Abstract：Commuting of residents in big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make possible the fine description of urban residents travel behaviors, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, travel behaviors of commuters are simulated: the spatial context of the model is set up using the existing urban road network and by dividing the area into travel units; then using the mobile phone call detail records (CDR) of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generated the OD matrix of travels at different time slots; and then the data are imported into the model for simulation. By the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can also induce backward the causes of traffic congestion using the simulation results and the OD matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

ORB (Oriented FAST and Rotated Brief) features are the most commonly used features in visual SLAM and visual odometry that have high computational speed, and rotation scale invariance. However, due to the homogenization of ORB features, the corner properties of specific features are poor in some artificial environments, and it can lead to poor matching performance when the environmental texture is not rich. The underlying reason is that the pixel grayscale changes in the less-richly textured regions on the image are not obvious, leading to a certain degree of mis-matching in the matching process. At the same time, when the camera motion speed is low, there is much overlap between adjacent frames. This results in minimal or almost no changes in the projection of feature points, and the system is highly sensitive to errors, In this case, even small errors can cause significant fluctuations in the calculation results. In response to the above issues, an improved feature point method for stereo vision mileage calculation is proposed to solve the problem of system stability deterioration when cameras move at low speeds in artificial envi-ronments. Firstly, weight calculation based on different texture regions is used to solve the problem of low corner properties of feature points caused by environmental texture differences. Then, keyframes are used for motion model estimation to improve the system's stability under low-speed motion. The experimental results of the dataset showed that the key frame rate was optimal between 10% and 12%, and the positioning accuracy was higher than these open-source systems compared with three common open-source VO systems.

This work aims to describe the dielectric behaviours of four main tissues in the human forearm using mathematical modelling, including fat, muscle, blood, and bone. Determining the electrical response of multiple tissues can help develop physiological monitoring of an organ or a body’s section by filtering out the impedance contributions from the surrounding tissues to blood flow-induced impedance variations. Multi-frequency bioimpedance analysis (MF-BIA) was initially performed using the finite element method (FEM) with a 3D forearm model followed by a pilot study to characterise the response of actual forearm tissues from 1 kHz to 349 kHz. Both the simulation and experimental results were fitted to a single-dispersion Cole model (SDCM) and a multi-dispersion Cole model (MDCM) to determine the Cole parameters for each tissue. The correlation analysis and Bland-Altman plot indicated a good fit between raw and fitted impedance values using both SDCM and MDCM. Overall, MDCM exhibited better performance in fitting and estimation of the Cole parameters with correlation coefficient (R2) of 0.99 and 0.97, root mean squared error (RMSE) of 0.09 Ω and 0.14 Ω, and mean difference (mean±standard deviation) of 0.00±0.09 Ω and -0.03±0.14 Ω for the real part and imaginary part of impedance, respectively.

Malaria is a critical fevered illness caused by Plasmodium parasites transmitted among people through the bites of infected female Anopheles mosquitoes. Public awareness about the disease is important for the control of disease. This article proposes a mathematical model to study the dynamics of malaria disease transmission with the influence of awareness-based control interventions. We found two equilibria of the model, namely the disease-free and endemic equilibrium. Disease-free equilibrium is stable globally if basic reproduction number (R0) is less than unity (R0<1). Some basic mathematical properties of the proposed model, such as nonnegativity and boundedness of solutions, the feasibility of the equilibrium points and their stability properties, have been studied. Finally, we adopted optimal control to minimize the cost of disease control and solve the problem by formulating Hamiltonian functional. The optimal use of insecticides for controlling the mosquito population is determined. Numerical simulations have been provided for the confirmation of the analytical results. We conclude that media awareness with optimal control approach is best for cost-effective malaria disease management.

This paper analyses and evaluate the precision and the accuracy the capability of low-cost terrestrial photogrammetry by using many digital cameras to construct a 3D model of an object. To obtain the goal, a building façade has imaged by two inexpensive digital cameras such as Canon and Pentax camera. Bundle adjustment and image processing calculated by using Agisoft PhotScan software. Several factors will be included during this study, different cameras, and control points. Many photogrammetric point clouds will be generated. Their accuracy will be compared with some natural control points which collected by the laser total station of the same building. The cloud to cloud distance will be computed for different comparison 3D models to investigate different variables. The practical field experiment showed a spatial positioning reported by the investigated technique was between 2-4cm in the 3D coordinates of a façade. This accuracy is optimistic since the captured images were processed without any control points.

This work presents a novel approach to construct surrogate models of parametric Differential Algebraic Equations based on a tensor representation of the solutions. The procedure consists in building simultaneously, for every output of the reference model, an approximation given in tensor-train format. A parsimonious exploration of the parameter space coupled with a compact data representation allows to alleviate the curse of dimensionality. The approach is thus appropriate when many parameters with large domains of variation are involved. The numerical results obtained for a nonlinear elasto-viscoplastic constitutive law show that the constructed surrogate model is sufficiently accurate to enable parametric studies such as the calibration of material coefficients.

In the context of carbon emissions peak, environmental issues highlight the importance of the green economy, how does economic agglomeration release growth potential and enable the coordinated development of the economy and environment? There are few works of literature to analyze it within the framework of spatial economy. This paper constructs a theoretical model to clarify the influence mechanism of economic agglomeration on green total factor productivity (GTFP), and then uses a dynamic SDM model to test the theoretical hypothesis. This contribution has three main findings. First, there is a "U-shaped" curve relationship between economic agglomeration and GTFP, and the formation mechanism is that economic agglomeration has a threshold effect on the agglomeration externalities such as infrastructure sharing, knowledge spillover, and labor market upgrading. Second, the mismatch of industrial structure is an important reason that the economic agglomeration in this region has not produced an obvious spatial spillover effect on other regions; Relaxing restrictions on the concentration of economic activity to regional centers would contribute to the improvement of GTFP. Third, GTFP has the classic "snowball effect" in the time dimension, but has the obvious "warning effect" in the space and time dimension. Based on this, this paper believes that at the present stage, it is necessary to adapt to the layout of economic geography, promote the rational allocation of human resources in the territorial space, promote the coordination between economic agglomeration and the development goal of green economy, and at the same time, it is necessary to cultivate the cooperative linkage mechanism of green economy development and transformation among cities.

Spatial planners are nowadays strongly interested in the benefits of greenspace for the health of urban residents. However, concrete and context-specific guidance on effective greening strategies is still lacking. We present a novel, model-based approach to support the development of such strategies. The approach is quantitative and spatially-explicit, and accounts for multiple health benefits as well as burdens. In this study, we applied the approach to the city of Maastricht (The Netherlands), and conducted an integrated, city-scale assessment of the health benefits and burdens of four urban greenspace designs. These included: ‘No greenspace’, ‘Current greenspace’, ‘Green parking lots and squares’, and ‘Optimized greenspace locations’. For each greenspace design, indicator values were calculated for five health determinants: heat stress, air pollution, perceived unsafety, unattractive views, and tick-bite risk. To assess the health contribution of urban greenspace in a given design, these indicator values were compared with the values in the ‘No greenspace’ design. The study produced clear, quantitative conclusions about the health benefits and burdens of the urban greenspace designs for the case of Maastricht, but also generated novel, more general insights relevant to planning of urban greenspace for health. These insights concern the importance of translating health policy objectives into specific target values or thresholds, and the importance of ‘smart’ choices in greenspace type and location that can effectively reduce trade-offs between health benefits and burdens. Priorities for future research, addressing limitations of the presented approach, concern a further expansion of the range of health benefits and burdens covered by the model, and the development of a common metric for the entire range of health benefits and burdens allowing optimization of greenspace design to maximize its overall net health benefit.

The paper proposed a modification of the GeoSER(D) model previously developed by us by detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents, this made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of 3 types of the strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. The paper showed that SARS COV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the consequence, not the initiator of the epidemic. Fluctuations in the dynamics of various indicators of the spread of SARS COV 2 associated with the difference in the daily schedule on weekdays and weekends. It has been shown that people's daily schedules strongly influence the spread of SARS COV 2. For the more contagious "rapid" strains of SARS COV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS COV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious – "fast" strain of the SARS COV 2 virus (omicron) spreads faster in public transport. For less contagious – "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.

To verify the authors’ mathematical model for water jet cooling of steel sheet, the previously performed experimental studies of temperature of the test plate in the roller quenching machine (RQM) were used. The calculated data of steel temperature evolution along the RQM length were compared with the readings of thermocouples inserted in the center of the test plate and at its surfaces. The core of the model is the dependence of the temperatures of film, transition and nucleate boiling regimes on the thickness of the oxide scale layer on the cooled surface. It was found that the model correctly takes into account the oxide scale on the sheet surface, flow rates and combinations of the RQM banks used, water temperature and other factors. For all experiments the calculated metal temperature corresponds well to the measured one. In the experiments with interrupted cooling, the calculated temperature plots repeat the characteristic changes in the experimental curves. The main uncertainty in the modeling of cooling in a wide temperature range can be contributed by the random nature of changes in the oxide scale thickness during water cooling. In this regard, the estimated thickness of the oxide scale layer should be considered as the main parameter for adapting the sheet temperature control process. The obtained data confirm the possibility of effective application of the model in the ACS of industrial TMCP (Thermo-Mechanical Controlled Process) systems.

Here we will model the curvature and contraction of space-time using as a basis the equation of state of an ideal gas and the Hawking´s equation for the temperature of a black hole. We will use this mathematical model to hypothesize that the Boltzmann´s constant depends on the state of matter, that is, there is a known Boltzmann´s constant for flat space-time and an effective Boltzmann´s constant for curved space-time. This model will allow us to quantify the structure of space-time and will serve as a basis to determine the origin of gravity and the origin of elementary particles. Using the Shannon-Boltzmann-Gibbs entropy relation, we will demonstrate that information is not lost and depends on the state of matter, the information is encoded and depends on the effective Boltzmann´s constant.

A previous related paper dealing with the density relaxation of polystyrene (PS) has shown that the equilibrium relaxation time (eq) has a purely exponential temperature dependence (ETD) below ≈ 100ºC. Such an ETD is now also confirmed based upon available dielectric-spectra data for PS. By combining the ETD behavior of eq (or aT) at low temperatures with a VFTH behavior at higher temperatures (based mainly on available recoverable-shear-compliance data), a composite correlation for eq (or aT) is developed which is continuous with continuous slope at a crossover temperature which is found to be 99.22ºC, where eq = 92.15 sec. This composite representation is shown to describe (without any adjustable parameters) available independent data for the segmental-relaxation time over a finite range both above and below Tcrossover (i.e. the glass-transition temperature).

We review some recent developments about strongly interacting relativistic Fermi theories in three spacetime dimensions. These models realize the asymptotic safety scenario and are used to describe the low-energy properties of Dirac materials in condensed matter physics. We begin with a general discussion of the symmetries of multi-flavor Fermi systems in arbitrary dimensions. Then we review known results about the critical flavor number $N_\mathrm{crit}$ of Thirring models in three dimensions. Only models with flavor number below $N_\mathrm{crit}$ show a phase transition from a symmetry-broken strong-coupling phase to a symmetric weak-coupling phase. Recent simulations with chiral fermions show that $N_\mathrm{crit}$ is smaller than previously extracted with various non-perturbative methods. Our simulations with chiral SLAC fermions reveal that for four-component flavors $N_\mathrm{crit}=0.80(4)$. This means that all reducible Thirring models with $\Nr=1,2,3,\dots$ show no phase transition with order parameter. Instead we discover footprints of phase transitions without order parameter. These new transitions are probably smooth and could be used to relate the lattice Thirring models to Thirring models in the continuum. For a single irreducible flavor, we provide previously unpublished values for the critical couplings and critical exponents.

In this paper, we propose "Synergized Efficiency Optimization for Large Language Models" (SEO-LLM), a groundbreaking approach that integrates advanced data utilization and model compression techniques to significantly enhance the performance, efficiency, and scalability of large language models (LLMs). Our method synergistically combines Adaptive Data Augmentation (ADA), Transfer- Active Learning (TAL), Adaptive Iterative Pruning (AIP), and Synergistic Quantization and Distillation (SQD). These components work together to reduce the training data requirement by 30%, compress model size by 67.6%, and improve inference speed by up to 50%, while preserving or even enhancing model accuracy across various NLP tasks. ADA dynamically adjusts augmentation strategies to optimize model generalization, while TAL leverages pre-trained models to focus learning on the most informative data samples. AIP intelligently prunes less significant weights, and SQD harmonizes quantization with knowledge distillation to achieve high compression rates without significant performance loss. The synergy between these techniques makes SEO-LLM a robust solution for deploying LLMs in resource-constrained environments, maintaining state-of-the-art performance with a fraction of the computational and data resources.

The rapidly evolving field of artificial intelligence (AI) continues to witness the introduction of innovative open-source pre-trained models, fostering advancements in various applications. One such model is Llama 2, an open-source pre-trained model released by Meta, which has garnered significant attention among early adopters. In addition to exploring the foundational elements of the Llama v2 model, this paper investigates how these early adopters leverage the capabilities of Llama 2 in their AI projects. Through a qualitative study, we delve into the perspectives, experiences, and strategies employed by early adopters to leverage Llama 2's capabilities. For the purpose of data analysis, the capabilities inherent in the Llama 2 model were employed to conduct keyword extraction from the context of the early adopters' case studies. The findings shed light on the model's strengths, weaknesses, and areas of improvement, offering valuable insights for the AI community and Meta to enhance future model iterations. Additionally, we discuss the implications of Llama 2's adoption on the broader open-source AI landscape, addressing challenges and opportunities for developers and researchers in the pursuit of cutting-edge AI solutions. The present study constitutes an early exploration of the Llama 2 pre-trained model, holding promise as a foundational basis for forthcoming research investigations.

Blackcurrant juice (Ribes nigrum L.) was subjected to supercritical carbon dioxide (SCCD) at 10, 30 and 60 MPa for 10 min at 45°C as well as thermally treated at 45°C and 85°C for 10 min to determine the stability, antioxidant capacity (AC) and bioaccessibility (BAc) of vitamin C, total anthocyanins and their individual monomers. An in vitro gastrointestinal digestion model completed with dialysis was used to assess BAc. The use of SCCD at each of the pressures applied improved the stability of vitamin C, total anthocyanins, and AC before in vitro digestion. As a result of digestion, L-ascorbic acid was oxidized to L-dehydroascorbic acid, and finally, the total content of vitamin C, anthocyanins, and AC decreased. SCCD did not significantly improve the BAc of vitamin C and total anthocyanins. The highest BAc of vitamin C was noted in fresh juice (FJ) (40%) and after mild heat treatment at 45°C (T45) (46%). The highest BAc of total anthocyanins was also noted in the FJ (4.4%). The positive effect of the application of SCCD on the BAc of the delphinidin-3-O-glycosides was observed compared to T45 and thermal pasteurization at 85°C (T85). Moreover, cyanidins were generally more bioaccessible than delphinidins in all samples. AC after digestion was higher in SCCD samples compared to thermally treated measured using ABTS+• and DPPH• assays, whereas in dialysate similar trends were observed only for AC measured using the ABTS+• assay. This phenomenon was justified by the formation of individual metabolites detected by UPLC-PDA-MS / MS in the model experiment with delphinidin-3-O-rutinoside. The protocatechuic acid which is well known as a strong antioxidant was detected in the model experiment after digestion. Further research is needed to better understand the metabolic pathway of anthocyanins and the possible uses of SCCD to improve the health properties of fruit products.

A compartmental epidemiological model with seven groups is introduced herein, to account for the dissemination of diseases similar to the Coronavirus disease 2019 (COVID-19). In its simplified version, the model contains ten parameters, four of which relate to characteristics of the virus, whereas another four are transition probabilities between the groups; the last two parameters enable the empirical modelling of the effective transmissibility, associated in this study with the cumulative number of fatalities due to the disease within each country. The application of the model to the fatality data (the main input herein) of five countries (to be specific, of those which had suffered most fatalities by April 30, 2020) enabled the extraction of an estimate for the basic reproduction number $R_0$ for the COVID-19 disease: $R_0=4.91(33)$.

Temporal lobe epilepsy is a common, chronic disorder with spontaneous seizures that is often refractory to drug therapy. A potential cause of temporal lobe epilepsy is primary brain injury, making prevention of epileptogenesis after the initial event an optimal method of treatment. Despite this, no preventive therapy for epilepsy is currently available. The purpose of this study was to evaluate the effects of anakinra, lamotrigine, and their combination on epileptogenesis using the rat lithium-pilocarpine model of temporal lobe epilepsy. The study showed that the treated and untreated animals showed no significant difference in the number and duration of seizures. However, the severity of seizures was significantly reduced after treatment. Anakinra and lamotrigine, alone or in combination, significantly reduced neuronal loss in the CA1 hippocampus compared to the control group. However, the drugs administered alone were found to be more effective for CA3 than their combination. The treatment alleviated the impairments in activity level, exploratory behavior and anxiety, but had a relatively weak effect on TLE-induced impairments in social behavior and memory. The efficacy of the combination treatment did not differ from that of anakinra and lamotrigine monotherapy. These findings suggest that anakinra and lamotrigine, alone or in combination, may have clinical utility in preventing epileptogenesis.

This is the first study to comprehensively evaluate the benefit of urban open spaces and cropland with different adjacent public facilities seen as locally undesirable (“not in my backyard,” NIMBY) or desirable (“yes in my backyard,” YIMBY). The total benefit increases or decreases for urban open space and cropland with adjacent NIMBY or YIMBY facilities in a municipality in Taiwan. The results show that for the city as a whole, the current arrangement of NIMBY and YIMBY in different zones decreases the total benefit of urban open spaces in highly urbanized zones and increases the total damage to cropland in extremely rural zones. This indicates a need to avoid further installing NIMBY or YIMBY facilities in already occupied urban open spaces. The results also demonstrate that locating NIMBY or YIMBY facilities near cropland fails to highlight the benefit of YIMBY facilities and magnifies opposition to NIMBY facilities. For individual housing units, the total damage is 1.87% of the average housing price for cropland-type open space with adjacent NIMBY or YIMBY facilities, and the total benefit is 7.43% of the average housing price for urban-type open space in a highly urbanized area. In contrast, the total benefit for open space with adjacent NIMBY or YIMBY facilities is a 2.95%-13.80% increase in the average housing price for areas with mixed urban open space and cropland.

The implementation of grey and green infrastructure is an effective mean to address urban flooding and nonpoint source pollution, but due to the complexity of the process and the diver-sity of benefits, there is a lack of measurement for their comprehensive benefits. Adopting a typ-ical university in Beijing as an example, this paper simulated the multidimensional benefits of the water quantity, water quality, and ecology of grey and green facility renovation by coupling the storm water management model (SWMM) and InfoWorks Integrated Catchment Manage-ment (ICM). Monetization methods and economical means were employed to characterize the comprehensive benefits. The results showed that grey and green infrastructure retrofitting re-duces the number of severe overflow nodes in the study area by 54.35%, the total overflow vol-ume by 22.17%, and the nonpoint source pollution level by approximately 80% under the heavy rain scenario and 60% under the rainstorm scenario. The annual benefits of grey and green infra-structure renovation reached ¥765,600/year, of this amount, ¥83,100/year is from hydrological regulation, ¥454,100/year is from nonpoint source pollution reduction, and ¥228,300/year is from ecological improvement. The benefits of green facilities were higher than those of grey facilities, and the combined benefits were negatively correlated with the rainfall level, with a total bene-fit–cost ratio of 1.19. The results provide methodological and data support for grey and green in-frastructure retrofitting within the context of sponge cities.

This study employs the phase-field regularized cohesion model (PF-CZM) to simulate crack propagation and damage behavior in porous granite. We investigate how pore radius (r), initial crack-pore distance (D), and pore-crack angle (θ) impact crack propagation. The simulation findings reveal that, with a fixed deflection angle and initial crack-pore distance, larger pores are more likely to induce crack extension under identical loading conditions. Moreover, with r and θ keeping constant, the crack extension can be divided into two stages: from its initiation to the lower edge of the pore and then from the lower edge to the upper boundary of the model. By varying the values of D and r, we derive multiple combinations of different D/r ratios and pore radii. These results demonstrate that with constant r, cracks tend to deflect toward the pore closer to the initial crack. Conversely, when D is maintained constant, cracks will preferentially deflect toward pores with a larger r. In summary, the numerical simulation of rock pores and initial cracks, based on the PF-CZM, exhibits remarkable predictive capabilities and holds significant potential in advancing rock fracture analysis.

Nitrile butadiene rubber (NBR) and its various composites are extensively studied as matrix materials for oil-free lubricated mechanical friction surfaces. While numerous macroscopic friction experiments can compare the frictional performance of different NBR composites, the complex material preparation and low experimental efficiency pose challenges. Nanoscale molecular dynamics (MD) simulations can help develop materials with improved friction properties, but they cannot directly provide the friction coefficients of actual macroscopic friction surfaces. Therefore, it is important to integrate the advantages of macroscopic and nanoscale friction studies and perform a synergistic analysis to modify the friction properties of composite materials. In this study, a multi-scale approach is proposed to simulate the frictional characteristics of carbon nanotube (CNT)-reinforced NBR by combining the MD friction simulation， micromechanical bridging model, and macroscopic finite element analysis (FEA) methods. The results of the multi-scale friction simulation of copper (Cu)-CNT/NBR composites show that the addition of CNTs significantly improves the frictional properties of NBR, and the extent of the improvement is related to the mass fraction of CNTs. As the mass fraction of CNTs increases (0%, 1.25%, 2.5%, 5%), the coefficient of friction decreases from 0.50 to 0.38. The results are validated by ring-block friction experiments at the same scale as the simulations.

Abstract: Self-regulated Learning has become one of educational psychology's most critical research areas. Contemporary research indicates that student creativity is crucial to the success of arts education. However, there is limited research on creativity in arts education in Self-regulated Learning. Therefore, this study aims to define the creativity characteristics that determine the quality of students’ work. This study implemented a creative competition in painting and composition for first-year undergraduates. It evaluated the characteristics of creativity during the three main phases of the task: planning, execution, and completion. We discovered through semi-open questionnaires (n=178)，and using the content analysis method. According to the competition results, the students with the highest scores in Flow, Inspiration, and Idea Generation in the Performance phase had the best quality work. In the Forethought phase, Analogical Thinking and Idea Generation worked best for most students, while in the Performance phase, Flow Sense, Idea Generation, and Idea Manipulation worked best.Data on creativity characteristics at different work phases and final quality might adequately guide and organize educational procedures. These findings can serve as a theoretical foundation for quantifying the creativity phase in Self-regulated Learning and offer arts educators a reference for creativity instruction.

Magnetorheological elastomers (MREs) are a type of smart material that can change their mechanical properties in response to external magnetic fields. These unique properties make them ideal for various applications, including vibration control, noise reduction, and shock absorption. This paper presents an approach for modeling the impact behavior of MREs. The proposed model uses a combination of exponential functions arranged in a multi-layer Preisach model to capture the nonlinear behavior of MREs under impact loads. The model is trained using particle swarm optimization (PSO) and validated using experimental data from drop impact tests conducted on MRE samples under various magnetic field strengths. The results demonstrate that the proposed model can accurately predict the impact behavior of MREs, making it a useful tool for designing MRE-based devices that require precise control of their impact response. The model's response closely matches the experimental data, with a maximum prediction error of 10% or less. Furthermore, the interpolated model's response shows good agreement with the experimental data, with a maximum percentage error of less than 8.5%.

We develop a mathematical model to provide epidemic predictions for the 2019-nCov epidemic in Wuhan, China. We use reported case data up to January 31, 2020 from the Chinese Center for Disease Control and Prevention and the Wuhan Municipal Health Commission to parameterize the model. From the parameterized model we identify the number of unreported cases. We then use the model to project the epidemic forward with varying level of public health interventions. The model predictions emphasize the importance of major public health interventions in controlling 2019-nCov epidemics.

Staphylococcus aureus (S. aureus) is a major human pathogen that requires new antibiotics with unique mechanism. A new pleuromutilin derivative, 14-O-[(4,6-Diaminopyrimidine-2-yl) thioacetyl] mutilin (DPTM), has been synthesized and proved as a potent antibacterial agent using in vitro and in vivo assays. In the present study, DPTM was further in vitro evaluated against Methicillin-resistant Staphylococcus aureus (MRSA) isolated from dairy farms and outperformed tiamulin fumarate, a pleuromutilin drug used for veterinary. Moreover, a murine skin wound model caused by MRSA infection was established and the healing effect of DPTM was investigated. The results showed that DPTM could promote the healing of MRSA skin infection, reduce the bacterial burden of infected skin MRSA and decrease the secretion of IL-6 and TNF-α inflammatory cytokines in plasma. These results provided the basis for further in-depth drug targeted studies of DPTM as a novel antibacterial agent.

Sudden water pollution accident is a tough environmental challenge to solve due to its uncertainty, unpredictability and destructiveness. Therein, the dangerous goods transportation accidents on the cross-tributary bridges are increasing worldwide with the acceleration of industrialization and urbanization, posing threats to the water ecological environment and human health. To prevent and control the risk, this study evaluated the risk of sudden water pollution accidents associated with dangerous goods transportation on the cross-tributary bridges by using the expert judgment fuzzy language method and Bayesian network model. Baiyangdian lake as the largest freshwater shallow lake in North China was chosen as a study case. Different accident scenarios were simulated to put forward risk prevention and control measures. The results showed that the primary risk factors were divided into five categories: personnel factors, vehicle factors, tank factors, weather factors, and road environment factors. The probability of dangerous goods transportation accidents was a possible event (P=0.115), with a high risk level. Furthermore, the Baiyangdian lake would suffer from serious environmental pollution and water quality degradation without prevention and control measures by simulating the oil leakage accident. Therefore, emergency prevention and control measures are expected to be formulated and implemented before and after the occurrence of the sudden water pollution accidents. This study provides useful insights into risk prevention and control strategies for sudden water pollution accidents, and the framework can be made applicable to other rivers facing similar risk challenges worldwide.

Dubermatinib (DMB, TP-0903), a benzenesulfonamide, is an inhibitor of the tyrosine kinase AXL, which is a member of the TAM family, and can prevent GAS6-mediated activation of AXL in cancer cells. Patients with previously treated chronic lymphocytic leukemia are being studied in phase I/II clinical trials to determine its antineoplastic potential (CLL). In the current work, the reactive intermediates of DMB were studied using liquid chromatography-mass spectrometry (LC-MS). Human liver microsomes (HLMs) were exposed to dimethylbenzene in a laboratory setting, and the resulting metabolites were collected through protein precipitation. Intense reactivity toward nucleophilic macromolecules was seen in the metabolites of the piperazine and pyrimidine rings in DMB, iminium and 2,5-quinone-imine, respectively. To assess the toxicities of the possibly reactive metabolites, DMB was incubated with HLMs in the presence of 1.0 mM KCN and 1.0 mM glutathione. The DMB metabolites found by LC-MS/MS were seven in vitro phase I metabolites, three cyano adducts, and two GSH conjugates. Phase I in vitro metabolic reactions included N-demethylation, hydroxylation, and dechlorination. The in vitro metabolism of DMB and the metabolites it produces have not been studied before.

Both climate change and anthropogenic activities contribute to the deterioration of terrestrial water resources and ecosystems worldwide. Central Asian endorheic basins are among the most affected regions through both climate and human impacts. Here, we used a digital elevation model, digitized bathymetry maps and Landsat images to estimate the areal water cover extent and volumetric storage changes in small terminal lakes in Burabay National Nature Park (BNNP), located in Northern Central Asia (CA), for the period of 1986 to 2016. Based on the analysis of long-term climatic data from meteorological stations, short-term hydrometeorological network observations, gridded climate datasets (CRU) and global atmospheric reanalysis (ERA Interim), we have evaluated the impacts of historical climatic conditions on the water balance of BNNP lake catchments. We also discuss the future based on regional climate model projections. We attribute the overall decline of BNNP lakes to long-term deficit of water balance with lake evaporation loss exceeding precipitation inputs. Direct anthropogenic water abstraction has a minor importance in water balance. However, the changes in watersheds caused by the expansion of human settlements and roads disrupting water drainage may play a more significant role in lake water storage decline. More precise water resources assessment at the local scale will be facilitated by further development of freely available higher spatial resolution remote sensing products. In addition, the results of this work can be used for the development of lake/reservoir evaporation models driven by remote sensing and atmospheric reanalysis data without the direct use of ground observations.

This paper proposes an analytical model of the double-stator spoke-type permanent-magnet vernier machine (DSSTVM) using the subdomain model (SDM), which can be used to calculate the mag-netic field distribution and corresponding electromagnetic parameters of the DSSTVM. The whole field domain is divided into several subdomains according to the magnetic characteristics of each region, within which Laplace's and Poisson's equations are solved accordingly in terms of magnetic vector potential (MVP). Then, the magnetic flux density distribution, back electromotive force (EMF), and electromagnetic torque of the DSSTVM can be obtained correspondingly. Ultimately, finite element analysis (FEA) is adopted to validate the proposed analytical model's effectiveness for quickly predicting the no-load and on-load performances of the DSSTVM.

When analyzing user geospatial information through deep learning methods, it is typically necessary to annotate existing geospatial data. Currently, manual annotation methods are commonly employed, suffering from issues related to low efficiency and accuracy. This design is based on the TensorFlow deep learning framework and first realizes the BiLSTM+CRF+AGG deep learning model. Among the models, AGG is the aggregation layer, which is introduced to solve the problem of solid particle size equilibrium. Secondly, based on the characteristics of original data and professional data, an automatic labeling algorithm is proposed. The algorithm first preprocesses the acquired original data set and professional data set, The most valuable unlabeled data set which can make the training model converge quickly is selected from the original data set as the target data set. Sort the target data set based on preset rules and set annotation parameters for the sorted target data set. Based on the set annotation parameters, the corpus is synthesized and used as the annotation result. Thirdly, based on the active learning strategy, a manual annotation auxiliary scheme is proposed, and an Excel generation module that is convenient for manual annotation correction is designed and developed to further improve the efficiency and quality of annotation through iterative processing. The combination of the BiLSTM+CRF+AGG deep learning model and high-quality annotation can accurately identify non-standard, incomplete, or even incorrect geographical information entities. This annotation method has found successful practical application within the context of the research project and has been granted an invention patent.

Our lives are comprised of moment-to-moment activity experiences. According to the Model of Human Occupation (MOHO), our occupational experiences can be affected by volition, which consists of personal causation, values, and interests. This study investigated how momentary volition affected occupational satisfaction and mind-wandering while performing occupations. This study also examined the relationship between momentary volition and the overall life perspectives of life satisfaction and life balance. Undergraduate students participated in this cross-sectional study. The experience sampling method (ESM) was used to measure students’ momentary states such as activity, volition, occupational satisfaction, and mind-wandering. After conducting the ESM, the participants’ life satisfaction was measured using the Satisfaction With Life Scale (SWLS), and their life balance was measured by the Life Balance Inventory (LBI). Forty-two participants and 1,092 sampling data were included in the analysis. Momentary personal causation, values, and interests contributed to occupational satisfaction. Mind-wandering was predicted negatively by interests but positively by personal causation. Momentary interests were positively correlated with SWLS and LBI scores. This study demonstrated that momentary volition was associated with occupational satisfaction and engagement, as well as life satisfaction and balance, in undergraduate students.

We provide methodology and numerical results for the Hattendorff differential equa- tion for the continuous time evolution of the variance of L(j)t , the loss at time t random variable for a multi-state process, given that the state at time t is j.

Achieving eco-efficiency in agriculture production at low environmental costs is key to sustainable agriculture. Using the DEA-SBM model, this study evaluated the agricultural eco-efficiency of the 77 counties and districts in China’s Jiangsu province from 1999 to 2018 and analyzed its spatio-temporal evolution pattern and influencing factors. The mains conclusions were as follows: (1) The overall agricultural eco-efficiency and its decomposition terms, pure technology efficiency and scale efficiency, exhibited a fluctuating downward trend. The regional inequality in agricultural eco-efficiency had been widening and exhibited a strong positive spatial association. (2) The agricultural eco-efficiency in Jiangsu province presented a “high south and low north” spatial pattern. High-level agricultural eco-efficiency areas were in the Taihu Plain in Sunan, while low-level agricultural eco-efficiency zones are distributed across Subei City. The High-High-type spatial association pattern is concentrated in the Suzhou-Wuxi-Changzhou region, while the Low-Low areas are mainly in the coastal regions of Subei and Suzhong. (3) The spatial pattern of PTE and SE generally exhibited a “high south and low north” distribution. Areas with positive growth in agricultural eco-efficiency, PTE, and SE, were situated in Xuzhou, Nanjing city, and the bordering regions between Yangzhou and Huai’an, and Changzhou and Wuxi. (4) The excessive redundant use and application of pesticides, chemical fertilizer, agricultural diesel, labor, land, and agricultural carbon emission have been the primary factor affecting Jiangsu's agricultural eco-efficiency. Irrigation had also signficantly impacted agricultural eco-efficiency, while mechanical power and agricultural film had minimal effect. The majority of counties and districts in Subei, Suzhong, and Ningzhen Yang Hilly region have issues regarding their excessive usage of chemical fertilizer, pesticide, chemical fertilizer, agricultural diesel, labor, and land. The findings of this study can contribute towards a better understanding of agricultural eco-efficiency and spatial association effect and can help policymakers increase agricultural eco-efficiency.

Multiple lines of evidence indicate that CD8$^+$ T cells are important in the control of HIV-1 (HIV) replication. However, CD8$^+$ T cells induced by natural infection cannot eliminate the virus or reduce viral loads to acceptably low levels in most infected individuals. Understanding the basic quantitative features of CD8$^+$ T-cell responses induced during the course of HIV infection may therefore inform us about the limits that HIV vaccines, which aim to induce protective CD8$^+$ T-cell responses, must exceed. Using previously published experimental data from a cohort of HIV-infected individuals with sampling times from acute to chronic infection we defined the quantitative properties of CD8$^+$ T-cell responses to the whole HIV proteome. In contrast with a commonly held view, we found that the relative number of HIV-specific CD8$^+$ T-cell responses (response breadth) changed little over the course of infection (first 400 days post-infection), with moderate but statistically significant changes occurring only during the first 35 symptomatic days. This challenges the idea that a change in the T-cell response breadth over time is responsible for the slow speed of viral escape from CD8$^+$ T cells in the chronic infection. The breadth of HIV-specific CD8$^+$ T-cell responses was not correlated with the average viral load for our small cohort of patients. Metrics of relative immunodominance of HIV-specific CD8$^+$ T-cell responses such as Shannon entropy or the Evenness index were also not significantly correlated with the average viral load. Our mathematical-model-driven analysis suggested extremely slow expansion kinetics for the majority of HIV-specific CD8$^+$ T-cell responses and the presence of intra- and interclonal competition between multiple CD8$^+$ T-cell responses; such competition may limit the magnitude of CD8$^+$ T-cell responses, specific to different epitopes, and the overall number of T-cell responses induced by vaccination. Further understanding of mechanisms underlying interactions between the virus and virus-specific CD8$^+$ T-cell response will be instrumental in determining which T-cell-based vaccines will induce T-cell responses providing durable protection against HIV infection.

A numerical mechanism connecting ice algal ecodynamics with the buildup of organic macromolecules is tested within modeled pan-Arctic brine channels. The simulations take place offline in a reduced representation of sea ice geochemistry. Physical driver quantities derive from the global sea ice code CICE, including snow cover, thickness and internal temperature. The framework is averaged over ten boreal biogeographic zones. Computed nutrient-light-salt limited algal growth supports grazing, mortality and carbon flow. Vertical transport is diffusive but responds to pore structure. Simulated bottom layer chlorophyll maxima are reasonable, though delayed by about a month relative to observations due to uncertainties in snow variability. Upper level biota arise intermittently during flooding events. Macromolecular concentrations are tracked as proxy proteins, polysaccharides, lipids and refractory humics. The fresh biopolymers undergo succession and removal by bacteria. Baseline organics enter solely through cell disruption, so that the internal carbon content is initially biased low. By including exudation, agreement with dissolved organic or individual biopolymer data is achieved given strong release coupled to light intensity. Detrital carbon then reaches hundreds of micromolar, sufficient to support structural changes to the ice matrix.

This paper addresses the modeling of the iron ore direct reduction process, a process likely to reduce CO₂ emissions from the steel industry. The shaft furnace is divided into three sections (reduction, transition, and cooling), and the model is two-dimensional (cylindrical geometry for the upper sections and conical geometry for the lower one), to correctly describe the lateral gas feed and cooling gas outlet. This model relies on a detailed description of the main physical–chemical and thermal phenomena, using a multi-scale approach. The moving bed is assumed to be comprised of pellets of grains and crystallites. We also take into account eight heterogeneous and two homogeneous chemical reactions. The local mass, energy, and momentum balances are numerically solved, using the finite volume method. This model was successfully validated by simulating the shaft furnaces of two direct reduction plants of different capacities. The calculated results reveal the detailed interior behavior of the shaft furnace operation. Eight different zones can be distinguished, according to their predominant thermal and reaction characteristics. An important finding is the presence of a central zone of lesser temperature and conversion.

A critical challenge underpinning the adoption of Additive Manufacture (AM) as a technology is the postprocessing of manufactured components. For Selective Laser Sintering (SLS) this can involve the removal of powder from the interior of the component, often by vibrating the component to fluidise the powder to encourage drainage. In this paper we develop and validate a computational model of the flow of metal powder suitable for predicting powder removal from such AM components. The model is a continuum Eulerian multiphase model of the powder including models for the granular temperature; the effect of vibration can be included through appropriate wall boundaries for this granular temperature. We validate the individual sub-models appropriate for AM metal powders by comparison with in-house and literature experimental results, and then apply the full model to a more complex geometry typical of an AM Heat Exchanger. The model is shown to provide valuable and accurate results at a fraction of the computational cost of a particle-based model.

Febrile neutropenia (FN) is a major concern in patients undergoing chemotherapy for diffuse large B-cell lymphoma (DLBCL); however, the overall risk of FN is difficult to assess. This study aimed to develop a model for predicting the occurrence of FN in patients with DLBCL. In this multicenter, retrospective, observational analysis, a multivariate logistic regression model was used to analyze the association between FN incidence and pretreatment clinical factors. We included adult inpatients and outpatients (aged ≥ 18 years) diagnosed with DLBCL who were treated with chemotherapy. The study examined 246 patients. Considering FN occurring during the first cycle of chemotherapy as the primary outcome, a predictive model with a total score of 5 points was constructed as follows: 1 point each for viral hepatitis, extranodal involvement, and a high level of soluble interleukin-2 receptor and 2 points for lymphopenia. The area under the receiver operating characteristic curve of this model was 0.844 (95% confidence interval: 0.777–0.911). Our predictive model can assess the risk of FN before patients with DLBCL start chemotherapy, leading to better outcomes.

The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that affected the lifestyle and economy. Various studies have been focused on the identification of COVID-19 impact to mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the change of mood state of children and adolescents during the first lockdown. Therefore, to this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best performing model and (v) a post-hoc interpretation of the features’ impact on the best performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of COVID-19 pandemic on the mood state of children and adolescents.

Despite the rising interest in smart city initiatives worldwide, governmental theories along with the managerial perspectives of city planning are a great lack in the literature. It is definitely understandable that the adoption of configurational pathways towards the ‘smart’ ‘governance’ models is required as key factor and smartness’ facilitator in modern cities. In this manuscript, we display an exhaustive analysis on the importance of the n-Helix models along with a benchmarking critical approach through selected European case-studies. The study, through the literature review, revealed the lack of exhaustive analyses for the methodological investigation, identification and adoption of the most appropriate governance model and collaborative approaches per project and collaborative approaches and create modular frameworks to address efficiently the continuous urban challenges, such as the rapid urbanization or the climate change.

Estimation of core and surface temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. While, it is impractical to measure core temperature using physical sensors, implementing a complex estimation strategy in on-board low-cost BMS is challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models having different levels of accuracy and complexity. Broadly, there are first-order and second-order heat capacitor-resistor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study to access suitability in online prediction and onboard BMS. The aim is to guide whether it’s worth investing towards developing a second-order model instead of a first-order model with respect to prediction accuracy considering modelling complexity, experiments required and the computational cost. Both the thermal models along with the parameter estimation scheme are modelled and simulated using MATLAB/Simulink environment. Models are validated using laboratory test data of a cylindrical 18650 LIB cell. Further, a Kalman Filter with appropriate process and measurement noise levels are used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models are analyzed for comparison purposes.

We propose a model for gravity based on the gravitational polarization of space. With this model, we can relate the density parameters within the Friedmann model, and show that dark matter is bound mass formed from massive dipoles set up within the vacuum surrounding ordinary matter. Aggregate matter induces a gravitational field within the surrounding space, which reinforces the original field. Dark energy, on the other hand, is the energy density associated with gravitational fields both for ordinary matter, and bound, or induced dipole matter. At high CBR temperatures, the cosmic susceptibility, induced by ordinary matter vanishes, as it is a smeared or average value for the cosmos as a whole. Even though gravitational dipoles do exist, no large scale alignment or ordering is possible. Our model assumes that space, i.e., the vacuum, is filled with a vast assembly (sea) of positive and negative mass particles having Planck mass, called planckions, which is based on extensive work by Winterberg. These original particles form a very stiff two component superfluid, where positive and negative mass species neutralize one another already at the submicroscopic level, leading to zero net mass, zero net gravitational pressure, and zero net entropy, for the undisturbed medium. It is theorized that the gravitational dipoles form from such material positive and negative particles, and moreover, this causes an intrinsic polarization of the vacuum for the universe as a whole. We calculate that in the present epoch, the smeared or average susceptibility of the cosmos equals, , and the overall resulting polarization equals, . Moreover, due to all the ordinary mass in the universe, made up of quarks and leptons, we calculate a net gravitational field having magnitude, . This smeared or average value permeates all of space, and can be deduced by any observer, irrespective of location within the universe. This net gravitational field is forced upon us by Gauss’s law, and although technically a surface gravitational field, it is argued that this surface, smeared value holds point for point in the observable universe. A complete theory of gravitational polarization is presented. In contrast to electrostatics, gravistatics leads to anti-screening of the original source field, increasing the original value, , to, , where is the induced or polarized field. In the present epoch, this leads to a bound mass, , where is the sum of all ordinary source matter in the universe, and equals the relative permittivity. A new radius, and new mass, for the observable universe is dictated by the density parameters in Friedmann's equation, and Gauss’s law. These lead to the very precise values, , and, , respectively, somewhat larger than current less accurate estimates.

Global models have been developed to tackle the challenge of forecasting sets of series that are related or share similarities, but not for heterogeneous datasets. Various methods of partitioning by relatedness have been introduced to enhance the similarities of the set, resulting in improved forecasting accuracy but often at the cost of a reduced sample size, which could be harmful. To shed light on how the relatedness between series impacts the effectiveness of global models in real-world demand forecasting problems we perform an extensive empirical study using the M5 competition dataset. We examined cross-learning scenarios driven by the product hierarchy commonly employed in retail planning, which allow global models to capture interdependencies across products and regions more effectively. Our findings show that global models outperform state-of-the-art local benchmarks by a considerable margin, indicating that they are not inherently more limited than local models and can handle unrelated time series data effectively. The accuracy of data partitioning approaches increases, as the size of the data pools and the models' complexity decrease. However, there is a trade-off between data availability and data relatedness. Smaller data pools lead to increased similarity among time series, making it easier to capture cross-product and cross-region dependencies, but this comes at the cost of a reduced sample, which may not be beneficial. Finally, it's worth noting that the successful implementation of global models for heterogeneous datasets can significantly impact forecasting practice.

As one of the unique destinations in Iran, Abdanan Black Twin Lake attracts many tourists yearly. Among striking features is the presence of minerals, boiling springs, and its beautiful landscape. Human beings are willing to spend money on such natural resources. An economic valuation can be interfered constructively and positively in improving environmental policies. So, quantifying these benefits is of the utmost importance. The paper mainly estimated the tourists' willingness to pay and their recreational value using the contingent valuation method. Random sampling was conducted on 384 people using the two-dimensional double-choice questionnaire in spring 2019. In the Twin Lake Recreational Value Questionnaire, the main questions were devoted to the visitors' willingness to pay, with three bids of 0.07 $, 0.14 $, and 0.22$. Among 384 respondents, 304 (79%) were willing to pay for recreational use of the lake, and 80 respondents (21%) were not. The likelihood, the model's parameters were estimated. The findings indicated the average tourists' willingness to pay for recreational value was estimated at 0.09$ per visit and the recreational value of this lake for each household was estimated at 0.40$. The findings revealed the effect of education, household income, household size and tourists' willingness to pay was significant.

In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance of the selected cell factory in larger reactors, has a major influence on the performance of the final process. To overcome this, scaledown approaches are essential to run screenings that show the real cell factory performance at industrial like conditions. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batches ran under the desired conditions generating sufficient information to define the fastest growing strain in an environment with varying glucose concentrations similar to industrial scale bioreactors.

A non-Newtonian fluid model is used to investigate the two-dimensional pulsatile blood flow through a tapered artery with a stenosis. The mixed convection effects of heat and mass transfer are also taken into account. By applying non-dimensionalization and radial coordinate transformation, we simplify the system in a tube. Under the finite difference scheme, numerical solutions are calculated for velocity, temperature concentration, resistance, impedance, wall shear stress and shearing stress at the stenosis throat. Finally, Quantitative analysis is carried out.

The out-of-equilibrium dynamics of many body systems has recently received a burst of interest, also due to experimental implementations. The dynamics of both observables, such as magnetization and susceptibilities, and quantum information related quantities, such as concurrence and entanglement entropy, have been investigated under different protocols bringing the system out of equilibrium. In this paper we focus on the entanglement entropy dynamics under a sinusoidal drive of the tranverse magnetic field in the 1D quantum Ising model. We find that the area and the volume law of the entanglement entropy coexist under periodic drive for an initial non-critical ground state. Furthermore, starting from a critical ground state, the entanglement entropy exhibits finite size scaling even under such a periodic drive. This critical-like behaviour of the out-of-equilibrium driven state can persist for arbitrarily long time, provided that the entanglement entropy is evaluated on increasingly subsytem sizes, whereas for smaller sizes a volume law holds. Finally, we give an interpretation of the simultaneous occurrence of critical and non-critical behaviour in terms of the propagation of Floquet quasi-particles.

Even brief epileptic seizures can lead to activity-dependent structural remodeling of neural circuitry. Animal models show that the functional plasticity of synapses and changes in the intrinsic excitability of neurons can be crucial for epileptogenesis. However, the exact mechanisms underlying epileptogenesis remain unclear. We induced epileptiform activity in rat hippocampal slices for 15 min using a 4-aminopyridine (4-AP) in vitro model and observed hippocampal hyperexcitability for at least 1 hour. We tested several possible mechanisms of this hyperexcitability, including changes in intrinsic membrane properties of neurons, presynaptic and postsynaptic alterations. Neither input resistance nor other essential biophysical properties of hippocampal CA1 pyramidal neurons were affected by epileptiform activity. The glutamate release probability also remained unchanged, as the frequency of miniature EPSCs and the paired amplitude ratio of evoked responses did not change after epileptiform activity. However, we found an increase in the AMPA/NMDA ratio, suggesting alterations in the properties of postsynaptic glutamatergic receptors. Thus, the increase in excitability of hippocampal neural networks is realized through postsynaptic mechanisms. In contrast, the intrinsic membrane properties of neurons and the probability of glutamate release from presynaptic terminals are not affected in a 4-AP model.

A temperature-modulated pyroelectricity measurement system for a small single crystal is developed and applied to standard sample measurements performed on a thin single crystal of lithium niobate. The modulation measurement is based on the AC technique, in which the temperature of the sample is periodically oscillated, and the synchronized pyroelectric signal is extracted using a lock-in amplifier. Temperature modulation is applied by irradiating periodic light on the sample placed in the heat exchange gas. To apply this technique to the transparent reference sample, a commercially available black resin is coated on the sample’s surface to absorb the light energy and transmits it to the specimen. The experimental results are analyzed using a two-layer heat transfer model to verify the effect of the light-absorbing layer as well as the non-contact temperature modulation system.

The study used ERA5 reanalysis SST dataset re-gridded to a common grid of 0.25° X 0.25° spatial resolution (latitude × longitude) for the historic (1940–2014) and projected (2015–2100). The SST simulation under the SSP5-8.5 scenarios was carried out with output from 8 General Circulation Models (GCMs). The bias-corrected dataset was developed using Empirical Quantile Mapping EQM for the historical (1940-2015) and the future (2030-2100) while the CMIP6 model simulation was evaluated against the ERA5 monthly rainfall observed data for monthly and annual sea surface temperatures (SST) over the Gulf of Guinea. ). The CMIP6 models simulation for SST projection from 2030-20100 based on SSPS 8.5 SST is projected to increase by 4.61o (31oC) in 2030 to 35oC in 20100 in the coastal GOG and 2.6oC in the Western GOG (Sahel). The correlation coefficient (r) was used to evaluate the performance of the CMIP6 models and the analysis showed ACCESS 0.1,CAMS CSM 0.2, CAN ESM 0.3, CMCC, 0.3 and MCM, 0.4 indicating that all models performed well in capturing the climatological pattern of the SST. The.CMIP6 bias corrected model simulations showed increased SST warming over the GOG will be higher in the Far period end than the Near-term climate. The study affirmed that the CMIP6 projections can be used for multiple assessments related to climate and hydrological impact studies and for the development of mitigation measures under a warming climate

To both survive and develop continuously, enterprises must overcome many kinds of competition and challenges. Cultivating employees' active and sustainable organizational citizenship behavior is important for enterprises to successfully cope with turbulence and uncertain events during their development. In this study, we investigated the development level of and factors influencing employees' organizational citizenship behavior in current organizations. By using the Belief in a Just World Scale, Organizational Citizenship Behavior Scale, and Interpersonal Intelligence Scale, we investigated 230 employees from 15 different enterprises. The results showed that belief in a just world, interpersonal intelligence, and organizational citizenship behavior were significantly positively correlated. Interpersonal intelligence played a moderating role between belief in a just world and organizational citizenship behavior; the organizational citizenship behavior of individuals with high interpersonal intelligence increased with the strengthening of the belief in a just world, and this increase was larger than that experienced by individuals with low interpersonal intelligence. This meant that under a certain level of belief in a just world, a high level of interpersonal intelligence was more conducive to promoting employees' sustainable organizational citizenship behavior.

This paper presents a study on the idle vibration reduction of a diesel sport utility vehicle (SUV). To reduce idle vibration, the transmission paths of vibration from the engine to the driver seat floor were investigated with the vehicle components related to idle vibration. Furthermore, operational deflection shape (ODS) tests were conducted to visualize the vibration shapes during engine idling. Experimental modal analyses were performed to obtain the natural frequencies and mode shapes. Through the ODS and modal tests, the vibration characteristics of the diesel SUV during idling were identified. Considering these vibration characteristics, a multi-body dynamic model for the diesel SUV described by differential equations of motion was established to evaluate the idle vibration. To implement the dynamic model effectively, the equivalent stiffnesses and damping coefficients included in the model were determined experimentally or analytically. The established dynamic model was verified by comparing the natural frequencies and idle vibration levels between simulations. Using this dynamic model, we analyzed the effects of various design variables on idle vibration and obtained an optimal design for reducing the idle vibration level. Finally, we present a design guide to reduce the idle vibration for diesel SUVs.

Recent studies investigated the detectability of ship wake signatures on SAR imagery using a large number of SAR images collocated with Automatic Identification System data for training machine learning models. These detectability models are in agreement with oceanographic expectations from preceding studies and can therefore be used for comparing the performance of different SAR sensors in terms of wake detectability. Previous model comparisons showed better wake detection performance of TerraSAR-X (TS-X) than of RADARSAT-2 (RS2) and Sentinel-1 (S1). A comparison between CosmoSkymed (CSK) and RS2 is performed here, to examine the hypothesis that X-Band is generally better for wake detection than C-Band. Finally, this hypothesis is not confirmed, as the detectability models for TS-X, CSK and RS2 reveal similar performances. A comparison of wake detection performance should take the individual wake components into account separately.

Under optimal conditions, just 3–6 ms of visual stimulation suffices for humans to see motion. Motion perception on this time scale implies that the visual system under these conditions reliably encodes, transmits, and processes neural signals with near-millisecond precision. Motivated by in vitro evidence for high temporal precision of motion signals in the primate retina, we investigated how neuronal and perceptual limits of motion encoding relate. Specifically, we examined the correspondence between the time scale at which cat retinal ganglion cells in vivo represent motion information and temporal thresholds for human motion discrimination. The time scale for motion encoding by ganglion cells ranged from 4.6–91 ms, depended nonlinearly on temporal frequency but not on contrast. Human psychophysics revealed that minimal stimulus durations required for perceiving motion direction were similarly brief, 5.6–65 ms, similarly depended on temporal frequency but, above ~10%, not on contrast. Notably, physiological and psychophysical measurements corresponded closely throughout (r = 0.99), despite more than a 20-fold variation in both human thresholds and optimal time scales for motion encoding in the retina. These results demonstrate that neural circuits for motion vision in cortex can maintain and make use of the high temporal fidelity of the retinal output signals.

This paper investigates the classical but yet intriguing question of modeling orthotropic RC floor systems endowed with lightening elements, by means of a uniform orthotropic slab, in order to describe accurately the building response under seismic loads. The simplified method, commonly adopted by engineers, based on the equivalence between the transverse stiffness of the RC elements of the real floor and those of the orthotropic slab is presented. A case study in which this simplified method is used is also provided. Then, an advanced finite elements (FE) based method to determine the elastic properties of the equivalent homogenized orthotropic slab is proposed. Based on the results obtained from the application of this method to a case study, a discussion on the adequacy of the simplified method is also provided.

This paper results from our investigation into novel means of electromagnetic propulsion. It requires the basis of our claims to be put on a sound theoretical footing regarding the purported momentum exchange with the electromagnetic field. One of these concerns is the huge discrepancy between the energy density of the Zeropoint and its purported manifestation as the Cosmological Constant. Here we state that it is manifestly wrong to introduce the zeropoint at zero order into the stress-energy tensor, because it is something which describes zero particle count. As a fluctuation, it belongs in a higher order Taylor expansion in frequency of the stress-energy tensor. Furthermore in the 3^rd order in the Einstein constant our procedure is some 9 orders of magnitude too small. We make up this difference by suggesting that vacuum energy is much higher still and that more degrees of freedom exist in physics beyond the Standard Model or that there is interaction energy between the modes.

Soil organic carbon (SOC) sequestration in arable soils is a challenging goal for soil management. Multiple factors should be considered for the prediction of the soil capacity to fix atmospheric carbon. In this study, we focused on the effect of crop rotation and previous land use for future carbon sequestration on two experimental fields with identical soils (Retisols) and input of organic fertilizers. We analyzed the SOC dynamics and used the Roth C model to forecast SOC changes under RCP4.5 and RCP8.5 scenarios. Our experimental and modelling results indicated a consistent increase in SOC stocks and the stable fractions of soil organic matter (SOM). The increase in SOC was higher in the experiment with the crop-grassland rotation that in the experiment with a rotation of row crops and barley. With similar total SOC stocks, the efficiency of soil management differed as reflected by the contrasting composition of SOM, as fields with a long cultivation history showed higher SOM stability. The goal of 4‰ annual increase of SOC stocks may be reached under crop- grassland rotation in 2020-40 and 2080-90 when applying mineral or organic fertilizer system for scenario RCP4.5, and mineral fertilizer system in 2080-2090 for scenario RCP8.5.

This study presents a strategic approach to developing applications focused on implementing Large Language Models using the Langchain framework in Python. Three language models are highlighted: GPT-3.5 (turbo mode), LLaMA 2, and Vicuna 7B, each with their distinctive features and capabilities. The methodology used is described in detail, including data extraction from medical reports using zero-shot prompting data extraction techniques, interaction with language models, and structured storage of results. The performance of the models in data extraction is evaluated, presenting metrics such as precision, recall, and F1 score. The results demonstrate high model capability in extracting information, although areas for improvement are identified, particularly in data extraction precision. In conclusion, the efficacy of the models in extracting information from medical histories is not considerably acknowledged, with an emphasis on the importance of improving precision and increasing the volume of trained data for future research in healthcare digitalization.

This research paper is about to obtain new soliton solutions to the M-fractional (1+1)-dimensional nonlinear Westervelt model by utilizing $\exp_a$ function and modified simplest equation techniques. The gained solutions involving dark, bright, periodic, dark-bright and other solitons. These results have many applications in wave propagation of sound waves, high amplitude in medical imaging and therapy. Achieved results are verified by Mathematica tool and the effect of M-fractional derivative on the solutions is explained through 2-Dimensional, 3-Dimensional and contour plots. At the end, these techniques are simple, fruitful and effective to deal with nonlinear FPDEs.

Entangled metallic wire material (EMWM) can be utilized as a novel elastic element in vibration isolation devices for mechanical actuators. This paper presents a vibration experiment aimed at investigating the degradation behavior of mechanical performance in EMWM under cyclic compressive environment. An electric vibration testing system, coupled with an isolation structure, is employed to apply compressive loads to the EMWM specimens. Through visual observations and quasi-static compression tests, the variations in geometric morphology and mechanical properties are studied, considering different relative densities and stress amplitudes. The results incidate a significant reduction in the compressed dimension of the specimens as the number of cycles increases, without any wire fractures or wear. Moreover, the mechanical properties exhibit an increasing secant modulus and a decreasing loss factor. These variations ultimately lead to a gradual deviation of the vibration characteristics of the isolation structure from its design state. To predict the mechanical property degradation of EMWM, prediction models are proposed, incorporating dimension, modulus and damping by fitting the obtained results. This research provides valuable experimental data and presents an effective method to determine the operational lifetime of vibration isolators utilizing EMWM.

Climate change has become the greatest threat to the survival of world and its ecosystem. With the irreversible impact on the ecosystem, problems like rise in sea level, food-insecurity, natural resources scarcity, seasonal disorders have increased over the past few years. Among these problems, the issue of water scarcity due to the lack of water resources and global warming has plagued several nations. Owing to the rising concerns over water scarcity United Nations (UN) has acknowledged water as a primary resource to the development of societies under the ‘Water Goal’ of the sustainable development goals. As the changing climate and intermittent availability of water resources pose major challenges to forecast demand, especially in countries like the United Arab Emirates (UAE) which has one of the highest per capita residential water consumption rates in the world. Therefore, the aim of this study is to propose an accurate water demand forecasting technique that incorporates all significant factors to predict the future water demands of the UAE. The forecasting model used is the Long Short Term Memory (LSTM), with the factors considered are mean temperature, mean rainfall, relative humidity, Gross Domestic Product (GDP), Consumer Price Index (CPI) and population growth. The LSTM model predicts the water demand forecasting in the UAE showing that the future demand will decrease from 1821 million m3 in 2018 to 1809.9 million m3 in 2027.

Mouse models of alcohol use disorder (AUD) revealed a subtype of purinergic receptors (P2X4Rs) as a promising target for AUD drug development. We have previously demonstrated that residues at the transmembrane (TM)-ectodomain interface and within TM1 segment contribute to the formation of an ethanol action pocket in P2X4Rs. In the present study, we tested the hypothesis that there are more residues in TM segments, which are important for ethanol sensitivity of P2X4Rs. Using site-directed mutagenesis and two-electrode voltage-clamp electrophysiology in Xenopus oocytes, we found that arginine at position 33 (R33) in the TM1 segment plays a role in ethanol sensitivity of P2X4Rs. Molecular models in both closed and open states provided evidence for interactions between R33 and aspartic acid at position 354 (D354) of the neighboring TM2 segment. Further work with mixtures of wild-type (WT) and reciprocal single (R33D:WT, D354R:WT) and double (R33D-D354R:WT) mutants confirmed the importance of this interaction for ethanol sensitivity, ivermectin action and channel function. Additionally, our findings suggest that valine at TM1 position 49 plays a role in P2X4R function by providing flexibility during channel opening. Collectively, these findings identified new activity sites, and suggest the importance of TM1-TM2 interaction for channel function and ethanol sensitivity of P2X4Rs.

The high demand for liquefied natural gas (LNG) requires more LNG carriers (LNGCs) to be in operation. During transportation, there is a high risk due to the required extremely low temperatures and the explosive nature of LNG cargo. Moreover, when there is a lack of experience in operating old LNGCs, there is a serious concern regarding operational accidents. A systematic maintenance strategy, especially for LNG cargo containment systems, is crucial for maintaining safe LNG transportation at sea. The purpose of this study is to develop preventive LNG cargo containment system maintenance models by using LNGC dock specifications from LNGCs of various ages. The dock specifications from a conventional LNGC repairing dock were analyzed using natural language processing techniques in order to develop preventive maintenance models of the LNG cargo containment system. From these results, and by considering the ship’s age, it was found that for young LNGCs the priority for repair should focus on checking routine consumable spare parts by tank inspections, whereas for older LNGCs the focus should be on tank condition maintenance rather than on other facilities. These results are expected to be useful in the development of a maintenance strategy of preventive LNG cargo containment systems in maritime LNG transportation.

Geometric Akaike Information Criteria (G-AICs) for generalized noise-level dependent crystallographic symmetry classifications of two-dimensional (2D) images that are more or less periodic in either two or one dimensions as well as Akaike weights for multi-model inferences and predictions are reviewed. Such novel classifications do not refer to a single crystallographic symmetry class exclusively in a qualitative and definitive way. Instead, they are quantitative, spread over a range of crystallographic symmetry classes, and provide opportunities for inferences from all classes (within the range) simultaneously. The novel classifications are based on information theory and depend only on information that has been extracted from the images themselves by means of maximal likelihood approaches so that these classifications are objective. This is in stark contrast to the common practice whereby arbitrarily set thresholds or null hypothesis tests are employed to force crystallographic symmetry classifications into apparently definitive/exclusive states, while the geometric feature extraction results on which they depend are never definitive in the presence of generalized noise, i.e. in all real world applications. Thus, there is unnecessary subjectivity in the currently practiced ways of making crystallographic symmetry classifications, which can be overcome by the approach outlined in this review.

Dust storms originating from Central Australia and western New South Wales frequently cause high particles concentration at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affect air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model – Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints namely mortality, respiratory and cardiac diseases hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. The top five Statistical Area Level 4 regions with the most impact in term of mortality, respiratory diseases hospitalisation and cardiac disease hospitalisation are Far West and Orana, Newcastle and Lake Macquarie, New England and North West, Sydney – Inner South West and either Central Coast (mortality) or Sydney – Parramatta (respiratory diseases hospitalisation) or Sydney – Inner West (cardiac diseases hospitalisation). Generally, the health impact is similar in order of magnitude to that caused by biomass burnings events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory diseases hospitalisation for this dust event (161) compared to fire event (24).

In the present trend of constructing taller and longer structures, the application of lightweight aggregate concrete is becoming an increasing important advanced solution in the modern construction industry. In engineering practice, the analysis of lightweight concrete elements is performed using the same algorithms used for normal concrete elements. As an alternative to traditional engineering methods, nonlinear numerical algorithms based on constitutive material models may be used. The paper presents a comparative analysis of curvature calculations for flexural lightweight concrete elements, incorporating analytical code methods EN 1992-1 and ACI 318-14, as well as a numerical analysis using the constitutive model of cracked tensile lightweight concrete recently proposed by the authors. To evaluate the adequacy of the theoretical predictions, experimental data of 51 lightweight concrete beams tested during five different programmes were collected. A comparison of theoretical and experimental results showed that the most accurate predictions are obtained using numerical analysis and the constitutive model proposed by the authors. In the future, the latter algorithm can be used as a reliable tool for improving the design standard methods or numerical modelling of lightweight concrete elements subjected to short-term loading.

The chemical reaction of ion-adsorption type rare earth during in-situ leaching process is accompanied by ion migration and charge movement, making the leaching process electrochemical in nature. The chemical reaction rate plays an important role in the leaching rate of rare earth elements. In this work, electrochemical impedance spectroscopy (EIS) was used to reveal characteristics of electrical resistance alteration and leaching rate of rare earth elements during in-situ leaching. The equivalent circuit model of the leaching process was established, and two critical parameters of solution resistance Rs and charge transfer resistance Rt were analyzed to reflect the electrochemical characteristics. According to the characteristics of electrical resistance alteration, the leaching process was divided into four stages: wetting, reaction, equilibrium, and top water stage. The resistance parameters Rs and Rt decreased first and then increased during the leaching process. The maximum value of Rs was 1330Ω∙cm2 at the end of the top water stage, and the minimum value was 135Ω∙cm2 at the beginning of the equilibrium stage. The maximum value of Rt was 8310Ω∙cm2 at the beginning of the leaching stage, and the minimum value was 21Ω∙cm2 at the end of the reaction stage. Rs and Rt were negatively correlated with the pore size and chemical reaction rate during leaching. With increasing of pore size and reaction rate , the resistance parameter decrease. This study provide a new idea for intelligent monitoring of rare earth ore.

Technology has transformed aeroponics, providing numerous advantages over traditional soil-based agriculture. However, there remains a need for comprehensive information on the types of technology employed in aeroponics and their impact on the growing process. This paper presents a literature review of 47 studies published between 2012 and 2023 in peer-reviewed journals and conference proceedings. As a result, it identifies the status and tendencies in the usage of technology in aeroponics as well as the main opportunities and challenges. Furthermore, this paper introduces the Technology Adoption and Integration in Sustainable Agriculture (TAISA) model. TAISA is a model that identifies the degree of technology integration in any sustainable agriculture system to determine how technology affects production and quality. Overall, the systematic review suggests that technology can play a vital role in improving the efficiency and sustainability of aeroponic farming. However, careful consideration must be given to the costs and technical requirements associated with using these technologies. Moreover, the TAISA model reveals that technology has primarily been employed in creating new processes that are not possible to implement without the help of technology. Therefore, we conclude that technology use has taken root in aeroponics and can be promoted to improve sustainable agriculture.

The application of warm-mixing technology brings considerable economical and environment benefits by decreasing the mixing temperature during warm asphalt mixture(WMA) production. However, the possible water residue also generates concerns in moisture susceptibility. For deep investigation on the influencing factors and mechanism of the moisture susceptibility of WMA, surface free energy(SFE) test and laboratory tests are applied in this research. A novel indicator based on SFE namely effective adhesion work is proposed to assess the asphalt-aggregate adhesion with different moisture contents. Then, given the mixing procedure of dry mixing method, an advanced three-phase model as form of asphalt-aggregate-warm mixing additive is introduced improving from the conventional two-phase asphalt-aggregate model for better reflecting the separate addition of warm mixing additive during mixing. Afterwards, the influence of aggregate types, asphalt type, aggregate moisture contents, warm-mixing agent types and warm-mixing process on the moisture susceptibility of WMA is analyzed utilizing the models and indicators proposed. Finally, the validity of the SFE indicator is verified by comparing the calculation of effective adhesion work with freeze-thaw splitting test result. The results show that all of the above factors impact the moisture susceptibility of WMA by influencing the interfacial adhesion, with the effect of moisture content being the most significant. Meanwhile, effective adhesion work and the three-phase model brought out in this research are proven to be feasible to characterize the adhesion properties of WMA, offering theoretical support to the research on warm mixing technology.

Accurate evaluation of health status of lithium-ion batteries must be deemed as of great significance, insofar as utility and safety of batteries are of concern. Lithium plating, in particular, is notoriously known to be a chemical reaction that can cause deterioration in, or even fatal hazards to, the health of lithium-ion batteries. Electrochemical impedance spectroscopy (EIS), which has distinct advantages such as fast or non-destructive over its competitors, suffices in detecting lithium plating and thus has been attracting increasing attention in the field of battery management, but its ability of assessing quantitatively the degree of lithium plating remains largely unexplored hitherto. On this point, this work seeks to narrow that gap, by proposing an EIS-based method that can quantify the degree of lithium plating. The core conception is to eventually circumvent the reliance on state-of-health measurement, and use instead the impedance spectrum, to acquire an estimate on battery capacity loss. To do so, the effects of solid electrolyte interphase formation and lithium plating on battery capacity must be first decoupled, so that the mass of lithium plating can be quantified. Then, based on an impedance spectrum measurement, the parameters of the fractional equivalent circuit model (ECM) of the battery can be identified. These fractional ECM parameters are received as inputs by an artificial neural network, which is tasked to establish a correspondence between the model parameters and the mass of lithium plating. The empirical part of the work revolves around the data collected from an aging experiment, and the validity of the proposed method is truthfully attested by dismantling the batteries, which is otherwise not needed during the actual uptake of the method.

This article presents a forecast model that uses a hybrid architecture of recurrent neural networks (RNN) with surface neural networks (ANN), based on historical records of exported active energy (EAE) and weather data. Two types of models were developed: the first type includes six models that use EAE records and weather variables as inputs, while the second type includes eight models that use only weather variables. Different metrics were applied to assess the performance of these models, and the best model of each type was selected. Finally, a comparison of the performance between the selected models of both types is presented, and they are validated with real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type has an RMSE of 0.19, MSE of 0.03, MAE of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower precision in the metrics (RMSE = 0.24, MSE = 0.06, MAE = 0.10, Corr. Coef. = 0.95, and Det. Coef. = 0.90). Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy production of the solar plant.

The induced draft (ID) fan is important auxiliary equipment in the thermal power plant. It is of great significance to monitor the operation of the ID fan for safe and efficient production. In this paper, an adaptive warning model is proposed to detect early faults of ID fans. First, a non-parametric monitoring model is constructed to describe the normal operation states with the multivariate state estimation technique (MSET). Then, an early warning approach is presented to identify abnormal behaviors based on the results of the MSET model. As the performance of the MSET model is heavily influenced by the normal operation data in the historic memory matrix, an adaptive strategy is proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the model. The proposed method is applied to a 300 MW coal-fired power plant for early fault detection, and it is compared with the model without an update. Results show that the proposed method can detect the fault earlier and more accurately.

Access to reliable, clean, modern cooking enhances life chances. One option is photovoltaic cooking systems. Accurate solar data is needed to ascertain to what extent these can satisfy the needs of local people. This paper investigates how to choose the most accurate satellite derived solar irradiance database for use in Africa. This is necessary because there is a general shortage of ground measurements for Africa. The solar data is needed to model the output of solar cooking systems, for instance, a solar panel, battery and electric pressure cooker. Four easily accessible satellite databases are validated against ground measurements using a range of statistical tests. Results demonstrate the impact of the mathematical measure used and the phenomenon of balancing errors. Fitting of the satellite model to appropriate climate zone and/or nearby measurements improves accuracy, as does higher spatial and temporal resolution of input parameters. That said, all the four databases reviewed were found to be suitable for simulating PV yield in East Africa.

Non-alcoholic fatty liver (NAFLD) is a metabolic disorder related with a chronic lipid accumulation within the hepatocytes. This disease is the most common liver disorder worldwide and it is estimated that is present in up to 25% of the world's population. However, the real prevalence of this disease and the associated disorders is unknown mainly because reliable and applicable diagnostic tools are lacking. It is known that the level of albumin, a pleiotropic protein synthetized by hepatocytes, is correlated with the correct function of the liver. The development of a complementary tool that allow the direct, sensitive, and label-free monitoring of albumin secretion in hepatocyte cell culture can provide insight about the mechanism and drugs action in NAFLD. With this aim, we have developed a simple integrated plasmonic biosensor based on gold nanogratings from periodic nanostructures present in commercial Blu-ray optical disc. This sensor allows the direct and label-free monitoring of albumin in a 2D fatty liver disease model under flow conditions using highly specific polyclonal antibody. This technology avoids both the amplification and blocking steps showing a limit of detection within pM range (≈ 0.39 ng/mL). Thanks to this technology, we identified the optimal fetal bovine serum (FBS) concentration to maximize the lipid accumulation within the cells. Moreover, we discovered that at third day from lipids challenge, the hepatocytes increased the amount of albumin secreted. These data demonstrate the ability of hepatocytes to respond to the lipid stimulation releasing more albumin. Further investigation needed to unveil the biological significance of that cell behaviour.

In this paper, we present an extended version of the Hadi-Vencheh model for multiple criteria ABC inventory classification. The proposed model is a nonlinear weighted product model (WPM) which determines a common set of weights for all the items. Our proposed nonlinear WPM incorporates multiple criteria with different measure units, without converting the performance of each inventory item in terms of each criterion into a normalized attribute value, thereby providing an improvement over the model proposed by Hadi-Vencheh. Our study mainly includes various criteria for ABC classification, and demonstrates an efficient algorithm for solving nonlinear programming problems in which the feasible solution set does not have to be convex. The algorithm presented in this study improves the solution efficiency of the Canonical Coordinates Method (CCM) algorithm substantially when applied to large scale, nonlinear programming problems. The modified algorithm was tested to compare our proposed model results to the results derived using the Hadi-Vencheh model and demonstrate the algorithm's efficacy. The practical implications of the study are to develop an efficient nonlinear optimization solver by optimizing the quality of existing solutions, thus improving time and space efficiency.

The impact of ponds on basins has recently started to receive its well–deserved scientific attention. In this study, pond–induced impacts on soil erosion and sediment transport were investigated at the scale of the French Claise basin. In order to determine erosion and sediment transport patterns under current conditions, the CORINE erosion and SWAT models were used. The impact of ponds on the studied processes was revealed by means of land cover change scenarios, using ponded versus pondless inputs. Results show that under current conditions (pond presence), 12.48% of the basin corresponds to no–erosion risk zones (attributed to the dense pond network), while 65.66% corresponds to low–erosion risk, 21.68% to moderate–erosion risk and only 0.18% to high–erosion risk zones. The SWAT model revealed that ponded sub–basins correspond to low sediment yields areas, in contrast to the pondless sub–basins, which yield appreciably higher erosion rates. Under the alternative scenario, erosion risks shifted to 1.12%, 0.52%, 76.8% and 21.56% for no, low, moderate and high–erosion risks, respectively, while the sediment transport pattern of the Claise completely shifted to higher sediment yield zones. This approach solidifies ponds as powerful man–induced modifications to hydro/sedimentary processes.

Strata and surface movement induced by mining under open-pit final slope is a huge threat to mine safety. Physical model test is an important method to study mining-induced strata and surface movement laws. Because of rock joints predominantly control rock mass deformation and failure, thus physical model test leaving out of consideration of rock joints is difficult to reflect the influence of rock joints on rock mass deformation. Therefore, this paper presents a three-dimensional physical model test considering simplified dominant rock joints. This test process includes the design of testing equipment, the construction of physical model with dominant rock joint sets, conduction of mining and deformation monitoring. And mining under eastern final slope of Yanqianshan iron mine was selected as a case to study the behavior of mining-induced strata and surface movement.

The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: 1. falling weight deflectometer and ground-penetrating radar are expensive tests, 2. back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, m5p model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

The accuracy of sales prediction models based on the big data of online word-of-mouth (eWOM) is still not satisfied. We argue that eWOM contains heat and sentiments of different product dimensions, which can improve the accuracy of these models. In this paper, we propose a dynamic topic analysis (DTA) framework in order to extract heat and sentiments of product dimensions from the big data of eWOM. Finally, we propose an autoregressive-heat-sentiment (ARHS) model, which integrates heat and sentiments of dimensions into the baseline predictive model. The empirical study in movie industry confirms that heat and sentiments of dimensions can improve the accuracy of sales prediction model. ARHS model is better for movie box-office revenue prediction than other models.

Background: With continuous advances in next-generation sequencing (NGS), novel hepadnaviruses have been discovered in many species over the last 10 years. Methods: In this study, the cloud search and analysis of NGS data published in Nature by Edgar et al. was used as a basis to re-mine and reanalyze public NGS data for new hepadnaviruses. Results: Ultimately, at least 41 new species of hepadnaviruses were identified, including hepadnaviruses from the model animals hamster and mouse, frog hepadnavirus with pan-species infectivity, and diverse African cichlid hepadnaviruses that circulate within populations. Conclusions: The discovery of the new species of hepadnaviruses not only provides new clues for the study of the origin and evolution of hepadnaviruses, but also can be used to construct new hepadnavirus animal infection models, which will be helpful for the research of eradicative drugs for hepatitis B virus (HBV).

Brazil has a large availability of natural resources and its economy was historically built around their exploitation. Changes in climate trends are already causing several environmental impacts, which affect the economic and social organization of the country. Impacts linked to the hydrological cycle are particularly concerning since water resources are used for electricity production, representing approximately 65% of the Brazilian electricity matrix. This study, therefore, aims to evaluate the hydroclimatic extreme trends of river basins located in the Northeast and South regions of the country. For this purpose, we carried out a flow analysis from 2020 to 2100, considering the precipitation data from the BCC CSM1-1, CCSM4, MIROC5, and NorESM1-M models presented in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change - IPCC. We used the SMAP rainfall-runoff model to obtain future flow projections for the RCP4.5 and RCP8.5 scenarios. As a result, we observed a trend toward water loss and intensification of extreme events, with an increase in variability in both scenarios. We also noted that these climate models have difficulty reproducing the natural variability of southern basins, as the simplification of atmospheric phenomena parameterization prevents them from correctly projecting the precipitation.

In this paper, initially a mathematical model is formulated for transient frequency of power system considering time delays which occur while transmitting the control signals in open communication infrastructure. Time delay negligence in a power system leads to improper measurement of frequency variation in power system. The study of impact of time delays on the stability of power system is performed by estimating the decay rate of frequency wave form using Kalman Filter (KF). In power system, there is a possibility of multiple time delays. This paper also focusses on developing Interacting Multiple Model (IMM) Algorithm with multiple model space using Kalman Filter (KF) as state estimator tool. The multiple time delays in power system is considered as multiple model space. The result shows that KF provides better estimate of correct model for a particular input-set. The qualitative properties of Riccati difference equation (RDE) in terms of state error covariance of IMMKF are also analyzed and presented.

The aim of this study was to investigate the spatio-temporal association between socio-economic deprivation and the incidence of COVID-19 and how this association changes through the seasons and due to the existence of restrictive public health measures. A retrospective observational study was conducted among COVID-19 cases that occurred in the Apulia region from 29 February 2020 to 31 December 2021, dividing the period into four phases with different levels of restrictions. A generalized estimating equations model was applied to test the independent effect of deprivation on the incidence rate of COVID-19, taking into account age, sex, and regional incidence rate as possible confounding effects and covariates such as season and levels of restrictions as possible modifying effects. The highest incidence rate was in areas with a Very High deprivation Index (DI) in winter (107.2 for 100,000 ab. ± 7.5), while in autumn, the highest Rate Ratio (RR) was estimated between Very High vs. Low DI (3.83, p<.001). During total lockdown, no RR between areas with different levels of DI was significant, while during soft lockdown, areas with Very High DI were more at risk than all other areas. The effects of social inequalities on incidence rate of COVID-19 change in as-sociation with the seasons and restrictions on public health. Disadvantaged areas showed a higher incidence rate of COVID-19 in the cold seasons and in the phases of soft lockdown.

Background Acute kidney injury (AKI) is a common comorbidity in the intensive care unit (ICU), with higher mortality rate among those who receive continuous renal replacement therapy (CRRT). This study aimed to investigate the association of white blood cell (WBC) levels with all-cause mortality in AKI patients undergoing CRRT. Method We conducted a post hoc analysis of ICU patient data from multiple centers in Korea. First, the levels of WBCs were classified into four groups based on quartiles. Second, the relationship between different WBC levels and mortality was investigated with the Kaplan-Meier (K-M) survival curves, Cox proportional hazards model, and generalized additivity model (GAM). Finally, we used multiple imputation to avoid bias caused by non-randomized missing data. The endpoints were short-term (28 days) and long-term (90 days) mortality. Result In our analysis, a total of 1,100 ICU patients were included. Among them, 670 (60.90%) and 781 (71.00%) died within 28 and 90 days, respectively. K-M survival curves showed that patients with the lowest WBC levels had the highest risk of mortality for both outcomes. Adjusted multivariate Cox regression analysis confirmed that compared with the reference level (quartile 2), low WBC levels increased the risk of both 28-day mortality (quartile 1, HR = 1.43, 95% CI: 1.13–1.83, P = 0.004) and 90-day mortality (quartile 1: HR = 1.32; 95% CI: 1.05-1.66, P = 0.016). GAM analysis showed a non-linear, U-shaped relationship between pre-CRRT WBC levels and all-cause mortality (non-linear P < 0.05). Conclusion The baseline WBC level is an independent predictor of prognosis for ICU patients receiving CRRT. Patients in the lowest WBC quartile had the highest risk of all-cause mortality. These results provide further evidence that early observation and intervention may have a prognostic value for these high-risk patients.

Nowadays, consumers have growing expectations for healthier and higher-quality food, and sustainable food choices are also taking on an increasingly central role. This paper assesses the environmental loads and energy resources of vegan, semi-vegetarian, and traditional restaurant soups and main dishes, focusing on preparation and cooking phases based on the life cycle assessment. The optimal menu samples were selected based on statistical methods. Mann-Whitney and Krus-kal-Wallis tests were applied to investigate restaurant products' distribution and carbon footprint. Based on the results, the preparation phases have a more significant impact. The environmental loads for the life cycle of vegan and semi-vegetarian menus are lower. Furthermore, a sustainability assessment model was developed by integrating lean and life cycle assessment approaches called "GreenCycLEAN." Research results are helpful for the sustainability of catering establishments.

The zebrafish is now an important model organism for cancer biology studies and provides some unique and complementary opportunities in comparison to the mammalian equivalent. The translucency of zebrafish has allowed in vivo live imaging studies of tumour initiation and progression at the cellular level thus providing novel insights into our understanding of cancer. Here we summarise and discuss available transgenic zebrafish tumour models and what we have gleaned from them with respect to cancer inflammation. In particular, we focus on the host inflammatory response toward transformed cells during the pre-neoplastic stage of tumour development. We discuss features of tumour associated macrophages and neutrophils in mammalian models and present evidence which supports the idea that these inflammatory cells promote early stage tumour development and progression. Direct live imaging of tumour initiation in zebrafish models has shown that the intrinsic inflammation induced by pre-neoplastic cells is tumour promoting. Signals mediating leukocyte recruitment to pre-neoplastic cells in zebrafish correspond to signals mediating leukocyte recruitment in mammalian tumours. The activation state of macrophages and neutrophils recruited to pre-neoplastic cells appears to be heterogenous, as seen in mammalian models, which provides an opportunity to study the plasticity of innate immune cells during tumour initiation. Although several potential mechanisms are described that might mediate the trophic function of innate immune cells during tumour initiation in zebrafish, there are several unknowns that are yet to be resolved. Rapid advancement of genetic tools and imaging technologies for zebrafish will facilitate research into the mechanisms that modulate leukocyte function during tumour initiation and identify targets for cancer prevention.

Background: New open-source electric-grid planning models have the potential to improve power system planning and bring a wider range of stakeholders into the planning process for next-generation, high-renewable power systems. However, it has not yet been established whether open-source models perform similarly to the more established commercial models for power system analysis. This reduces their credibility and attractiveness to stakeholders, postponing the benefits they could offer. In this paper, we report the first model intercomparison between an open-source power system model and an established commercial production cost model. Results: We compare the open-source Switch 2.0 to GE Energy Consulting’s Multi Area Production Simulation (MAPS) for production-cost modeling, considering hourly operation under 17 scenarios of renewable energy adoption in Hawaii. We find that after configuring Switch with similar inputs to MAPS, the two models agree closely on hourly and annual production from all power sources. Comparing production gave a coefficient of determination of 0.996 across all energy sources and scenarios, indicating that the two models agree on 99.6% of the variation. For individual energy sources, the coefficient of determination was 69–100. Conclusions: Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here. Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here.

Human footprint is having a unique set of ridges unmatched by any other human being, and therefore it can be used in different identity documents for example birth certificate, Indian biometric identification system AADHAR card, driving license, PAN card, and passport. There are many instances of the crime scene where an accused must walk around and left the footwear impressions as well as barefoot prints and therefore it is very crucial to recovering the footprints to identify the criminals. Footprint-based biometric is a considerably newer technique for personal identification. Fingerprints, retina, iris and face recognition are the methods most useful for attendance record of the person. This time world is facing the problem of global terrorism. It is challenging to identify the terrorist because they are living as regular as the citizens do. Their soft target includes the industries of special interests such as defense, silicon and nanotechnology chip manufacturing units, pharmacy sectors. They pretend themselves as religious persons, so temples and other holy places, even in markets is in their targets. These are the places where one can obtain their footprints easily. The gait itself is sufficient to predict the behaviour of the suspects. The present research is driven to identify the usefulness of footprint and gait as an alternative to personal identification.

In this study, the values of the interfacial energies of seven different polymer-water systems obtained by Sessile Drop Accelerometry (SDACC) are compared with the values obtained by the Young’s-equation-based Owens-Wendt method. The SDACC laboratory instrument –a combination of a drop shape analyzer with high-speed camera and a microgravity tower- and the evaluation algorithms, are designed to measure the interfacial energies as a function of the geometrical changes of a sessile droplet shape due to the effect of “switching off” gravity during the experiment. The method bases on Thermodynamics of Interfaces and differs from the conventional aproach of the two hundred-years-old Young’s equation in that it assumes a thermodynamic equilibrium between interfaces, rather than a balance of forces on a point of the solid-liquid-gas contour line. A comparison of the mathematical model that supports the SDACC method with the widely accepted Young`s equation is discussed in detail in this study.

The storage of biochemical information, which is a prerequisite for the development of the first cell, is an unsolved problem affecting all concepts of the origin of life. However, if the protected environment in the continental crust is taken into account, completely new possibilities emerge for identifying processes that may have been crucial for the formation of the first cell. Under this background, we can hypothesize that cellular life began, with a self-sustaining cycle of molecular reaction steps and information storage of peptide sequences in RNA in a crustal depth of approximately 1000 m. This cycle was made possible in an open system bound to gas-permeable tectonic fracture zones with a high proportion of CO₂ and/or N₂. It can be assumed that a large number of RNA-like molecular strands formed in the crustal environment, from which a special RNA was selected by flotation processes, which formed the basis of a proto-tRNA. The formation of peptides and vesicles in supercritical CO₂ and the chemical evolution of peptides have already been proven at conditions of the upper continental crust. The behavior of individual amino acids in connection with vesicle formation deserves special interest. Hydrophobic amino acids accumulate in the vesicle membrane, with their position in the membrane depending on the degree of hydrophobicity. Selection of a proto-tRNA with an acceptor arm ending with the bases CCA resulted in a distinctive property. The position of adenine at the tip of the acceptor arm permitted it to penetrate the membrane, allowing it to be linked to an amino acid at the 3'-OH position of the terminal ribose. The penetration depth was controlled by the hydrophobicity of the opposite anti-codon, with adenine always occupying the middle position for the hydrothermally formed hydrophobic amino acids. When the vesicle membrane was closely occupied by proto-tRNAs, the anti-codons acted as templates and stored the sequence of the peptides that form. In the cases of hydrophilic anti-codons, the acceptor arm does not reach far enough into the membrane. This allowed hydrophilic amino acids to be linked to 2´-OH.

The article suggests methods that allow creating the most complicated type of polygonal masonry found in Peru. This masonry type consists of large stone blocks weighing from several hundred kilograms to several tons fitted close to each other almost without a gap between complicated curved surfaces over a large area. The work provides a description of techniques, which apparently were used by builders who arrived from Europe. The techniques under discussion are based on the use of a reduced clay model, 3D-pantograph, topography translator and replicas. The use of the topography translator, reduced clay model and pantograph provides not only the unique appearance and high quality of masonry of large blocks, but also allows to increase the productivity of the builders significantly. As machines coping-scaling three-dimensional objects are known since the beginning of the 18th century, the stone structures under consideration should be approximately dated by this time. The remaining simpler types of polygonal masonry, when the stones are small or the fitted surfaces are almost flat, or the stones contact each other over a small area, or there are significant gaps between the stones, are quite consistent with the well-known methods of stone processing at that time or earlier, and, therefore, they do not require any additional explanations. The Fortress Sacsayhuaman is considered as an example of early star fortresses that has survived to our time. The polygonal structures in Peru, the polygonal Face Towers and polygonal bas-reliefs in Cambodia, symmetrical statues of pharaohs in Egypt are based on the same construction technologies, working methods, tools and technical contrivances. Therefore, with a high probability one can state that all these monuments were created by the same group of architects, sculptors, builders, and could not have appeared before the 17th century.

To investigate the variation patterns of spectral characteristics of main branches in fruit trees due to uneven distribution of growth, pruning, and fruit quality, as well as to determine the optimal fruit vi-bration harvesting, a vibration differential model of tree branches was constructed using the concen-trated mass method. A three-dimensional model of the fruit tree was built using finite element software. The spectral characteristics of each branch were obtained using the linear sweep frequency method. The method of changing the mass of each branch was employed to simulate the variation in branch mass. The frequency of occurrence, mean acceleration, and coefficient of variation of the inherent frequencies were used as evaluation indicators. The results revealed that when the vibration frequency of the fruit tree was at the peak frequency in the tree trunk spectrum, the frequency of occurrence and mean accel-eration were optimal. And the coefficient of variation of branch acceleration was less than 1, indicating a moderate dispersion. Additionally, reducing the branch mass resulted in an increase in the peak fre-quency of the tree trunk spectrum. Therefore, the growth, pruning, and uneven distribution of the branches in fruit trees can alter the spectral characteristics. When harvesting fruit by vibration, selecting the peak frequency in the trunk vibration spectrum as the excitation force frequency yields the best fruit detachment.

By using the Black-Derman-Toy (BDT) model, we predict the future trend of the riskless rate, and then we build an equation that relates the market price of zero-coupon bonds and the theoretical price of zero-coupon bonds calculated using a binomial option pricing model. Based on this, we can find the implied daily return μ, implied natural upturn probability p, and implied daily volatility σ with respect to different time-to-maturity values of zero-coupon bonds. With these results, we can give some suggestions to bond traders.

In the development of the safety-critical systems, it is important to perform Failure Modes and Effects Analysis (FMEA) process to identify potential failures. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks. To compensate for the difficulty of the FMEA task, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. This paper explains an Automatic FMEA tool which integrates the Model-based Design (MBD), FMEA, and Simulated Fault Injection techniques in a single environment. The Automatic FMEA tool has the following advantages compared to the existing FMEA analysis tool. First, the Automatic FMEA tool automatically generates FMEA reports compared to the traditional spreadsheet-based FMEA tools. Second, the Automatic FMEA tool analyzes the causality between the failure modes and the failure effects by performing model-based fault injection simulation. In order to demonstrate the applicability of the Automatic FMEA, we used the electronic fuel injection system (EFI) Simulink model. The results of the Automatic FMEA were compared to that of the legacy FMEA.

With the pressure of the increasing density of urban areas, some public infrastructures are moving to the underground to free up space above, such as utility lines, rail lines and roads. In the big data era, the three dimensional (3D) data can be beneficial to understand the complex urban area. Comparing to spatial data and information of the above ground, we lack of the precise and detailed information about underground infrastructures, such as the spatial information of underground infrastructure, the ownership of underground objects and the interdependence of infrastructures in the above and below ground. How to map reliable 3D underground utility networks and use it in the land administration? First, to explain the importance of this work and find a possible solution, this paper observes the current issues of the existing underground utility database in Singapore. A framework for utility data governance is proposed to manage the work process from the underground utility data capture to data usage. This is the backbone to support the coordination of different roles in the utility data governance and usage. Then, an initial design of the 3D underground utility data model is introduced to describe the 3D geometric and spatial information about underground utility data and connect it to the cadastral parcel for land administration. In the case study, the newly collected data from mobile Ground Penetrating Radar is integrated with the existing utility data for 3D modelling. It is expected to explore the integration of new collected 3D data, the existing 2D data and cadastral information for land administration of underground utilities.

In this research work the study of Buongiorno Model Darcy Forchheimer flow of Hybrid nanofluid flow on isothermal permeable stretch/shrink sheet with homogeneous and heterogeneous reaction is investigated. The effects of MHD, thermal radiation, viscous dissipation, Joule heating, and heat generation/absorption are taken into account. Hybrid nanofluids can act as a medium for heterogeneous-homogeneous reactions. The nanoparticles dispersed in the fluid can influence reaction kinetics and species transport, potentially, altering reaction rates and pathways. The applications of HNF in reactions, both homogeneous and heterogeneous, lie in leveraging their enhanced thermal properties, catalytic potential, and improved mixing capabilities to optimize reaction efficiency and control reaction conditions. Similarity transformation is used to convert a set of PDEs into a set of ODEs. HAM is used for the solution of the obtained ODEs system. In a heterogeneous reaction, the concentration gradient is amplified; in a homogeneous reaction, the impact is opposite. Moreover, at higher compositions of HNPs, the concentration and velocity rise while the temperature falls. Furthermore, as the values of homogeneous and heterogeneous reactions increase, the concentration falls. It is reliable with the idea that a greater reaction rate lowers the diffusion rate. Nevertheless, these parameters do not affect temperature or velocity.

This study proposes a 3D particle-base (discrete) multiphysics approach for modelling calcification in the aortic valve. Different stages of calcification (from mild to severe) were simulated, and their effect on the cardiac output assessed. The cardiac flow rate decreases with the level of calcification. In particular, there is a critical level of calcification below which the flow rate decreases dramatically. Mechanical stress on the membrane is also calculated. The results show that, as calcification progresses, spots of high mechanical stress appear. Firstly, they concentrate in the regions connecting two leaflets; when severe calcification is reached, then they extend to the area at the basis of the valve.

Modeling dense nonaqueous phase liquids (DNAPLs) contamination in groundwater is challenging because of its multiphase distribution. To understand the research trends of DNAPL modeling in groundwater, a bibliometric analysis was conducted using CiteSpace based on 614 publications from the WoS Core Collection database (1993-2023). The publications were statistically analyzed, and the research hotspots and trends were summarized. The statistical analysis of the publications indicates that: the United States is leading the international research on DNAPL models, followed by China and Canada; collaboration between countries and disciplines in this field needs to be strengthened. The summary of keyword clustering and burst detection reveals that: the current research hotspots focus on multiphase flow models, mass transfer models, back diffusion, and practical applications of the models; the research trends are centered on back diffusion mechanisms, characterization of contamination source zones and prediction of contaminant distribution in real-world sites, as well as the optimization of remediation strategies.

Computational fluid dynamics (CFD) modeling has emerged as a valuable tool for investigating complex processes like microencapsulation. This paper aims to validate the ability of CFD simulations to predict particle size distribution in a polymer microencapsulation process. The CFD modeling approach employed an Eulerian multiphase framework, incorporating a discrete population balance model to track the evolution of the droplet population. A realizable k-ε turbulence model and a multiple reference frame strategy were utilized to capture the system’s flow dynamics. The results reveal that while the CFD simulations align well with experimental data at higher agitation speeds (>10000 rpm), discrepancies arise at lower speeds (<7500 rpm), indicating a challenge in accurately capturing turbulent viscous regimes. Despite these challenges, the CFD model demonstrates robust predictive capabilities for droplet formation and distribution in microencapsulation processes, validated by error margins within acceptable limits. The validated model can be used as a reliable tool to guide experimental efforts and optimize process parameters, contributing to an enhanced understanding and control of microencapsulation processes.