Due to poor ventilation conditions in the tunnel, if gas pipeline leaks, the consequence of the accident will be more serious. Therefore, before the emergency repair, gas in the tunnel needs to be excharged so as not to explode during the repair process. Therefore, it is necessary to study the ventilation of gas in the tunnel. Based on the computational fluid dynamics (CFD) theory and taking the Yanyingshan tunnel section of China-Myanmar pipeline as an example, this paper uses Fluent software to establish the leakage model of the gas pipeline and fan model in the tunnel and analyzes the influence of different fan locations and number of fans on gas concentration. It can be concluded that: (1) the use of press-in method makes it more efficient to discharge gas out of the tunnel. (2) In order to make ventilation efficient, the fan should be arranged in a higher position and needs to be at a distance from the top of the tunnel. (3) Parallel use of two fans has better ventilation effect than single fan.

The COVID-19 pandemic over the past years has shown a great need for rapid, low-cost and on-site detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this paper, an aptamer-based colloidal gold nanoparticle lateral flow test strip was well developed to realize the visual detection of wild type SARS-CoV-2 spike protein (SP) and multiple variants. Under the optimal reaction conditions, a low detection limit of SARS-CoV-2 S protein of 0.68 nM was acquired, and the actual detection recovery was 83.3% to 108.8% for real world samples. This suggests a potential tool for the prompt detection of SARS-CoV-2 with good sensitivity and accuracy, and a new method for the development of alternative antibody test strips for the detection of other viral targets.

In the decarbonization of the steel, cement, and chemical industry in Germany, green hydrogen is expected to play a crucial role. The utilization of green hydrogen in the production processes of said industries requires organizations to modify their business model, requiring sustainable business model innovation (SBMI). Numerous tools and frameworks that support organizations in the process of SBMI have been proposed in the literature in recent years. However, the applicability of these tools and frameworks for steel, cement, and chemical companies that intend to utilize green hydrogen to produce their goods remains unexplored. By conducting a systematic literature review on tools and frameworks for SBMI, a literature and practice review to identify and analyze existing green hydrogen projects of steel, cement, and chemical companies, and an evaluation of the identified tools and frameworks in an evaluation matrix, this paper aims to assess the suitability of SBMI tools and frameworks for steel, cement, and chemical companies that plan to use green hydrogen to produce their goods. Based on the evaluation, the Cambridge Business Model Innovation Process (CBMIP) was identified as the most suitable SBMI framework.

Solid tumors universally possess the ability to evade contact inhibition of proliferation (CIP), a mechanism halting cell proliferation when cell-cell contacts occur. Merlin, an ERM-like protein, crucially regulates CIP and is frequently deactivated in various cancers, indicating its signifi-cance as a tumor suppressor in cancer biology. Despite extensive investigations into Merlin's role in cancer, its lack of intrinsic catalytic activity and frequent conformation changes has made it notoriously challenging to study. To address this challenge, we harnessed innovative luciferase technologies to create and validate a split-luciferase biosensor system. This system enables precise quantification of Merlin's conformation and activity both in vitro and within living cells. This biosensor significantly enhances the study of Merlin's molecular functions, serving as a potent tool to explore its contributions to CIP and tumorigenesis.

Nitrogen (N) is an essential macro nutrient for crop growth; therefore, N deficit can greatly limit crop growth and production. In the North China Plain (NCP), winter wheat is one of the main food crops, and its yield has increased from approximately 4000 kg ha-1 to 6000 kg ha-1 in the last two decades. Determining the proper N application rates at different growth stages and in all seasons is very important for the sustainable and high production of wheat in the NCP. A field experiment with five N application rates (250, 200, 150, 100, and 40 kgN·ha-1, designated as N250, N200, N150, N100, and N40, respectively) was conducted during the 2017-2018 and 2018-2019 winter wheat seasons to investigate the effects of the N application rate on water- and fertiliz-er-utilization efficiency, and on the crop growth and yield of winter wheat under sprinkler fer-tigation conditions. The results showed that, in the N application range of 40-200 kg ha-1, crop yield and water- and fertilizer-use efficiencies increased as the N application rate increased; however, further increases in the N application rate did not have additional benefits. The N up-take after regreening of winter wheat linearly increased with crop growth. Considering the wheat yield and N-use efficiency, the optimal N application range was 200 kg ha-1, and the best top-dressing strategy was equal amounts of N applied at the regreening, jointing, and grain-filling stages. The results of this study will be useful for optimizing field N management to achieve high wheat yield production in the NCP and in regions with similar climatic and soil environment conditions.

A method is proposed for describing the dynamics of systems of interacting particles in terms of an auxiliary field, which in the static mode is equivalent to given interatomic potentials, and in the dynamic mode is a classical relativistic composite field. It is established that for interatomic potentials, the Fourier transform of which is a rational algebraic function of the wave vector, the auxiliary field is a composition of elementary fields that satisfy the Klein-Gordon equation with complex masses. The interaction between particles carried by the auxiliary field is nonlocal both in space variables and in time. The temporal non-locality is due to the dynamic nature of the auxiliary field and can be described in terms of functional-differential equations of retarded type. Due to the finiteness mass of the auxiliary field, the delay in interactions between particles can be arbitrarily large. A qualitative analysis of the dynamics of few-body and many-body systems with retarded interactions has been carried out, and a non-statistical mechanisms for both the thermodynamic behavior of systems and synergistic effects has been established.

Growth models of uneven-aged forests on the diameter class level can support silvicultural decision making. Machine learning brings added value to the modeling of dynamics at the stand or individual tree level based on data from permanent plots. The objective of this study is to explore the potential of machine learning for modeling growth dynamics in uneven-aged forests at the diameter class level based on inventory data from practice. Two main modeling approaches are conducted and compared: i) fine-tuned linear models differentiated per diameter class, ii) an artificial neural network (multilayer perceptron) trained on all diameter classes. The models are trained on the inventory data of the Canton of Neuchâtel (Switzerland), which are area-wide data without individual tree-level growth monitoring. Both approaches produce convincing results for predicting future diameter distributions. The linear models perform better at the individual diameter class level with test R2 typically between 50 and 70% for predicting increments in the numbers of stems at the diameter class level. From a methodological perspective, the multilayer perceptron implementation is much simpler than the fine-tuning of linear models. The linear models developed in this study achieve sufficient performance for practical decision support.

The presence of antibiotic residues in honey has recently become a growing public health concern due to the overuse of these products in beekeeping and their transfer to the food industry. The aim of this study was to develop and validate a simple, selective and sensitive method for the simultaneous identification and quantification of residues of twenty-two sulfonamides, trimethoprim and dapsone in honey using a rapid and reliable liquid chromatography–tandem mass spectrometry (LC–MS/MS) method. The developed method showed good linearity, specificity, precision (repeatability and intra-laboratory reproducibility) and recovery. The decision limit (CCalpha) ranged between 2.16-22.12 µg/kg for CCα, while the detection capability (CCbeta) was between 2.16- 2.67-27.36 µg/kg. The method proved to be rapid, reliable and effective for the determination of sulfonamides, trimethoprim and dapsone from honey samples. The average recoveries were estimated to be between 70 and 106%. The relative standard deviations (RSDs) were between 6-18%. In practice, the method is essential to check the honey sold on the market and ensure its safety before it reaches the consumer.

The fast and reliable processing of medical images is of paramount importance to adequately generate data to feed machine learning algorithms that can prevent and diagnose health issues. Here we benchmark different compressed sensing techniques applied to magnetic resonance imaging as a means to reduce the acquisition time spent in the collection of data and signals that form the image. We show that by using these techniques, it is possible to reduce the number of signals needed and, therefore, substantially decrease the time to acquire the measurements. To this end, we have considered and compared different algorithms: the Iterative Re-Weighted Least Squares, the Iterative Soft Thresholding Algorithm, the Iterative Hard Thresholding Algorithm, the Primal Dual Algorithm and the Log-Barrier Algorithm. We have implemented such algorithms in different analysis programs that have been used to perform the reconstruction of the images and found that the Iterative Soft Thresholding Algorithm gives the optimal results. We found that the images obtained with this algorithm have less quality than the original ones, but the quality is good enough to distinguish each body structure and detect any health problems.

In the same manner that there are several statements of the second law of thermodynamics but all of them are equivalent, it is possible that most of the entropic methods applied to the nervous system and brain in particular share a similar outcome, or essence, that helps understand some aspects of the fundamentals of basic neurodynamics. In this short review focused on certain aspects of the entropic metrics some results are examined that indicate the fundamental importance of the natural tendency towards a maximal energy distribution for healthy brain activity and thus cognition to emerge.

The fault diagnosis of wind turbine systems represent a challenging issue, especially for offshore installations, thus justifying the research topics developed in this work. Therefore, this paper addresses the problem of the fault diagnosis of wind turbines, and it present viable solutions of fault detection and isolation techniques. The design of the so--called fault indicator consists of its estimate, which involves data--driven methods, as they result effective tools for managing partial analytical knowledge of the system dynamics, together with noise and disturbance effects. In particular, the suggested data--driven strategies exploit fuzzy systems and neural networks that are employed to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, as they approximate the dynamic evolution of the system along time. The designed fault diagnosis schemes are verified via a high--fidelity simulator, which describes the normal and the faulty behaviour of an offshore wind turbine plant. Finally, by taking into account the presence of uncertainty and disturbance implemented in the wind turbine simulator, the robustness and the reliability features of the proposed methods are also assessed. This aspect is fundamental when the proposed fault diagnosis methods have to be applied to offshore installations.

This study aims to construct simple devices to ascertain the concentrations of ammonium (NH4+) and nitrate (NO3-). The device structure comprises a visible and near-infrared absorption meas-urement room, along with an RGB (red, green, and blue) absorption measurement room, con-stituting the two measuring rooms. Both the measurement rooms are equipped with light sources and detectors configured to operate in opposed or thru-beam modes. A tiny processor is utilized for processing the measurements, with an algorithm implemented within its memory. During the algorithm development phase, ranges for measuring the quantity of nutrients were categorized using a discriminant analysis approach. Thirty percent of the soil sample data were utilized as verification data, while the remaining 70 percent served as training data. Nutrient content values from reliable measuring instruments were incorporated into all datasets. Discriminant analysis was employed to classify the amount of NH4+ into five ranges based on the test results. When tested with soil samples, the accuracy was 66.13% compared to the reference value of a reliable measuring device. Similarly, NO3- was classified into four ranges with an accuracy of 81% com-pared to the reference value of a reliable measuring device.

This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR- Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4,372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently in healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that has the ability to take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state-of-the-art published in each of those subsets between the years of 2014-2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.

Semi-continuous data are very common in the social science and economics. In this paper, a Bayesian variable selection procedure is developed to assess the influence of exogenous factors including observed and unobserved on the semi-continuous data. Our formulation is based on the two-part latent variable model with polytomous response. We consider two schemes for the penalties of regression coefficients and factor loadings: the Bayesian spike and slab bimodal prior and the Bayesian lasso prior. Within the Bayesian framework, we implement Markov Chains Monte Carlo sampling method to conduct posterior inference. To facilitate posterior sampling, we recast the logistic model in part one as the norm-like mixture model. Gibbs sampler is designed to draw observations from the posterior. Our empirical results show that with suitable hyper-parameters, the spike and slab bimodal method slightly outperforms the Bayesian lasso in the current analysis. Finally, a real example related to the China household finance survey is analyzed to illustrate the application of the methodology.. .

The literature offers limited information on the effect of obesity on the rate of force development, a critical parameter for mobility in older adults. The objectives of this study were; to explore the influence of obesity on RFD in older adults and to examine the association between this neuromuscular parameter and walking speed. 42 older adults were classified into two groups: the control group (CG, n=22; age=81.13±4.02 years; Body mass index (BMI)=25.13±3.35 kg/m²), and the obese group (OG, n=20; age=77.71±2.95 years; BMI=34.46±3.25 kg/m²). Walking speed (m/s) was measured using the 10-meter walking test. Neuromuscular parameters of the plantar flexors were evaluated during a maximal voluntary contraction test using a dynamometer. The RFD was calculated from the linear slop of the force – time curve in two phases: from the onset of the contraction to 50 ms (RFD50-100) and from 100 to 200 ms (RFD100-200). The gait speed was lower in the OG compared to the CG (p

Digital twin technology is revolutionizing traditional manufacturing paradigms. In modern manufacturing systems, digital twin technology is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their reliance on voluminous training data, find their applicability constrained in real-world production settings. This paper introduces an unsupervised 3D reconstruction approach tailored for industrial applications, aimed at bridging the data void in creating digital twin models. Our proposed model, by ingesting high-resolution 2D images, autonomously reconstructs precise 3D digital twin models without the need for manual annotations or prior knowledge. Through comparisons with multiple baseline models, we demonstrate the superiority of our method in terms of accuracy, speed, and generalization capabilities. This research not only offers an efficient approach to industrial 3D reconstruction but also paves the way for the widespread adoption of digital twin technology in manufacturing.

This work has its origin in intuitive physical and statistical considerations. The problem of optimizing an artificial neural network is treated as a physical system, composed of a conservative vector force field. The derived scalar potential is a measure of the potential energy of the network, a function of the distance between predictions and targets. Starting from some analogies with wave mechanics, the description of the sys-tem is justified with an eigenvalue equation that is a variant of the Schr˜odinger equation, in which the potential is defined by the mutual information between inputs and targets. The weights and parameters of the network, as well as those of the state function, are varied so as to minimize energy, using an equivalent of the variational theorem of wave mechanics. The minimum energy thus obtained implies the principle of minimum mutual information (MinMI). We also propose a definition of the potential work produced by the force field to bring a network from an arbitrary probability distribution to the potential-constrained system, which allows to establish a measure of the complexity of the system. At the end of the discussion we expose a recursive procedure that allows to refine the state function and bypass some initial assumptions, as well as a discussion of some topics in quantum mechanics applied to the formalism, such as the uncertainty principle and the temporal evolution of the system. Results demonstrate how the minimization of energy effectively leads to a decrease in the average error between network predictions and targets.

Camelina (Camelina sativa L.) is an oilseed with potential for use as a raw material in second-generation biofuels. Camelina has a seed yield of up to 2380 kg ha^-1 and contains around 45% fatty acids. Selection of a suitable site is critical for production optimization. The objective of this study was to determine Chilean agro-climatic suitability for establishing camelina as a productive alternative. Climate and soil requirements and geographical restraints were evaluated for the species, considering the climatological characteristics of its regions of origin, as well as regions where camelina is successfully grown in the rest of the world. The variables considered include factors (maximum temperatures of the warmest month, water deficits, and degree days) and limitations (altitude, geomorphology, and current land use), which permitted the evaluation of the national territory for a certain level of suitability. It was determined that 1.3% of the national territory (960,664 ha) has some degree of suitability for camelina adoption. Between the Biobío and Los Lagos regions, 49.0% of land (471,203 ha) is in the category of without thermic restrictions, with mild water restrictions, and mild soil restrictions or without information, which can be used for camelina production. The Los Ríos region had 21.4% surface area (321,176 ha) with some level of suitability for camelina, the most suitable region to establish this crop in Chile.

Much evidence suggests a correlation between degeneration and mitochondrial impairment. Typical cases of degeneration can also be observed in physiological phenomena (aging) as well as in neurological neurodegenerative diseases and cancer. All these pathologies have as a common denominator the dyshomeostasis of mitochondrial bioenergy. Even neurodegenerative diseases show a bioenergetics imbalance in their pathogenesis or progression. Huntington's chorea and Parkinson's disease are both neurodegenerative diseases, but while Huntington's disease is a genetic, and progressive disease with early manifestation and severe penetrance, Parkinson's disease is a pathology with a multifactorial aspect. Indeed, there are different types of Parkinson/Parkinsonism. Many forms are early onset diseases linked to gene mutation, others can appear in young adults and senescent only post-injury, and a final group is idiopathic. Huntington's was defined as a hyperkinetic disorder, while Parkinson's is a hypokinetic disorder; but in the middle, there are a lot of similarities as well as neuronal excitability, the loss of striatal function, psychiatric comorbidity, etc. In the review, we would embrace the theories that both diseases start and develop in light of mitochondrial dysfunction. These dysfunctions act on energy metabolism and reduce the vitality of neurons in many different brain areas.

Much evidence suggests a correlation between degeneration and mitochondrial impairment. Typical cases of degeneration can also be observed in physiological phenomena (aging) as well as in neurological neurodegenerative diseases and cancer. All these pathologies have as a common denominator the dyshomeostasis of mitochondrial bioenergy. Even neurodegenerative diseases show a bioenergetics imbalance in their pathogenesis or progression. Huntington's chorea and Parkinson's disease are both neurodegenerative diseases, but while Huntington's disease is a genetic, and progressive disease with early manifestation and severe penetrance, Parkinson's disease is a pathology with a multifactorial aspect. Indeed, there are different types of Parkinson/Parkinsonism. Many forms are early onset diseases linked to gene mutation, others can appear in young adults and senescent only post-injury, and a final group is idiopathic. Huntington's was defined as a hyperkinetic disorder, while Parkinson's is a hypokinetic disorder; but in the middle, there are a lot of similarities as well as neuronal excitability, the loss of striatal function, psychiatric comorbidity, etc. In the review, we would embrace the theories that both diseases start and develop in light of mitochondrial dysfunction. These dysfunctions act on energy metabolism and reduce the vitality of neurons in many different brain areas.

A mass mortality event of Asian seabass Lates calcarifer juveniles occurred in Zhuhai, the main Asian seabass cultured area in mainland China. The fish samples were pooled for pathogen identification and both high viral loads of ISKNV and NNV were detected by real-time microfluidic quantitative PCR. Immunohistochemistry and immunofluorescence showed that strong ISKNV signals were examined in spleen and liver, while strong NNV signals were detected in brain and eye. The multi-tissue homogenates were inoculated into MFF-1 cell and SSN-1 cell, respectively. After several viral passages, both ISKNV and NNV were purely isolated from each other, and designated as ASB-ISKNV-23 and ASB-NNV-23, respectively. The whole genome sequences of ASB-ISKNV-23 and ASB-NNV-23 were determined and annotated. The result showed that ASB-ISKNV-23 and ASB-NNV-23 are composed of 112,236 bp and 4,538 bp, respectively. Phylogeny analysis showed that ASB-ISKNV-23 belongs to ISKNV-II sub-genotype and ASB-NNV-23 belongs to RGNNV genotype. Collectedly, coinfection of ISKNV-II and RGNNV were firstly documented in mass mortality of Asian seabass in mainland China. Importantly, both ISKNV-II and RGNNV were purely isolated using two different permissive cell lines. Our study provides useful information for better understanding the complex pathogenesis regarding the coinfection with ISKNV and NNV in farmed fish.

Preterm birth accounts for about 10% of births worldwide. Studying risk factors for perinatal brain damage is essential, as findings suggest that almost 20% of disabilities are linked to risks in the early stages of development. This research aimed to study longitudinal changes in intelligence from 6 to 8 years of age in a sample of 39 preterm children with a history of risk of brain damage and a control group of 35 children born at term. The Wechsler Intelligence Scale (WISC-IV) was used for cognitive measurement. The results showed that the preterm group obtained significantly lower scores than the control group. The working memory indicator significantly affected the interaction between age and prematurity. We consider that it is crucial to expand the knowledge we have about the neurocognitive development of premature infants both in specific cognitive domains and in age ranges so that the information obtained helps to predict the probability of presenting cognitive alterations from early stages and, therefore, to implement intervention strategies and programs based on scientific evidence and complemented in their design by clinical experience, empirical and theoretical knowledge of the different professionals involved in infant cognitive intervention.

The Imbalance Cost Pass-Through (ICPT) is a flexible component of the Incentive Regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. However, the cost of electricity generation is significantly influenced by the volatility of commodity prices, including coal, gas and LNG. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Due to that reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed by a Mean Absolute Percentage Error (MAPE). In findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the Moving Average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July-December 2022 and January-June 2023 is MYR 0.21/ kWh for ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for the forecasting, the ICPT price is expected to drop for the next announcement. The findings of this study may contribute to a positive impact on the sustainability of the energy sector in Malaysia.

Liver fibrosis, a consequence of chronic liver damage or inflammation, is characterized by the excessive buildup of extracellular matrix components. This progressive condition significantly raises the risk of severe liver diseases like cirrhosis and hepatocellular carcinoma. The lack of approved therapeutics underscores the urgent need for novel anti-fibrotic drugs. Hepatic stellate cells (HSCs), key players in fibrogenesis, are promising targets for drug discovery. This study investigated the anti-fibrotic potential of Citrus hystrix DC. (KL) and its bioactive compound, β-citronellol (β-CIT), in a human HSC cell line (LX-2). Cells exposed to TGF-β1 to induce fibrogenesis were co-treated with crude KL extract and β-CIT. Gene expression was analyzed by Real-Time qRT-PCR to assess fibrosis-associated genes (ACTA2, COL1A1, TIMP1, SMAD2). The releasing of matrix metalloproteinase 9 (MMP-9) was measured by ELISA. Proteomic analysis and molecular docking identified potential signaling proteins and modeled protein-ligand interactions. Results showed that both crude KL extract and β-CIT suppressed HSC activation genes and MMP-9 levels. The MAPK signaling pathway emerged as a potential target of β-CIT. This study demonstrates the ability of KL extract and β-CIT to inhibit HSC activation during TGF-β1-induced fibrogenesis, suggesting a promising role of β-CIT in anti-hepatic fibrosis therapies.

This research, which focuses on wave energy generating technology, suggests a brand-new oscillating float-type wave energy conversion system that is appropriate for the offshore wave environment. To accurately model the device, a three-dimensional viscous numerical pool is created based on STAR-CCM+. The array-type power generation device is numerically simulated using theoretical analysis and numerical simulation, and the wave energy power generation device is arranged as double floats in the front and rear rows, representing triangular array type and rectangular array type, respectively. The triangular array type is a superior layout and can make greater use of marine resources, according to this paper's analysis of the hydrodynamic and power generation impacts of the three array types. The findings are meant to serve as a guide for engineering practice.

Today, computer programming is an essential pillar in the education of individuals. Even though technological evolution has eased the massive need for computer programming, a systematic lit-erature review shows some problems remain with teaching computer programing. To address such issues, in this paper we propose an adapted theory, a method, and a model for teaching some knowledge objects associated with the concepts of variables, expressions, comparisons, loops, and functions in programming fundamentals in computer sciences. The main contribution of this study comprises the extension of the concept of didactic transposition to improve teaching in context; in this paper, the idea is called didactic transposition in extensa sensu. The proposal involves the development of a linguistic corpus based on syllabi, textbooks, reference manuals, and a survey applied to experts in teaching programming fundamentals based on seventy-eight universities worldwide. An adapted theory, a method, and a model for teaching programming fundamentals using computational linguistics are produced. In addition, a template is created for designing teaching practices in this regard. The proposal generates an important alternative for the devel-opment of teaching strategies where the conditions of the context are addressed as a priority; ad-ditionally, several aspects are considered for elaborating teaching strategies using computational linguistic techniques from written documents.

Microfluidic devices or microdevices refer to systems with a characteristic length in the micrometer range. Systems in this size allow handling small quantities of reagents and samples, with reduced residence time, better control of chemical species concentration, high heat and mass transfers, and high surface/volume ratio. These characteristics led to the application of these microdevices in several areas, such as biological systems, energy, liquid-liquid extraction, food, agricultural sectors, pharmaceuticals, flow chemistry, microreactors, and biodiesel synthesis. Microreactors are devices that have interconnected microchannels, in which small amounts of reagents are manipulated and react for a certain period of time. The traditional characteristics of microreactors are less quantities of reagents and samples, high surface area in relation to volume (10000 m² m^-3), reduction of resistance to heat and mass transfer, reduced reaction times, and narrower residence time distributions. In recent years, several studies have been carried out on biodiesel production in microreactors that explore the influence of operating conditions, mixing and reaction yield, numbering, and especially the microdevices design. Despite all the advantages of microreactors, the literature shows that there are only a few applications on an industrial scale. Two main reasons that hinder the adoption of this technology are the scale-up to a large enough volume to deliver the necessary production capacity and the costs related to industrial manufacturing microreactors. It is often stated that large-scale production of microreactors can be easily achieved by numbering-up. However, researches show that an incredibly high number of microdevices would be needed, which results in a technical unfeasibility and a strong impact on the construction costs of the industrial system. The present review aims to show whether microreactors can replace conventional biodiesel production processes and how this replacement technology could be carried out. The current chapter was divided into the following sections: Introduction, Synthesis and Purification of Biodiesel in Microreactors, Fundamentals of CFD, and Fundamentals of Scale-up. Finally, conclusions and future perspectives are exposed.

Aim: Streptococcus pneumoniae and influenza H1N1 virus are common organisms associated with human infections. These infections could play a significant role in immune regulation. The study was performed to analyse the genome sequences of these organisms with human genome and study its functional significance. Methods: The study was performed to analyse the overlapping of genome sequences in Streptococcus pneumoniae and Influenza (H1N1) virus against human genome sequences by BLASTn sequence analysis. The alignments are studied against the corresponding genes for their functional significance with DAVID and NDEx software.Results: Several hits or overlapping nucleotide segments were identified. Between streptococcus and homo sapiens 287 overlaps were identified, and among influenza and homo sapiens 124 hits were identified. A wide range of functional significance of these genes were identified, and the results are presented in this study. The results show insights into functional pathways and biological activities associated with the respective vaccinations or infections by these microorganisms. Conclusion: The common organisms like Streptococcus pneumoniae and Influenza H1N1 virus actively interact with the immune system and result in a wide range of immune regulations.

Human development encompasses the integration of neuromotor, psychological, social-emotional, and cognitive processes across time. However, a better understanding of the interconnectedness of motor and cognitive development throughout the lifespan is needed. The integrative and dynamic nature of short-term motor skill acquisition and long-term developmental processes may foster executive functions. Unfortunately, assessments of motor competence have traditionally focused on skillfulness from a movement perspective, using restrictive task protocols that decontextualize performance and limit cognitive involvement. Additionally, traditional motor-cognitive dual-task assessments centered on cognitive involvement during specific movement protocols, have minimized impacts of task complexity and motor competence on cognitive performance. To gain a more ecologically valid understanding of the relation between motor and cognitive development, a novel assessment paradigm using an integrative approach to motor-cognitive dual-tasking is needed. The purpose of the present paper is to: 1) provide a conceptual bridge, based on multidisciplinary evidence, to effectively link the concurrent development of motor competence and executive functions via learning-related and exercise-related neurotrophic mechanisms, and 2) use this conceptual bridge to inform the development of novel motor-cognitive dual-task assessments that account for the role of task complexity, current levels of motor competence, and the continuous decision-making inherent in “real world” performance environments.

RNA sequencing techniques, like bulk RNA-seq and Single Cell (sc) RNA-seq, are critical tools for the biologist looking to analyze the genetic activity/transcriptome of a tissue or cell during an experimental procedure. Platforms like Illumina's next-generation sequencing (NGS) are used to produce the raw data for this experimental procedure. This raw FASTQ data must then be prepared via a complex series of data manipulations by bioinformaticians. This process currently takes place on an unwieldy textual user interface like a terminal/command line that requires the user to install and import multiple program packages, preventing the untrained biologist from initiating data analysis. Open-source platforms like Galaxy have produced a more user-friendly pipeline, yet the visual interface remains cluttered and highly technical, remaining uninviting for the natural scientist. To address this, SeqMate is a user-friendly tool that allows for one-click analytics by utilizing the power of a large language model (LLM) to automate both data preparation and analysis (differential expression, trajectory analysis, etc). Furthermore, by utilizing the power of generative AI, SeqMate is also capable of analyzing such findings and producing written reports of upregulated/downregulated/user-prompted genes with sources cited from known repositories like PubMed, PDB, and Uniprot.

In the sphere of urban renewal in historic districts, preserving and innovatively reinterpreting traditional architectural styles remains a primary research focus. However, the modernization and adaptive reuse of traditional buildings often necessitate changes in their functionality. To cater to the demands of tourism in historic districts, many traditional residential buildings require conversion to commercial use, resulting in a mismatch between their external form and internal function. This study explores an automated approach to transform traditional residences into commercially viable designs, offering an efficient and scalable solution for the modernization of historic architecture. We developed a methodology based on diffusion models, focusing on a dataset of nighttime shopfront facades. By training a Low-Rank adaptation (LoRA) model and integrating the ControlNet model, we enhanced the accuracy and stability of generated images. The methodology’s performance was validated through qualitative and quantitative assessments, optimizing batch size, repetition, and learning rate configurations. These evaluations confirmed the method’s effectiveness. Our findings significantly advance the modern commercial style transformation of historical architectural facades, providing a novel solution that maintains aesthetic and functional integrity, thereby fostering breakthroughs in traditional de-sign thinking and exploring new possibilities for the preservation and commercial adaptation of historical buildings.

Previous work has shown that perturbation analysis in algorithmic information dynamics can uncover generative causal processes of finite objects and quantify each of its element's information contribution to computably constructing the objects. One of the challenges for defining emergence is that the dependency on the observer's previous knowledge may cause a phenomenon to present itself as emergent for one observer at the same time that reducible for another observer. Thus, in order to quantify emergence of algorithmic information in computable generative processes, perturbation analyses may inherit such a problem of the dependency on the observer's previous formal knowledge. In this sense, by formalizing the act of observing as mutual perturbations, the emergence of algorithmic information becomes invariant, minimal, and robust to information costs and distortions, while it indeed depends on the observer. Then, we demonstrate that the unbounded increase of emergent algorithmic information implies asymptotically observer-independent emergence, which eventually overcomes any formal theory that any observer might devise. In addition, we discuss weak and strong emergence and analyze the concepts of observer-dependent emergence and asymptotically observer-independent emergence found in previous definitions and models in the literature of deterministic dynamical and computable systems.

The emphasis in the automotive industry towards sustainable mobility has led to a significant interest in hybrid-electric propulsion systems with high energy density batteries. Addressing the needs of this strategy, the battery market is exploring new technologies to improve the safety and lifespan of electric vehicles. To this end, there is a focus on the all-solid-state battery (ASSB) technology for its cycle capabilities. Filling the current void in the literature pertaining to accurate ageing models for ASSBs, in this work, we present an enhanced version of the numerical ageing model, originally developed for liquid electrolyte based batteries, to forecast the development of the solid electrolyte interface layer that is the major cause of battery capacity fading. The model has been tested on prototype batteries and reveals an accuracy of 99%. The capacity fade in ASSBs has been investigated under different conditions, and the enhanced ageing model has been validated using experimental data from these batteries. The findings suggest that there is potential for solid-state batteries to be commercialized, although significant work is needed to match the manufacturing level of lithium-ion batteries with liquid electrolytes.

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. Emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

Outliers seriously affect the accuracy in geometric model fitting. Previous works in coping with outliers involve threshold selection and scale estimation. However most scale estimators suppose the inlier distribution as a Gaussian model, which usually poorly meets the cases in geometric model fitting. Outliers, considered as the points with big residuals to all the true models, share common items with big values in quantized residual preferences, thus making the outliers gather away from the inliers in quantized residual preference space. In this paper we make use of this outlier consensus in quantized residual preference space by extending the usage of energy minimization to combine the model error and the spatial smoothness in quantized residual preference space for outlier detection. The energy minimization based outlier detection process follows an alternate sampling and labeling framework. After the outlier detection, an ordinary energy minimization method is employed to optimize the inlier labels, which also follows the framework of alternate sampling and labeling. The experimental results in this study show that the energy minimization based outlier detection method can detect most of the outliers in the data. Furthermore, the proposed energy minimization based inlier segmentation process segments the inliers into different models quite accurately. Overall, the performance of the proposed method is better than most of the state-of-the-art methods.

Nowadays the relationship between planning land use and actual land use is not so clear in general. A lot of efforts have been put in the failures of regulation for the expansion of construction land. However, it still lacks an integrated approach to study the effectiveness of land use regulation in terms of different land use types. Furthermore, the existing evaluation of land use plan mainly focuses on a general level, a detailed research on the regulation effectiveness of each construction land use type is absent. Therefore, this research tries to evaluate regulation effectiveness of land use plan, which takes Cangwu country, Guangxi Province as an example. The finding by analysis is that the total area of construction land expansion was about 3494.73 ha, nearly 1.1 times of the plan quota. Moreover, the effectiveness differs greatly in various construction land use types. Town, industrial/mining sites can be well regulated through the quota of land use plan. While, the quota regulation system is not as effective for other type of construction land. Thus, we suggest to improve the regulation effectiveness of construction land through different plan instruments.

The term Concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. The Concepts are also studied computationally through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of Abstract Concepts by exploiting the geometrical view of Concepts, without supervision. In the article, the IRIS data was used to demonstrate: the RAN's modeling; flexibility in concept identifier choice; and deep hierarchy generation. Data from IoT's Human Activity Recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with 8 UCI benchmarks and the comparisons with 5 Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RAN's hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size.

The usage of Quantum Similarity through the equation Z = {∀θ ∈ Z → ∃s ∈ S ∧ ∃t ∈ T : θ = (s, t)}, represents a way to analyze the way communication works in our DNA. Being able to create the object set reference for z being (s, t) in our DNA strands, we are able to set logical tags and representations of our DNA in a completely computational form. This will allow us to have a better understanding of the sequences that happen in our DNA. With this approach, we can also utilize mathematical formulas such as the Euler–Mascheroni constant, regression analysis, and computational proofs to answer important questions on Quantum biology, Quantum similarity, and Theoretical Physics.

In this article, we consider a class of nonsmooth interval-valued multiobjective semi-infinite programming problems with vanishing constraints (in short, NIMSIPVC). We introduce the VC-Abadie constraint qualification (in short, VC-ACQ) for NIMSIPVC and employ it to establish Karush-Kuhn-Tucker (in short, KKT)-type necessary optimality conditions. Related to NIMSIPVC, we formulate interval-valued vector Lagrange type dual and scalarized Lagrange type dual problems. Subsequently, we establish weak, strong, and converse duality results relating NIMSIPVC and corresponding dual problems. In addition, we introduce the notions of saddle points for interval-valued vector Lagrangian and scalarized Lagrangian of NIMSIPVC. Moreover, we establish the saddle point optimality criteria for NIMSIPVC. Various non-trivial examples are provided to demonstrate the validity of established results. To the best of our knowledge, optimality conditions, Lagrange type duality, and saddle point optimality criteria for NIMSIPVC have not been investigated yet via Clarke subdifferentials.

The development of offshore wind farms requires robust bonding solutions that can withstand harsh marine conditions for the easy integration of secondary structures. This paper investigates the durability performance of two adhesives: Sikadur 30 adhesive and Loctite UK 1351 B25 for use in offshore wind environments. Tensile tests on adhesive samples and accelerated aging tests were carried out under a variety of temperature and environmental conditions, including both dry and wet conditions. The long-term effects of aging on adhesive integrity are investigated by simulating the operational life of offshore installations. The evolution of mechanical properties, studied under accelerated aging conditions, provides an important indication of the longevity of structures under normal conditions. The results show significant differences in performance be-tween the two adhesives, highlighting their suitability for specific operating parameters. It should also be noted that for both adhesives, their exposure to different environments (seawater, distilled water, humid climate) over a prolonged period has shown that i) Loctite adhesive has a slightly faster initial uptake than Sikadur adhesive, but the later reaches an asymptotic plateau with a lower maximum absorption rate than Loctite adhesive; and ii) a progressive deterioration in the tensile properties following an exponential function. Therefore, aging behavior results showed a clear correlation with Arrhenius' law, providing a predictive tool for the aging process. Thus, the aging process of the two adhesives follows Arrhenius kinetics.

In detecting DDoS, deep learning faces challenges and difficulties such as high computational demands, long training times, and complex model interpretation. This research focuses on overcoming these challenges by proposing an effective strategy for detecting DDoS attacks in unbalanced network environments. This research uses SMOTE to increase the class distribution of the data set by allowing models using LSTM to learn time anomalies effectively when DDoS attacks occur. The experiments carried out have shown significant improvement in the performance of the LSTM model when integrated with SMOTE. These include validation loss results of 0.048 for LSTM SMOTE and 0.1943 for LSTM without SMOTE, accuracy of 99.50 and 97.50. Apart from that, there was an increase in the f1 score from 93.4% to 98.3%. In this research, it is proven that SMOTE can be used as an effective strategy to improve model performance in detecting DDoS attacks on heterogeneous networks, as well as increasing model robustness and reliability.

Caudal regression syndrome (CRS) is a congenital abnormality characterized by an incomplete development of the spinal cord (SC) and other abnormalities. We studied a 9-months old CRS child presenting: interruption of SC at L2-L3 level, sacral agenesis, lack of innervation of the inferior limbs (flaccid paraplegia) and neurogenic bladder and bowel. Given the effects of growth hormone (GH) on the proliferation, differentiation and migration of neural stem cells (NSCs), we treated him with GH and rehabilitation, trying to induce the recovery of main sequelae. GMFM-88 test score was 12.31%. After a blood analysis, GH treatment (0.3 mg/day, 5 days/week, 3 months and then 15 days without GH) and rehabilitation commenced. This protocol was followed during 5 years, being the last GH dose 1 mg/day. Blood analysis and physical exams were performed every 3 months initially and every 6 months later. Six months after commencing the treatment GMFM-88 score increased to 39.48%. Responses to sensitive stimuli appeared in most of the territories explored; 18 months later sensitive innervation was complete and the patient moved any muscle over the knees and controlled his sphincters. Three years later he walked with the help of canes, there was plantar flexion and GMFM-88 score was 78.48%. In summary, GH plus rehabilitation may be useful for innervating distal territories, below the level of the incomplete spinal cord in CRS. Most likely, GH acts on ependymal SC NSCs, as the hormone does in the neurogenic niches in the brain.

In this paper, we establish some inequalities bounding the ratio Г_p,k(x)/Г_p,k(y), where Г_p,k(.) is the (p,k)-analogue of the Gamma function. Consequently, some previous results are recovered from the obtained results.

Soil contamination with toxic heavy metals, such as lead (Pb), is a critical environmental issue with adverse effects on ecosystems. Cymbopogon citratus, known as lemongrass, is a medicinal plant with phytoremediation potential for Pb-contaminated soils, besides producing essential oil (EO) and bioactive compounds. Azospirillum brasilense is a plant growth promoting bacteria, and its inoculation into the soil can benefit plants in many ways, such as increased root growth and improved nutrient absorption. This study aimed to investigate the phytochemistry of lemongrass inoculated with A. brasilense and grown in Pb-contaminated soil to assess its responses to inoculation under different Pb levels. The experimental design was completely randomized in a 2 x 5 factorial scheme: two levels of A. brasilense (absence or presence) and five Pb levels (0, 50, 100, 300, and 500 mg Pb kg–1 of soil), totaling 60 experimental plots in ten treatments with six replicates. After four months of treatment, was analyzed total and reducing sugars, total phenolic content, flavonoids, antioxidant activity, antioxidant enzymes, proline, and EO content and composition. Soil Pb levels and A. brasilense inoculation affected phytochemical in lemongrass plants. Azospirillum inoculation reduced total sugars in the roots at all soil Pb levels, while increasing Pb levels favored a rise in sugar contents. There was an increase in flavonoid content in treatments associated with Pb and inoculated with A. brasilense. Antioxidant capacity was lower at lower Pb levels, regardless of bacterial inoculation. Enzymatic response was mainly affected by Pb concentrations between 50 and 100 mg kg–1 soil. EO content was influenced by soil Pb levels, with higher EO production at 500 mg Pb kg–1 soil and without A. brasilense inoculation. Overall, lemongrass cultivation in Pb-contaminated areas can be an alternative for phytoremediation and EO production for the industry. Moreover, A. brasilense inoculation proved to be beneficial in mitigating stress caused by high Pb concentrations in contaminated soils.

Cancer cells are originated by normal cells and with those share the need to be fed. Metabolic reprogramming in cancer cells is not yet fully understood, and there is a vast literature suggesting that any macronutrient may be efficiently utilized by cancer for duplication purposes. Metabolic hyperactivity of cancer and continuous duplication requires huge amounts of both energy and substrates for syntheses of components of new cells, and the modifications of metabolism induced by cancer cells to survive and grow damaging lethally, the whole organism. In rodents, we have recently observed that by modifying diet in a way not existent in nature, changing radically the ratios among essential and non-essential amino acids (AA) (usually less than 0.9), largely increasing essential-AA (EAA) percentages, lifespans increased. In vitro, EAA supplementation promoted colon cancer cells (CT116) apoptosis by enhanced autophagy. Therefore, we tested in mice if injection of colon cancer cells would have been followed by unvaried development of cancer volumes in animals fed with standard laboratory diet (controls) versus EAA rich modified diets (EAAmd). Both diets perfectly matched the same total macronutrients and micronutrients contents; only quality of nitrogen was radically different. Results of the first set of 8 animals, 4 controls and 4 EAAmd-fed, showed that controls develop cancers near 5 times larger and heavier than EAAmd-fed (P<0,002) on the same time. We discuss which epigenetic modifications would be involved and worth to be studied, and what kind of scenarios those preliminary findings, if confirmed, would open.

Pigmented corn is a gramineae food of great biological, cultural and nutritional importance for many Latin American countries, with more than 250 breeds in the American continent. It confers a large number of health benefits due to its diverse and abundant bioactive compounds. In this narrative review we decided to organize the information on the nutrients, bioactive compounds and phytochemicals present in pigmented corn, as well as their effects on human health. Phenolic compounds and anthocyanins are some of the most studied and representative compounds in these grasses, with a wide range of health properties, mainly the reduction of prooxidant molecules. Carotenoids are a group of molecules belonging to the terpenic compounds, present in a large number of pigmented corn breeds, mainly the yellow ones, whose biological activity incorporates a wide spectrum. Bioactive peptides can be found in abundance in corn, having very diverse biological effects that include analgesic, opioid, antihypertensive, etc. activity. Other compounds with biological activity found in pigmented corn are resistant starches, some fatty acids, phytosterols, policosanols, phospholipids, ferulic acid and phlobaphenes, as well as a great variety of vitamins, elements, and fibers. This review aims to disseminate and integrate the existing knowledge on compounds with biological activity in pigmented corn, in order to promote their research, interest and use by scientists, nutrition professionals, physicians, industries and the general population.

Mutual exclusion algorithms are at the heart of concurrent, distributed/parallel, real-time and operating systems. It is well-known that such algorithms are very difficult to analyze and in the literature different conjectures about starvation-freedom and the number of by-passes (also said the overtaking factor), which in turn affects the (hopefully) bounded waiting time that a process competing for entering the critical section has to suffer before accessing the shared resource, have been formulated for specific algorithms. This paper proposes a novel modelling approach based on Timed Automata and the Uppaal toolset, which proves effective for studying all the properties of a mutual exclusion algorithm for N≥2 processes, by exhaustive model checking. Although the approach, as already confirmed by similar experiments reported in the literature, is not scalable due to state explosion problems, and can be practically applied until N≤5, it is of great value for revealing true properties of analyzed algorithms. For dimensions N>5 the statistical model checker of Uppaal can be used which, although based on simulations, can confirm properties by estimations and probabilities. The paper describes the proposed modelling and verification method and applies it to several mutual exclusion algorithms, thus retrieving known properties but also showing new results about properties often studied only by peer-review and intuitive reasoning.

High-precision conical threads are widely used in responsible joints in the drilling of oil and gas wells. Difficult underground drilling conditions due to the occurrence of significant mechanical loads and an aggressive environment in wells require the use of workable materials that are often difficult to machine. The huge need for such connections dictates the use of high-performance cutting tools, which means that high demands are placed on their effective geometric performance. The study describes the theoretical dependence of the profile precision of the tapered drill-string tool-joint threads of all standard sizes on the complex of a cutting tool geometric parameters and cutting tool setting accuracy. The experiments include an assessment of the geometric accuracy of the profile of the obtained NC23 thread connector, depending on the values of the installation angles and the declared tangential displacement of the standard threaded carbide insert.

This qualitative research explores negotiation dynamics within procurement, focusing on strategies, challenges, and outcomes in contemporary business environments. Negotiation in procurement plays a crucial role in shaping organizational strategies, supplier relationships, and operational efficiencies. In a globalized economy characterized by rapid technological advancements and market uncertainties, effective negotiation practices are essential for organizations seeking competitive advantage and sustainable growth. Through semi-structured interviews with procurement professionals, supply chain executives, and industry experts, this study examines the nuanced strategies employed in negotiation processes. Thematic analysis of the data reveals key themes including negotiation strategies (e.g., preparation, flexibility), challenges (e.g., price volatility, regulatory constraints), and outcomes (e.g., cost savings, innovation incentives). Relationship management emerges as pivotal, highlighting the importance of trust, transparency, and mutual respect in fostering collaborative supplier partnerships. The study also explores contextual factors (e.g., organizational culture, industry dynamics) and emotional dimensions (e.g., emotional intelligence, interpersonal dynamics) that influence negotiation effectiveness.

This study delves into the intricate interplay between cosmology and gravity in dilaton-based models, enhancing our understanding. Exploring boundary theories coupled to gravity, we derive a four-dimensional effective theory from the cosmological constant. Dilaton gravity is then scrutinized, revealing elegant solutions and insights into brane embedding. The analysis extends to probe scalar fields, employing the WKB approximation in both conformal and non-conformal cases. In summary, this work contributes to a nuanced understanding of the complex relationship between cosmology and gravity in dilaton-based frameworks.

Maintaining the high overall health of farmed animals is a central tenant of their well-being and care. Intense animal crowding in aquaculture promotes animal especially in the absence of straightforward methods for monitoring their health. Here, we used bacterial 16S ribosomal RNA gene sequencing to measure population dynamics occurring during P. salmonis outbreaks. Our findings revealed a complex bacterial community consisting of a previously undescribed core pathobiome. Notably, we detected Aliivibrio wodanis and Tenacibaculum dicentrarchi on the skin ulcers of salmon infected with P. salmonis, while Vibrio spp. were enriched on infected gills. The prevalence of these co-occurring networks indicates the influence that coinfection plays in P. salmonis pathogenicity.

Biogeochemistry of waters is an essential regulator of phytoplankton dynamics, determining the level of species bloom and the change of dominants. The paper investigated the seasonal dynamics of phytoplankton and the nutrient concentration and their ratios in the NE Black Sea in 2017-2021. Two taxonomic groups, diatoms, and coccolithophores, determine seasonal dynamics and significantly contribute to the total phytoplankton biomass. Coccolithophores annually, except in 2020, formed bloom in early June. Large diatoms in summer annually, except in 2019, dominated with biomass exceeding 1000 mg m-3. During the bloom of these taxonomic groups, their contribution to the total phytoplankton biomass exceeded 90%. Each group has characteristic biogeochemical niches in the nitrogen and phosphorus concentrations coordinates. The regulation of the biogeochemistry of water is the position of the seasonal thermocline. With a high-lying and sharp gradient thermocline (the average for five years is 6.87 m), low nitrogen concentrations and a nitrogen-to-phosphorus ratio below the Redfield ratio are created in the upper mixed layer. These conditions are optimal for the dominance of coccolithophores. When the thermocline is deepened (the average for five years is 17.96 m), the phosphorus concentration decreases significantly and the ratio of nitrogen to phosphorus is significantly higher than the Redfield ratio, and these conditions lead to the dominance of large diatoms. The results of experimental studies with nitrogen and phosphorus additives in the natural phytoplankton population confirm the above statements. The addition of phosphorus leads to an increased role of coccolithophores in the total phytoplankton biomass, the addition of nitrogen alone contributes to the growth of large diatoms, and the combined addition of phosphorus and nitrogen in a ratio close to the Redfield ratio leads to the dominance of small diatoms.

In this paper the dynamics of spatially extended infected phytoplankton with the Holling type II functional response and logistically growing susceptible phytoplankton system is studied. The proposed model is an extension of temporal model available [6], in spatiotemporal domain. The reaction diffusion system exhibits spatiotemporal chaos in phytoplankton dynamics. This is particularly important for the spatially extended systems that are studied in this paper as they display a wide spectrum of ecologically relevant behavior, including chaos. In this system stability of the system is studied with respect to disease contact rate and the growth fraction of infected phytoplankton indirectly rejoin the susceptible phytoplankton population. The results of numerical experiments in one dimension and two dimensions in space as well as time series in temporal models are presented using MATLAB simulation. Moreover, the stability of the corresponding temporal model is studied analytically. Finally, the comparison of the three types of numerical experimentations are discussed in conclusion.

This study analyzes the modeling and dynamics of a novel passive in Multi-Degree-of-Freedom (MDOF) vibration isolation platform which can achieve significant isolation effect. Symmetrical Scissor-Like structures (SLSs) are utilized in the proposed MDOF isolation platform as the supporting and isolation elastic components. Based on the mathematical modeling and theoretical analysis of the MDOF vibration isolation system with SLSs, the effect of structural parameter and joint friction on stiffness and damping properties is investigated. It is shown that due to geometric relations within the SLSs, the natural frequencies can be reduced via adjusting structural parameters of the SLS for different direction vibration isolation. Theoretical and experimental results show that the SLS isolation platform can achieve much better loading capacity and vibration isolation performance simultaneously by only using linear passive components because of the MDOF adjustable stiffness property. Therefore, with low costing and energy consumption, the proposed novel isolation platform can provide the improvement of vibration suppression in various engineering practices.

The joint extraction of entities and relations is a critical task in information extraction, and its performance directly affects the performance of downstream tasks. However, existing joint extraction models based on deep learning exhibit weak processing capabilities for the phenomenon of multiple pronunciations of one character and multiple characters of one pronunciation when processing Chinese texts, resulting in performance loss. To address these issues, this paper introduces part-of-speech (POS) and pinyin features to aid the model in learning semantic features that are more contextually appropriate. A Chinese Joint Entity and Relation Extraction Model based on Progressive Contrastive Learning and Heterogeneous Feature Fusion is proposed (CJE-PCHF). During model training, an interactive fusion network based on progressive contrastive learning is employed to learn the dependencies between pinyin, POS, and semantic features. This guides the model in heterogeneous feature fusion, capturing higher-order semantic associations between heterogeneous features. On the commonly used DuIE evaluation dataset for joint extraction, this model achieved a significant improvement, with the F1 score increasing by 5.4% compared to the benchmark model CasRel.

In this paper, an adaptive proportional and integral (PI) controller for speed control of electric drives is developed. The adaptive PI controller is designed based on model reference adaptive identification and the Lyapunov function so that the system stability can be proved. To identify the inertia, the viscous friction torque coefficient, and the load torque simultaneously and smoothly, sinusoidal speed reference is usually employed. A modified motion equation considering the low-pass filter for feedback speed and the Coulomb friction torque is introduced and the relationship between the identified parameters and their actual values are clarified. A high-precision digital simulation model is built based on MATLAB/Simulink. The effectiveness of the proposed method is verified by simulations under various conditions.

A time-sequential working, spatially multiplexed autostereoscopic 3D display design consisting of a fast switchable RGB-color filter array is presented. The wavelength-selective filter barrier emits the light from a display over a larger active area than common autostereoscopic barrier displays. An optical modelling of wavelength-selective barriers has been used for instance to calculate the light ray distribution properties of that arrangement. To find well working display designs, automated searches by simulation and computational evaluation has been proceeded. Wavelength-selective filter barrier arrangements exhibit characteristics different from common barrier displays with similar barrier pitch and ascent. In particular, these constructions show strong angular luminance dependency under barrier inclination specified by correspondent slant angle. In time-sequential implementation it is important to avoid that quick eye or eyelid movement lead to visible color artifacts. In the millisecond regime tunable liquid crystal Fabry-Pérot color filters for the colors red, green and blue are presented. They consist of a sub-micrometer thick nematic layer sandwiched between dielectric mirrors and ITO-electrodes. These cells shall switch narrow-banding light of red, green or blue. An array for a glasses-free 3D display has to be equipped with several thousand switchable filter elements having different color apertures. The newly introduced design is usable as a multi user display as well as a single user system with user adaptive control.

This paper proposes a new dynamic analysis of reasoning. It makes the following contributions. First, the current states of the psychology of reasoning are critically reviewed from theoretical and empirical perspectives. The long-standing controversies between logicians and psychologists are clarified by introducing the gauge theoretic structure. The debates between the mental logic theory and the mental models theory are carefully reviewed by identifying the problem types used in the empirical studies. Certain mental fluctuations are found and a quantum theoretic solution is examined. Second, it solves the measurement problem within the Hamiltonian framework. The modeling structure underlying reasoning dynamics is the electrodynamics. They are both the single-charge dynamic systems sharing the U(1) symmetry. The mental logic theory and the mental models theory are now characterized as wavefunctions in a unified account of the dynamic analysis with the yes/no measurements. The stochastic sampling method is introduced to solve the measurement problem, which has a wide range of applications in social sciences. Third, a novel metatheoretic framework of reasoning dynamics within Lagrangian framework is proposed. It solves the “Man vs. Men” problem via gauge theoretic modeling, by making the distinction between the global level and the local level. At each level, the gauge transformations are applied to achieve the global symmetry and the local gauge symmetry. It explains the meanings of the gauge principle and the Nether theorem. Fourth, the special theory of relativity is applied to address the “Man vs. Men” problem. It explains how to draw the global language cone and the individualized local language cones. Finally, it points out that borrowing modeling methods from physics has a long tradition in psychology. The present work utilizes the conceptual and modeling methods found in modern theoretical physics.

Introduction: The trophoblast derived angiogenic factors are considered to play an important role in the pathophysiology of various complications of pregnancy. The aim of this study was to establish relationship between concentrations of soluble Human Leukocyte Antigen-G (sHLA-G) in maternal serum and amniotic fluid at 16-22 weeks of gestation and the sonographic measurements of the fetal and placental growth. Materials and methods: sHLA-G in serum and amniotic fluid, as well as fetal biometric data and placental volume and perfusion indices were determined in 41 uncomplicated singleton pregnancies. Results: The sHLA-G levels were unchanged both in amniotic fluid and in serum during mid-pregnancy. The sHLA-G level in serum correlated positively with amniotic sHLA-G level (β=0.63, p<0.01). Significant correlation was found between serum sHLA-G level and abdominal measurement (β=0.41, p<0.05) and estimated fetal weight (β=0.41, p<0.05). The amniotic sHLA-G level and placental perfusion (VI: β = -0.34, p<0.01 and VFI: β = -0.44, p<0.01, respectively) were negatively interrelated. A low amniotic sHLA-G level was significantly associated with nuchal translucency (r = -0.102, p<0.05). Conclusions: sHLA-G assayed in amniotic fluid might be a potential indicator for placental function, whereas the sHLA-G level in serum can be a prognostic modality for feto-placental insufficiency.

The research investigates the factors affecting the financial performance of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia and their impact on the country’s economic growth. The research methods of the studies involve data collection through surveys or interviews, followed by statistical analysis. The study found that entrepreneurial qualities such as motivation, entrepreneurial orientation, and risk behavior positively influence the financial performance of MSMEs. MSMEs that manage working capital optimally tend to increase profitability and growth, despite operational difficulties like worker shortages. MSMEs engaged in foreign trade usually achieve higher profitability. Bank loans can enhance profitability and sales, but the collateral requirement may hinder growth. The owner’s involvement doesn’t affect growth but can enhance profitability. Factors like the Human Capital Index, interest rates, the Indonesian Rupiah exchange rate, and inflation do not significantly impact MSME investment. However, business units, labor, investment, and exports of MSMEs significantly influence Indonesia’s economic growth. MSMEs play a crucial role in job creation, rural development, economic diversification, and GDP contribution in Indonesia. Despite challenges, they have the potential for sustainable growth. The research also explores strategies for overcoming challenges faced by MSMEs and investigates the role of universities in supporting the MSME sector. The University of Indonesia (UI) supports the MSME sector through strategic initiatives, including education, training, mentoring, and internship programs. The research provides valuable insights into the operation of MSMEs in Indonesia and their contribution to the economy, highlighting areas for further exploration and potential strategies for supporting the growth and sustainability of these enterprises.

UV-A (315–400 nm) is a component of solar radiation impacting plant physiology. Consequently, the impact was investigated of UV-A supplementation on various aspects of kale cultivation in a plant factory with artificial lighting. Three UV-A intensity treatments (5, 10, or 15 W/m2) were applied to kale plants for 5 days before harvesting. The results revealed that 5 or 10 W/m2 UV-A supplementation provided significant advantages for kale growth. The fresh weight of the kale shoots increased by over 35–50% after UV-A exposure. In particular, the 10 W/m2 UV-A exposure significantly improved the photosynthetic rate, water use efficiency and quantum efficiency of photosystem II (Y(II)). In addition, the treatment with UV-A positively impacted changes in leaf spectral indices, resulting in decreases in the simple ratio index (SR), the nor-malized phaeophytinization index (NPQI), and the normalized difference red edge index (NDRE). Furthermore, the phytonutrients in kale (vitamin C, soluble protein, and total phenolics) were enhanced using the 10 W/m2 UV-A treatment. One notable outcome was the consistent reduction in nitrate contents across all UV-A treatment intensities. Therefore, supplementing kale cultiva-tion with UV-A radiation was a valuable strategy for enhancing kale yield and quality in plant factory production.

A crop-weed simulation model is presented to compare and evaluate Integrated Weed Management (IWM) strategies. Specifically, the model was parameterized for soybean crops in competition with Euphorbia davidii Subils. We used both, weed and crop demographic data surveyed in agronomic fields of the central zone of the Buenos Aires province, Argentina, throughout two crop cycles (2011/2012 and 2013/2014). The proposed model underwent a calibration process and subsequent validation with 70/30 % data split respectively (N=37). Two annual-based and one multiannual-based case studies were simulated to demonstrate the performance of the model. Different IWM strategies were compared under both, operational and tactical planning horizons through the evaluation of different model outcomes (i.e. crop yield, interspecific competition, economic return, and environmental impact). Our case studies results suggest that the inclusion of cultural management practices could reduce weed interspecific competition by 46 to 97 % and weed seed production by 40 to 89%. An increment in both expected crop yield, by 6 to 20%, and annual gross margin, by 44 to 199 US$.ha-1, were obtained in silico for similar levels of environmental impact.

Disulfiram (DSF), an anti-alcoholism medicine, exerts treatment effects in patients suffering from persistent Borreliosis and also exhibits anti-cancer effects by its copper chelating derivatives. Since chronic/persistent borreliosis is characterized by increased amounts of proinflammatory macrophages, this study investigated opsonin-independent phagocytosis, migration and surface marker expression of in vivo activated human monocyte-derived macrophages and dendritic cells with and without DSF treatment. Phagocytosis of non-opsonized Dynabeads® M-450 and migration of macrophages and dendritic cells were monitored using live cell analyzer Juli™ Br for 24h by imaging every 3.5 min. Results were analyzed by a newly developed software based on the differential phase contrast images of cells before and after ingestion of Dynabeads bearing an electron dense iron core. As a control, phagocytosis was also monitored by visual counting of ingested beads. DSF decreased the phagocytic capacities exhibited by global macrophages and dendritic cells. DSF also impaired the migration of human monocyte-derived macrophages and dendritic cells (hMDMD) and significantly reduced the expression densities of surface antigens CD45 and CD14. In cells consisting of anti-inflammatory M2 macrophages, DSF led to a remarkable cell death and also increased oxidative stress as determined by MitoSox staining. Mitochondrial oxidative stress was a prominent feature in M2 macrophages which were more sensitive to DSF-induced cytotoxicity.

Lung cancer is the leading cause of cancer related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterised by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation with their metabolic pathways and in light of the altered cancer metabolism.

This study analyses regional development in one of the poorest provinces in China, Guizhou Province, between 2000 and 2012 using a multiscale and multi-mechanism framework. In general, regional inequality has been declining since 2000. In addition, economic development in Guizhou Province presented spatial agglomeration and club convergence, which shows how the development pattern of one core area, two-wing areas and a contiguous area at the edge of the province have been developed between 2006 and 2012. Multilevel regression analysis revealed that industrialization and investment level were the primary driving forces of regional economic disparity in Guizhou Province. The influences of marketization and decentralization on regional economic disparity were relatively weak. Investment level reinforced regional economic disparity and the development of core-periphery structure in the province. However, investment level actually weakened the regional economic disparity in Guizhou Province when the variable of time was considered. In addition, both the topography and urban–rural differentiation were the two main reasons for forming a core-periphery structure in Guizhou Province.

Abstract: The VSI represents a significant advancement in the assessment of gastrointestinal-specific anxiety among patients with Irritable Bowel Syndrome (IBS) and other Chronic Inflammatory Bowel Diseases (IBD). However, an Italian version of the instrument is not yet available for the reference population. This study utilized a national sample of 500 individuals, including healthy controls and patients with Ulcerative Colitis and Crohn's Disease, to test the validity and reliability of the Italian VSI. Using back-translation methodology to ensure translation fidelity, the research applied a questionnaire and the VSI through an online format to 500 participants. Data analysis revealed that the Italian VSI had excellent psychometric properties, demonstrating high internal consistency (Cronbach’s α = 0.95) and construct validity. The scale proved sensitive in detecting significant differences in visceral sensitivity among groups, highlighting its utility as a clinical and research assessment tool. Specifically, the Italian VSI exhibited a unidimensional factorial structure and maintained a strong correlation with other measures of anxiety and the severity of gastrointestinal symptoms, confirming its role in enhancing the understanding and management of IBS and IBD in Italy.

The novel coronavirus disease (COVID-19) pandemic has resulted in over 720 million confirmed cases and 7 million deaths worldwide, with insufficient treatment options. Innumerable efforts are being made around the world for faster identification of therapeutic agents to treat the deadly disease. Postacute sequelae of SARS-CoV-2 infection or COVID-19 (PASC), also called Long COVID, is still being understood and lacks treatment options as well. A growing list of drugs are being suggested by various in silico, in vitro and ex vivo models, however currently only two treatment options are widely used: the RNA-dependent RNA polymerase (RdRp) inhibitor remdesivir, and the main protease inhibitor nirmatrelvir in combination with ritonavir. Computational drug development tools and in silico studies involving molecular docking, molecular dynamics, entropy calculations and pharmacokinetics can be useful to identify new targets to treat COVID-19 and PASC, as shown in this work and our recent paper that identified alendronate as a promising candidate. We have now investigated all bisphosphonates which can bind competitively to nidovirus RdRp-associated nucleotidyl (NiRAN) transferase domain, and systematically down selected seven candidates (CHEMBL608526, CHEMBL196676, CHEMBL164344, CHEMBL4291724, CHEMBL4569308, CHEMBL387132, CHEMBL98211), two of which closely resemble the approved drugs minodronate and zoledronate. This work and our recent paper together provide an in silico mechanistic explanation for alendronate and zoledronate users having dramatically reduced odds of SARS-CoV-2 testing, COVID-19 diagnosis, and COVID-19-related hospitalizations, and indicate that similar observational studies with minodronate could be valuable.

Herein, we present a combination of experimental and computational study on the mitochondrial F0F1-ATPase nanotoxicity inhibition induced by single-walled carbon nanotubes (SWCNT-pristine, SWCNT-COOH). To this end, the in vitro inhibition responses in submitochondrial particles (SMP) as F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/ml) for both types of carbon nanotubes. Besides, both SWCNTs show an interaction inhibition pattern like the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor). Furthermore, the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB), fit well with the previous in vitro evidences from the thermodynamics point of view. Following an affinity order as: FEB (oligomycin A/F0-ATPase complex) = -9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = - 6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = -5.9 kcal/mol. With predominance of van der Waals hydrophobic nanointeractions with key F0-ATPase binding site residues (Phe 55 and Phe 64). By the other hand, results on elastic network models, and fractal-surface analysis suggest that SWCNTs induce significant perturbations by triggering abnormal allosteric responses and signals propagation in the inter-residue network which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). Besides, the performed Nano-QSTR models for both SWCNTs show that this method may be used for the prediction of the nanotoxicity induced by SWCNT. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug-design based nanotechnology, and potential biomedical application in precision nanomedicine.

We introduce optically clear and resilient free-form micro-optical of pure (non-photosensitized) organic-inorganic SZ2080 material made by femtosecond 3D laser lithography (3DLL). This is advantageous for rapid printing of 3D micro-/nanooptics, including their integration directly onto optical fibers. A systematic study on the fabrication peculiarities and quality of resultant structures is performed. Comparison of microlenses’ resiliency to CW and femtosecond pulsed exposure is determined. Experimental results prove that pure SZ2080 is ∼3 fold more resistant to high irradiance as compared with a standard photo-sensitized material and can sustain up to 1.91 GW/cm² intensity. 3DLL is a promising manufacturing approach for high-intensity micro-optics for emerging fields in astro-photonics and atto-second pulse generation. Additionally, pyrolysis is employed to shrink structures up to 40% by removing organic SZ2080 constituents. This opens a promising route towards downscaling photonic lattices and creation of mechanically robust glass-ceramic structures.

The increased prevalence of obesity worldwide has been implicated for the alarming rise of the incidence of gestational diabetes and preeclampsia as well, both considered as threatening conditions for both mother and fetus. We studied gene polymorphisms of the proinflammatory cytokine Interleukin 6 (IL-6) and the gene expression levels of VEGF and VEGF-receptor all known to be involved in pregnancy complications aiming to identify possible predisposing risk factors in pregnancies with obesity. G allele of IL-6 was found to correspond with an increased risk for gestational diabetes and preeclampsia occurrence. Furthermore, in obese pregnant mothers with either gestational diabetes or pre-existing type 2 diabetes and to those developed preeclampsia it was confirmed that gene expression levels of VEGF were reduced while they were increased for VEGF receptor. We conclude that the genetic profile of the obese pregnant woman shares a common background with that of a pre-existing type 2 diabetes mellitus and therefore predisposes to complications in pregnancy.

Integrated information theory (IIT) proposes a measure of integrated information (Φ) to capture the level of consciousness for a physical system in a given state. Unfortunately, calculating Φ itself is currently only possible for very small model systems, and far from computable for the kinds of systems typically associated with consciousness (brains). Here, we consider several proposed measures and computational approximations, some of which can be applied to larger systems, and test if they correlate well with Φ. While these measures and approximations capture intuitions underlying IIT and some have had success in practical applications, it has not been shown that they actually quantify the type of integrated information specified by the latest version of IIT. In this study, we evaluated these approximations and heuristic measures, based not on practical or clinical considerations, but rather based on how well they estimate the Φ values of model systems. To do this, we simulated networks consisting of 3–6 binary linear threshold nodes randomly connected with excitatory and inhibitory connections. For each system, we then constructed the system’s state transition probability matrix (TPM), as well as its state transition matrix (STM) over time for all possible initial states. From these matrices, we calculated, approximations to Φ, and measures based on state differentiation, state entropy, state uniqueness, and integrated information. All measures were correlated with Φ in a state dependent and state independent manner. Our findings suggest that Φ can be approximated closely in small binary systems by using one or more of the readily available approximations (r > 0.95), but without major reductions in computational demands. Furthermore, Φ correlated strongly with measures of signal complexity (LZ, r_s = 0.722), decoder based integrated information (Φ*, r_s = 0.816), and state differentiation (D1, r_s = 0.827), on the system level (state independent). These measures could allow for efficient estimation of Φ on a group level, or as accurate predictors of low, but not high, Φ systems. While it’s uncertain whether the results extend to larger systems or systems with other dynamics, we stress the importance that measures aimed at being practical alternatives to Φ are at a minimum rigorously tested in an environment where the ground truth can be established.

Among the many Lysosomal Storage Disorders (LSDs) that would benefit from the establish-ment of novel cell models, either patient-derived or genetically engineered, is Mucopolysaccha-ridosis type II (MPS II). In fact, even though a specific therapeutic approach does exist for this disorder (Enzyme Replacement Therapy, ERT, with recombinant human IDS) and haematopoiet-ic stem cell transplantation (HSCT) may also hold promise whenever the disorder is diagnosed soon enough, both approaches have their own drawbacks. Here we present our results on the establishment and characterization of two MPS II pa-tient-derived stem cell line(s) from deciduous baby teeth. To the best of our knowledge, this is the first time a stem cell population is isolated from LSD patient samples obtained from the oral cavity. Taking into account our results on the molecular and biochemical characterization of those cells and our first insights on their subcellular MPS phenotype, we consider these cells will be a valuable tool for both pathophysiological assessments and drugs screenings. Ultimately, we believe patient-derived dental pulp stem cells (DPSC) may represent a feasible alternative to induced pluripotent stem cells (iPSC) in many labs with standard cell culture con-ditions and limited (human and economic) resources.

Local food has continued to become more popular in both the home and as featured ingredients in all segments of food service. Numerous studies have explored the motivating factors for consumer purchasing motivators. However, no known study has explored if there is a correlation between ego and intent to buy local foods. This study adopted a modified version of the Theory of Trying and surveyed a sample of 237 food service buying agents in South Carolina to examine their perceptions of buying local. Results suggest that ego is a predictor of trying to purchase local foods for restaurants. It was also found that the modified model is an acceptable model for predicting buyer intent. These findings support the majority of the studies looking at ego-centric behaviors with green purchases and could potentially help local food suppliers to better market their products to foodservice establishments. This study could also act as the foundation for future studies to explore the full extent of ego’s motivational effect on purchasing intentions in the food service industry.

: To improve the efficiency of rhizobial inoculation operations and address the current situation of rhizobial inoculation being labor-intensive, a spraying and control system for variable rate application (VRA) of liquid rhizobia was developed. The system can accurately regulate the spraying rate of liquid rhizobia according to the desired rate of rhizobia by incremental Proportional Integral Derivative (PID) closed-loop control algorithm, making it particularly suitable for precise spraying with small amounts. In the laboratory experiment of precise flow control, variation coefficient of spraying nozzle from 0.10 MP to 0.20 MP pressure were no more than 1.2%. A clear linear relationship was observed between the spraying rate and pressure. The maximum response time was 1.93 seconds, while the average response time was 1.62 seconds when controlling the spraying system for small amounts. Field trial was conducted in Heilongjiang Agricultural Xianghe Farm of China. Average seeds number and 100-seed weight of soybean plant with rhizobia inoculation treatment were better than control group, and average yield of spraying liquid rhizobia agent with full base fertilizer increased by 232.5 kg/hm2, representing a significant 7.4% improvement over the control group. The results of these trails clearly showed the positive effect of the spraying and control system for liquid rhizobial inoculation, which has superior to existing inoculation manners, and provides an important technical support for the widespread adoption of rhizobia technology.

The paper examines the various uncertainties encountered in high-frequency trading (HFT) environments and delves into the multiple challenges faced by HFT firms in navigating the Dodd-Frank Wall Street Reform and Consumer Protection Act (referred to as the "Dodd-Frank Act"), particularly during the initial stages of its enactment. These challenges include the ambiguity surrounding the definition of HFT, the lack of clarity regarding regulatory requirements and boundaries, inconsistencies in enforcement resulting from deviations in understanding the content, and the absence of detailed descriptions of the Act’s provisions. These hurdles significantly impact not only the daily operations of HFT firms but also pose higher demands on their long-term strategic planning and risk management. Drawing upon the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, this study employs an innovative analytical framework. Through the comprehensive application of conceptual space, cognitive space, and semantic space, it provides a systematic methodology for identifying and analyzing the aforementioned issues. This approach not only aids firms in better comprehending and adhering to complex regulatory requirements but also enables them to explore new business opportunities and competitive advantages while ensuring compliance.

Sustainable development and education in sustainable development (ESD) are crucial and interconnected topics under wide discussion today. Young adults need to be educated in how to help bring about change. Thus, the dynamic relationship between school and home is important. In education, use could be made of English language classes, especially since English is a global language that enables access to the sustainability literature and facilitates discussion in global conferences and meetings. Consequently, English as a foreign language (EFL) students could gain confidence from being able to understand and use the terminology of, for example, sustainability. In this paper, the use of competence-based language teaching (CBLT) is proposed as a means of introducing sustainability topics into EFL writing classes at a university in Saudi Arabia. Students’ competence in sustainable development is therefore considered based on their ability to write about suggested sustainable development topics, measured on a scale for five dimensions of ESD proposed by Michelsen and Fischer (2017). The results demonstrate that the students possessed competence in sustainable development and intended to participate in its implementation. Thus, recommendations are made for the stakeholders in the education sector to include sustainable development topics as an initial offering in the curricula of various specialties using the CBLT approach.

The antimicrobial properties of the new strains of microorganisms isolated from natural sources of various ecological niches in the Moscow Region and the Republic of Tatarstan were studied. An antifungal activity of isolates was established in a test culture of toxin-producing microscopic fungi that cause various plant diseases: Fusarium oxysporum, Aspergillus flavus, Penicillium spp. and Candida albicans. Of the 46 studied microorganisms, 4 isolates (Bacillus subtilis, Propionibacterium freudenreichii, Lactobacillus plantarum and Streptomyces spp.) showed an ability to produce biologically active metabolites with a pronounced antimicrobial potential. This is promising in the growth and protection of the Scots pine, pedunculate oak and small-leaved linden forest crops from diseases caused by fungi Lophodermium pinastri Chev. and Microsphaera alphitoides stimulation. Based on selected isolates, a Bacterial product LRV composition has been developed. A Scots pine, pedunculate oak and small-leaved linden seedlings single and double foliar treatment with Bacterial product LRV at a concentration of 10 ml/l led to an increase in the growth of an above-ground part by 31.8, 51.9 and 25.4 %, respectively, and an underground part – by 25.0, 37.2 and 25.7 %, respectively, compared with the control. The weight of seedling at the end of the study exceeded the control variant by an average of 26.0, 44.0 and 78.0 %, respectively. A use of a plant protection Bacterial product LRV did not have a significant effect on a powdery mildew and Schütte disease damage to forest crops. A Biogeosystem Technique methodology has been developed to improve a long-term forest growing.

The flux growth of InVO4 bulk single crystals has been explored for the first time. The reported eutectic composition at a ratio of V2O5:InVO4 = 1:1 could not be used as a self-flux, since no sign of melting was observed up to 1100 °C. Crystals of InVO4 of typical size 0.5×1×7 mm3 were obtained using copper pyrovanadate (Cu2V2O7) as a flux, using Pt crucibles. XRD confirmed the orthorhombic Cmcm structure. Rests of flux material were observed on the sample surface, with occasional traces of Pt indicating some level of reaction with the crucible. X-ray absorption spectroscopy showed that oxidation states of indium and vanadium ions are +3 and +5, respectively. The size and high quality of the obtained InVO4 crystals makes them excellent candidates for the further study of their physical properties.

For a significant period, conventional breeding and genetic modification were the key techniques that were effective in managing biotic and abiotic stresses in crops and adding desirable traits. However, the recent appearance of novel diseases and unexpected climatic changes that have substantial implications for agriculture worldwide have urged scientists to look for alternative methods to quickly manage seasonal crises. The primary emphasis of this chapter is on the obstacles and diverse biotechnological methods employed to enhance crop resilience against a range of biotic and abiotic stressors in plants. Furthermore, we consider gene transformation, omics techniques, genome editing, and other sophisticated biotechnological tools that utilize transcriptomic, proteomic, metabolomic, phenomics, RNA interference, and epigenome modifications for enhancing plant resilience. Lastly, we examine the potential of merging these eco-friendly and innovative methods with conventional breeding to promote modern agriculture and aid in enhancing tolerance to different biotic, abiotic stresses and growth promotion.

Sclerotinia sclerotiorum is a devastating fungal pathogen that causes severe crop losses worldwide. It is of vital importance to understand its pathogenic mechanism for disease control. Through a forward genetic screen combined with next-generation sequencing, a putative protein kinase, SsCak1, was found to be involved in growth and pathogenicity of S. sclerotiorum. Knockout and complementation experiments confirmed that deletions in SsCAK1 caused defects in mycelium and sclerotia development, as well as appressoria formation and host penetration, leading to complete loss of virulence. These findings suggest that SsCak1 is essential for growth, development and pathogenicity of S. sclerotiorum. Therefore, SsCak1 could serve as a potential target for the control of S. sclerotiorum infection through host-induced gene silencing (HIGS), which could increase crop resistance to the pathogen.

Studies suggest that elevated postnatal blood lead levels (BLLs) are negatively associated with child growth. This study aimed to investigate associations of childhood BLLs at age one-year and growth outcomes at age six-years (n=661) in a cohort of children in Allada, Benin. The growth outcomes studied are weight-for-age Z-score (WAZ), height-for-age Z-score (HAZ) and BMI-for-age Z-score (BMIZ), head circumference (HC), underweight, stunting, and wasting. Multivariable regression models examined associations between blood lead levels, growth outcomes with adjustment for potential confounders. The geometric mean BLLs was 59.3 μg/L and 82% of children had BLLs &gt;35 μg/L at one year. After adjusting for confounding factors, no association was found between BLLs quartiles and HAZ, WAZ, BMIZ, and height and weight growth velocities. However, boys in the highest quartile had 1.02 cm lower HC (95% CI: [-1.81, -0. 24]) as compared to those in the lowest quartile with a dose-response trend across quartiles (Ptrend =0.02). Furthermore, an increased risk of being stunted was observed in children in the highest quartile of exposure compared to the first (OR: 2.43; 95% CI: [1.11 – 5.33]) with a dose-response trend (Ptrend =0.03). Blood lead was found to be associated with an increased risk of childhood stunting and lower head circumference in a resource-limited setting.

Growth and yield of boreal tree species are discussed. A growth model is applied, along with verified yield models of sawlogs and veneer logs. Using the normal forest-principle, thinning schedules and rotation ages maximizing the estate-level capital return rate are clarified. Regeneration expenses are amortized at the end of any rotation. Consequently, capitalizations are greater and rotations longer than in recent studies. The capital return rate is a weak function of initial stem count but differs by tree species. The initial stem count strongly contributes to biomass stored in trees. The most promising way of increasing the capital return rate is the reduction of regeneration expenses. Thinnings are triggered by stand volumes of at least 200 m3/ha. The average commercial trunk volume of trees removed in thinnings always exceeds 200 liters. Risk aversion theory proposes short rotations and low stem count in seedling planting unless carbon storage compensation exists.

We examined the causality between growth opportunities and earnings quality of the emerging economies. We develop an argument that growth opportunities should lead to high-quality earnings of the developing economies against the prior studies from developed economies which posit that growth opportunities result in a lower quality of earnings. The findings revealed that Growth Opportunities had a positive significant effect on Earnings Quality (EQ). Thus, the study concluded that growth opportunities are useful in determining the earnings quality of firms. The study recommends that managers should take advantage of their firms' growth opportunities to provide quality accounting information which will directly provide expanded opportunities for business growth. Though literature confirms that generally, earnings management might not be necessarily bad in business operational practice, managers are advised to avoid extreme aggressiveness in managing earnings as this may culminate in the negative manipulation of accounting information.

Oil revenues and external debt might have stimulated economic growth in the oil exporting countries via investment in capital projects. The paper estimated economic growth on oil revenues and external debt after controlling public investment and population growth over the period 1970-2015. Following the confirmation of the order of integration, our analysis is based on autoregressive distributed lag bound testing to cointegration approach. The key findings are that oil revenues and public investment contributes to Nigeria’s economic growth. However, our findings also indicate that external debt and population growth retards growth. The study suggests that minimizing fiscal deficits and unnecessarily foreign loans by creating tax avenues through the development of the non-oil sectors would reduce the dependency syndrome on a single commodity (oil) in Nigeria.

In recent years, technical processes for the sequestration of CO2 through industrial waste mineral carbonation have been explored and developed. There is a large portfolio of Carbon Capture, Uti-lisation and Storage (CCUS) techniques that have been employed on a laboratory and at pilot scale. These include geological storage, ocean storage and mineralisation by carbonate ores. In view of this, the main purpose of this research was to investigate and explore chemical variables, particularly ammonium salts as lixiviants for calcium mineral extraction from iron making slag. The slag in use was acquired from a Steel Mill in the Vaal Triangle Region in Gauteng, South Af-rica. The experimental test work was conducted using different ammonium lixiviants, namely, NH4NO3, NH4Cl and CH3COONH4 to understand the influence of anion type as well as possible differences in mechanisms of interactions. Lixiviant concentration as well as the reaction time were varied in this research study. The three selected ammonium based lixiviants showed different extents of calcium extraction owing to differences in the anion groups. NH4NO3, NH4Cl and CH3COONH4 were found capable of dissolving 50% to 80% of calcium from the selected slag for different molar concentrations. Anion type and leaching time also had significant influences on the leaching of calcium from the slag. Rapid pH degradation resulted in better calcium extraction ca-pabilities. This work has shown that the selected ammonium salts have a potential as lixiviants for calcium dissolution from iron making blast furnace slags. These lixiviants would therefore be important to consider during calcium mineral carbonation for CO2 sequestration.

Interaction with social agents employing natural language have been discussed especially since Weizenbaum (1966) observed the social effects of his chatbot named 'Eliza', which imitated a cli-ent-centered conversation. With Nass and Reeves’ media equation theory and computers as social actors paradigm, a well-tested framework has been introduced to the study of human-computer interaction. In the 1990s, there was a renewed focus on natural language analysis, which has be-come a crucial area of research in machine learning. Recent advancements in data processing and storage capacities have led to the development of AI-generated utterances that closely resemble human utterances. As a result, AI-based systems can now mimic social relationships to a signifi-cant extent. In light of these technological developments, the article explores whether quasi-social relationships should be considered within the field of social psychology and examines the poten-tial advantages and challenges of this approach.

In the majority of children with growth hormone (GH) deficiency (GHD), normal GH secretion may occur before the attainment of final height. The aim of the study was to assess the incidence of persistent and transient GHD and the effectiveness of recombined human GH (rhGH) therapy in children with isolated idiopathic GHD, with respect to the moment of therapy withdrawal and according to different diagnostic criteria of GHD. Analysis included 260 patients (173 boys, 87 girls) with isolated idiopathic GHD who completed rhGH therapy and have reassessed GH and IGF-1 secretion. The incidence of transient GHD with respect to different pre- and post-treatment criteria was compared together with assessment of GH therapy effectiveness. Incidence of transient GHD, even with respect to pediatric criteria was very high. Normal GH secretion occurred before the attainment of near-final height. Application of more restricted criteria decreased the number of children diagnosed with GHD but not the incidence of transient GHD among them. Poor response to GH therapy was observed mainly in patients with normal IGF-1 before treatment, suggesting that diagnosis of GHD may be false positive. Further efforts should be made to avoid overdiagnosis GHD and overtreatment of the patients.

This paper proposes a methodology for the development and its Verification and Validation(V&amp;V) of safety-rated software. The development methodology is based on the structured analysis and structured design methodology, which has been proven in the field of safety software development during long period. The suggested the development and the V&amp;V methodology additionally focuses on the quality attributes 3C+T (completeness, Correctness, Consistency and traceability), which are important criteria in IEC international standards. The software development life cycle is compiled from IEC international standardization, which are of the requirements analysis phase, design phase, implementation phase, testing phase, installation/commissioning phase, and operation &amp; maintenance phases. The activities of the development process method were defined from requirements analysis to implementation, and the activities of the verification and validation were defined to include the scope of the developer's activities and then to include the test phase, installation/commissioning phase, and operation phases additionally. In other words, testing phase performed by the developer was defined as informal testing, and testing performed by the verifier according to test criteria, test plans, and test procedures was defined as formal testing. According to proposed methodology, the requirements phase defines the sequence of Entity Relationship Diagram (ERD)-Context Diagram-Data Flow Diagram (DFD), functional decomposition process and defines operational scenario. In the design phase, module design such as task analysis and task allocation were defined. In the implementation phase, we listed the coding guidelines that must be followed for safety-related system. The test phase defined the test criteria, test plan, test procedure, and test result report activities. In the installation/commissioning and operation phases, recommendations for improving reliability, quality, and safety were proposed for deliverables such as user documentation and technical documentation.

Ecuador is an important center of biodiversity of the plant family Amaryllidaceae, specially the subfamily Amaryllidoideae, known as a source of important bioactive molecules. The aim of this study was to assess the chemical and biological potential of four different species of Amaryllidoideae collected in Ecuador: Urceolina formosa, Urceolina ruthiana, Clinanthus incarnatus, and Stenomesson aurantiacum. Sixteen alkaloids were identified in the bulb extract of these species by GC-MS, which the extract of S. aurantiacum exhibiting the greatest structural diversity. The potential of these three genera against Alzheimer’s disease was evaluated by measuring their AChE and BuChE inhibitory activity, revealing that C. incarnatus and U. formosa (from Sucumbíos province) showed the best results with IC50 values of 1.73 ± 0.25 and 30.56 ± 1.56 g·ml-1, respectively. Molecular dynamic assays were conducted to elucidate the possible interactions that occurs among 2-hydroxyanhydrolycorine and AChE enzyme, suggesting some similarity with those observed for galanthamine. This study enhances our understanding of the biodiversity of Amaryllidoideae species from Ecuador, highlighting their potential as a source of chemical compounds with pharmaceutical potential.

In order to help the transportation industry achieve the goal of carbon peak and carbon neutrality, this study is based on the selection of eight variables, such as population size, per ca pita GDP, personal vehicle ownership, passenger and freight turnover, and green space coverage, as the factors influencing the carbon emissions of the transportation industry in Jiangsu Province, and the prediction and analysis of the transportation carbon emission trends from 2000 to 2021, and the prediction and analysis of the carbon emissions of the transportation industry and the time of the peak of the carbon emissions in Jiangsu Province. The trend of transportation carbon emissions from 2000 to 2021 was predicted and analyzed, and the carbon emissions and peak time of carbon emissions in the transportation industry in Jiangsu Province were predicted and analyzed. Comparing the predicted results with other models to verify the accuracy of the results, it was found that the WOA-SVM model had the smallest error among several models. On this basis, targeted measures are proposed to accelerate the process of carbon peak and ensure the smooth achievement of carbon neutrality goals in Jiangsu Province. The results indicate that under the current policy measures, the peak carbon emissions in Jiangsu Province will occur in 2038, with a peak of 48.72 million tons. Jiangsu Province should actively adopt energy-saving and emission-reduction measures, build a green and low-carbon transportation development model, and achieve the carbon peak target ahead of schedule.

The corporate strategic planning of businesses in sub-Saharan Africa (SSA) largely focuses on immediate financial performances with minimal credence to social sustainability. Thus, studies on the linkage between corporate governance and sustainability reporting have focused on developed economies. This study therefore investigated the role of institutional ownership in the impact of board committee characteristics on social sustainability reporting. The study involved strongly balanced panel data with 1,969 observations of 275 publicly listed non-financial firms in SSA within the timeframe of 2012 to 2021. Data was analyzed using STATA 14.1. The hypotheses were tested using the two-step system of the generalized method of moment (GMM) using the Arellano-Bond dynamic panel-data estimation method. The rate of social sustainability reporting was 39.4%. Relatively, Mauritius and South African firms had the most effective board committee characteristics and higher levels of social sustainability reporting. Institutional ownership had no significant effect on social sustainability reporting. Institutional ownership moderated the effect of sustainability committee independence and sustainability committee gender diversity on social sustainability reporting negatively. The paper presents a new perspective on corporate governance and social sustainability literature by examining the effect of institutional ownership on board committee characteristics and social sustainability reporting in SSA.

Over recent years, the swine industry has witnessed the withdrawal of antibiotics and continuous regulation of zinc and copper oxides in the early-life nutrition of piglets. Due to this development, alternative ingredients or additives from plant sources have been tremendously explored. Therefore, this study's objective was to evaluate the effect of dietary supplementation with tannins on weaned piglets’ growth performance, serum antioxidant capacity, and immune status using a meta-analysis approach. With only the inclusion of non-challenged weaned piglets, a total of 16 studies with parameters of interest were deemed eligible after a two-step screening process following a comprehensive literature search in the scientific databases of Web of Science, Scopus, ScienceDirect, PubMed, and Google Scholar. Two main categories of data, including the study’s characteristics and response variables (growth performance, serum antioxidant, and immune indices), were extracted. Applying the random-effects models, Hedges’ g effect size of supplementation with tannins was calculated using the R software to determine the standardized mean difference (SMD) at a 95% confidence interval. Sub-group analysis and meta-regression further explored heterogeneity (PSMD 0.05, I2 50%, n ≥10). Supplementation with tannin reduced average daily gain (p = 0.20) and feed conversion ratio (p 0.01); however, it increased average daily feed intake (p = 0.07) and final body weight (p 0.01). Meta-regression models indicated that tannin dosage and supplementation duration were directly associated with tannins' effectiveness on productive performance. In the serum, the concentration of glutathione peroxidase, superoxide dismutase, and total antioxidant capacity were elevated (p 0.01) in response to tannins supplementation, while malondialdehydes was reduced (p 0.01). Likewise, decreased immunoglobin A (p = 0.47) and increased immunoglobin M and G levels (p 0.01) were detected. In conclusion, dietary supplementation with tannins, particularly with chestnut and grape seed proanthocyanidins, increases the productivity of weaned piglets. At the same time, it is a possible nutritional strategy to mitigate oxidative stress and stimulate gut health. Thus, supplementing chestnut and grape seed proanthocyanidin tannins in the early phase of swine production could be used to alleviate the incidence of diarrhea.

Measurement of Globe temperature is essential for investigating thermal comfort in street canyon. Globe thermometer is the most common instrument used to measure globe temperature (GT), however its application in scale models is still uncertain and not thoroughly investigated as of today. Therefore, this study is explicitly conducted to investigate globe thermometer measurements and proposed an improvised customization of the globe thermometer size to enhance reliability in scale models. The experiments were carried out outdoors using a typical street canyon model (scale 1:100) with East-West street orientation. The experiment presented a comparison between a low solar reflective street canyon (albedo of 0.4) and a high solar reflective canyon (albedo of 0.6) in terms of surface temperatures, heat flux and globe temperature. Furthermore, this study also investigated the influence of solar orientation on the globe temperature using both experimental using scale models and microclimatic modelling using ENVI-met. It is observed that although the surface temperatures are lower with high solar reflective canyon compared to that with low solar reflective canyon, GT is higher in high reflective canyon during daytime, which could be due to combined effect of direct radiation and shortwave reflection. However, for hours after the sun set, GT becomes lower (up to 1°C) in the case of high reflective canyon compared to low reflective canyon.

The current disruptive innovation model has a limitation in incorporating customers’ demands while the customer can be a co-creator of value on performance, design, and price innovation. Thus, this research attempted to explore the disruptive innovation model which can include both the incumbent’s innovation trajectory and customer’s demand, and how incumbents behave to enable disruptive innovation in the smartphone market. Based on the Apple iPhone case study, this study verified that the current disruptive innovation model can incorporate customers’ demand by reflecting their demand in the direction of innovating the performance, design, or price of smartphones with the price-adjusted innovation trajectory. The secondary innovation trajectory which was found uniquely in this study is meaningful since it can be a creative solution for the innovator’s dilemma. Besides, Apple, as an incumbent, has behaved to innovate iPhone performance and design demands of customers creating significant papers and patents as outcomes of innovation activities. This study has practical implications for incumbents’ managers as well as customers to enable disruptive innovation in the smartphone market.

Education on native tree species using indigenous knowledge systems is crucial in elementary school environmental education because it sensitizes learners on the significant roles played by the native trees and their interconnections with their culture and existence. Unfortunately in Ghana, there is no pedagogical model that would aid elementary school teachers in carrying out such education.This study was aimed at developing, implementing and evaluating an indigenous knowledge inspired pedagogical model for native tree education among elementary school learners in Ghana. The convergent parallel mixed methods design that aimed at collecting quantitative and qualitative datasets on the development and evaluation of the developed pedagogical model. The results show that the pedagogical model was deemed very satisfactory in all its aspects for native tree education among elementary school learners in Ghana. The model was able to assist in developing the sustainability consciousness and competence of elementary school learners toward caring for native trees through tree planting, making of nurseries and gardens, and engaging in native tree conservation campaigns. The study recommends that the Ministry of Education should adopt, improve and widely disseminate the pedagogical model and help in its broader implementation in various elementary schools across the country.

This study aimed to develop chitosan-based apigenin spray dried solid dispersion using 1:1,1:1.5,1:2 w/w ratios. The results of process yield and drug assay were found to be (87.5%, 94.2%, and 95.86%), (95.2 ±1.34%), (99.5 ±0.85%), and (97.6 ±2.42%), respectively, for AC1, AC2, and AC3. Flow properties indicate the free flow of powders. FTIR revealed compatibility among chitosan and apigenin, and DSC and XRD showed prepared solid dispersion was on a molecular level and available in amorphous. In contrast, SEM images reflect irregular block and near-spherical shape elongated particles in the selected AC2. The antimicrobial examination reflects that AC2 was more effective against Gram-positive, negative, and fungal strains. AC2 SDSDs has more anti-oxidant property compared to pure flavonoid (AGN). Anti-proliferative activity against A549 lung cancer cell lines showed better anti-cancer activity by AC2 SDSDs. The selected AC2 SDSD filled in a 3D shell pill and further characterized for 3D shell pill for dissolution profiles and stability studies. The product has sustained release and similar release profiles after three months of storage. The findings suggest that the AC2 SDSDs may be a promising candidate for further development as a 3D printed drug delivery system for treating multiple disease conditions using model flavonoid- apigenin.

The crosstalk between tumor progression and ferroptosis is largely unknown. Here, we identify malate dehydrogenase 2 (MDH2) as a key regulator of ferroptosis. MDH2 deficiency inhibits hepatocellular carcinoma (HCC) cell growth and promotes sensitivity of HCC cells to RSL3-induced ferroptosis. MDH2 knockdown enhances RSL3-induced intracellular reactive oxygen species (ROS), free iron ions and lipid peroxides levels, leading to HCC ferroptotic cell death which is rescued by Ferrostatin-1 and iron chelator Deferiprone. Importantly, inhibition of HCC cell growth caused by MDH2 deficiency is partially rescued by ferroptosis blockade. Mechanistically, MDH2 resists RSL3-induced ferroptosis sensitivity by dependence on GPX4, which is stabilized by MDH2 in HCC. The protein expression of MDH2 and GPX4 is positively correlated with each other in HCC cell lines. Furthermore, through UALCAN website analysis, we find that MDH2 and GPX4 are highly expressed in HCC samples. These findings reveal a critical mechanism by which HCC evades ferroptosis via MDH2-mediated stabilization of GPX4 to promote tumor progression and underscore the potential of MDH2 inhibition in combination with ferroptosis inducers for the treatment of HCC.

ESG (Environmental, Social, and Governance) responsibility fulfillment increasingly affects enterprise valuation. Although researchers debate about the precise effects, the prevailing view suggests a linear relationship between ESG performance and enterprise value. This study introduces a novel ESG responsibility performance metric through the Regulated Intermediary Model to delve into this relationship within the Chinese context. Our findings reveal an inverted U-shaped relationship between ESG performance and enterprise value, with financing constraints having a significant moderating effect. These findings remain robust after employing instrumental variables to mitigate potential endogeneity. Heterogeneity analysis demonstrates that this inverted U-shaped relationship is particularly pronounced in non-polluting and non-state-owned enterprises. Moreover, a comparison between equity and debt financing mechanisms underscores that improved ESG performance is associated with lower cost of equity financing, thereby enhancing enterprise value. Financial institutions are encouraged to leverage innovative financial instruments to diversify enterprise financing channels and alleviate financing constraints of enterprises that fulfill their ESG responsibilities.

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring SARS-CoV-2 prevalence in a city in Northern Italy from October 2021 to May 2023. Starting from a widely applied deterministic model, this study proposed a variation to take into account population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated with healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred in the considered period, demonstrating the model’s capacity to predict relevant fluctuations in the number of cases. Population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied for comparing virus load values across cities with varying population demographics and sewer network structures, enhancing the comparability of WBE data for effective surveillance and intervention strategies.