the recurrence of HBV-related HCC.

At the Rio Conference in 1992, the sustainable development agenda promised a new era for natural resource management, where the well-being of human society would be enhanced through the sustainable use of natural capital. Several decades on, economic growth continues unabated at the expense of natural capital, as evidenced by biodiversity loss, climate change and further environmental issues. Why is this happening and what can be done about it? In this research, we present three Agent-Based Models that explore the social, economic and governance factors driving (un)sustainability in complex social-ecological systems. Our modelling results reinforce the idea that the current economic system does not protect the natural capital on which it depends. This is due to a disjunction between the economic and environmental elements upon which the sustainable development paradigm is founded. Additionally, various factors appear to enhance social-ecological system unsustainability: the role of financial entities and monetary debt; economic speculation; technological development and efficiency; lack of long-term views and late government interventions; inefficient tipping point management; and the absence of strong top-down and bottom-up conservation forces. Interestingly, alternative scenarios showed that these same factors could be redirected to enhance sustainable development. The current economic system may, therefore, not be inherently unsustainable, but rather specific economic mechanisms, agents’ decision-making, and the kinds of links between economic and natural systems could be at the root of the problem. We argue that short- and medium-term sustainability can be enhanced by implementing mechanisms that shift capitalist forces to support environmental conservation. Long-term sustainability, however, requires further paradigm change: where the economy integrates, and fully accounts for, externalities and recognises the actual value of natural capital.

The Genji firefly is a key symbol of the conservation and management of rural environments. The species lives underwater as larvae, in the soil when they pupate, and emerges on the ground as adults. Because of the large differences in habitat conditions between larvae and adults, the habitat requirements of the Genji firefly must be clarified over a wide range, from the instream to the surrounding riparian environment. In this study, we built a habitat suitability model for the Genji firefly by relating local landscape features to the number of individuals surveyed in a small river in an agricultural watershed. For habitat suitability modelling, tree-based machine learning methods, namely Random Forests (RF) and classification and regression trees (CART), are used to model species distributions and extract quantitative information on their ecological characteristics. Because the model performance on the test dataset was poor, we removed noisy absences using model outputs from the RF computation using the entire dataset. As a result, we observed improved model performance, in which there seems to be an optimal level for removing noisy absences. Variable importance and partial dependence plots were then used to visualize the relationship between the local landscape features and habitat suitability of the adult firefly. Detailed information on landscape features extracted using RF and CART is useful for a deeper understanding of the ecology of the Genji fireflies.

The phenomenon of decreased serum uric acid (UA) levels in patients with Parkinson’s disease (PD) and its relationship with purine metabolic pathways remain unclear. We compared inosine, hy-poxanthine, xanthine, and UA levels in serum and cerebrospinal fluid (CSF) of PD patients with those of controls, and investigated the effect of changes in the purine metabolic system on UA levels in PD. Overall, 134 samples (serum, 45 PD patients and 30 controls; CSF, 39 PD patients and 20 controls), were analyzed using liquid chromatography-tandem mass spectrometry. A general linear model (GLM) was used to investigate relationships among purine metabolites. Compared to controls, PD patients had significantly lower UA levels in the serum and CSF, and serum and CSF UA levels were significantly correlated. Additionally, in PD, decreased serum hypoxanthine levels were observed with decreased CSF inosine and hypoxanthine levels, suggesting the involvement of the purine recycling system. GLM analysis indicated that the reduced UA levels in PD were mainly due to sources other than the purine metabolic system, such as exercise, nutritional indices, muscle volume, or adipose tissue. Our results highlight the impairment of purine recycling pathways in PD, as evidenced by the decreased serum hypoxanthine and CSF inosine.

Acid in-situ leaching (ISL) is a common approach to the recovery of uranium in the subsurface. As some toxic and harmful substances might be produced by the chemical reactions among the injected sulphuric acid, the groundwater, and the porous media during leaching processes, the pollution control of the mining plan for ISL is important. In this study, a three-dimensional reactive transport modeling (3DRTM) was applied to decide the pollution control mining plan, considering the partial penetration through wellbore in confined aquifer and complex chemical reactions between main minerals. Based on the 3DRTM, different pumping ratio and non-uniform injection schemes were compared. The results show that the preferential pollution control mining plan is non-uniform injection ratio equal 0.1. By analyzing the characteristics of water table and streamline, it is concluded that the scheme has a strong hydraulic capture effect. In this scheme, the concentration of UO22+, H+, SO42- obtained by 3DRTM is lower. The inner well injection rate is 194.09 m3/d, the outer well injection rate is 158.89 m3/d, and the pumping rate is 264.00 m3/d. A reasonable suggestion is to adopt non-uniform injection mining mode in ISL.

Ultrasonic feedback energy is affected by the variety, planting and growth state of crops, it is difficult to fine application in precision agriculture systems. To this end, an ultrasonic sensor was mounted in a spray boom height detection system. Winter wheat was used as the test object to obtain feedback energy values for the spray boom height from the top of the wheat in the field during six critical growth stages: the standing stage, jointing stage, booting stage, heading stage, filling stage and maturity stage. The relationship between the actual value of the height from the spray boom to the top of wheat (Habw) and the detected value of the height from the spray boom to the top of wheat (Hdbw) was analyzed. A spray boom height detection model based on the ultrasonic sensor during the full growth cycle of wheat was determined. Field validation tests showed that the applicability of the spray boom height detection distance (Dd) of the spray boom height detection model proposed in the present study was 450 mm~950 mm. Within the applicable Dd range, the detection error of the detection model was ≤5 cm during the full growth cycle. This study provides an ultrasonic detection model construction method, which improves the reliability and accuracy of ultrasonic boom height detection for different wheat growth stages.

Based on the teaching material of "Robotics" course, this paper studied the automatic knowledge graph construction, including knowledge points extraction and knowledge point relation extraction. We proposed a new method of extracting the first-level, second-level, and third-level knowledge points as well as their prerequisite relations of knowledge points in the textbook. For the problem of insufficient knowledge of the pre-trained language model in the specific field, methods such as incremental pre-training and optimization of cost functions are employed to integrate subject knowledge into the pre-trained language model, thus improving its effectiveness. To overcome the problem that the traditional method of relationship extraction can not be applied directly to the extraction of teaching materials, a new scheme for knowledge point relationship extraction based on keyword relationship is proposed. The experimental data from textbooks shows that the F1 score of knowledge point extraction reaches 93%, considerably improved compared to the traditional model. Consequently, the knowledge point entity extraction and relationship extraction methods based on the pre-trained model can effectively extract structured information and facilitate the automatic construction of knowledge graphs.

This study investigates why young adults live with their parents in Sweden. As young adults’ living arrangements affect decisions about marriage, education, childbirth, and participation in the workforce, more knowledge for policy makers is crucial to implementing effective policies to support young adults and promote financial independence and well-being. Using a data set from 1998 to 2021 at the municipal level in Sweden, we used a spatial autoregressive panel data model to examine the proportion of young adults living at home and regional disparities. The study uncovers intraregional variations, illustrating how different municipalities within Sweden exhibit different patterns of young adults living at home. Our findings reveal that economic factors, such as unemployment, significantly impact this pattern. The dynamics of the housing market, demographic factors, cultural differences, and location-specific characteristics also play an essential role in explaining this pattern. The findings suggest that the key drivers are the lack of rental housing, high unemployment rates, a high degree of urbanisation, interregional migration, and lack of social capital.

Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

In this work, an entropy-stable and well-balanced numerical scheme for a one-dimensional blood flow model is presented. Such scheme is obtained from an explicit entropy conservative flux along with a second order discretization of the source term by using centered finite differences. We prove that the scheme is entropy-stable and preserves steady-states solutions. In addition, some numerical examples are included to test the performance of the proposed scheme.

The fatigue delamination damage is one of the most important fatigue failure modes of laminated composite structures. However, the developments of the theoretical framework, mathematical/physical model and numerical simulation of fatigue delamination are still challenging issues, and there is a lack of systematic classification. This article reviews the experimental phenomena of the onset and propagation stage of delamination under fatigue loading, and summarizes the commonly used phenomenological models for laminated composite structures. The research methods, general modeling formulas and development prospects of the phenomenological models are presented in detail. In the light of the analysis of finite element model (FEM) for the laminated composite structures, a number of simulation methods of fatigue delamination crack propagation damage model are carefully classified. Furthermore, within the theoretical framework of strength theory model (SM), fracture mechanics model (FM), damage mechanics model (DM) and hybrid model (HM), the complete procedure, application, capability assessment, advantages and limitations of the models are also discussed in detail. Finally, several valuable suggestions are given for the research interests, and challenging technical problems that need to be overcome on the fatigue delamination prediction of laminated composite structures in the future.

Considering future tasks in soil health, resources management and environmental protection, farmers are challenged to develop sustainable strategies for managing soil and land resources. In this study, the long-term sustainability of two fertilization strategies, - current, with synthetic fertilizers (SYN) vs conservative, with organic sources of nitrogen (organic amendments plus green manure with a legume - CONS) - was assessed in a processing tomato/durum wheat rotation. The EPIC model was used, validated with field data then run to simulate the management for 30 years under 3 current and future climates. Yield, soil organic carbon (SOC) stock change, nitrogen use efficiency (NUE), water use efficiency (WUE) and nitrate leaching were considered as sustainability indicators. Under all the future climate scenarios, tomato yield increased in CONS, remaining almost stable in SYN. Wheat yield increased both in CONS and SYN, but the average yield in CONS was considerably lower than in SYN. NUE and nitrate leaching followed the same trend, both decreasing in CONS, while WUE was higher in CONS compared to SYN. The effect of CONS on SOC was always positive. Thus, the alternative N fertilization strategy proposed can be a favorable option for maintaining soil health and a sustainable crop production.

Flood modeling and forecasting are key to managing and preparing for extreme flood events. Hydrological flood forecasting aims to predict the system response to different input changes with minimum uncertainties. In that sense, streamflow Data Assimilation (DA) seeks to combine errors between hydrological model and water discharge observations through the update of model states. This paper aims to assess a sub-daily flood forecast system in a basin of the Peruvian Tropical Andes using two sequential data assimilation algorithms called the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF). The study was conducted in the Vilcanota River basin during the rainiest months in 2022 to assess recent potential river floods. This basin is in the southern Peruvian Andes and was selected because it is continually affected by river floods such as occurred in 2010. For this purpose, the lumped GR4H rainfall-runoff model was run forward with 100 ensemble members using two different Satellite Precipitation sources (IMERG-E' and GSMaP-NRT'). Also, four DA experiments (IMERG-E'+EnKF, IMERG-E'+PF, GSMaP-NRT'+EnKF, and GSMaP-NRT'+PF) were conducted by assimilating real-time hourly discharges at the Pisac stream gauge station to examine the improvement of forecast accuracy for lead times of 1—24 hours. Results display good forecast performances during the first 10 hours, especially for the GSMaP'+EnKF scheme. Finally, this work benchmarks the application of streamflow DA in and Andean basin of Peru with sparse data availability and will support the development of more accurate climate services in Peru through hydrologic ensemble predictions.

This article aims at presenting the results that were found in the design and implementation of a new management model of Critical Success Factors for improving the effects of research projects. The model was based on the theory of structural equations derived from factor analyses and multiple linear regression models. For achieving the purpose, the authors developed an empirical evidence analysis founded on a literature review in the knowledge area of project management. Afterwards, the process of identification of the CSFs was carried out for a group of research projects launched in Colombia as a result of the application and measurement of an instrument. They were the basis for the development of a structural relationship modeling. Also, this contributed to determine the CSFs that explain the generation of effects of research projects, and the project management execution process from which it can be carried out based on said measurement indicators.

The Cancer Genome Atlas (TCGA) has classified papillary thyroid carcinoma (PTC) into indolent RAS-like and aggressive BRAF-like based on its distinct driver gene mutations. This study aimed to assess clinicopathology and pERK1/2 expression variations between BRAF-like and RAS-like PTCs and establish predictive models for BRAFV600E and RAS-mutated PTCs. A total of 222 PTCs underwent immunohistochemistry staining to assess pERK1/2 expression and Sanger sequencing to analyze the BRAF and RAS genes. Multivariate logistic regression was employed to develop prediction model. Independent predictors for the BRAFV600E mutation include a nuclear score of 3, the absence of capsules, an aggressive histology variant, and pERK1/2 levels exceeding 10% (X2=0.128, P&gt;0.05, AUC=0.734, P&lt;0.001). RAS mutation predictive model includes follicular histology variant and pERK1/2 expression &gt;10% (X2=0.174, P&gt;0.05, AUC=0.8, P&lt;0.001). We proposed using the prediction model concurrently with four potential combination group outcomes. PTC cases included in combination of low BRAFV600E-scoring group and high RAS-scoring group are categorized as RAS-like (adjOR=4.857, P=0.01, 95% CI=1.470-16.049). PTCs included in combination of high BRAFV600E-scoring group and low RAS-scoring group are categorized as BRAF-like PTCs (adjOR=3.091, P=0.001, 95% CI=1.594-5.995). The different prediction models indicate variations in biological behaviour between BRAF-like and RAS-like PTCs, necessitating adjustments in treatment approaches.

Matrix-variate Gaussian type or Wishart type distributions in the real domain are widely used in the literature. When the exponential trace has an arbitrary power and when a factor involving a determinant enters into the model or a matrix-variate gamma type or Wishart type model with exponential trace having an arbitrary power, is extremely difficult to handle. Evaluation of the normalizing constant in such a model is the most important part because when studying the properties of such a model, the method used in the evaluation of the normalizing constant will be the relevant steps in all the computations involved. One such model with a factor involving a trace and the exponential trace having an arbitrary power, in the real domain, is known in the literature as Kotz' model. No explicit evaluation of the normalizing constant in the model involving trace with an exponent and determinant with an exponent entering into the model and at the same time the exponential trace having an arbitrary exponent seems to be available in the literature. The normalizing constant widely used in the literature and interpreted as the normalizing constant in the general model and refers to as a Kotz' model does not seem to be correct. Corresponding model in the complex domain, with the correct normalizing constant, does not seem to be available in the literature. One of the main contributions in this paper is the matrix-variate distributions in the complex domain belonging to Gaussian type, gamma type, type-1 and type-2 beta type when the exponential trace has an arbitrary power. All these models are believed to be new. A second main contribution is the explicit evaluation of the the normalizing constants, in the real and complex domains especially in the complex domain, in a matrix-variate model involving a determinant and a trace as multiplicative factors and at the same time the exponential trace having an arbitrary power. Another main contribution is the introduction of matrix-variate models with exponential trace having an arbitrary exponent, in the categories of type-1 beta, type-2 beta and gamma distributions or in the family of Mathai's pathway models [1], both in the real and complex domains. Another new contribution is the logistic-based extensions of models in the real and complex domains with exponential trace having an arbitrary exponent and connecting to extended zeta functions introduced by this author recently. Some properties of such models are indicated but not derived in detail in order to limit the size of the paper. The techniques and steps used at various stages in this paper will be highly useful for people working in multivariate statistical analysis as well as people applying such models in engineering problems, communication theory, quantum physics and related areas, apart from statistical applications.

As the demands in reliability and high-degree of automation in industrial applications are increasing exponentially, the current Industry 4.0 trend lies in migrating towards smarter technologies that increase flexibility and provide autonomous operation. To tackle this, manufacturers shifted towards Real-Time Ethernet communication which supports autonomous operation, faster sensor/actuator configuration and high degree of determinism. Nevertheless, the uninterrupted operating principle of legacy industrial systems as well as the system complexity and heterogeneity constitutes the transition to Real-Time Ethernet very challenging. This article proposes a novel method allowing to automate the generation of executable code for Real-Time Ethernet architectures based on high-level programming models. The method is based on a programming model and modular code artifacts for the hardware architecture, employing Real-Time Ethernet communication. Through an tool-supported algorithm the high-level model is linked to the architecture's communication primitives and interfaces. The tool-support reduces significantly the development and debugging effort for Real-Time Ethernet applications and increases the application reliability, since the generated code is based on verified programming models. Additionally, the tool creates dedicated files, which allow automated industrial network configuration without any human intervention. Finally, by embedding firewall policies into the code generation process, the method guarantees cyber-resilience for the Real-Time Ethernet architecture. We demonstrate the proposed method in Programmable Logic Controllers (PLCs) of a Public Power Corporation's Hydroelectric Power Plant, which control the temperature and rotor speed of a power generator. The results demonstrate the method's ability to generate rapidly trustworthy and fault-tolerant code for the autonomous plant operation. The hurdles in time and effort for developing, configuring and debugging Real-Time Ethernet applications can be minimized through a high-level programming model that allows automated generation of executable code that is reusable for similar applications. Such time and effort reduction may pave the way towards the Industry 4.0 area.

Compared with ordinary asphalt pavement, the application of modified asphalt will improve the performance of asphalt pavement. However, using the same modification method for the whole road section can cause modification waste or result in some parts not meeting performance standards. This paper proposes a whole road section modification method. First of all, a finite element model was established using Abqus according to the actual road pavement material to analyze the mechanical response of each layer under different working conditions and establish the grading standard under comprehensive working conditions. Secondly, different modification methods were analyzed according to investigation and their modification effects were graded through clustering analysis method. Finally, different working conditions and different modification methods were reasonably matched and a section of highway was selected for verification. The results show that before modification, the calculated rutting is 6.23mm and after modification, it is reduced to 2.45mm which indicates that reasonable modification has a better effect and can achieve the purpose of rutting equalization control on the whole road section. It is of great significance to carry out reasonable modification on different conditions to control rutting equalization of asphalt pavements.

: There remains no acquiescence that confirms the extent to which unemployment and entrepreneurship influence each other in developing countries. Thus, an incessant invite for further debate attracted the need to assess the relationship between unemployment and entrepreneurship in terms of business formation in Namibia Given that, this study examines the relationship between unemployment and entrepreneurial activities in Namibia. Time series data were collected from the World Bank database to investigate the relationship between unemployment and entrepreneurship in Namibia. Unit root test was conducted using both the Augmented Dickey-Fuller test and the Phillips-Perron test to measure stationarity of the data and found all the variables to have no unit root after the first difference. Thus, we employed the Johansen-Juselius test of cointegration, and the results revealed the absence of a long-run relationship between the variables. Hence, we performed a vector autoregressive (VAR) model to estimate the short-run relationships and found that Namibia exhibit both the refugee and Schumpeter effects. Finally, the direction of causality between the variables was measured using the Pairwise Granger causality test and the results revealed that none of the variables was proven to granger cause the other, concluding that they are all independent of each other. Therefore, in pursuit of unemployment mitigation, we recommend policymakers to focus on other aspects beyond entrepreneurship when devising policies without jeopardizing the ease of business start-up procedures. This will incentivize entrepreneurial activities, which can eventually address other macroeconomic issues beyond unemployment.

Among the main elements that contribute to climate change are degradation processes and the ecological level of the landscape. These two topics have been discussed and researched for many years and many studies have been conducted. The idea of the article is to determine the correlation between the ecological stability of the territory and the intensity of degradation processes and find out how the ecological stability affects the intensity of soil erosion and vice versa. The ecological stability was calculated based on various methods during the years analyzed, i.e., 1990, 2006, 2012, and 2018. The soil water erosion was performed for the same period in order to identify the relationship between the ecological stability and intensity of soil erosion. The investigated area is located in the Slovak Republic while each year reflects different management of the territory reflecting the current situation in the catchment according to the year evaluated. The intensity of the erosion process was conducted using a phycially-based EROSION-3D model and based on the precipitation derived using Community Land Model (the CLM model). In addition to identifying the relationship between the level of ecological stability and the intensity of erosion, this study also describes the development the ecological stability during the evaluated period together with changes in soil erosion processes.

Biomass gasification acquired great interest over the past decades as an effective and trustable technology to produce energy and fuels with net-zero carbon emissions. Moreover, using biomass waste as feedstock enables to recycle organic wastes and to fit the circular economy goals thus reducing the environmental impacts of waste management. Even if many studies have been already carried on, this kind of process must still be investigated and optimized with the final aim to develop industrial plants for different applications, from the hydrogen production to net-negative emission strategies. Modeling and developing of process simulations became an important tool to investigate the chemical and physical behavior of the plant, allowing to make a first raw optimization of the process and to define heat and material balances of the plant as well as to define optimal geometrical parameters with cost and time effective approaches. The present review paper focuses on the main literature models developed until now to describe biomass gasification process, and in particular on kinetic models, thermodynamic models, and computational fluid dynamic models. The aim of this study is to point out the strengths and the weakness of those models, comparing them and indicating in which situation is better to use an approach instead of another. Moreover, theoretical shortcut models and software simulations, not explicitly addressed by prior reviews, are taken into account. For researchers and designers this review provides a detailed methodology characterization as a guide to develop innovative study or project.

Peripheral nerve injury remains a medical challenge with no easy resolution. Over the last few decades, significant advances have been achieved in promoting peripheral nerve regeneration, and new assessment tools have been developed, both functional and imaging, to quantify the proportion and quality of nerve recovery. The exploration of new animal models, larger, more complex and with more similarities to humans, has made it possible to reduce the gap between the results obtained in classic animal models such as rodents and the application of new therapies in humans and animals of clinical interest. Ultrasonography is an imaging technique recurrently used in clinical practice to assess the peripheral nerves, allowing its anatomical and topographic characterization, and aiding in the administration of anesthesia and in the performance of nerve blocks. The use of this technique in animal models is scarce, but it could be a useful tool in monitoring the progression of nerve regeneration after the induction of controlled experimental lesions. In this work, sheep, a promising animal model in the area of peripheral nerve regeneration, were subjected to an ultrasonographic study of three peripheral nerves of the hind limb, the common peroneal, tibial and sciatic nerves, in order to establish values of dimensions and ultrasound appearance in healthy nerves and also landmarks for their identification. At the same time, an echographic evaluation of the cranial tibial muscle, an effector muscle of the common peroneal nerve, was also carried out, establishing normal values for the echographic aspect and dimensions. The results obtained will allow the use of these data as control values in future work exploring new therapeutic options for nerve regeneration after induction of common peroneal nerve injuries in sheep.

This study analyzes the effect of economic growth and income distribution on health inequality using data from the Organization for Economic Cooperation and Development (OECD). A panel analysis was conducted by combining 21 years of data (from 2000 to 2020) from 38 OECD countries. Life expectancy and avoidable mortality were set as the dependent variables; gross domestic product and poverty gap were set as the independent variables; body mass index, consumption, smoking rate, healthcare personnel, number of beds in health service facilities, national medical expenses, and unemployment were set as control variables. First, economic growth and poverty gap significantly affect life expectancy and avoidable mortality. It has been confirmed that to resolve health inequality, growth during growth and distribution must be prioritized. Second, it has been confirmed that as the poverty gap increases, life expectancy increases and the avoidable mortality rate decreases. A mechanism different from the existence of a medical security system targeting the lowest class or an increase in the welfare of society worked. In conclusion, as a policy proposal to alleviate health inequality, this study suggests that policy intervention is necessary to block or alleviate the negative impact of income inequality in health care policies.

: Introduction: Discovering compounds with antibacterial activity is crucial in the current fight against antibiotic-resistance. Material and Methods: We generate two QSAR model to design six new heteroaryl drug candidates; we assessed the antibacterial properties of the compounds against nine ATCC strains, the majority being part of the ESKAPE group, that include Enterococcus faecalis, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and also Salmonella enterica and Escherichia coli. We performed a synergic assay in which we combined PB4, a compound that we have previously tested in published studies, with GC-VI-70, one of the new compounds with the best cytotoxicity/MIC profile. We tested sub-MIC concentrations of PB4 with five antibiotics (linezolid, gentamycin, ampicillin, erythromycin, rifampin and imipenem) to examine the synergic effect on the ATCC strains. To evaluate compounds’ cytotoxicity, we performed a 24-hour and 48-hour 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay on Colorectal Adenocarcinoma (CaCo-2) cells. The antibiotics were tested, alone and in combination with PB4. Results: PB4 lowered the MIC measured for GC-VI-70 and for the various antibiotics employed in clinical use, and all the compounds presented in this study have cytotoxic activity against cells. Conclusions: These compounds show promising use in combination with antibiotics to improve their effectiveness at low concentrations, to prevent unwanted cytotoxic effects.

The emergence of Digital Twin (DT) technology is creating unique opportunities for human so-ciety in terms of real-time data transfer from the physical environment to its digital replica. DT technology has two-way interaction capabilities that enable data exchange between physical and virtual environments, creating bidirectional data flows. Progress has been made in different in-dustry sectors, but the Architecture, Engineering, Construction, and Operation (AECO) sector still requires further development. Recent studies have shown the potential for DT-based platform implementation in the lifecycle management of buildings and infrastructure. This paper examines the development and implementation of DT technology in the construction sector, identifying key definitions, enablers and use cases, while comparing its use with other industries and evaluating benefits and future potential.

Chipless radio frequency identification (RFID) technology is expected to replace barcode technology due to its ability to read in non-line-of-sight (NLOS) situations, long reading range, and low cost. Currently, there is extensive research being conducted on frequency-coded (FC) co-polarized radar cross-section (RCS)-based tags, which are widely used. However, detecting co-polarized chipless RFID tags in cluttered environments is still a challenge, as confirmed by measuring two co-polarized tags in front of a perfect metal reflector (30.5cm×22.5cm). To address this challenge, a realistic mathematical model for a chipless RFID system has been developed that takes into account the characteristics of the reader and the tag, as well as reflections from cluttered objects. This model has been simulated and verified with measurement results by placing a single flat metal reflector behind two co-polarized one-bit designs: a dipole array tag and a square patch tag. The results showed that the interfering signal completely overlaps the ID of the co-polarized tag, severely limiting its detectability. To solve this issue, the proposed solution involves reading the tag in cross-polarization mode by etching a diagonal slot in the square patch tag. This proposed tag provides high immunity to the environment and can be detected in front of both dielectric and metallic objects.

In this study, Brillouin optical time-domain analysis (BOTDA) sensing technology was utilized for monitoring settlement in a similarity model of a highway subgrade. As contact winding cannot be used for an optical fiber that is buried directly in soil, uncoupling between. the fiber and the soil can occur. Thus, an optical-fiber-embedded beam (OFEB) was developed, and a method for measuring and calculating the beam deformation was proposed. A calibration test and a test on a similarity model of a subgrade were carried out to investigate the applicability and monitoring accuracy of the OFEB. It was concluded that the OFEB can accurately measure beam deflection, where the maximum relative error between measurements by the optical fiber and a displacement transducer was approximately 5%. The OFEB was embedded directly into a similarity model of a subgrade to monitor settlement. The deflection deformation of the OFEB was found to be close to the subgrade settlement over a certain settlement range with a relative error below 8.1%. Thus, the OFEB can be used to realize measure large-range distributed settlement in a subgrade. A numerical simulation was performed to identify appropriate beam dimensions and material design parameters, thereby extending the measurement range before decoupling of the OFEB and soil occurs. The enhancement of the measurement range and accuracy of the OFEB based on the preliminary experiments carried out in this study enables further investigation of settlement monitoring.

The anaerobic digestion (AD) of rumen residues and wheat straw in different proportions of inoculum (66.67, 50, 33.33 %V/V) to substrate and their co-digestion potential have been exhaustively studied in this research. It explores variation of feedstock characteristics such as biodegradability and methane potential during AD and anaerobic co-digestion (ACoD) of rumen residues (RR) with wheat straw (WS), under mesophilic conditions. Comparative performance was made with a organic load rate of 18 gVS/L (with 2:1, 1:1 and 1:2 g/g VS of inoculum to substrate). Among different kinetic models studied (modified Gompertz model, transfer model and logistic function), the AD and ACoD of substrates showed better fit to Gompertz model (R2: 0.977–0.997) indicating variation in lag phase and methane production rate depending on the substrate characteristics. During AD, the methane yield improved as the inoculum ratio increased, however, there was no significant difference in ACoD yield from RR to WS for the inoculum to substrate ratios studied, due to the synergistic effect as a result. of greater biodegradability and optimal conditions (such as the C/N ratio). A comparison of methane generation means indicated that maximum methane production can be achieved by mixing rumen residue (75 %) and wheat straw (25 %) with a C/N ratio of (38.15).

Attention Deficit-Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder with high incidence in children and adolescents characterized by motor hyperactivity, impulsivity, and inattention. MRI-based evidences support that neuroanatomical abnormalities as the volume reduction of neocortex and hippocampus are shared by several neuropsychiatric diseases as schizophrenia, autism spectrum disorder and ADHD. In addition, it is well documented the abnormal development and postnatal pruning of dendritic spines of neocortical neurons in schizophrenia, autism spectrum disorder and intellectual disability. A recent report using the prenatal nicotine exposure murine model of ADHD support a delay in spine maturation in CA1 neurons correlated with impaired working memory and hippocampal long-term potentiation (LTP). In vivo spine imaging show that dendritic spines are dynamic structures exhibiting Hebbian and homeostatic plasticity triggering intracellular cascades involving glutamate receptors, calcium influx and remodeling of F-actin network. The LTP-induced insertion of postsynaptic glutamate receptors is associated to the enlargement of spine head and long-term depression (LTD) to the spine shrinkage. In this review, we summarize recent evidence emerged from meta-analysis of brain imaging data from ADHD patients, risk loci from global genome-wide analysis and new reports focused on spine molecular structure and dynamics using in vivo imaging in neocortex and hippocampus.

This study investigates the influence of expectations and trust on farmers' decisions regarding contractual choices in land transfer. The dataset used for analysis consists of 1101 households in Guizhou Province. The research focuses on two aspects: the selection between written and oral contracts, and the decision to continue the contractual relationship. The findings reveal that farmers' choices are significantly affected by their expectations and levels of trust.Specifically, when farmers' expectations regarding the land transfer process are not met, they tend to prefer written contracts over oral ones. This preference can be attributed to the greater clarity and risk reduction offered by written contracts. On the other hand, higher levels of trust increase the likelihood of farmers continuing the land transfer contract, as trust fosters a sense of confidence and security in the ongoing relationship.Interestingly, trust also plays a moderating role. Farmers who have aligned expectations and high levels of trust are more inclined to opt for oral contracts. This preference for oral contracts can be attributed to their flexibility and emphasis on interpersonal relationships, allowing for better adaptation to the changing dynamics of land transfer.These findings contribute to a deeper understanding of the factors influencing farmers' decision-making processes regarding contractual choices in land transfer. They provide valuable insights for policymakers involved in land transfer, enabling them to optimize policies and facilitate farmers' decision-making processes.

Water management systems help allocate water resources effectively, considering various demands such as agriculture, industry, domestic use, and environmental needs. They optimise water distribution and ensure equitable access, minimising water scarcity and conflicts. Critical elements of this system are often the target of various attacks. Depending on the target of the attack, different scenarios based on physical, cyber, or combined forms of attacks can be used. Requirements for the protection of water objects forming part of the critical infrastructure system are determined primarily by generally binding legal regulations, technical standards, or other requirements of third parties. These requirements imply the need to adopt certain protective measures. Physical protection system (PPS), as a convenient way of organising protective measures, makes it possible to prevent an unauthorised person from achieving his goal. Current procedures aimed at protecting objects use a qualitative or quantitative approach. The article presents the use case of a possible way to protect a selected water reservoir that has been identified as a national element of critical infrastructure in the subsector Drinking Water Provision. The use case is based on the analysis of safety requirements and subsequent design of the PPS water reservoir. To verify the functionality of the proposed PPS, a quantitative PPS model was created using a software tool, and four possible attack scenarios were simulated.

The merge areas of freeway work zones include relatively significant safety hazards that have continuously led to urgent safety issues to be solved by the management departments. In order to make up for the cumbersome process of independent identification of rear-end collision and lane change collision on complex road sections, an appropriate identification method of traffic conflicts in the merge area of freeway work zone was explored, this study collected vehicle running tracking data from the merge areas of multiple work zones, using an unmanned aerial vehicle video technique. Based on an inter-frame difference method and the principle of a spatio-temporal context visual tracking algorithm, the vehicles were detected and tracked, and the coordinate data of the vehicles in continuous motion were parsed using MATLAB extension tools. Based on the behavior characteristics of vehicle conflict avoidance, a new identification method for evading severe traffic conflicts is proposed according to the initial velocity, acceleration and accident rate of section traffic. Then, a statistical analysis was performed on the spatial distribution characteristics of the traffic conflicts in typical merge areas. The impacts of the road conditions in work zones, vehicle factors, and traffic flow factors on the traffic conflicts were analyzed. A binomial logistic model was established to identify the main influencing factors. The results show that in the merge area of the freeway work zone, there are serious traffic conflicts between vehicles in the following two situations: (Ⅰ) v∈[7,13.5] m/s and a∈[-3.96,-0.65]m/s2; and (Ⅱ) v∈[13.5,24.3] m/s, and a∈[-3.96,-1.57] m/s2. The probabilities of serious traffic conflicts in the first and last 25 m of the merge area are greater than those in the other sections. The smaller the space between the upstream work zone and the merge area, the greater the probability of serious traffic conflicts between vehicles. When the average vehicle speed is relatively high, the probability of serious conflicts is the highest, i.e., by a multiple of 5.95 from the baseline. Moreover, the probability of serious conflicts between vehicles is higher for larger vehicles, i.e., 4.765 times that for small vehicles. The research results can serve as a reference for freeway management departments to improve the safety levels of merge areas during road work. For example, the probability of serious conflicts can be effectively reduced by setting up reasonable speed limit signs in the work zone, increasing the spacing between work zone and merge area, and appropriately diverting large vehicles.

Effective marketing decision-making benefits from a rigorous, data-driven process that systematically evaluates alternatives based on insights and analysis. This study develops and evaluates a 5-stage decision-making process model integrating marketing engineering principles aimed to optimize marketing decisions. An experiment randomized 150 participants into groups following either the proposed model or an unaided approach. Results indicate the model-following group achieved significantly higher ROI from marketing decisions (19.3% vs 16.4%) and employed key elements like customer segmentation, experimentation, and optimization to a greater extent. However, limitations including the experiment's scenario, self-report measures, and cross-sectional design constrain implications. Future research employing probability sampling, multiple decision contexts, longitudinal designs, manipulation checks, and objective metrics can further validate the proposed model. Overall, the study advances the understanding of how structured decision processes based on marketing engineering principles may optimize marketing decisions and outcomes.

Most petrochemical plants still maintain a Proportional-Integral-Differential controller(PID) system, which is a feedback control system. However, gradually, the PID system is being extended and introduced to the Advanced Process Controller (APC) system, which is an integrated control system of feedforward and feedback that predicts external influences in advance. In the process of conducting on-site plant tests and calculating APC model parameter for the application of APC systems, a problem arises that Model Parameter are implemented differently depending on the proficiency of APC engineers. To minimize this problem, a technique for estimating APC model parameter without a plant test is required. In order to estimate the APC model parameter, it is necessary to train on dynamic interval data. In this paper, we use statistical techniques such as PELT, Linear Kernel, and Radial Basis Function Kernel of Change Point Detection (CPD) to extract dynamic data with minimum Mean Absolute Error (MAE) from time series data of a real petrochemical plant. Then, the hyper parameter is fixed and the APC model parameter is estimated by learning the dynamic section data. By applying the estimated APC model parameter to the APC Model Tool and measuring the fitting rate, it was confirmed that it is possible to estimate the APC model parameter with excellent control performance without plant test.

Freezing/thawing indices are important indicators of dynamics of frozen ground on the Qinghai-Tibet Plateau (QTP), especially for areas with limited observations. Based on the numerical outputs of Community Land Surface Model version 4.5 (CLM4.5) from 1961 to 2010, this study compared the spatial and temporal variations between air freezing/thawing indices (2 m above the ground) and ground surface freezing/thawing indices in permafrost and seasonally frozen ground (SFG) across the QTP, after presenting changes of frozen ground distribution in each decade in the context of warming and wetting. The results indicate that an area of 0.60×106 km2 permafrost of QTP degraded to SFG in 1960s-2000s, and the primary shrinkage period occurred in 2000s. The air freezing index (AFI) and ground freezing index (GFI) decreased dramatically at the rates of 71.00℃·d/decade and 34.33℃·d/decade from 1961 to 2010, respectively. In contrast, the air thawing index (ATI) and ground thawing index (GTI) increased strikingly with the values of 48.13℃·d/decade and 40.37℃·d/decade in the past five decades. Permafrost showed more pronounced changes in freezing/thawing indices since 1990s compared to SFG. The changes of thermal regimes in frozen ground shows closely relations to air warming until the late 1990s, especially in 1998 when the QTP has undergone the most progressively warming. However, a sharp increase in the annual precipitation from 1998 seems to take a stronger control role on thermal degradation in frozen ground than the air warming in 2000s. Meanwhile, the prominent vegetation expansion further promotes thermally unstable of frozen ground in this highly wetting period.

: Submerged aquatic vegetation (SAV) are highly efficient at carbon sequestration and, despite their relatively small distribution globally, are recognized as a potentially valuable component of climate change mitigation. However, SAV mapping in tidal marshes presents a challenge due to optically complex constituents in the water. The emergence and advancement of deep learn-ing-based techniques in the field of habitat mapping with remote sensing imagery provides an opportunity to address this challenge. In this study, an analytical framework was developed to quantify the carbon sequestration of SAV habitats in the Atchafalaya River Delta Estuary from field and remote sensing observations using deep convolutional neural network (DCNN) tech-niques. A U-Net based model, Wetland-SAV Network, was trained to identify SAV percent cover (high, medium, and low) as well as other estuarine habitat types from Landsat 8/9-OLI data. The areal extent of SAV was up to 8% of the total area (47,000 ha) with a significant loss of SAV habitats observed post-Hurricane Barry (~2,300 ha) in 2019. The habitat areas and habitat-specific carbon fluxes were then used to quantify net greenhouse gas (GHG) flux of the study area for with/without SAV scenarios in a Carbon Balance Model. The total net GHG flux was in the range of -0.13 ± 0.06 to -0.86 ± 0.37 ×105 tonne CO2e yr-1 and increased up to 40% (-0.23 ± 0.10 to -0.90 ± 0.39 ×105 tonne CO2e yr-1 ) when SAV was accounted for within the calculation. At the hectare scale, inclusion of SAV resulted in an increase of ~60% for net GHG sink in shallow areas adjacent to emergent marsh where SAV was abundant. This is the first attempt at remotely mapping SAV in coastal Louisiana as well as a first quantification of net GHG flux at the scale of hectares to thousands of hectares, accounting for SAV within these sub-tropical coastal delta marshes. Remote sensing and deep learning models have high potential for mapping and monitoring of SAV in turbid sub-tropical coastal deltas as a component of increasing accuracy of net GHG flux estimates at small (hectare) and large (coastal basin) scales.

Abstract: The study aimed to investigate the antitumor efficacy of anti-LMP1 antibodies in EBV-positive nasopharyngeal and stomach cell lines and xenograft models. The study also exam-ined the NF-κB expression and cell cycle activation of NPC serum exosome-associated LMP1. An-ti-LMP1 antibody treatment before or during cell implantation prevented tumor growth in nude mice. A small dose of antibodies resulted in complete tumor regression for at least three months af-ter the tumors had grown in size. The consumption of antigen-antibody complexes by tumor cells limited tumor growth. In vitro experiments showed that anti-LMP1 antibodies killed EBV-positive NPC- or GC-derived epithelial cell lines and EBV-positive human B cell lines but not EBV-negative cell lines. Treatment with anti-LMP1 reduced NF-κB expression in cells. The animal model experi-ments showed anti-LMP1 inhibited and prevented NPC- or GC-derived tumor growth. The results suggest that LMP1 antibody immunotherapy could cure nasopharyngeal cancer, EBV-positive gastric carcinoma, and EBV-associated lymphomas. However, further validation of these findings is required through human clinical trials.

Gram-negative bacterial infections pose a significant threat to public health. Toll-like receptor 4 (TLR4) recognizes bacterial lipopolysaccharide (LPS) and induces innate immune responses, autophagy and cell death, which have major impacts on the body physiological homeostasis. However, the role of TLR4 in bacterial LPS-induced autophagy and apoptosis in large mammals remains unknown, which are closer to humans than rodents in many physiological characteristics. So far, few reports focus on the relationship between TLR, autophagy and apoptosis in large mammal levels. We urgently need more tools to further explore their crosstalk. Here, we generated a TLR4-enriched mammal model (sheep) and found that a high-dose LPS treatment blocked autophagic degradation and caused strong innate immune responses and severe apoptosis in PBMCs of transgenic offspring. Excessive accumulation of autophagosomes/autolysosomes might contribute to LPS-induced apoptosis in PBMCs of transgenic animals. Further study demonstrated that inhibiting TLR4 downstream NF-κB or p38 MAPK signaling pathways reversed the LPS-induced autophagy activity and apoptosis. These results indicate that the elevated TLR4 aggravates LPS-induced PBMCs apoptosis by leading to lysosomal dysfunction and impaired autophagic flux, which is associated with TLR4 downstream NF-κB and MAPK signaling pathways. This study provides a novel TLR4-enriched mammal model to study its potential effects on autophagy activity, inflammation, oxidative stress and cell death. These findings also enriched the biological functions of TLR4 and provided powerful evidence for bacterial infection.

The results of measuring the capacity of a thin-film solid-state lithium-ion battery (SSLIB) Ti|Si@O@Al|LiPON|LiCoO₂|Ti at different charge currents are reported. It is shown that the dependence of the capacity on the current density follows the Peukert's law, which is characterized by a low value of the Peukert’s exponent in the region of low currents and a high value of the exponent in the region of high currents. A capacity model for a SSLIB based on the balance of diffusion and migration currents is proposed and the dependence of the capacity on the charge current is derived. The obtained dependence is well approximated by Peukert's law with an exponent of 1.39, which is intermediate between the exponents for lithium-ion batteries with liquid electrolyte and SSLIBs.

In this study, the focus is on the drop test simulation of an MR (Magnetorheological) damper-based main landing gear (MRMLG), aiming to explore multi-degree-of-freedom (DOF) dynamic models during aircraft landing. Three different 6-DOF dynamic models are proposed in this work and their drop performances are compared with results achieved one of commercial softwares. The proposed models include a nonlinear aircraft model (NLAM), a linearized approximated aircraft model (LAAM) linearizing from the nonlinear equations of motion in NLAM, and a fully approximated aircraft model (FAAM) which linearizes the MRMLG's strut force model. In order to evaluate drop performance of the aircraft landing gear system with MR dampers, a 7-DOF aircraft model incorporating the nonlinear MRMLG is formulated using RecurDyn. The principal comparative param-eters are the coefficient of determination (R2) for system response of each model with Re-curDyn model and root mean square error (RMSE) which is the ensembles of CG dis-placement data for each model. In addition, the ensemble of time series data is created for diverse drop scenarios, providing valuable insights into the performance of the proposed drop test models of the aircraft landing gear system featuring MR dampers.