Peanut production contributes to food security in northern Ghana due to its ability to tolerate drought and survive on marginal lands. This notwithstanding, poor handling along the value chain favors aflatoxin contamination, a threat to human and animal life. Farmer-led improved storage practice, a potential solution to aflatoxin contamination, have been promoted in northern Ghana. However, there is limited evidence on the factors influencing the use of the improved storage system that may guide dissemination efforts. Using the Cragg’s two stage model, our results show that the probability and intensity of adopting the improved storage structure is principally and significantly influenced by economic active household members, storing of peanut in other places relative to the field, and location of farmer residence. In disseminating the improved peanut storage structure, the identified factors must be incorporated in the selection criteria to ensure maximum uptake and usage. Farmer training programs that incorporate both good agronomic practices and good storage practices must be intensified to reduce postharvest losses due to aflatoxin contamination. These strategies will help mitigate the harmful effect of aflatoxin, ensure economic sustainability, and enhance food security.

PEM fuel cell is a technology successfully used in the production of energy from hydrogen, allowing the use of hydrogen as an energy vector. It is scalable for stationary and mobile applications. However, the technology demands more research. An important research topic is fault diagnosis and condition monitoring to improve the life and the efficiency and to reduce the operation costs of PEMFC devices. Consequently, there is a need of physical models that let deep analysis. These models must be accurate enough to represent the PEMFC behavior and to allow the identification of different internal signals of a PEM fuel cell. This work presents a PEM fuel cell model that uses the output temperature in a closed loop, so it can represent the thermal and the electrical behavior. The model is used to represent a NEXA Ballard 1.2 kW; therefore it is necessary to fit the coefficients to represent the real behavior. Five optimization algorithms were tested to fit the model, three of them were taken from literature and two were proposed. Finally, the model with the parameters identified was validated with real.

The etiology of Autism Spectrum Disorder (ASD) has been linked to both genetic and epigenetic factors. Among the epigenetic factors, exposure to valproic acid (VPA), an antiepileptic and mood-modulating drug, has been shown to induce characteristic traits of ASD when exposed during embryogenesis. Conversely, in animal models, Enriched Environment (EE) has demonstrated positive behavioral and neural effects, suggesting its potential as a complementary treatment to pharmacological approaches in central nervous system disorders. In this study, we utilized zebrafish to model ASD characteristics induced by VPA and hypothesized that sensory stimulation through EE could ameliorate the behavioral and neuroanatomical features associated with ASD. To test this hypothesis, we assessed social behavior, cerebellar volume, and Purkinje cell populations via histology and immunohistochemistry after exposing the fish to EE. The results revealed that zebrafish exposed to VPA exhibited social deficits, reduced cerebellar cortex volume, and a decrease in c-Fos-positive cells in the Purkinje layer. In contrast, VPA-exposed fish treated with EE showed increased socialization, augmented cerebellar cortex volume, and an elevation in c-Fos-positive Purkinje cells. These findings suggest that alterations induced by VPA may be ameliorated through EE treatment, highlighting the potential therapeutic impact of sensory stimulation in conditions related to ASD.

In the context of growing global political tension and introduction of world trade barriers, the urgent task is to develop new tools for assessing their consequences. In the paper we present the agent-based model of trade wars, considering organizations, states and residents generated using initial statistical data. Simulation determines changes in output and supplies of organizations under trade restrictions. Results of calculations on the developed model and comparison of various model complexes forecasts with real consequences of trade wars between the USA and China in 2018 and Western countries against Russia in 2022 are presented. Within calculations four scenarios were considered: (1) baseline, (2) new restrictions between China and the USA, (3) more serious sanctions against China and Russia by the USA and the EU, (4) a global trade war. In the second scenario deviation GDP of the USA and China from the baseline scenario does not exceed 0.5%. In the third scenario, the range of countries involved is expanding, and the fall in GDP in them is expected at the level of 0.7-1%. In the fourth scenario, the entire world economy experiences a serious slowdown, and the EU are facing the most severe consequences, going into recession.

The capacity of 3D printing (3DP) technologies to initiate speedy polymerization of solvent free resins accounts for their utility in the manufacturing of medical devices. Nonetheless, independent biological evaluation of 3D printed materials is recommended due to the unique parameters of the manufacturing process, which can influence their physical, chemical, and biological properties. In this study, E-Shell 450 material indicated for 3DP of hearing aid shells and inner ear devices was examined for biological safety using zebrafish bioassays adapted to OECD fish embryo test. In addition, the proprietary material was characterized for composition using headspace gas-chromatography mass spectrometry (GC-MS). To initiate test, newly fertilized zebrafish eggs were cultured on non-treated and ethanol-treated materials in glass petri dishes with ultrapure water, incubated at 28.5°C and assessed for developmental endpoints of toxicity at 24h interval until 96h. Data confirmed non-treated material was extremely toxic in bioassays within 24h whereas ethanol-treated material showed a relative lower toxicity possibly due to ethanoic-aqueous interactions as observed by GC-MS. With the current influx of 3D printing materials, users are urged to exercise caution. Operators must also take cognizance of the potential toxicity of the chemicals used in 3DP and implement safety measures to limit their exposure.

This article considers the concepts of sustainability and sustainable development in relation to disaster risk reduction and climate change adaptation. We conceptualize sustainability from a social systemic perspective, that is, from a perspective that encompasses the multiple functionalities of a social system and their interrelationships in particular environmental contexts. The systems perspective is applied in our consideration and analysis of disaster risk reduction (DRR), climate change adaptation (CCA), and sustainable development (SD). Section 1 introduces briefly sustainability and sustainable development, followed by a brief presentation of the theory of complex social systems (Section 2). The theory conceptualizes interdependent subsystems, their multiple functionalities, and the agential and systemic responses to internal and external stressors on a social system. Section 3 considers disaster risk reduction (DRR) and climate change adaptation (CCA), emerging in response to one or more systemic stressors. It illustrates these with disaster risk reduction in the cases of food and chemical security regulation in the EU. CCA is illustrated by initiatives and developments on the island of Gotland, Sweden and in the Gothenburg Metropolitan area, which go beyond a limited CCA perspective, taking into account long-term sustainability issues. Section 4 discusses the limitations of DRR and CCA, not only their technical limitations but economic, socio-cultural, and political limitations, as informed from a sustainability perspective. It is argued that DRRs are only partial subsystems and must be considered and assessed in the context of a more encompassing systemic perspective. Part of the discussion is focused on the distinction between sustainable and non-sustainable DRRs and CCAs. Section 5 presents a few concluding remarks about the importance of a systemic perspective in analyzing DRR and CCA as well as other similar subsystems in terms of sustainable development.

Stemming from the urgency of the actual situation, commercial banks need an effective credit risk management tool to limit risks. The authors went to survey, study and propose a set of factors affecting the ability of debt repayment of individual customers and conducting surveys. The topic uses data sets including 240 observation samples. Using the SPSS software to clean data and run the model based on Maddala's Binary logistics regression published in 1984 to find out the impact of each individual element of customers affecting their ability to repay such debts. Come on. The authors also specify the order of influence of each factor determining the ability to repay individual customers, thereby helping bank managers have a better visual view to make decisions for borrowing accurately, limiting risks.

The predictability of wind information in a given location is essential for the evaluation of a wind power project. Predicting wind speed accurately improves the planning of wind power generation, reducing costs and improving the use of resources. This paper seeks to predict the mean hourly wind speed in anemometric towers (at a height of 50 meters) at two locations: a coastal region and one with complex terrain characteristics. To this end, the Holt-Winters (HW), Artificial Neural Networks (ANN) and Hybrid time-series models were used. Observational data evaluated by the Modern-Era Retrospective analysis for Research and Applications-Version 2 (MERRA-2) reanalysis at the same height of the towers. The results show that the hybrid model had a better performance in relation to the others, including when compared to the evaluation with MERRA-2. For example, in terms of statistical residuals, RMSE and MAE were 0.91 and 0.62 m/s, respectively. As such, the hybrid models are a good method to forecast wind speed data for wind generation.

{\bf Abstract.} \\ {\bf Background:} Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 burden among the Rohingya refugees of Bangladesh to keep up with the disease outbreak's pace, health needs, and disaster preparedness. \noindent{\bf Methodology and Findings:} To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious Recovered (SEIR) transmission model. All the values of different parameters used in this model were from the Bangladesh Government’s database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and good fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be good control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number ($ \mathcal{R}_{0} $) has been estimated to be 0.7563. \noindent{\bf Conclusion:} Our analysis suggests that, due to the extensive precautions from the Bangladesh government as well as other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. Although detailed and pragmatic preparedness should be adopted for the worst scenario.

Bipolar disorder (BD) is a complex mental health condition characterized by alternating periods of mania and depression. This review provides a comprehensive overview of BD, focusing on its genetics, causal hypotheses, clinical signs and evolution, potential biomarkers with an emphasis on electroencephalography (EEG), and treatment options. Genetic studies have identified several susceptibility genes and polygenic risk scores, highlighting the heritable nature of BD. Various causal hypotheses, including monoamine dysregulation, kindling, circadian rhythm disruptions, and neuroinflammation, have been proposed to explain its pathophysiology. The clinical course of BD is characterized by recurrent episodes of mania/hypomania and depression, with significant heterogeneity among individuals. EEG and other biomarkers hold promise for improving diagnosis, predicting treatment response, and elucidating the neurobiological underpinnings of BD. Current treatment options include pharmacotherapy, psychotherapy, and neuromodulation techniques, which can be tailored to individual needs based on clinical presentation and response to treatment. Future research should focus on identifying more specific biomarkers, developing novel treatments, and optimizing existing therapies to improve outcomes for individuals with BD.

The potential effect of implementing environmental regulations on economic growth is a controversial issue for a long time. As portrayed by Porter hypothesis, environmental regulations may impact on economic growth by reconstructing the economic structure. A number of previous literature proved the connections between the above three parties. With the intention of exploring the nature of this mechanism, it is important to understand the internal inter-relation of the three parties, requiring data from a large economy experiencing the transition in economic structure. By constructing a panel containing data from 30 Chinese provinces over 10 years (2008-2019), this paper investigate the dynamic inter-relation of the three parties by introducing PVAR model with Granger Test. Results reveal that environmental regulations give a “U” shaped impact to economic growth. In return, economic growth promotes the development of economy with a weakening strength. In addition, results also support the hypothesis that economic structure is the intermediate of economic growth and environmental regulations. There is a rooftop for the effect of economic structure to environmental regulations. The rooftop may signify the best optimization of primary structure.

model checking techniques are often used for the verification of software systems. Such techniques are accompanied with several advantages. However, state space explosion is one of the drawbacks to model checking. During recent years, several methods have been proposed based on evolutionary and meta-heuristic algorithms to solve this problem. In this paper, a hybrid approach is presented to cope with the SSE problem in model checking of systems modeled by GTS with an ample state space. Most of existence proposed methods that aim to verify systems are applied to detect deadlocks by graph transformations. The proposed approach is based on the fuzzy genetic algorithm and is designed to decline the safety property by verifying the reachability property and detecting deadlocks. In this solution, the state space of the system is searched by a fuzzy genetic algorithm to find the state in which the specified property is refuted/verified. To implement and evaluate the suggested approach, GROOVE is used as a powerful designing and model checking toolset in GTS. The experimental results indicate that the presented hybrid fuzzy method improves speed and performance by comparing other techniques

It is the aim of this article to introduce a concise notation that is be able to capture the most important dimensions of sexuality and thus facilitate discussions. Moreover, the major three dimensions - sex, gender, and sexual orientation - lead to a straightforward classification which motivates the quest for stringent explanatory models, some of which are also discussed.

Standard Model is well-performing under the three-generation, but in the wake of more physics, fourth-generation leptons and quarks are introduced. In this paper, We will be reviewing various constraints surrounding quarks and leptons of 4th Generation and tried to show the importance of quarks to understand the baryogenesis. We talked about, reviewed existing parameters and set the updated lower limits of t’ and b’ by analysis. And predicted an updated version of lower mass bounds quarks and leptons. It is also reviewed that fourth generation leptons are primarily not possible because of disintegration of Higgs Boson and an interpretation has been setup. And in final, we talked about the Unified Standard Model.

This study explores the application of numerical analysis and material models to predict ice impact loads on ships and offshore structures operating in polar regions. An explicit finite element analysis (FEA) approach was employed to simulate an ice and steel plate collision experiment conducted in a cold chamber. The pressure and strain history during the ice collision were calculated and compared with the experimental results. Various material model configurations were applied to the FEA to account for the versatile behavior of ice—whether ductile or brittle—its elastic-plastic yield criteria, and its dynamic strain rate dependency. In addition to the standard linear elastic perfectly plastic and linear elastic-plastic relationships, this study incorporated the crushable foam and Drucker-Prager models, based on the specific ice yield criteria. Considering the ice’s strain rate dependency, collision simulations were conducted for each yield criteria model to compute the strain and reaction force of the plate specimens. By comparing the predicted pressures for each material model combination with the pressures from ice collision experiments, our study proposes material models that consider the yielding, damage, and behavioral characteristics of ice. Lastly, our study proposes a combination of ice material properties that can accurately predict collision force.

The propagation of electromagnetic waves beyond the line of sight can be caused by atmospheric ducts, which are a significant concerns in the fields of radar and communication. This paper utilizes data from seven automatic stations and five radio-sounding stations to statistically analyze the characteristics of the atmospheric ducts in the northwest region of the South China Sea (SCS). After verifying the practicality of numerical analysis data from NCEP CFSv2 and ERA5 in studying atmospheric ducts using measured data, we analyzed the space-time distribution characteristics of the height of the regional evaporation duct and the bottom height of the elevated duct. Using the parabolic equation model, we simulated electromagnetic propagation loss under different frequencies and radiation elevation angles in both uniform and non-uniform duct environments within a typical atmospheric duct structure. The study found that the NCEP CFSv2 data accurately captures the evaporation duct height and duct occurrence rate in the study area, and the elevated duct bottom height obtained from the inversion of ERA5 and the measured data has a good consistency. The occurrence rate and height of evaporation duct in coastal stations in the northwest of the SCS vary significantly by month, demonstrating clear monthly distribution patterns. Conversely, changes in the Xisha station are minimal, indicating good temporal uniformity. For lower atmospheric ducts, the difference in occurrence rates between 00:00 and 12:00 (UTC) is negligible. The occurrence probability of elevated ducts in the Beibu Gulf area is relatively high, mainly concentrated from January to April, and the Xisha area is dominated by surface ducts without foundation layer, mainly concentrated from June to August. The monsoon plays a critical role in the generation and evolution of atmospheric ducts in the northwest of the SCS, with the height of the evaporation duct increasing and the bottom height of the elevated duct decreasing after the onset of the summer monsoon. Electromagnetic propagation simulations demonstrate that higher frequency and lower elevation angles of radiation sources in the trapping layer of the evaporation duct make it easier to be trapped. As the evaporation duct height decreases, the amplitude of the "sinusoidal fluctuation" of the propagation loss also decreases. Frequency changes of the radiation source in the surface duct environment have minimal impact on electromagnetic propagation loss, but the elevation angle of the radiation source is a critical factor. The frequency of the "sinusoidal fluctuation" in propagation loss is higher in a hybrid duct environment compared to a uniform surface duct. Additionally, the propagation loss increases faster with distance at the height of the evaporation duct, resulting in greater electromagnetic propagation loss.

Physics of high-Tc superconducting cuprates is obscured by the effect of strong electronic correlations. One way to overcome the problem is to seek for an exact solution at least within the small cluster and expand it to the whole crystal. Such an approach is in the heart of the cluster perturbation theory (CPT). Here we develop CPT for the dynamic charge and spin susceptibilities (spin-CPT), within which the correlation effects are explicitly taken into account by the exact diagonalization. We apply spin-CPT to the effective two-band Hubbard model for the cuprates obtained from the three-band Emery model and calculate one- and two-particle correlation functions, namely, spectral function and spin and charge susceptibilities. Doping dependence of the spin susceptibility was studied within spin-CPT and CPT-RPA that is the CPT generalization of the random phase approximation (RPA). Both methods produce the low energy response at four incommensurate wave vectors in qualitative agreement to the results of the inelastic neutron scattering on overdoped cuprates.

This paper presents the experimental and numerical studies of the vertical stress distribution beneath the centerline of circular footings resting on the sand. Vertical stress values due to circular load in medium-dense sand were measured using soil stress transducers. Reduced-scale laboratory model tests were carried out in a square-shaped test box and vertical stresses were measured at the centerline of the circular footing at different depths of the sand. In order to determine the effect of footing width on vertical stress distribution, laboratory experiments were performed on circular footings of different diameters. In addition, a series of finite element analyses was carried out on the test model to validate the findings of the experimental study. In the numerical analysis, the sand was modeled as a non-linear elastoplastic material. The analyses were performed using the finite element method with two-dimensional axisymmetric and three-dimensional conditions. The results obtained from laboratory model tests and numerical analysis were compared with the vertical stress values obtained from the theoretical solution in the literature. Vertical stress distribution in horizontal planes at any depth from the footing base shows the same characteristic form in all methods. However, some differences were observed between experimental, numerical, and theoretical results and discussed.

The runtime environment is an important concern for self-adaptive systems (SASs). Although researchers have proposed many approaches for developing SASs that address the issue of uncertain runtime environments, the understanding of these environments varies depending on the objectives, perspectives, and assumptions of the research. Thus, the current understanding of the environment in SAS development is ambiguous and abstract. To make this understanding more concrete, we describe the landscape in this area through a systematic literature review (SLR). We examined 128 primary studies and 14 unique environment models. We investigated concepts of the environment depicted in the primary studies and the proposed environment models based on their ability to aid in understanding. This illustrates the characteristics of the SAS environment, the associated emerging environmental uncertainties, and what is expressed in the existing environment models. This paper makes explicit the implicit understanding about the environment made by the SAS research community and organizes and visualizes them.

This review describes the subjective experience of visual aura in migraine, outlines theoretical models of this phenomenon, and explores how these may be linked to neurochemical, electrophysiological and psychophysical differences in sensory processing that have been reported in migraine with aura. Reaction-diffusion models have been used to model the hallucinations thought to arise from cortical spreading depolarisation and depression in migraine aura. One aim of this review is to make the underlying principles of these models accessible to a general readership. Cortical spreading depolarisation and depression in these models depends on the balance of the diffusion rate between excitation and inhibition, and the occurrence of a large spike in activity to initiate spontaneous pattern formation. We review experimental evidence, including recordings of brain activity made during the aura and attack phase, self-reported triggers of migraine, and psychophysical studies of visual processing in migraine with aura, and how these might relate to mechanisms of excitability that make some people susceptible to aura. Increased cortical excitability, increased neural noise, and fluctuations in oscillatory activity across the migraine cycle are all factors likely to contribute to the occurrence of migraine aura. There remain many outstanding questions relating to the current limitations of both models and experimental evidence. Nevertheless, reaction-diffusion models, by providing an integrative theoretical framework, support the generation of testable experimental hypotheses to guide future research.

Empirically based modeling is an essential aspect of design for a wave energy converter. These models are used in structural, mechanical and control design processes, as well as for performance prediction. The design of experiments and methods used to produce models from collected data have a strong impact on the quality of the model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followed for wave tank testing. The general system identification processes are shown to have a number of advantages. The experimental data is then used to produce multiple models for the dynamics of the device. These models are validated and their performance is compared against one and other. While most models of wave energy converters use a formulation with wave elevation as an input, this study shows that a model using a hull pressure sensor to incorporate the wave excitation phenomenon has better accuracy.

The lack of reliable rainfall projection records remains a major challenge to Uganda. In the advent of extreme wetness or drought events, reliable rainfall estimates for local planning and adaptation are essential. The present study used two main datasets to conduct a historical analysis from 1981 to 2019, coupled with future projections under representative concentration pathway (RCP 8.5) for the period 2020-2050. Historical analysis revealed bimodal annual rainfall pattern for March-May (MAM) and September-November (SON) gradients representing heavier to lighter rainfall events respectively over the study area. Investigation of recent trends in rainfall patterns revealed an upward trend from 2010 onwards in annual and seasonal rainfall. Moreover, results for future projections show wet conditions are projected to occur over the study area between the months of April/May and October. Contrarily, March is likely to experience a reduction in wet conditions. Mann-Kendall test employed to make future projections of rainfall depicted decreasing patterns during MAM season whilst increasing tendencies with strong shift was highlighted for SON season over the study region. Meanwhile, annual projections indicate huge variations with linear trends showing a marginal increase as compared to historical trends. Findings would serve as baseline print to propel further studies that could delve into impact analysis of drought extreme events which pose significant threats to the agricultural sector which is heavily reliant on rainfall.

Infectious diseases continue to be a significant threat to human health and civilization, and finding effective methods to combat them is crucial. In this research, we investigate the impact of awareness campaigns and optimal control techniques on infectious diseases without proper vaccines. Specifically, we develop an SIRS type mathematical model that incorporates awareness campaigns through media and treatment for disease transmission dynamics and control. The model displays two equilibria, a disease-free equilibrium and an endemic equilibrium, and exhibits Hopf bifurcation when the bifurcation parameter exceeds its critical value, causing a switch in the stability of the system. We also propose an optimal control problem that minimizes the cost of control measures while achieving a desired level of disease control. By applying the minimum principle to the optimal control problem, we obtain analytical and numerical results that show how the infection rate of the disease affects the stability of the system and how awareness campaigns and treatment can maintain system stability. This study highlights the importance of awareness campaigns in controlling infectious diseases and demonstrates the effectiveness of optimal control theory in achieving disease control with minimal cost.

Using the 2011-2020 digital inclusive finance data released by Peking University and the index system constructed by the Blue Book of China Rural Revitalization and Development Index (2018), this paper analyzes the dynamic evolution of the digital inclusive finance and the rural revitaliza-tion from time and space dimensions with the help of kernel density estimation, Markov chain and Moran index. Using The Gini coefficient decomposition method to analyze the source of differ-ences in digital inclusive finance, and then using the spatial autoregressive (SAR) model and ge-ographically weighted regression (GWR) model to study the boosting effect of digital inclusive finance on rural revitalization from the perspective of spatial aggregation and spatial differentia-tion, respectively. The results show that: (1) The growth rate of China's digital inclusive finance slows down year by year, and the inter-provincial differences increase year by year and show gradient characteristics, indicating that there may be a trend of multipolar differentiation. The overall level of Rural Revitalization shows an increasing trend, and the gap between provinces is still apparent. (2) The evolution of digital inclusive finance and rural revitalization is a slow ad-justment process, and there has been no cross-level jump in the past ten years. In digital inclusive finance, the liquidity from the highest and lowest levels to the medium level is high. However, the liquidity in the states of rural revitalization development level is not high. (3) Rural revitalization has a positive spatial spillover effect. The level of rural revitalization in the western area is signif-icantly lower than in the eastern area. At the same time, there is no significant difference between the east and central areas. The depth of the use of digital inclusive finance has a significant positive impact on the revitalization and development of rural areas, indicating that the further promotion of digital inclusive finance business in rural areas can substantially boost the revitalization of rural areas. (4) The boosting effect of digital inclusive finance on rural revitalization shows prominent spatial differentiation characteristics. The depth of use and the degree of digitization generally show a positive impact. The central and eastern coastal cities have the highest impact, decreasing toward the southwest and northeast. The areas with the lowest usage depth impact are clustered in the northeast, and the areas with the lowest digitization impact are clustered in the southwest.

The Earth's water resources, totaling 1.386 billion cubic kilometers, predominantly consist of saltwater in oceans. Groundwater, plays a pivotal role, with 99% of usable freshwater supporting 1.5–3 billion people as drinking water source and 60–70% for irrigation. Climate change, with temperature increase and altered precipitation patterns, directly impacts groundwater systems, affecting recharge, discharge, and temperature. Hydrological models are crucial for assessing climate change effects on groundwater, aiding in management decisions. Advanced hydrological models, incorporating data assimilation and improved process representation, contribute to understanding complex systems. Recent studies, employ numerical models to assess climate change impacts on groundwater recharge that could help in management of Groundwater. Groundwater vulnerability assessments vary with the spatial and temporal considerations, as well as assumptions in modelling groundwater susceptibility. The review assesses the vulnerability of groundwater to climate change and stresses the importance of accurate assessments for sustainable water resource management. It highlights challenges in assumptions related to soil and aquifer properties, multiple stressors, adaptive capacity, topography, aquifer properties, and groundwater contamination processes and gradual sea level rise scenarios and realistic representations of the region of study. The advancements in hydrological modelling, including the integration of uncertainty quantification and remote sensing data, artificial intelligence, could assist in the efforts to improve models for assessing the impacts of climate change on hydrological.

We have recently proposed a pre-quantum, pre-space-time theory as a matrix-valued Lagrangian dynamics on an octonionic space-time. This pre-theory offers the prospect of unifying the internal symmetries of the standard model with gravity. It can also predict the values of free parameters of the standard model, because these parameters arising in the Lagrangian are related to the algebra of the octonions which define the underlying non-commutative space-time on which the dynamical degrees of freedom evolve. These free parameters are related to the algebra $J_3(\mathbb O)$ [exceptional Jordan algebra] which in turn is related to the three fermion generations. The exceptional Jordan algebra [also known as the Albert algebra] is the finite dimensional algebra of 3x3 Hermitean matrices with octonionic entries. Its automorphism group is the exceptional Lie group $F_4$. These matrices admit a cubic characteristic equation whose eigenvalues are real and depend on the invariant trace, determinant, and an inner product made from the Jordan matrix. Also, there is some evidence in the literature that the groups $F_4$ and $E_6$ could play a role in the unification of the standard model symmetries, including the Lorentz symmetry. The octonion algebra is known to correctly yield the electric charge values (0, 1/3, 2/3, 1) for standard model fermions, via the eigenvalues of a $U(1)$ number operator, identified with $U(1)_{em}$. In the present article, we use the same octonionic representation of the fermions to compute the eigenvalues of the characteristic equation of the Albert algebra, and compare the resulting eigenvalues with the known mass ratios for quarks and leptons. We find that the ratios of the eigenvalues correctly reproduce the [square root of the] known mass ratios for quarks and charged leptons. We also propose a diagrammatic representation of the standard model bosons, Higgs and three fermion generations, in terms of the octonions, exhibiting an $F_4$ and $E_6$ symmetry. In conjunction with the trace dynamics Lagrangian, the Jordan eigenvalues also provide a first principles theoretical derivation of the low energy value of the fine structure constant, yielding the value $1/137.04006$. The Karolyhazy correction to this value gives an exact match with the measured value of the constant, after assuming a specific value for the electro-weak symmetry breaking energy scale.

The objective of this paper is to present a mathematical formalism that states a bridge between Physics and Psychology, concretely between analytical dynamics and personality theory in order to open new insights in this theory. In this formalism energy plays a central role. First, the short-term personality dynamics can be measured by the General Factor of Personality (GFP) response to an arbitrary stimulus. This GFP dynamical response is modelled by a stimulus-response model: an integro-differential equation. The bridge between Physics and Psychology is provided when the stimulus-response model can be formulated as a linear second order differential equation and, subsequently, reformulated as a Newtonian equation. This bridge is strengthened when the Newtonian equation is derived from a minimum action principle, obtaining the current Lagrangian and Hamiltonian functions. However, the Hamiltonian is a non-conserved energy. Then, some changes provide a conserved Hamiltonian function: the Ermakov-Lewis energy. This energy is presented, as well as the GFP dynamical response that can be derived from it. An application case is presented: an experimental design in which 28 individuals consumed 26.51 g of alcohol. This experiment provides an ordinal scale for the Ermakov-Lewis energies that predicts the effect of a single dose of alcohol.

Here we mathematically model black holes and the early universe following dynamics similar to RLC electrical models, focusing on their similarities at the singularity. We use this mathematically modelling to hypothesize the evolution of an expanding universe as the result of a black hole collapse followed by its evaporation. Or model consists of several steps defined by: (1) the formation of a black hole following general relativity equations; (2) growth of the black hole modelled as a resistance-capacitance-like electrical circuit; (3) expansion of space-time following the disintegration of the black hole, modelled by RLC-like dynamics. In updating this article, version 2, we will expand by presenting three additional examples related to the theory of the generalization Boltzmann's constant in curved space-time and the theory of general and especial relativity. We will calculate the critical mass to produce a black hole in the LHC, the existence of a high temperature Bose-Einstein condensate and finally we will demonstrate the existence of a tangential force to the repulsion force in the disintegration of subatomic particles. In the update of this article, version 3, we will expand to develop two examples. First, we will present the relationship between M-theory, extra dimensions with the theory of the generalization of Boltzmann's constant in curved space-time. Secondly, we will propose the idea of ​​updating the Lambda-CDM model, considering the primordial gravitational waves to correctly describe the evolution of the universe.

The educational and play-related activities of children are proceeded mainly indoors in a kin-dergarten. The exposure of children to high concentrations of PM2.5 and CO2 indoors have leaded to various harmful effects and negatively impact educational outcomes in a kindergarten. Therefore, this study presents a numerical model of CO2 and PM2.5 concentration based on two classrooms in a kindergarten. Using this numerical model, we present different scenarios of oper-ating mechanical ventilation and air purifiers in kindergartens with the aim of optimizing for reducing the concentrations of CO2 and PM2.5. We found that the amount of ventilation required to maintain good air quality, per child, was approximately 20.4 m3/h. However, we also found that as the amount of ventilation increased, so did the concentration of indoor PM2.5; we found that this issue can be resolved through the use of a high-grade filter (i.e., a MERV 13 grade filter with a collection efficiency of 75%). This study provides a scientific basis for reducing PM2.5concentrations in kindergartens, while keeping CO2 levels low.

In this paper, a capacitor microphone system is presented to improve the conversion of mechanical energy to electrical energy using a nonlinear auto regressive moving average-L2 (NARMA-L2) and model predictive control (MPC) controllers for the analysis of the open loop and closed loop system. The open loop system response shows that the output voltage signal need to be improved. The comparison of the closed loop system with the proposed controllers have been analyzed and a promising result have been obtained using Matlab/Simulink.

The sudden pervasive of severe acute respiratory syndrome Covid-19 has been leading the universe into a prominent crisis. It has influenced each zone, for example, industrial area, horticultural zone, Public transportation, economic zone, and so on. So as to see how Covid-19 affected the globe, we conducted an investigation characterizing the effects of the pandemic over the world using Machine Learning (ML) method. Prediction is a typical data science exercise that helps the administration with function planning, objective setting, and anomaly detection. We propose an additive regression model with interpretable parameters that can be naturally balanced by experts with domain intuition about the time series. We focus on global data beginning from 22^nd January 2020, till 26^th April 2020 and performed dynamic map visualization of Covid-19 expansion globally by date wise and predicting the spread of virus on all countries and continents. The major advantages of this work include accurate analysis of country-wise as well as province/state-wise confirmed cases, recovered cases, deaths, prediction of pandemic viral attack and how far it is expanding globally.

Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.

This study investigated whether export performance is correlated with innovation. We explicitly considered five types of innovation: a) process innovation; b) product innovation; c) process and product innovation; d) product innovation new to the firm; and e) product innovation new to the market. We en-larged the findings of R&D-export and R&D-investment relations using a vast sample of cross-European firms. We considered innovation as endogenous and determined by R&D through a process characterized by the role of the regional technological environment. Our analysis enabled us to propose an integrated model incorporating R&D, innovation and export, in a framework of simultaneous equations which consider their mutual correlations. To the best of our knowledge, this work is the first in recent years to empirically compute the relation between R&D and tangible investment at the micro level. Econometric results showed that product and process innovation are positively linked to the export intensity of European manufacturing firms. The results also showed complementarity effects between the two kinds of innovation. The av-erage effect on export intensity from carrying out process innovation is larger than that found for product innovation, except in cases where the product is new to the market. When both types of innovation have been carried out, a larger effect results than that found for product or process innovation alone. Furthermore, the average marginal effect on export intensity from innovations of any kind among those studied here is highly positive and significant.

One important goal of research on heterogeneous catalysis for Fischer Tropsch synthesis (FTS) is to understand the connection linking catalyst morphology and its activity. Development of these effective catalysts highlights the potential for understanding the effectiveness of promoted supported catalysts and revealing their complex and dynamic effects in FTS. The metal-support interaction (MSI) is influenced by the physicochemical and textural features provided by the modified support and can directly influence the sizes of active metal crystallites, reactants and/or products mass transfer, dispersion, mechanical strength, stability and consequently the adsorption-desorption characteristics of the catalyst. Herein, this article explores the general strategies for modifying silica supports (achieved through doping, coating, or promoting the support or support precursor) and how these strategies can be manipulated to further alter catalyst performance and product selectivity by enhancing the amount of active metal species and improving the intrinsic turnover frequency (TOF). Furthermore, the review emphasises a new strategy (the inverse model of the silica support modification) aiming at overcoming the challenges posed by active metal coating or coverage, as well as pore blockages by promoters that occur during conventional preparation of promoted silica supported FT catalysts.

The employment rate of people who face severe mental health issues is extremely low, while the vast majority expresses their willingness to work. There are various obstacles that impede their work re-integration process. Apart from the illness’ symptoms and the employers’ stigma, these barriers are strongly associated with the effects of long-term unemployment and the lack of positive psychosocial resources, such as career adaptability and resilience. The present study aims at investigating career adaptability and resilience of mental health service users who receive career counseling services. The career counseling approach that was used combines elements from the IPS model and the career construction approach that has been developed to address the contemporary world of work challenges. We investigated how mental health service users view themselves in terms of career adaptability and resilience, and which factors contributed to their development or impeded them. We used a qualitative approach, which allows for an in-depth exploration of the participants’ views. Fifteen mental health users who receive career counseling services were interviewed. Results showed that mental health service users believe that they can overcome any difficulties and setbacks, when they have adequate support from their social network and when they receive career counseling services. They highlighted the importance of counseling services to maintain their work and cope with stressful events. Further implications of the results regarding vocational rehabilitation of mental health users as means for social inclusion are discussed.

Environmental decentralization (ED), or the allocation of environmental protection affairs and responsibilities among various administrative authorities, affects the effectiveness of environmental regulation in promoting green total factor productivity (GTFP). Based on panel data of 30 Chinese provinces from 2001 to 2015, this paper employs dynamic panel models to test the effects of environmental regulations (environmental protection investment, ENV; pollutant discharge fees, PDF) on GTFP, with or without being influenced by ED. Without the impact of ED, GTFP is significantly inhibited by ENV while significantly promoted by PDF. Considering the impact of ED, with the strengthening of ED, the negative effects of ENV on GTFP is significant; contrarily, the positive effects of PDF on GTFP is significant; improving provincial ED adds negative effects of ENV, while reduces the positive effects of PDF; increasing prefectural ED reduces negative effects of ENV; expanding county-level ED adds the positive effects of PDF. Therefore, to boost GTFP growth, prefectural environmental protection authorities should have more autonomy in ENV, while the county-level should have more autonomy in PDF.

Outbreaks of the desert locust Schistocerca gregaria affect some of the poorest parts of Africa, causing devastating catastrophes. Key to understanding and dealing with this problematic adaptation to environmental changes is comparing locusts that are gregarious (associated with outbreak states) and solitarious (associated with non-outbreak states) either in nature or after experimental treatments in laboratories. Categorising locusts and detecting changes in their phase status is key to such comparisons. Such comparisons are hitherto based on applying mathematical models that use behavioural parameters and that each laboratory has to build a new for each experiment. All such models used thus far for research on locusts are different from each other. That implies differences in the tools used for the different experiments and by the different laboratories and, thus, potential noise in the scientific results and interpretations too. Standardizing the way how we categorise locusts between laboratories and experiments is needed if we want to reduce noise and errors. It is even a must if we are to make the results and interpretations transferable and comparable between experiments and laboratories that work in such an important research area. Here, we use samples from independent S. gregaria population in order to further test the two models that were suggested earlier as standardizing tools for S. gregaria categorization. The outcomes of both models were largely replicated and reproducible. We report on how successful the two models were at categorizing solitarious, intermediate (transient) and gregarious nymph and adult samples. We highlight shortcomings and make more specific recommendations on the use of these models based on the differences they show as to their precision when categorizing the solitarious and gregarious S. gregaria nymph and adult samples.

Multimodal therapy including surgery, radiation treatment and temozolomide (TMZ) is performed on glioblastoma (GBM). However, the prognosis is still poor and there is an urgent need to develop effective treatments to improve survival. Molecular biological analysis was conducted to exam-ine the signal activation patterns at GBM specimens and remains an open problem. Advanced macrolides, such as azithromycin, reduce the phosphorylation of p70 ribosomal protein S6 kinase (p70S6K), a downstream mammalian target of rapamycin (mTOR) effector, and suppress the proliferation of T-cells. We focused on its unique profile and screened for the antitumor activity of approved macrolide antibiotics. Clindamycin (CLD) reduced the viability of GBM cells in vitro. We assessed the effects of the candidate macrolide on the mTOR pathway through Western blotting. CLD attenuated p70S6K phosphorylation in a dose dependent manner. These effects of on GBM cells were enhanced by co-treatment with TMZ. Furthermore, CLD inhibited the expression of O6-methylguanine-DNA methyltransferase (MGMT) protein in cultured cells. In the mouse xenograft model, CLD and TMZ co-administration significantly suppressed the tumor growth and markedly decreased the number of Ki-67 (clone MIB-1) positive cells within the tumor. These results suggest that CLD suppresses GBM cell growth by the inhibiting mTOR signaling. Moreover, CLD and TMZ showed promising synergistic antitumor activity.

We consider Pisot family substitution tilings in $\R^d$ whose dynamical spectrum is pure point. There are two cut-and-project schemes(CPS) which arise naturally: one from the Pisot family property and the other from the pure point spectrum respectively. The first CPS has an internal space $\R^m$ for some integer $m \in \N$ defined from the Pisot family property, and the second CPS has an internal space $H$ which is an abstract space defined from the property of the pure point spectrum. However it is not known how these two CPS's are related. Here we provide a sufficient condition to make a connection between the two CPS's. In the case of Pisot unimodular substitution tiling in $\R$, the two CPS's turn out to be same due to [5, Remark 18.5].

In cotton cultivars, an insect that causes irreversible damage is the Spodoptera frugiperda, known as the fall armyworm. Since the visual detection of plants is a burdensome task for human inspection, the spectral information related to plant damage, registered on a spectral scale, can be useful. These measurements, associated with machine learning techniques, produce useful information for a rapid and non-invasive inspection method development. To contribute to this gap fulfillment, this paper proposes a machine learning framework to model the spectral response of cotton plants under the attack of the fall armyworm. Additionally, a theoretical model is presented, built from the results of the machine learning analysis, to infer this damage with up-to-date orbital sensors. The data was composed of the reflectance measurements collected at a cotton field with control plants and plants submitted to Spodoptera frugiperda damage. Their spectral response was recorded with a hand-held spectroradiometer ranging from 350 to 2,500 nm, for eight consecutive days. Different machine learning models were evaluated and the overall best model was defined by accuracies comparisons on a testing-set. A ranking approach was adopted based on the model accuracy, returning the most contributive wavelengths for the classification. Sequentially, an unsupervised neural network (Self-Organizing Map - SOM) was implemented to reduce data-dimensionality and assist in the definition of important spectral regions. The regions were associated with the spectral bands of the two sensors (OLI and MSI) and a theoretical model using a band simulation process with the overall best machine learning model was proposed. The results indicated that the Random Forest (RF) algorithm is the most suitable to predict cotton-plants damaged by insects and that the last day of analysis (8th day) was better to separate it, with F-measure equals 0.912. The ranking approach combined with the SOM method indicated the spectral regions at the red to near-infrared (650 to 1,350 nm) and shortwave infrared (1,570 to 1,640 nm) as the most important regions to the analysis. The proposed theoretical model simulated with the OLI and MSI sensor-bands returned an F-Measure of 0.865 and 0.886, respectively. In conclusion, this framework can be used to map cotton-plants under insect-attack. The theoretical model presents high accuracy to infer the insect-damaged on cotton plants based on multispectral bands from other sensors, being a useful tool for future research that intends to evaluate it in other areas and at different field scales.

Hydro-climatic projections in West Africa are attributed with high uncertainties that are difficult to quantify. This study assesses the influence of the parameter sensitivities and uncertainties of three rainfall runoff models on simulated discharge in current and future times using meteorological data from 8 Global Climate Models. The IHACRES Catchment Moisture Deficit (IHACRES-CMD) model, the GR4J and the Sacramento model were chosen for this study. During model evaluation, 10,000 parameter sets have been generated for each model and used in a sensitivity and uncertainty analysis using the Generalized Likelihood Uncertainty Estimation (GLUE) method. Out of the three models, IHACRES-CMD recorded the highest Nash-Sutcliffe Efficiency (NSE) of 0.92 and 0.86 for the calibration (1997-2003) and the validation (2004-2010) period respectively. The Sacramento model was able to adequately predict low flow patterns on the catchment while the GR4J and IHACRES-CMD over and under estimate low flow respectively. The use of multiple hydrological models to reduce uncertainties caused by model approaches is recommended along with other methods of sustainable river basin managements.

In MBSE there is yet no converged terminology. The term ’system model’ is used in different contexts in literature. In this study we elaborated the definitions and usages of the term ’system model’, to find a common definition. 104 publications have been analyzed in depth for their usage and definition as well as their meta-data e.g., the publication year and publication background to find some common patterns. While the term is gaining more interest in recent years it is used in a broad range of contexts for both analytical and synthetic use cases. Based on this three categories of system models have been defined and integrated into a more precise definition.

Objective: To use mathematical models to predict the epidemiological impact of lifting the lockdown in London, UK, and alternative strategies to help inform policy in the UK. Methods: A mathematical model for the transmission of SARS-CoV2 in London. The model was parametrised using data on notified cases, deaths, contacts, and mobility to analyse the epidemic in the UK capital. We investigated the impact of multiple non pharmaceutical interventions (NPIs) and combinations of these measures on future incidence of COVID-19. Results: Immediate action at the early stages of an epidemic in the affected districts would have tackled spread. While an extended lockdown is highly effective, other measures such as shielding older populations, universal testing and facemasks can all potentially contribute to a reduction of infections and deaths. However, based on current evidence it seems unlikely they will be as effective as continued lockdown. In order to achieve elimination and lift lockdown within 5 months, the best strategy seems to be a combination of weekly universal testing, contact tracing and use of facemasks, with concurrent lockdown. This approach could potentially reduce deaths by 48% compared with continued lockdown alone. Conclusions: A combination of NPIs such as universal testing, contact tracing and mask use while under lockdown would be associated with least deaths and infections. This approach would require high uptake and sustained local effort but it is potentially feasible as may lead to elimination in a relatively short time scale.

Increasingly, there is a focus on utilising renewable energy resources in a bid to fulfil increasing energy requirements and mitigate the climate change impacts of fossil fuels. While most renewable resources are free, the technology used to usefully convert such resources is not and there is an increasing focus on improving the conversion economy and efficiency. To this end, advanced control technologies can have a significant impact and is already a relatively mature technology for wind turbines. Though hydroelectric plants can use simple regulation systems, significant benefits have been shown to accrue from the appropriate use of the same control methods designed for wind turbine plants. This represents the key point of the paper. In fact, to date, the application communities connected with wind and hydraulic energies have had little communication, resulting in little cross fertilisation of control ideas and experience, particularly from the more mature wind area to hydrodynamic systems. Therefore, this paper examines the models and the application of control technology across both domains, both from a comparative and contrasting point of view, with the aim of identifying commonalities in models and control objectives, as well as potential solutions. Key comparative reference points include the articulation of the exployed models, specification of control objectives, development of high--fidelity simulators, and development of solution concepts. Not least, in terms of realistic system requirements are the set of physical and constraints under which such renewable energy systems must operate, and the need to provide reliable and robust control solutions, which respect the often remote and relatively inaccessible location of many onshore and offshore deployments.

In recent years, both domestic and international research on quadruped robots has advanced towards high dynamics and agility, with a focus on high-speed locomotion as a representative motion in high-dynamic activities. Quadruped animals like cheetahs exhibit high-speed running capabilities, attributed to the indispensable role played by their flexible spines during the flight phase motion. This paper establishes dynamic models of quadruped robots with different degrees of spine flexibility, providing a parameterized description of the flight phase motion for both rigid trunks and flexible spine quadruped robots. By setting different initial values for the spine joint and calculating the flight phase results for both types of robots at various initial velocities, the study compares and analyzes the impact of a flexible spine on the flight phase motion of quadruped robots. Through comparative experiments, the research aims to validate the influence of a flexible spine during the flight phase motion, providing insights into how spine flexibility affects the flight phase motion of quadruped robots.

This paper investigates the polarization and heat generation characteristics of batteries under different ambient temperatures and discharge rates by means of using a coupled electric-thermal model. The study found that the largest percentage of polarization is ohmic polarization, followed by concentration polarization and electrochemical polarization. The values of the three types of polarization are generally small and stable under normal-temperature environments and low-discharge rates. However, they increase significantly in low-temperature environments and high-discharge rates and continue to rise during the discharge process. Additionally, ohmic heat generation and polarization generation also increase significantly under these conditions. Reversible entropy heat is less sensitive to ambient temperature but increases significantly with the increase of the discharge rate. Ohmic heat generation and polarization heat generation contribute to the total heat generation of the battery at any ambient temperature, while reversible entropy heat only contributes to the total heat generation of the battery at the end of discharge.

The main obstacle affecting the natural and herbal products industry is the infringement of trade patents. This resulted in losing the opportunity to compete with others in this business. Creating additional value to this business is, therefore, a way of leading to patenting for trade protection for entrepreneurs. This study was conducted to develop the structural equation modeling of guidelines for creating additional value for Thai herbs to support trade patent. The study comprises a mixed method with three parts. In the first part, data comes from interviews with selected 9 specialists. The second part was supported by interviews to create the questionnaire and distributed it to 500 entrepreneurs, the next step was the data analysis using structural equation modeling (SEM). The third part used data from the SEM model to discussion focused on developing the conceptual model by 7 experts. The analysis of the developed model passed the criteria for evaluating consistency and empirical data. All hypotheses proposed in this study were supported, the findings revealed that all factors had a direct effect at a statistically significant level of 0.001. Furthermore, this investigation allows knowing the Brand Identity factor were practically important for creating value of Thai herbs leading to patent protection.

The rise of multi-sided digital platforms has transformed the global economy, enabling individuals and companies to create, deliver, and capture value through innovative connections and network effects. While scholars have studied the core strategy concepts and proposed platform architectures, less attention has been paid to the long-term sustainability of these platforms. This paper addresses this gap by examining the relationship between sustainability and platform architecture. Specifically, it asks what mechanisms characterize digital platforms for sustainability, and whether there are design choices that optimize the business model for sustainability. Using a mixed-methods approach that draws on platform literature, business models, and sustainable innovation, the paper develops an integrative framework to identify elements and design choices in platform businesses which support sustainability outcomes. The framework is then used to classify the business models of the identified platforms, revealing specific design choices for platform architecture that can optimize their business models for sustainability. This contribution to existing literature provides an artifact that allocates key decisions on value creation, delivery, and capture that are key to platforms and includes an additional layer to optimize them for long-term sustainability. As digital platforms continue to drive the economy, this research is relevant not only to platform practitioners, but also to policy makers and society at large.

Several studies have shown that the traditional way of learning is not optimal when we aim to improve ICT users’ actual privacy behavior. In this research, we present a literature review of the theories that are followed in other fields to modify human behavior. Our findings show that the cognitive theory and the health belief model present optimistic results. Further, we examine various learning methods and we conclude that experiential learning is advantageous compared to other methods. In this paper aggregate the privacy behavior determinant factors found in the literature and we use the cognitive theory to we synthesize a theoretical framework. The proposed framework can be beneficial to educational policy makers and practitioners, such as public and private schools, and universities. Also, our framework provides a fertile ground for more research on experiential privacy learning and privacy behavior enhancement.

Skin cancer is an uncommon but serious malignancy. Dermoscopic images examination and biopsy are required for cancer detection. Deep learning (DL) is extremely effective in learning characteristics and predicting malignancies. However, DL requires a large number of images to train. Image augmentation and transferring learning were employed to overcome the lack of images issue. In this study we divided images into two categories: benign and malignant. To train and test our models, we used the public ISIC 2020 database. Melanoma is classified as malignant in the ISIC 2020 dataset. Along with categorization, the dataset was studied to demonstrate variation. The performance of three top pretrained models was then benchmarked in terms of training and validation accuracy. Three optimizers were employed to optimize the loss: RMSProp, SGD, and ADAM. Using ResNet, VGG16, and MobileNetV2, we obtained training accuracy of 98.73%, 99.12%, and 99.76%, respectively. Using these three pretrained models, we attained a validation accuracy of 98.39%.

Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this paper presents approaches that help to interpret and explain the results of deep learning algorithms by depicting the anatomical areas which influence the decision of the algorithm most. Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generate visual interpretations and explanations for clinicians to corroborate their clinical findings. In addition, this will aid in gaining confidence in such methods. The framework builds on existing interpretability and explainability techniques that are currently focusing on classification models, extending them to segmentation tasks. In addition, these methods have been adapted to 3D models for volumetric analysis. The proposed framework provides methods to quantitatively compare visual explanations using infidelity and sensitivity metrics. This framework can be used by data scientists to perform post-hoc interpretations and explanations of their models, develop more explainable tools and present the findings to clinicians to increase their faith in such models. The proposed framework was evaluated based on a use case scenario of vessel segmentation models trained on Time-of-fight (TOF) Magnetic Resonance Angiogram (MRA) images of the human brain. Quantitative and qualitative results of a comparative study of different models and interpretability methods are presented. Furthermore, this paper provides an extensive overview of several existing interpretability and explainability methods.

In this paper, the computational fluid dynamics (CFD) method has been used to simulate the gas flow and the catalytic hydrogenation process of the C-2 hydrogenation adiabatic reactor in a practical ethylene production unit of a chemical plant. The laminar finite-rate model was used for the chemical reaction model. The standard two-equation model was used for the turbulence model. The exponent-function kinetic model was used for the reaction dynamics model. The simulation results were in good agreement with the actual industrial data obtained from the C-2 hydrogenation reactor, and the distributions of reaction parameters such as temperature and component concentrations in the reactor were further analyzed. Based on the validated CFD models, the structure and operating conditions of the reactor, as well as hydrogen load distribution ratios, are simulated for comparing the comprehensive performance metrics of ethylene selectivity and acetylene conversion rate; as a result, we can obtain the optimal temperature of 312 K, the optimal hydrogen/acetylene ratio of 0.10 as well as the optimal hydrogen load distribution ratio of 4:4:2, and the diameter/height ratio exerts a marginal effect on the selectivity, conversion rate and harmonic mean of the reactor.

This study explores wind energy resources in different locations through the Gulf of Oman and also their future variability due climate change impacts. In this regard, EC-EARTH near-surface wind outputs obtained from CORDEX-MENA simulations are used for historical and future projection of the energy. The ERA5 wind data are employed to assess the suitability of the climate model. Moreover, the ERA5 wave data over the study area are applied to compute sea surface roughness as an important variable for converting near-surface wind speeds to those of wind speed at turbine hub height. Considering the power distribution, bathymetry and distance from the coats, some spots as tentative energy hotspots to provide a detailed assessment of directional and temporal variability and also to investigate climate change impact studies. RCP8.5 is a common climatic scenario is used to project and extract future variation of the energy in the selected sites. The results of this study demonstrate that the selected locations have a suitable potential for wind power turbine plans and constructions.

Glioma accounts for the highest proportion of primary intracranial malignant tumors. Microenvironment enormously influences the process of glioma progression. Our study is to establish an individualized prognostic nomogram for glioma patients with microenvironment signature. Glioma samples of Chinese Glioma Genome Atlas (CGGA) were grouped by the immune and stromal score based on ESTIMATE algorithm. Microenvironment-related genes (MRGs) in glioma were analyzed by R. To determine the best prognostic correlation genes, univariate and multivariate Cox regression analysis were used to analyze MRGs. Use the selected genes (CHI3L1, SOCS3, SLC47A2, COL3A1, SRPX2 and SERPINA3), we established the prognostic risk score model (microenvironment signature) and validated it. Gene Set Enrichment Analysis (GSEA) showed that the high-risk group was mainly enriched in immune and stromal function KEGG pathways. Finally, the nomogram was constructed and evaluated. The receiver operating characteristic (ROC) curve, Calibration plots and decision curve analysis (DCA) of training and validation set indicated the excellent predictive performance of nomogram. In conclusion, the 6-gene microenvironment signature can not only provide directions for the basic research of glioma, but also can be included as an independent prognostic index in nomogram for individual prediction to guide clinical treatment.

This paper describes a memory model with 2 levels of information. The lower-level stores source data, is Markov-like and unweighted. Then an upper-level ontology is created from a further 3 phases of aggregating source information, by transposing from an ensemble to a hierarchy at each level. The ontology is useful for search processes and the aggregating process transposes the information from horizontal set-based sequences to more vertical typed-based clusters. The base memory is essentially neutral, where any weighted constraints or preferences should be sent by the calling module. This therefore allows different weight sets to be imposed on the same linking structure. The success of the ontology typing is open to interpretation, but the author would suggest that when clustering text, the result was types based more on use and context, for example, 'linking' with 'structure' or 'provide' with 'web,' for a document describing distributed service-based networks. This allows the system to economise over symbol use, where links to related symbols will be clustered together. The author then conjectures that a third level would be more neural in nature and would include functions or operations to be performed on the data, along with related memory information.

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

The relation between CO2 emissions and atmospheric CO2 concentration has traditionally been treated with more or less complex models with several boxes. Our approach is motivated by the question of how much CO2 must necessarily be absorbed by sinks. Observations lead to the model assumption, that carbon sinks like oceans or biosphere are linearly dependent on CO2 concentration on a decadal scale. In particular this implies the falsifiable hypothesis that oceanic and biological CO2 buffers have not significantly changed in the past 50 years and are not saturated in the forseeable future. The simple model with 2 parameters explains very well the CO2 emission and historical CO2 concentration data. The model gives estimates of the natural emissions, the pre-industrial CO2 equilibrium concentration levels, the half-life time of an emission pulse, and the future CO2 concentration level from a given emission scenario. This is validated by an ex-post forecast of the last 20 years. The important result is that with the stated polices scenario of the IEA future CO2 concentrations will not rise above 475 ppm. The model is compared with the Carbon modul of the Bern model, mapping their complex IRFs to a single time variant parameter.

The water resources carrying capacity (WRCC) system comprises multiple complex and nonlinear interactions related to society, economy, water resources and the water environment. A full understanding of its internal mechanism is conducive to the further improvement of the regional WRCC. This article focuses on the temporal and spatial variability of WRCC to explore a method of dynamic successive assessment. Firstly, the Pressure-State-Response (PSR)framework is used to develop a systematic and causal indicator system. Then, The Variable Fuzzy Pattern Recognition (VFPR) model and an analytic hierarchy process-entropy (AHP-E) model are combined to successively and dynamically assess WRCC. The proposed method is applied to the dynamic successive assessment of WRCC in Hebei Province, and it is obtained that the poor water resources carrying capacity in Hebei Province is mainly due to the basic attribute of the decision of the water resources shortage, but Hebei Province actively adopts a variety of measures to save water and pressurize mining, which makes the province's water resources carrying capacity have a tendency to become better gradually. At the same time, the system dynamics model (SD) of water resources carrying capacity based on the analysis of the model structure was established. Moreover, three scenarios were designed, including existing continuation, high-efficiency water-saving, cross-regional water transfer and coordinated development, each scenario divided into high and low-speed economic development and population growth schemes, then total 6 schemes between 2023 and 2030 were simulated and predicted respectively. The results indicate that if the current development model is adopted, the water resource carrying capacity will continue to maintain low levels. It was concluded that the high-speed development of economy and population, the efficient water conservation and inter-basin transfer scenario (scenario 2 with high-speed) is the best choice for sustainable development of water resources and social economy in Hebei.

Turbulence is a fluid dynamic problem refractory to mathematical treatment. Examining a theoretical model of liquid water flowing in a cylinder at different Raleigh numbers, we propose a novel approach to elucidate the first stages of turbulent flows. The weakly bonded molecular assemblies of liquid water form a fluctuating branched polymer in which every micro-cluster displays different density. Against the common view of liquid water as an incompressible and continuous fluid, we suggest that the occurrence of transient local aggregates could be able to generate the vortices and eddies that are the hallmarks of turbulence. We quantify the local changes in velocity, diameter and density required to engender “obstacles” to the average flow. Then, we show how these microstructures, equipped with different Raleigh numbers and characterized by high percolation index, could generate boundary layers that contribute to micro-vortices production. We conclude that the genesis of turbulence cannot be assessed in terms of collective phenomena, rather is sustained, among many other factors, by the underrated microscopic inhomogeneities of fluids like liquid water.

In this age of mass media and, in particular, social media-driven perception of reality, coupling disease and prophylactic opinion dynamics models can provide better insights into disease evolution than using a disease model alone. We develop in this work two disease-opinion dynamics models based on the epidemiology of the new coronavirus disease (COVID-19) and the availability or not of imperfect vaccines. We assume that susceptibility to infection decreases with the level of prophylactic attitude (personal hygiene, social distancing), and changes in prophylactic attitudes of susceptible individuals occur in response to perceived disease prevalence and vaccination coverage and efficacy in the population. We derive and discuss the disease-free equilibriums and reproduction numbers in the introduced models. We further assess the impacts of the distribution of opinions at disease introduction, the ability to detect presymptomatic, asymptomatic and symptomatic positive COVID-19 cases, the behavioural responses to the outbreak and the introduction of vaccination, and the effects of distortions of disease prevalence by public policy and mass media on disease dynamics. The insights highlighted from the proposed models are expected to make informative contributions to public policy in a context of opinion fluxes in response to perceived disease prevalence.

The present work highlights some of the dynamic couplings observed in a series of tests performed in a wave basin with a scaled-model of a FOWT with semi-submersible substructure. The model was moored by means of a conventional chain catenary system and an actively controlled fan was used for emulating the thrust loads during the tests. A set of wave tests comprising regular and irregular waves was done for different wave angles and wind velocities. The experimental records illustrate the main coupling effects involved and how they affect the FOWT motions in waves, especially when the floater presents a non-negligible tilt angle.. In addition, an analysis of the frequency-domain dynamic model was made in order to evaluate its ability to capture these effects properly. The influence of different modes of fan response, floater trim angles (changeable with ballast compensation) and variations of the mooring stiffness with the offsets were investigated in the analysis. Results attest that significant changes in the FOWT responses may indeed arise from coupling effects, thus indicating that caution must be taken when simplifying the hydrodynamic frequency-domain models often used as a basis for the simulation of FOWTs in waves and in in optimization procedures for the design of the floater and mooring lines.

Multimission satellite altimetry (e.g. ERS, Envisat, TOPEX/Poseidon, Jason) data have enabled a synoptic view of ocean variations in the past decades, including sea-level rise and mesoscale circulations. Since 2016, the Sentinel-3 mission has provided better spatial and temporal sampling compared with its predecessors. The Sentinel-3 Ku/C Radar Altimeter (SRAL) is one of the synthetic aperture radar altimeters (SAR Altimeter) which is more precise in coastal and lake observations. In this study, we validate Sentinel-3 Level-2 products in Baltic Sea coastal areas and two lakes in Estonia. Moreover, the Copernicus Marine Environment Monitoring Service (CMEMS) Level-3 sea-level anomaly data and the Nucleus for European Modelling of the Ocean (NEMO) reanalysis model outcomes are compared with measurements from a tide gauge network. A dense in situ water level network deployed along the coast for geodetic observation was utilised to provide ground truths for validating altimetry results. Three validation methods were used for Level-2 data: (i) collocated Sentinel-3 and GNSS ship measurements; (ii) a national geoid model (EST-GEOID2017) with sea-level anomaly correction; (iii) collocated Sentinel-3 and buoy measurements. The validations were carried out in seven Sentinel-3A/B overpasses in 2019. Our results show that the uncertainty of the Sentinel-3 Level-2 altimetry product is below decimetre level on the Estonian coast and the targeted lakes. Results from CMEMS Level-3 showed a correlation of 0.8 (RMSE 0.19 m) and 0.91 (RMSE 0.27 m) when compared against tide gauge measurements and NEMO model, respectively.

The ageing global population and changing labor force participation are having a profound impact on state pension programs worldwide, including Malaysia. However, the effects of an ageing workforce and declining fertility rates are having a significant impact on future trends in labor force participation and the duration of retirement. This paper presents an innovative examination of labor force participation rate (LFPR) forecasting within the context of an aging workforce, utilizing the Lee-Carter and Cairns-Blake Dowd stochastic models, traditionally applied to mortality data. By adapting these models to LFPR data, the study offers a novel perspective on predicting workforce participation trends and understanding the dynamics of retirement planning from year 2001 to 2047. Additionally, by incorporating the mortality forecasts from the United Nations, the research navigates through the complexities of applying actuarial methods to labor economics, revealing differentiated pathways for male and female participation in the labor market and the anticipated length of retirement. This study offers a unique comparison between the Lee-Carter and the Cairns-Blake-Dowd (CBD) LFPR models. It focuses on fitting and forecasting the LFPR of senior employees aged 50 to 64. The results highlighted discrepancies among models that demonstrated evidence of model risk for extended retirement duration.

In order to explore the effect of fertilization patterns on the growth of rapeseed seedlings under waterlogging stress, three fertilization patterns (conventional fertilization, supplemental organic fertilization, and supplemental microbial fertilization) were set up using the variety Xiangyou 708 as the material, and waterlogging treatment was carried out during the seedling stage of rapeseed. The effects of fertilization patterns on the growth of rapeseed seedlings and rhizosphere microorganisms under waterlogging stress were investigated. The results showed that all three fertilization patterns exhibited that waterlogging stress inhibited the growth of rapeseed seedlings, inhibited root activity, and changed the structure of rhizosphere bacterial colonies. However, supplemental organic fertilization and microbial fertilization could better promote the growth of rapeseed seedlings, reduce the impact of waterlogging stress on the growth of rapeseed seedlings, and accelerate the recovery of rapeseed seedlings after waterlogging stress. In addition, supplemental microbial fertilization could also change the structure of rhizosphere bacterial colonies. Under normal water supply, supplemental organic fertilization enriched P_Bacteroidota, P_Actinobacteriota, P_Chloroflexi, and G_Flavisolibacter in the rhizosphere soil of rapeseed, while supplemental microbial fertilization enriched P_Bacteroidota and G_Flavisolibacter in the rhizosphere soil of rapeseed. After 7 days of waterlogging treatment, supplemental organic fertilization enriched P_Verrucomicrobiota in the rhizosphere soil of rapeseed, while supplemental microbial fertilization enriched P_Actinobacteriota, G_SC-I-84, and G_Ellin6067 in the rhizosphere soil of rapeseed. The enrichment of these bacteria may be related to the growth promotion and waterlogging tolerance of rapeseed. This study provides evidence that microbial fertilizer and organic fertilizer can promote the growth of rapeseed and enhance its waterlogging tolerance, as well as evidence that some rhizosphere microorganisms have a potential role in promoting the growth and waterlogging tolerance of rapeseed.

The split of the brain into two hemispheres is pivotal to human existence, enabling incompatible versions of the world, with quite different priorities and values, to exist concomitantly. Neuroimaging techniques are usually the adopted methods to assess hemispherical bias. However, can differences in thinking orientation, behavioural style, and personality emerging from laterality be observed without the use of neuroimaging techniques? This paper investigates whether a personality test based on the Five-factor model (FFM) is also able to identify hemispherical bias. It does so by testing five hypotheses on personality differences related to handedness. The aim is to contribute to the instantiation of the FFM in neuroscience by testing its validity against the neuroscientific literature on brain laterality. The findings pointed to no statistically significant differences due to handedness in three out of five personality traits (namely, Neuroticism, Conscientiousness and Extraversion). As to the other two traits – Openness and Agreeableness – the hypotheses were only partially confirmed by the data, as the relevant difference found was not between left-handed and right-handed groups, but between ambidextrous and the others.

The world population is aging, which along with recent shifts in the labor force participation (LFP), is having a significant longevity influence on state pension programs across the board, including Canada, Finland, Japan, and Germany. Except Japan, these countries have set their statutory re-tirement age at 65, but the impact of aging workforce and declined fertility rates create wonders on the estimate future trends in the LFP, as well as the length of retirement. In this study, we fit the LFP rates of these countries, representing continents from Asia, Europe and North America among the OECD countries, using the Lee-Carter and Cairns-Blake-Dowd (CBD) stochastic models. The es-timates are then used for the projection of future LFP rates (1989-2066), and by combining the mortality forecasts from the United Nations, we project the expected length of retirement (1989-2066). This study provided a novel comparison between the Lee-Carter and the CBD LFP models by fitting and forecasting the LFP rates of senior employees between 50- to 74-year-olds. The results revealed disparities between models that provided proof of the presence of model risk for longer retirement durations. The study findings emphasized the importance for decision-makers in pension industry to have awareness of potential risks and limits associated with the LFP models. The Lee-Carter model outperformed the CBD model even though the CBD model is known for its ac-curate prediction in higher ages. Population aging should be considered in any analysis of the long-term viability of pensions, together with the participation rate trends for a sustainable labor market future.

Identification of drug targets and biochemical investigations on mechanisms of action is a are major issues in modern drug development. The present article reviews the classical “one drug” – “one target” paradigm. Novel methods for target deconvolution and for investigation of resistant strains based on protein mass spectrometry have shown that multiple gene products and adapta-tion mechanisms are involved in responses of pathogens to xenobiotics. This review is focused on protozoan pathogens. The conclusions can, however, be extended to chemotherapies against other pathogens or cancer. For this review we have searched Pubmed (pubmed.ncbi.nlm.nih.gov) and Web of Science (webofscience.com) using the key words listed below (status May 2024)

This paper investigates the relationship between public debt and economic growth in the context of a panel kink regression with latent group structures. The proposed model allows us to explore the heterogeneous threshold effects of public debt on economic growth based on unknown group patterns. We propose a least squares estimator and demonstrate the consistency of estimating group structures. The finite sample performance of the proposed estimator is evaluated by simulations. Our findings reveal that the nonlinear relationship between public debt and economic growth is characterized by a heterogeneous threshold level, which varies among different groups, and highlight that the mixed results found in previous studies may stem from the assumption of a homogeneous threshold effect.

This paper studies the pilot control strategy and workload of tilt-rotor aircraft dynamic conversion procedure between helicopter mode and fixed-wing mode. A nonlinear flight dynamics model of tilt-rotor aircraft with full flight modes is established. On this basis, a nonlinear optimal control model of dynamic conversion is constructed, considering factors such as conversion corridor limitations, pilot control, flight attitude, engine rated power, and wing stall effects. To assess pilot workload, an analytical method based on wavelet transform is proposed, which examines the mapping relationship between pilot control input's amplitude, constituent frequencies, and control tasks. By integrating the nonlinear optimal control model and the pilot workload evaluation method, the analysis of pilot control strategy and workload during the conversion procedure is conducted, leading to the identification of strategies to reduce pilot workload. The results indicate that incorporating the item of pilot workload in the performance index results in a notable reduction in the magnitude of collective stick inputs and longitudinal stick inputs. Moreover, it facilitates smoother adjustments in altitude and pitch attitude. Additionally, the conversion of the engine nacelle can be achieved at a lower and constant angular velocity. In summary, the conversion and reconversion procedures are estimated to have a low workload (Level 1 ~ Level 2), with relatively simple and easy manipulation for the pilot.

The first attempt to control and mitigate an epidemic outbreak caused by a previously unknown virus occurs primarily via non-pharmaceutical interventions (NPIs). In case of the SARS-CoV-2 virus, which since the early days of 2020 caused the COVID-19 pandemic, NPIs aimed at reducing transmission enabling contacts between individuals. The effectiveness of contact reduction measures directly correlates with the number of individuals adhering to such measures. Here, we illustrate by means of a very simple compartmental model how partial noncompliance with NPIs can prevent these from stopping the spread of an epidemic.

Major marine construction projects, resulting in the release of sediments, are subject to environmental assessment and other regulatory approval processes. An important tool used for this is the development of specialized numerical methods for these marine activities. An integrated set of numerical methods addresses four distinct topics: the near-field release and mixing of suspended sediments into the water column (i.e. the initial dilution zone); the transport of the suspended sediments under the influence of complex ocean currents in the far-field; the settling of the transported suspended sediments onto the seabed; and the potential for resuspension of the deposited sediments due to sporadic occurrences of unusually large near-bottom currents. A review of projects subjected to environmental assessment in the coastal waters of British Columbia from the year 2006 to 2017, is presented to illustrate the numerical models being used and their ongoing development. Improvements include higher resolution model grids to better represent the near-field, the depiction of particle size dependent vertical settling rates and the computation of resuspension of initially deposited sediments, especially in relation to temporary subsea piles of sediments arising from trenching for marine pipelines. The ongoing challenges for this numerical modeling application area are also identified.

Study aimed to evaluate the impact of integrating family planning with maternal, newborn, and child health (FP-MNCH) on uptake of modern contraceptive methods and related health outcomes in two districts of the province of Sindh, Pakistan. Impact of intervention was evaluated using a quasi-experimental control before-after study design. Intervention included capacity building of healthcare providers and outreach workers, ensuring sustained supplies of family planning commodities, data-driven decision-making, and community engagement activities. Data was collected through household surveys at baseline (December2020) and endline (December 2022). The sample size was estimated as 880 married women of reproductive age (MWRA) in each district. The Difference and Difference(DiD) analytical method was used to estimate the impact of intervention adjusted for potential confounding factors. There was statistically significant increase of 11.3% in the current use of MCM in the intervention group compared to the control group (p value <0.001), with increases observed in the uptake of injection , implants, and condoms. Additionally, there was an increase in the proportion of women who had ANC visits (DiD 10.5% p value 0.003), FP counselling during ANC (DiD 15.6% p value < 0.001), LHW visits during pregnancy (DiD 15.1% p value 0.021), PNC check-ups for mother (DiD 25.2% p value <0.001), LHW visits after delivery (DiD 20.4% p value <0.001), and LHW advised for family planning at PNC visit n (DiD 14.5% p value 0.030). This study provides strong evidence for the scaling-up of integrated interventions through existing health care platforms utilizing human resources deployed by the government. This FP-MNCH model has the potential to be adopted in other provinces and at federally administered areas for both health and population planning. This model should be considered for replication in other districts of Sindh to accelerate current level of integration already existing.

Remote Sensing and Geographic Information System are recognized as crucial instruments for conducting geographical and geospatial research. RS and GIS technology was employed to assess the morphological Characteristics of the Kasura River Watershed situated within the Parbhani and Jalna Districts of Maharashtra, India. The entire drainage area of the Kasura River basin measures 490.13 square kilometers (km²). Various morphometric characteristic were calculated and examined, encompassing linear features such as stream number, stream order, mean stream length, stream length, and stream length ratio, areal attributes including elongation ratio, circularity ratio, form factor, constant of channel maintenance, drainage density and drainage texture and relief characteristics such as relief, relief ratio, maximum relief, ruggedness number, relative relief, and length of overland flow. These findings underscore the significance of employing morphometric analysis in watershed assessment, offering a quantitative method for characterizing the surface attributes of a drainage pattern and yielding valuable insights into the topography and runoff dynamics of the region.

This study proposes an image recognition method to assist swine farm managers in collecting health data related to pig feeding and excretion. Analyzing the correlation between sow health data and indoor air quality in the pigsties revealed significant influences of air quality indicators on sow health. Increased levels of TVOC, CO2, and temperature were found to negatively affect feeding health, while increased temperature, humidity, and PM10 were found to negatively impact excretion health. These findings provide a basis for evaluating variables when constructing future sow disease prediction models. Analyzing the correlation between pig health status and air quality can help swine farm managers improve sow production environments, reduce disease risks, enhance production efficiency, and provide valuable insights for future research on disease prediction models.

As Cybercrimes continue to rise, researchers and industry players have continued to work hard to come up with solutions that could reduce and possibly eradicate these criminalities. This research has been able to discover the major factor that usually causes people to fall prey to these cyber criminals. This was why a school of thought came up with the Zero Trust Access model to strengthen the network framework. While previous network assets relied on the "trust but verify" philosophy, this technology's primary tenet is "never trust, always verify," which applies to every user and device connected to the network. This study interrogated the existing network models of the Integrative Trust Model (ITM) and Zero Trust Access (ZTA) in addition to the trust model (TA) to understand their concepts, implementation needs, strengths, and shortcomings. This made it possible for the study to establish the position of the ZTA model in repositioning the network security architectures vis-a-vis protecting the users

This paper aims to implement histogram pyramids with marching cubes method for 3D medical volumetric rendering. The histogram pyramids are used for feature extraction by segmenting the image into the hierarchical order like the pyramid shape. The histogram pyramids can decrease the number of sparse matrixes that will occur during voxel manipulation. The important feature of the histogram pyramids is the direction of segments in the image. This feature will be then used for connecting pixels (2D) to form up voxel (3D) during marching cubes implementation. The proposed method is fast and easy to implement and it also produces a smooth result (compared to the traditional marching cubes technique). The experimental results show that time consuming for generating 3D model can be reduced by 15.59% in average. The paper also shows the comparison between the surface rendering using the traditional marching cubes and the marching cubes with histogram pyramids. Therefore, for the volumetric rendering such as 3D medical models and terrains where a large number of lookups in 3D grids are performed, this method is a particularly good choice for generating the smooth surface of 3D object.

This paper based on systems - theoretic approach to the definition of a country's GDP as not directly observable characteristic of system state. Leontief dynamic model is generalized to take into account the stimulating effect of consumption on GDP growth. In consumption, apart from final consumption, terms are considered: balance of foreign trade, fictitious investments and hidden costs. The Kalman filter uses Rosstat's gross output (for system output) and final consumption (for system control) data from 1995 to 2015. It is concluded that if in the years 2014, 2015 it was possible to increase consumption by 5% by, say, price cuts or some increase in money supply, then GDP would be greater by about 2.5%. GDP real values in recent years are most likely greater than official values. Fictitious investments and hidden costs are found in the amount of up to third the value of final consumption. The accuracy of one-year forecasts of true GDP by the methodology of this article is approximately 1.5%.

Synthetic materials such as Carbon, Kevlar, and Glass fibers are extensively utilized in various industries due to their superior characteristics including high stiffness, great strength, low den-sity, and high wear resistance, these advantages contributed to making them widespread in ap-plications like; aerospace, automobile, civil and electronics. This research is carried out to enhance the creep compliance of epoxy composite material by considering two types of fibers as reinforcement. A composite material is produced by combining two fibers, Kevlar fiber mixed with sika epoxy resin, along with Carbon fiber also mixed with sika epoxy resin using a Hand-Layup process. Experiments and numerical modeling are examined to offer a comprehensive understanding creep behavior of epoxy composite material in different weight ratios. Burger and Prony models are used to represent stress relaxation and creep strain of Visco-elastic material. According to the results, sika epoxy resin reinforced with Kevlar and Carbon fibers exhibits enhancement in the mechanical properties compared with pure epoxy. The use of Kev-lar/Sika epoxy composite results in higher creep compliance and modulus of elasticity compared with Carbon/Sika epoxy composite.

This article introduces an analytical framework that interprets individual measures of entropy-based mobility derived from mobile phone data. We explore and analyze two widely recognized entropy metrics: random entropy and uncorrelated Shannon entropy. These metrics are estimated through collective variables of human mobility, including movement trends and population density. By employing a collisional model, we establish statistical relationships between entropy measures and mobility variables. Furthermore, our research addresses three primary objectives: firstly, validating the model; secondly, exploring correlations between aggregated mobility and entropy measures in comparison to five economic indicators; and finally, demonstrating the utility of entropy measures. Specifically, we provide an effective population density estimate that offers a more realistic understanding of social interactions. This estimation takes into account both movement regularities and intensity, utilizing real-time data analysis conducted during the peak period of the Covid-19 pandemic.

As a radiant light source within the dynamic range of most spacecraft payloads, the moon pro-vides an excellent reference for on-orbit radiometric calibration. This research hinges on the pre-cise simulation of lunar spectral irradiances and the Earth-based Moon observation geometry. The paper leverages the Hapke model to simulate the temporal changes in lunar spectral irradi-ances, utilizing datasets obtained from Lunar Reconnaissance Orbiter Camera (LROC). The re-search also details the transformation process from the lunar geographic coordinate system to the instantaneous projection coordinate system, thereby delineating the necessary observational geometry. The insights offered by this study have the potential to enhance future in-orbit space-craft calibration procedures, thereby boosting the fidelity of data gathered from satellite obser-vations.

Background. The treatment of coronary stenosis relies on invasive high risk surgical assessment to generate the fractional flow reserve diagnostics index, a ratio of distal to proximal pressures in respect of coronary atherosclerotic plaque causing stenosis. Non-invasive methods are therefore a need of the times. This study proposes an extensible mathematical description of the coronary vasculature that permits rapid estimation of the coronary fractional flow reserve. Methods. By adapting an existing closed loop model of human coronary blood flow, the effects of large vessel stenosis and microvascular disease on fractional flow reserve were quantified. Several simula-tions generated flow and pressure information which was used to compute fractional flow re-serve under a spectrum of conditions including focal stenosis, diffuse stenosis, and microvascular disease. Sensitivity analysis stratified the influence of model parameters on the index. The model was simulated as coupled non-linear ordinary differential equations and numerically solved us-ing an implicit higher order method. Results. Large vessel stenosis affected fractional flow re-serve. The model predicts that the presence, rather than severity, of microvascular disease affect coronary flow deleteriously. Sensitivity analysis revealed that heart rate may not affect the index. Conclusions. The model provides a computationally inexpensive instrument for future in silico coronary blood flow investigations as well as clinical-imaging decision making. A combination of focal and diffuse stenosis appears to be essential in reducing the index. In addition to pressure measurements in the large epicardial vessels, diagnosis of microvascular disease is essential. The independence of the index with respect to heart rate suggests that computationally inexpensive steady state simulations may provide sufficient information to reliably compute the index.

Soils provide important regulating ecosystem services and have crucial implications for human well-being and environmental conservation. However, soil degradation and particularly soil erosion jeopardize the maintenance and existence of these services. This study explores the spatio-temporal relationships of soil erosion to understand the distribution patterns of sediment retention services in mainland Portugal. Based on Corine Land Cover maps from 1990 to 2018, the InVEST Sediment Delivery Ratio (SDR) model was used to evaluate the influence of sediment dynamics for soil and water conservation. Spatial differences in the sediment retention levels were observed within the NUTS III boundaries, showing which areas are more vulnerable to soil erosion processes. Results indicated that the Region of Leiria, Douro and the coastal regions have decreased importantly sediment retention capacity over the years. However, in most of the territory (77.52%) changes in sediment retention were little or not important (i.e., less than 5%). The statistical validation of the model proved the consistency of the results, highlighting the usefulness of this methodology to analyse the state of soil erosion in the country. These findings can be relevant to support strategies for more efficient land use planning regarding soil erosion mitigation practices.

Understanding the strength of the associations between perceived risk and individuals’ behavioral intentions to protect their health is important for determining appropriate risk communication strategies in communities impacted by lead contamination. We conducted a survey within communities of northern Idaho, USA (n = 306) near a Superfund megasite with legacy mining contamination. We empirically test a theoretical model based on the Health Belief Model. Survey respondents had higher intentions to practice health protective behaviors when they perceived the risk of lead contamination as severe, recognized the benefits of health protective behaviors, and considered the risks of lead contamination. Women reported higher behavioral intentions than men, but age and mining affiliation did not have an association. Survey comments indicated that perceptions about the long-term environmental remediation in the region influenced risk perceptions. Understanding risk perceptions, behavioral intentions, and related factors can aid public health agencies in tailoring risk communication for increasing protective behaviors in mining-impacted communities internationally.

In the present work, we evaluate the prediction capability of six Pedotransfer functions (PTFs), reported in the literature, for the saturated hydraulic conductivity estimations (Ks). We used a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. Additionally, six new PTFs were construct for Ks from clay percentage, bulk density and saturation water content data. The results show, for the evaluated models, that one model present an overestimation for Ks>0.5 cm h-1 values, three models have a underestimation for Ks>1.0 cm h-1 and two models have a good correlation (R2>0.98) but are necessary more than three parameters. Nevertheless, the last two models requires from three to four parameters in order to get the optimization. By other hand, the models proposed in this work have a similar correlation with a less number of parameters: the fit is seen to be much better than using the existing ones, achieving a correlation of R2 = 0.9822 with only one variable and a R2 = 0.9901 when we use two.

The change in both streamflow and baseflow in urban catchments has received significant attention in the latest decades as a result of their drastic variability. In this research, effects of climate variation and dynamics of land use are measured separately and in combination on streamflow and baseflow in the Little Eagle Creek (LEC) watershed (Indianapolis, Indiana). These effects are examined using land use maps, statistical tests, and hydrological modeling. Transition matrix analysis was used to investigate the change in land use between 1992 and 2011. Temporal trends and changes in meteorological data were evaluated from 1980-2017 using the Mann-Kendall test. Changes in streamflow and baseflow were assessed using the Soil and Water Assessment Tool (SWAT) hydrological model using multiple scenarios that varied in land use and climate change. Evaluation of the model outputs showed streamflow and baseflow in LEC are well represented using SWAT; however, comparing the calibration and validation period showed SWAT performs better for the calibration. During 1992-2011, roughly 30% of the watershed experienced change, typically cultivated agricultural areas became urbanized. Baseflow is significantly affected by the observed urbanization; however, the combination of land and climate variability has a larger effect on the baseflow in LEC. Generally, the variability in the baseflow and streamflow appears to be heavily driven by the response to climate change in comparison to variability due to altered land use. The results reported herein expand the current understanding of variation in hydrological components, and provides useful information for management planning regarding water resources, as well as water and soil conservation in urban watersheds in Indiana and beyond.

Introduction: Overuse of antibiotics has been increasing in recent years and is currently the most prescribed drug in the paediatric age. It is estimated that 72% of patients in the neonatal intensive care unit (NICU) are treated with antibiotics. One of the effects observed with their use is the alteration of the microbiota, which is associated with metabolic disorders including obesity. In this study, the aim was to determine in newborn rats, whether most commonly prescribed antibiotics in Mexican NICUs have a differential effect on weight gain, development of adiposity, and modification of carbohydrate and lipid metabolism. Methods: This is an experimental study in newborn rats, comparing Ampicillin/Meropenem (Access/Watch groups) 100 mg/g every 12 h /10 mg/g every 12 h (each of these two antibiotics was adjusted to the required dosages based on weight); Cefotaxime 200 mg/g every 24 h; (Watch group) and Amikacin 15 mg/g every 24 h (Access group) versus Saline Solution as Control. Each antibiotic was intraperitoneal administered daily for 5 days in males (to avoid gender differences) to 14 newborn rats in each group. A comparison of anthropometric and biochemical parameters registered on day 28 was made using ANOVA analysis. Differences were considered if p < 0.05.Results: Amikacin was the antibiotic with the greatest effect on anthropometric measurements and LDL-cholesterol; as well as Cefotaxime on Glucose and triglycerides, while Meropenem plus Ampicillin showed the weakest effect on measured parameters. Conclusion: The administration of antibiotics in the neonatal stage has repercussions on the body composition of adult rats, as well as on lipids and carbohydrates serum levels. Future studies might evaluate the toxicity of antibiotics on the immature neonatal organs.

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.

Air pollution poses a significant risk to public health and ecosystems. The use of sophisticated models to simulate the transformation and dispersion of pollutants in the atmosphere is a recognized method to assess air pollution levels and its sources and inform policy decisions for effective mitigation strategies in urban and industrial environments. The reliability of models depends, in part, on the sensitivity to the variance of input parameters, which can introduce uncertainty into the model's predictions. This uncertainty is especially challenging when the input parameters exhibit inherent variability. In this study, we analyse the uncertainty of an integrated emission/dispersion model by evaluating the relationship between input parameters and model outcomes. We uncover new aspects of this relationship while clarifying some previously known dependencies. Wind speed was found to have the most significant effect on calculated concentrations, with small differences leading to substantial concentration variations. The time of day, which reflects prevailing vertical turbulence conditions, was also found to have a notable impact on concentrations, whereas ambient temperature can also significantly influence model’s results. Based on these findings, we offer specific recommendations for regulators and scientists involved in environmental impact assessment to improve the accuracy and reliability of air pollution modelling.

The increase in the number of firms manipulating financial reports has misled shareholders' investment decisions and resulted in an indelible blot on foreign investors’ trust. Due to earnings management (EM) practice, managers' inefficiency, and lack of transparency in Iraq companies. This study tested the influence of the corporate governance mechanisms (CG), (board independence, audit committee, meeting frequency) on EM based on agency theory, as well, to link between EM and firm's performance (FP) in Iraqi listed companies and the impact of moderating role of corporate social responsibility (CSR) based on the Stakeholder Theory. The study's sample consists of 65 companies for the 2013-2018 financial years. Data were collected mainly from the annual reports (secondary data) of the Iraqi listed firms. This study uses the M-score model to detect EM practices as practical techniques in detecting earnings manipulation practices. The panel static model estimators. Hence, this paper adds to the CG literature from the perspective of stakeholder theory using Iraq's unique industrial environment. Based on the research results, policy-makers might use the study‘s findings to recognize the essential roles of several CG mechanisms in alleviating the opportunistic practices in Iraq. Further, companies should also be encouraged to enhance the CSR disclosure quality.

Helicobacter pylori, a gram-negative bacteria usually found in stomach and first part of small intestine, is known to cause infections and ulcers and may have association with cancer. Normally, the treatment options include combinations of antibiotics with proton pump inhibitors and antiinflammatory drugs. However, all of them have high systemic exposure, and hence, unfavourable side effects, whereas their exposure in stomach mucus, the predominant location of the bacteria, is very limited. We have attempted to develop a predominantly gut-restricted formulations of antibiotic + adjuvant combinations as a basis for potentially more safe and efficacious treatment of gastrointestinal tract infections. We have considered E. coli (as a model of H. pylori) and Lactobacilli to study the antibacterial activity of nanogel with combined drugs (classical antibiotics and individual components of essential oils). It has been shown that levofloxacin (LF) in combination with zephirol demonstrates synergy effect against E. coli (cell viability has decreased by about 50%) and weakens the effect against Lactobacilli. A number of other combinations of antibiotic + adjuvant is also shown to be effective. Using FTIR and UV spectroscopy, it has been proved that chitosan nanogels with the drug are well adsorbed on the mucosal model, thereby increasing the bioavailability of drugs. Using ABTS assay, the antioxidant properties of flavonoids and other drugs are shown, which is potentially necessary to minimize the harmful effects of toxins and radicals produced by pathogens. Thus, chitosan nanogels loaded with a combination of drugs can be considered as a new strategy for the treatment of infectious diseases of the gastrointestinal tract, and applicable to difficult-to-treat bacteria.

There are many features of homeostasis worth investigating such as how it works (information pathways, set point processing), where its command and control processors are located in the cell, the language used and how homeostasis interacts with all aspects of an organism's functional maintenance and development. All are necessary and valid inquiries, but this paper examines the implications of ignoring it as a factor of disease causation. Alternatively when properly understood the homeostasis profile becomes a rational explanation for any individual’s range of health; where some areas are very resistant to disease (resilient) and other areas of homeostasis are dysfunctional leading to minor, moderate or very susceptible tendencies to disease (lack of resilience in homeostasis). An understanding of this phenomenon, its interactions with gene expression (epigenetics) is necessary in order to evaluate the solutions for acute and chronic diseases. By this method diseases can be defined more precisely as specific susceptibilities in homeostasis, leading to a method to restore specific homeostasis functions.

In this report, we present a study demonstrating how machine learning methods can reveal the interaction of different risk factors of post-transplant leukemic relapse and obtain robust predictions even with a modest clinical dataset. Using a cohort of 63 pediatric patients with acute lymphocytic leukemia (ALL) and 46 patients with acute myeloid leukemia (AML) who underwent stem cell transplant at a single institution, we built predictive models of leukemic relapse with both pretransplant and posttransplant patient variables (specifically lineage-specific chimerism) using the random forest classifier. The random forest classifier revealed different important predictive factors between ALL and AML in our relapse models consistent with previous knowledge. Furthermore, it also distinguished donor CD34 chimerism as most impactful in relapse prediction compare to donor chimerism of other cellular lineages in our dataset for ALL but CD3 for AML. Our models greatly improved sensitivity and specificity at predicting relapses in cross validation compared to a reference model based on our own institutional incidence of relapse and inferential statistical principles. Local Interpretable Model-Agnostic Explanations, an interpretable machine learning tool, confirmed our Random Forest Classification result and provided an intuitive explanation of how our machine learning models made the relapse prediction for each individual patient.

The detrimental consequences of soil pollution caused by crude oil or petroleum products are immense, leading to land degradation, property damage, and rendering agricultural practices ineffective. Extensive research has been conducted in the field of soil remediation, but further studies are still required to explore additional details of the remedial process. As a result, this study focuses on evaluating the effectiveness of Vernonia Galamensis and Vernonia Amygdalina, commonly known as bitter leaf, in remediating hydrocarbon-contaminated soil. In the analysis of micro-organisms, it was found that the bitter leaf extracts contained three types of bacteria: Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli. The leaf extracts were prepared using different methods, including sun drying, room drying, and using them in their wet form, which were then blended into the contaminated soil. The study also took into consideration three different types of soil: sandy-loamy soil, clay soil, and swamp soil. These advanced techniques and considerations are relevant to the topic of revolutionizing soil remediation, as they explore the potential of bitter leaf extracts and different soil types in effectively mitigating the effects of hydrocarbon contamination.The findings revealed that the wet blended extracts of Vernonia performed exceptionally well in the remediation process, surpassing a 50% reduction in the initial contamination levels. The study involved utilizing a quantity of bitter leaf ranging from 10g to 40g, which was added to the contaminated soils and monitored for a duration of 40 days. Remarkably, this approach led to a significant decrease in the concentration of contaminants within the soil, indicating the effectiveness of the bitter leaf extracts in the remediation process. Towards the conclusion of the study, predictive models were constructed to forecast the impact of hydrocarbon content, as well as the levels of lead, zinc, and chromium in the soil. These variables served as the dependent variables in the models, while the mass of bitter leaf, the duration of treatment, and the pH of the soil were considered as independent variables. Significantly, the models achieved a level of significance of less than 0.05, indicating their statistical validity. Furthermore, the r2 value, which represents the goodness of fit, demonstrated an appreciable level of accuracy in predicting the remediation effects. These results highlight the potential of the developed models in assessing and predicting the remedial outcomes of hydrocarbon contamination using bitter leaf extract.

The purpose of this paper is to show how the self-exciting threshold autoregressive (SETAR) model could be an appropriate econometric framework for characterizing the dynamics of the U.S. public debt/GDP ratio after the Bretton Woods collapse. Our preferred SETAR specifications are capable in capturing the main stylized facts of the U.S. public debt/GDP ratio between 1974 and 2024. In addition, the estimated SETAR models are consistent with several theoretical frameworks that seek to explain the behavior of the U.S. public debt/GDP ratio before and after the Global Financial Crisis (GFC). Finally, the paper provides some arguments on why the previous studies that use the exponential smooth threshold autoregressive (ESTAR) models, or SETAR-type models for the first differences of the U.S. public debt/GDP ratio, are potentially mis-specified, both on econometric and economic grounds.

In this study, a new structure is proposed based on the body-centered cubic (BCC) lattice structure: adding a cubic truss in the center of the BCC structure, which is denoted by TLC (Truss-lattice-cube). The different dimensions of the central cube can affect the mechanical properties of the lattice structure notably. With a fixed length (15mm) of a unit cell, the optimal size for the central cube is determined to be 5 mm. Quasi-static compressive tests are performed on specimens made of Polylactic Acid (PLA) using additive manufacturing technology. The deformation characteristics of the new structure is analyzed in detail by experiments and numerical simulations. Compared to the BCC structure, the mechanical properties of the TLC structure are significantly improved. The initial flow stress of the TLC has been increased by 122% at a strain of 0.1; the specific strength has been enhanced by 293% at a strain of 0.5; and the specific energy absorption has been improved by 312% at the strain of 0.6. Printing defects in the lattice structure could deteriorate the mechanical property remarkably. In this work, incorporation of microcracks into the finite element model makes the simulation capture the influence of printing defects and improve the predictive accuracy of simulation significantly.

The sustainable development of mines has been the focus of attention in recent years, in China, there are a large number of untreated mined-out areas, and a series of disasters caused by instability of the goaf will bring heavy blows to people’s safety and financial resources. Mined-out areas management is an important part that cannot be ignored in mine sustainable development, and filling management is one of the most effective ways to solve the hidden danger of mined-out areas. Among them, cemented filling with tailings and the ultra-high-water backfilling are commonly used in mining enterprises. However, filling treatment will lead to increasing cost and the decreasing profits, which seriously depresses the motivation of mining enterprises and even leads to mor-al hazard. Therefore, the analysis of the economic benefits of goaf control plays a vital role in the sustainable construction and long-term development of mines. This paper proposed the mine-out area treatment economic model, the proposed method employs the guiding philosophy of news-boy model to create a mathematical economy model that provides the basis of goaf management mode for mine. The following research results are achieved: (1) The economic model of mined-out area backfilling treatment is constructed, and which is classified three different modes. (2) Com-bined with mathematical derivation and simulation, the influence of relevant variable parameters on each type of filling mode is discussed. (3) Various types of goaf filling treatment mode are com-pared with non-filling scheme (benchmark mode), so as to provide theoretical support for mining enterprises to choose the appropriate filling scheme. The results shows that the economic model of mined-out area management provides the optimal mode for mined-out area filling, the balance of tailings and ultra-high water filling materials procurement is realized, to making maximum profits. In this paper, the use of economic thinking has an important impact on the sustainable development of safety goaf management. To evaluate the sustainable development of goaf management by building economic models. It has important theoretical basis and practical guiding significance for the development and exploration of mined-out area management by combining economy with sustainable development.

This research investigates a model for the COVID-19 outbreak in Indonesia by analyzing comorbid diseases, self-quarantine, government-provided quarantine, and immunization regimens. Some model parameters are derived from academic journals, others are extrapolated from data collected by the Indonesian Ministry of Health in 2020, and others are assumed. Evaluation of the model reveals non-endemic equilibrium points, endemic equilibrium points, and the basic reproduction number (BRN). The contact rate and infected probability factors impact the increase in the number of COVID-19-infected individuals with comorbid disease within a group. According to the sensitivity analysis of the BRN, the key parameters impacting the spread of COVID-19 are the rate of susceptible recruitment, the rate of contact, the COVID-19 infection death rate, and the likelihood of infection. In addition, sensitivity analyses are provided to examine the effect of parameter changes on subpopulations. We discovered that the natural death rate is the most sensitive parameter based on the sensitivity index after reaching equilibrium. Symmetry aspects appear in some of the visualization of the model’s solution and the BRN and parameters’ sensitivity.

The main purpose of this paper is to present a comprehensive view of the application mathematical models in the designing and implementing SSCM beside to solving problems and making decision. The research questions are: what mathematical models are used for designing and implementing sustainable supply chain management, how to use them, which industries implemented in, what modules of SSCM depth in and finally finding the gaps of researches. The methodology of research is Systematic Literature review through peer review papers which are published in high ranking journals. In this paper, First, we search all papers through scientific data bases like Scopus, science direct, MDPI, Springer, Google Scholar, then, screening papers based on the criteria such as subject of paper, journals impact factor which is published in-should be peer review journal- and relative content of the papers. Finally, we selected 245 papers with three steps screening through 2806 papers that they have enough quality and relative to our research goals for context analysis. Through context analysis, first we categorized the information of the papers and drew the current situation of researches in the framework of our topic. Then, we evaluate and compare the goals of sustainability and current situation and found the gapes, then, offered new suggestions like implementing SSCMs models in pollutant industries like casting industry, Heavy industry, coal Industry and so on. On the other hand, there are gaps in researches in some modules of SSCM such as packaging, designing products, etc.

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, support quantative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation and multi-year daily use for public health and clinical support decision-making of a particle filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian Sequential Monte Carlo algorithm of Particle Filtering allows the model to be re-grounded daily and adapt to new trends within daily incoming data – including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of dynamic model state, support probabilistic model projection of epidemiology and health system capacity utilization and service demand and probabilistically evaluate trade-offs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision making across Canada, such methods offer strong support for evidence-based public health decision making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

Youth is the essential entrepreneurial force for every country's social, political, and economic development. Urban youth make up about 34.27 percent of the population in Ethiopia. The youth unemployment crisis is one of the serious challenges that Ethiopia is facing nowadays, and it should be mentioned in the nation's public discourse. Therefore, the main objective of this study is to specify the socioeconomic and demographic factors that influence urban youth unemployment in the Somali Regional State, specifically in Jigjiga, Degahbour, Kebridahar, Gode, and Dollo Ado towns. Both primary and secondary data sources were employed. A multi-stage sampling technique has been used to select 385 sample respondents from the total population of the study. This study found that almost 63% of youth in the study area were unemployed, and a large number of unemployed youths live in Jigjiga and Dollo Ado Towns as compared to other city administrations. The econometric model's findings indicate that factors like gender, education level, work experience, access to credit, access to information, time applied for job vacancy, active job seeker, social network (network with clan elders, government officials, businessmen, and other friends), and residence in Jigjiga and Gode town have a significant association with the likelihood of youth unemployment in the Somali regional state. Therefore, this study recommends that the regional government and local city administration should develop effective policies and strategies to address the underlying causes of gender inequality, improving access and quality of education, supporting freshman graduates to find jobs, providing access to credit and job vacancies, ensuring fair and free job competition, and managing youth migration for job seeking.