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Hydrogen sulfide and polysulfides are increasingly recognized as bioactive signaling molecules to produce various actions and regulate (patho)physiological processes. Here we examined the effects of sodium sulfide (Na

Background: Alzheimer’s disease is a specific form of dementia characterized by the aggregation of amyloid-β plaques and tau tangles. New research has found that the formation of these aggregates occurs after dysregulation of cellular respiration and the production of radical oxygen species. Proteomic data shows that these changes are also related to unique gene expression patterns. Objective: This study is designed to incorporate both proteomic and gene expression data into a testable mathematical model for AD. Manipulation of this new model allows the identification of potential therapeutic targets for AD. Methods: We investigate the impact of these findings on new therapeutic targets via metabolic flux analysis of sirtuin stress response pathways while also highlighting the importance of metabolic enzyme activity in maintaining proper respiratory activity. Results: Our results indicate that protective changes in SIRT1 and AMPK expression are potential avenues for therapeutics. Conclusion: Combining our mitochondrial gene expression analyses with available protein data allowed the construction of a new mathematical model for AD that provides a useful approach to test the efficacy of potential AD therapeutic targets.

The primary objective of the paper is to identify the effective way to enhance the conductive and convective heat transfer of the FPSC. The performance enhancements of different FPSC components such as absorber plate, absorber tube, and heat transfer fluid are reviewed in detail. The influence of absorber plate configurations, material properties, a center-to-center distance of the absorber tube, plate thickness, coatings, and tube geometry have been assessed to increase the conduction heat transfer. Also, the augmentations of convective heat transfer using different nanofluids in FPSC such as Al

Current emergency management research mainly specifies the positions of evacuation guides from a knowledge base of experience, disregarding the subjective perceived decision-making of pedestrians caught in an emergency situation. Therefore, in this paper, a fuzzy inference system for pedestrians to select guides is designed from the perspective of pedestrians, and a crowd evacuation model with guides under limited vision is constructed. First, selecting the indoor evacuation of people with limited vision as the context, the number and optimal initial positions of guides are determined by a Gaussian fuzzy clustering algorithm. Next, a two-layer fuzzy inference system based on a multifactor pedestrian selection guide is established. Then, from the comprehensive perspective of managers and pedestrians, an improved social force evacuation model with guides is constructed. A comparison of the evacuation times and evacuation processes of known methods with different scene population distributions is analyzed through simulations. The results show that the guide setting scheme of the improved model is more conducive to reducing evacuation times and balancing exit utilizations. The model can provide a basis for emergency management decision-making departments to formulate more flexible guidance strategies.

In order to further improve regional economic innovation capability and governance level and solve the problems of lack of attention to evaluation indicators in traditional evaluation methods of regional economic innovation capability and easy to be affected by subjective factors, an evaluation model based on neural network algorithm is proposed. Through re-analysis of regional economic innovation capability evaluation indexes, the model defines the most reasonable combination of characteristics by combining information gain characteristic selection strategy and finally builds a scientific evaluation index system. By testing the prediction accuracy of the experimental discovery model and evaluation index, the neural network model improves by 41% compared with the traditional subjective evaluation method, and the accuracy increases by 20% compared with the GA-BP neural network model. The experiment proves the stability and good convergence effect of the evaluation model.

The exponential growth of electrical demand and the integration of renewable energy sources (RES) brought new challenges in the traditional grid about energy quality. The transition from traditional grid to smart grid is the best solution which provides necessary tools and information and communication technologies (ICT) for service enhancement. In this study, variation of energy demand and some factors of atmospheric change are considered to forecast production of photovoltaic energy that can be adapted for evolution of consumption in smart grid. The contribution of this study concerns a novel optimized hybrid intelligent model made of the artificial neural network (ANN), support vector machine (SVM), and particle swarm optimization (PSO) implemented for long term photovoltaic (PV) power generation forecasting based on real data of consumption and climate factors of the city of Douala in Cameroon. The accuracy of this model is evaluated using the coefficients such as the mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and regression coefficient (R). Using this novel hybrid technique, the MSE, RMSE, MAPE, MAE, and R are 14.9721, 3.8693, 3.32%, 0.867, and 0.9984, respectively. These obtained results show that the novel hybrid model outperforms other models in the literature and can be helpful for future renewable energy requirements. However, the convergence speed of the proposed approach can be affected due to the random variability of available data.

This study explores a control strategy for a flexible Modular Multilevel Converter-High Voltage Direct Current (MMC-HVDC) system when there is an asymmetrical fault with the AC side system voltage. Aiming at the characteristic that the fault system has a negative sequence current path, this paper proposes a positive/negative sequence controller to ensure the output of original active power, and the validity of the proposed control strategy is verified by using simulation software PSCAD/EMTDC.

Game theory is an excellent mathematical tool to describe the interaction between the immune system and cancerous leukocytes c . l e u . The feature of cancerous leukocytes to differentiate and mutate to give rise to leukemia is in the domain of ecological models as well. In this work, the dynamic of leukemia is described and compared by two models: firstly by a simple probabilistic mathematical model using the zero-sum two player game of Hawk and Dove, and secondly by Leslie Predator Prey model of ecology. The main goal of this study is to compare the results of both models and then discuss the treatment of leukemia i.e., Hematopoietic Stem cell transplant with the best model among them. Hawk and Dove model also describes the cell to cell interaction of cancerous leukocytes and healthy leukocytes l e u after diagnoses and the condition of the patient before and after treatments. In this work, Hematopoietic Stem cell transplant is discussed by using concepts of a zero-sum three player game. Also, both models will be characterized by determining the stability properties, identifying basins of attraction, and locating the equilibrium points to see, at what extent the patient’s survival is possible with leukemia in its body. Results for both models will be presented graphically.

Several computational studies have been undertaken to explore the Ca2+-induced Ca2+ release (CICR) events in cardiac myocytes and along with experimental studies it has given us invaluable insight into the mechanism of CICR from spark/blink initiation to termination and regulation, and their interplay under normal and pathological conditions. The computational modelling of this mechanism has mainly been investigated using coupled differential equations (DEs). However, there is a lack of computational investigation into (1) how the different formulation of coupled DEs capture the Ca2+ movement in the cytosol and sarcoplasmic reticulum (SR), (2) the buffer and dye inclusion in both compartments, and (3) the effect of buffer and dye properties on the calcium behaviour. This work is set out to explore (1) the effect of different coupled formulation of DEs on spark/blink occurrence, (2) the inclusion of improved sarcoplasmic buffering properties, and (3) the effects of cytosolic and sarcoplasmic dye and buffer properties on Ca2+ movement. The simulation results show large discrepancies between different formulations of the governing equations. Additionally, extension of the model to include sarcoplasmic buffering properties show normalised fluorescent dye profiles to be in good agreement with experimental and amongst its one- and two-dimensional representations.

This work is aimed at formulating and analyzing a compartmental mathematical model to investigate the impact of rodent-born leptospirosis on the human population by considering a load of pathogenic agents of the disease in an environment and the incidence rate of human infection due to the interaction between infected rodents and the environment. Firstly, the basic properties of the model, the equilibria points, and their stability analysis are studied. We also found the basic reproduction number R 0 of the model using the next-generation matrix approach. From the stability analysis, we obtained that the disease-free equilibrium (DFE) is globally asymptotically stable if R 0 < 1 and unstable otherwise. The local stability of endemic equilibrium is performed using the phenomenon of the center manifold theory, and the model exhibits forward bifurcation. The most sensitive parameters on the model outcome are also identified using the normalized forward sensitivity index. Finally, numerical simulations of the model are performed to show the stability behavior of endemic equilibrium and the varying effect of the human transmission rates, human recovery rate, and the mortality rate rodents on the model dynamics. The model is simulated using the forward fourth-order Runge-Kutta method, and the results are presented graphically. From graphical stability analysis, we observed that all trajectories of the model solutions evolve towards the unique endemic equilibrium over time when R 0 > 1 . Our numerical results revealed that decreasing the transmission rates and increasing the rate of recovery and reduction of the rodent population using appropriate intervention mechanisms have a significant role in reducing the spread of disease infection in the population.

In order to study the mixed time window vehicle routing optimization problem based on customer priority, a customer differentiation management strategy based on customer priority is proposed. Combined with the main factors affecting customer priority evaluation and the characteristics of vehicle routing problem with mixed time windows, a comprehensive evaluation index affecting customer priority was first established and DBSCAN clustering algorithm was used for clustering analysis of customer priority to solve the optimization problem of cold chain distribution route considering customer priority. Fuzzy time window of refrigerated vehicles was then constructed with trapezoidal fuzzy number, and a mathematical programming model was built with an objective function for minimizing the sum of fixed, green, penalty, refrigeration, and cargo damage costs. Two scenarios of out-of-stock and not-out-of-stock were designed. Finally, an improved genetic algorithm was used to solve the model, and the rationality of the model was verified through a case of imported fruit distribution in Xiamen City. Results showed that the proposed method can effectively solve the routing problem of refrigerated trucks considering customer priority. Moreover, the findings of this study can provide a new approach for solving the routing optimization problem of refrigerated trucks considering customer priority.

The increasingly rampant telecom network fraud crime will cause serious harm to people's property safety. The way to reduce telecom fraud has shifted from passive combat to active prevention. This paper proposes a victim analysis and prediction method based on Bayesian network (BN), which models victims from age, gender, occupation, marriage, knowledge level, etc. We describe the fraud process in terms of whether to report to the police, property loss, and realizing the reasoning of the whole process of telecom fraud. This paper uses expert experience to obtain a Bayesian network structure. 533 real telecom fraud cases are used to learn Bayesian network parameters. The model is capable of quantifying uncertainty and dealing with nonlinear complex relationships among multiple factors, analyzing the factors most sensitive to property damage. According to the characteristics of victims, we conduct situational reasoning in the Bayesian network to evaluate property damage and alarm situations in different scenarios and provide decision support for police and community prevention and control. The experimental results show that male staff in government agencies are the most vulnerable to shopping fraud and women in schools are the most vulnerable to phishing and virus fraud and have the greatest property loss after being deceived; victim characteristics have very limited influence on whether to report to the police.

Water-related tragedies are the highest common of all proven natural calamities and pose severe attacks on people and on socioeconomic status development. Due to the obvious controversy surrounding, their volume, location and time of incidence, geological swaths, and geophysical interrelations, flood events are difficult to completely control. Hence, complete flood prevention is always considered to be a viable choice. The specialized flood occurrences are investigated by developing a structural measure. In this paper, nonlinear flood event circumstance is determined by using a statistical Bayesian parametric approach for parameter estimation. A popular tool for estimating a flood design is model of nonlinear flood event. Nonlinear flood event models are subjected to a Bayesian technique for estimating parameter. The approach is based on the minimization function of square for models with nonlinear calculated peak discharges in terms of parameters. The observed and calculated peak discharges for numerous storms in the watershed, data on the pattern of error observed, and previous information on values of parameter all influence this objective function. The subsequent matrix for covariance is a measure of the calculated parameters’ accuracy. Rainfall and runoff data from a Harvey River sample are used in this study to show the process.

People’s lives are undergoing tremendous changes with the development of the times. Compared with the past, people’s pursuit of spiritual and cultural life also makes our education field usher in a huge development to adapt to the changes in the context of the times. But, at the same time, the development of labor education is gradually being downplayed by people, resulting in a series of problems such as people preferring comfort and not working. Aiming at this common problem, this paper will use the ant colony algorithm and particle swarm optimization algorithm in the computer intelligent algorithm to optimize the way of labor education. It includes the principle and basic process of the ant colony algorithm, the establishment of the mathematical model of the original ant colony algorithm, and the improved algorithm of the ant colony algorithm. The research results of the optimization method of labor education showed the following: when the number of ant colonies reaches 51, the number of iterations of the algorithm will be the least, and the corresponding shortest path is also the best solution; when the combination of pheromone intensity and volatility factor is 3, the optimal solution can be quickly found, and the algorithm inflection point of MMAS is 44.82. From the research results, it can be seen that the computer intelligent algorithm has a good choice for the optimization of labor education and can achieve a major breakthrough in the traditional model of labor education.

Our aim in this paper is to study a mathematical model for high grade gliomas, taking into account lactates kinetics, as well as chemotherapy and antiangiogenic treatment. In particular, we prove the existence and uniqueness of biologically relevant solutions. We also perform numerical simulations based on different therapeutical situations that can be found in the literature. These simulations are consistent with what is expected in these situations.

The use of multimodal magnetic resonance imaging (MRI) to autonomously segment brain tumors and subregions is critical for accurate and consistent tumor measurement, which can help with detection, care planning, and evaluation. This research is a contribution to the neuroscience research. In the present work, we provide a completely automated brain tumor segmentation method based on a mathematical model and deep neural networks (DNNs). Each slice of the 3D picture is enhanced by the suggested mathematical model, which is then sent through the 3D attention U-Net to provide a tumor segmented output. The study includes a detailed mathematical model for tumor pixel enhancement as well as a 3D attention U-Net to appropriately separate the pixels. On the BraTS 2019 dataset, the suggested system is tested and verified. This proposed work will definitely help for the treatment of the brain tumor patient. The pixel level accuracy for tumor pixel segmentation is 98.90%. The suggested system architecture's outcomes are compared to those of current system designs. This study also examines the suggested system architecture's time complexity on various processing units with neuroscience approach.

Racism and corruption are mind infections which affect almost all public and governmental sectors. However, we cannot find enough published literatures on mathematical model analyses of racism and corruption coexistence. In this study, we have contemplated the dynamics of racism and corruption coexistence in communities, using deterministic compartmental model to analyze and suggest proper control strategies to stakeholders. We used qualitative and comprehensive mathematical methods and analyzed both the racism model in the absence of corruption and the corruption model in the absence of racism. We have computed basic reproduction numbers by applying the next generation matrix method. The developed model has a disease-free equilibrium point that is locally asymptotically stable whenever the reproduction number is less than one. Additionally, we have done sensitivity analysis to observe the effect of the parameters on the incidence and transmission of the mind infections that deduce the transmission rates of both the racism and corruption are highly sensitive. The numerical simulation we have simulated showed that the endemic equilibrium point of racism and corruption coexistence model is locally asymptotically stable when max R r , R c > 1 , the effects of parameters on the basic reproduction numbers, and the effect of parameter on the infectious groups. Finally, the stakeholders must focus on minimizing the transmission rates and increasing the recovery (removed) rate for both racism and corruption action which can be considered prevention and controlling strategies.

In this article, the principles of the gray model and BP neural network model are analyzed, and the characteristics of land-use change and spatial and temporal distribution are studied in-depth, and at the same time, to explore the influence of land-use change on ESV, the relationship between the two is analyzed using gray correlation degree, and a mathematical model is constructed to maximize the benefits of the regional system, coupling economic and ecological benefits, combined with Geo SOS-FLUS model to achieve the optimization of land use. This article constructs a combined prediction model of a gray neural network. The gray differential equation parameters correspond to the weights and thresholds of the neural network, and the optimized parameters are determined by training the neural network to make it stable. Then the training results of the BP neural network are fitted with the results obtained from the gray GM (1.1) model. Finally, the prediction results of the three models, gray GM (1.1), BP God Meridian, and gray neural network model, are compared and analyzed. The global spatial autocorrelation and local spatial aggregation patterns of regional soil erosion and its erosion factors are analyzed using the Exploratory Spatial Data Analysis (ESDA) method in spatial measurement theory.

The products of the enterprise are the logistics objects of the enterprise. Therefore, the company’s products are the primary factor affecting logistics costs. The products of different enterprises may be different in terms of the type, nature, volume, quality, and physical and chemical properties of the products, which will have different impacts on the cost of logistics activities such as warehousing, transportation, and material handling of enterprises. More and more enterprises have taken the cost of logistics distribution as one of the important indicators affecting the development of enterprises; especially, cold chain logistics has been rapidly developed and valued in recent years, because the requirements of such products for delivery time, distribution efficiency, and distribution environment are very strict, whether the goods can be distributed in a standard and reasonable environment and whether the delivery vehicle can deliver the goods within the time specified by the customer have greatly affected the safety of frozen and refrigerated food. Therefore, this paper reduces the cost of the distributor through the optimization of the distribution path of cold chain logistics and makes the goods distributed can be delivered to the customer faster and more reasonably by establishing an integrated optimization platform, which is of great significance for how to reduce the cost of enterprises. Therefore, this paper starts from the function with the lowest distribution cost as the goal, comprehensively considers the specific characteristics of cold chain logistics, given the relevant constraints, uses the improved genetic algorithm to iterate on the given scheme, sends the improved new scheme to the simulation software for simulation operation, then sends the results obtained by the operation to the genetic algorithm for the next iteration, and repeats it in turn until the prespecified conditions can be terminated. Therefore, this paper summarizes some problems and development status in cold chain logistics and distribution routes by consulting relevant literature, optimizes the scheme by using VRP model combined with constraints, establishes a distribution system model, and finally verifies and analyzes to obtain a more reasonable and satisfactory solution. The innovation of this paper is that the research on the VRP problem is optimized through an improved genetic algorithm, certain improvements have been made in the coding method and the operation of selection, crossover, variation, etc., and the improved genetic algorithm can greatly reduce the number of program iterations. Then, we use the integrated optimization platform to import the solution into FlexSim for simulation, each simulation of the new solution will be transmitted to MATLAB through the Excel table for the next optimization iteration, and we repeat the above steps until the preset conditions are met after the termination. This would lead to a more realistic and satisfactory solution.

Cervical cancer is one of the main causes of cancer death all over the world. Most diseases such as cervical epithelial atypical hyperplasia and invasive cervical cancer are closely related to the continuous infection of high-risk types of human papillomavirus. Therefore, the high-risk types of human papillomavirus are the key to the prevention and treatment of cervical cancer. With the accumulation of high-throughput and clinical data, the use of systematic and quantitative methods for mathematical modeling and computational prediction has become more and more important. This paper summarizes the mathematical models and prediction methods of the risk types of human papillomavirus, especially around the key steps such as feature extraction, feature selection, and prediction algorithms. We summarized and discussed the advantages and disadvantages of existing algorithms, which provides a theoretical basis for follow-up research.

Virtual interprofessional education (IPE) has emerged as a promising alternative to traditional in-person IPE. However, theoretical frameworks to support virtual interprofessional learning are not well established. Two theoretical frameworks emerged as relevant to virtual IPE: (1) the Canadian Interprofessional Health Collaborative (CIHC) interprofessional learning framework and (2) Dornan's Experience-Based Learning Model (ExBL) of workplace learning. In this study, we sought to explore virtual IPE using both frameworks to develop new theoretical understandings and identify assumptions, gaps and barriers.This was a qualitative study. Semi-structured interviews were conducted with medical and nursing student participants (n = 14) and facilitators (n = 3) from virtual IPE workshops. Transcripts were analysed using directed content analysis methodology, informed by the CIHC and ExBL frameworks. Themes were explored using mind-mapping transitional coding. Data collection and analysis were continued iteratively until themes with adequate conceptual depth, relevance and plausibility were identified.Three themes were identified: (1) a shift in the balance of personal and professional, (2) blunted sociologic fidelity and (3) uncertainty and threats to interpersonal connections. Professional distinctions and hierarchies are blurred virtually. This contributed to an increased sense of psychological safety among most learners and lowered the threshold for participation. Separation from workplace sociologic complexity facilitated communication and role clarification objectives. However, loss of immersion may limit deeper engagement. Interprofessional objectives that rely on deeper sociological fidelity, such as conflict resolution, may be threatened. Informal interactions between learners are hindered, which may threaten organic development of interprofessional relationships.Role clarification and communication objectives are preserved in virtual IPE. Educators should pay close attention to psychological safety and sociologic fidelity-both to leverage advantages and guard against threats to connection and transferability. Virtual IPE may be well suited as a primer to in-person activities or as scaffolding towards interprofessional workplace practice.

The US Environmental Protection Agency (USEPA) has a long history of leveraging environmental models and integrated modeling frameworks to support the regulatory development of numeric ambient water quality criteria for the protection of aquatic life and human health. Primary modeling types include conceptual, mechanistic, and data-driven empirical models; Bayesian and probabilistic models; and risk-based modeling frameworks. These models and modeling frameworks differ in their applicability to and suitability for various water quality criteria objectives. They require varying knowledge of system processes and stressor-response relationships, data availability, and expertise of stakeholders. In addition, models can be distinguished by their ability to characterize variability and uncertainty. In this work, we review USEPA recommendations for model use in existing regulatory frameworks, technical support documents, and peer-reviewed literature. We characterize key attributes, identify knowledge gaps and opportunities for future research, and highlight where renewed USEPA guidance is needed to promote the development and use of models in numeric criteria derivation. These outcomes then inform a decision-based framework for determining model suitability under particular scenarios of available knowledge, data, and access to technical resources. Integr Environ Assess Manag 2023;19:191-201. © 2022 SETAC.

INTRODUCTION: Prolactinomas are the most frequent pituitary tumor subtype. Despite most of them respond to medical treatment, a proportion are resistant and become a challenge in clinical management. Wnt/β-Catenin pathway has been implicated in several cancers including pituitary tumors and other sellar region malignancies. Interestingly, Wnt/β-Catenin inhibition augments the cytotoxicity of the chemotherapeutic agent Temozolomide (TMZ) in different cancers. TMZ is now being implemented as rescue therapy for aggressive pituitary adenoma treatment. However, the molecular mechanisms associated with TMZ action in pituitary tumors remain unclear. OBJECTIVES: Our aims in the present study were to evaluate differential β-Catenin expression in human resistant prolactinomas and Wnt/β-Catenin signaling activation and involvement in Prolactin (PRL) secreting experimental models treated with TMZ. RESULTS: We first evaluated by immunohistochemistry β-Catenin localization in human resistant prolactinomas in which we demonstrated reduced membrane β-Catenin in prolactinoma cells compared to normal pituitaries, independently of the Ki-67 proliferation indexes. In turn, in vivo 15 mg/kg of orally administered TMZ markedly reduced PRL production and increased prolactinoma cell apoptosis in mice bearing xenografted prolactinomas. Intratumoral β-Catenin strongly correlated with Prl and Cyclin D1, and importantly, TMZ downregulated both β-Catenin and Cyclin D1, supporting their significance in prolactinoma growth and as candidates of therapeutic targets. When tested in vitro, TMZ directly reduced MMQ cell viability, increased apoptosis and produced G2/M cell cycle arrest. Remarkably, β-Catenin activation and VEGF secretion were inhibited by TMZ in vitro. CONCLUSIONS: We concluded that dopamine resistant prolactinomas undergo a β-Catenin relocalization in relation to normal pituitaries and that TMZ restrains experimental prolactinoma tumorigenicity by reducing PRL production and β-Catenin activation. Together, our findings contribute to the understanding of Wnt/β-Catenin implication in prolactinoma maintenance and TMZ therapy, opening the opportunity of new treatment strategies for aggressive and resistant pituitary tumors.

The forecast of capital demand has the characteristics of uncertainty. There are known and unknown information about the capital demand for regional economic development. In fact, there are also some in between, that is, uncertain. Consumption is the ultimate goal of production and a key link in realizing a virtuous circle of economic development. This paper uses the GM (1, 1) model to compare the predicted value of the test area with the actual value in 5 years, and the loudness is as high as 90%. Under the guidance of the profit model, the regional economic capital demand has a decisive influence on the regional economic development. The predictive analysis model of capital needs is conducive to fully mobilizing the impact of infrastructure construction of all parties and is an important factor affecting economic development. The mathematical model proposed in this paper is helpful for deepening the research on the management of regional economic development and enriching the theoretical system of regional economic development.

Background: Non-newtonian fluids, especially shear thinning fluids, have several applications in the polymer industry, food industry, and even everyday life. The viscosity of shear thinning fluids is decreased by two or three orders of magnitude due to the alignment of the molecules in order when the shear rate is increased, and it cannot be ignored in the case of polymer processing and lubrication problems. Objective: So, the effects of viscosities at the low and high shear rates on the heat and mass boundary layer flow of shear thinning fluid over moving belts are investigated in this study. For this purpose the generalized Carreau model of viscosity relate to shear rate is used in the momentum equation. The Carreau model contains the five parameters: low shear rate viscosity, high shear rate viscosity, viscosity curvature, consistency index, and flow behavior index. For the heat flow, the expression of the thermal conductivity model similar to the viscosity equation due to the non-Newtonian nature of the fluid is used in the energy equation. Methods: On the mathematical model of the problem, boundary layer approximations are applied and then simplified by applying the similarity transformations to get the solution. The solution of the simplified equations is obtained by numerical technique RK-shooting method. The results are compared with existing results for limited cases and found good agreement. Results: The results in the form of velocity and temperature profiles under the impact of all the viscosity’s parameters are obtained and displayed in graphical form. Moreover, the boundary layer parameters such as the thickness of the regions, momentum thickness, and displacement thickness are calculated to understand the structure of the boundary layer flow of fluid. Conclusion: The velocity and temperature of the fluid are decreased and increased respectively by all viscosity’s parameters of the model. So, the results of the boundary layer fluid flow under rheological parameters will not only help engineers to design superior chemical equipment but also help improve the economy and efficiency of the overall process.

The jet pipe servo (JPS) valve is one key component, whose dynamic performance directly influences the aircraft’s maneuverability. In this paper, a more accurate mathematical model and a novel multiobjective hierarchical encourage particle swarm optimization genetic algorithm (HEPGA) are proposed to improve the dynamic performance of the jet pipe servo valve. By optimizing the main structure parameters of the jet pipe servo valve, the adjustment and overshoot in the dynamic performance are reduced by 24.28% and 51.39%, respectively, compared with the prototype before optimization. To obtain a more accurate mathematical model, the computational fluid dynamics (CFD) is introduced to modify the analytical model considering the turbulent submerged free jet. Different from conventional numerical simulation, the dynamic mesh technique is used to analyze the flow field distribution by considering the force interaction of various parts of the jet pipe servo valve under actual working condition. Then, the HEPGA with better convergence is utilized because of the conflict of adjustment and overshoot. This proposed hybrid algorithm introduces the concept of staff welfare system to divide the population into elite individuals and excellent individuals of particle swarm optimization and general individuals of genetic algorithm. Meanwhile, the convergency performance of the HEPGA is evaluated through the Rosenbrock function by comparing with other particle swarm genetic hybrid methods. Subsequently, the experimental platform is constructed and the dynamic performance tests are conducted on the prototype after optimization. The experimental results verify the accuracy of the established mathematical model and the significant improvement of dynamic performance of the jet pipe servo valve.

Since the outbreak of the COVID-19 epidemic, several control strategies have been proposed. The rapid spread of COVID-19 globally, allied with the fact that COVID-19 is a serious threat to people’s health and life, motivated many researchers around the world to investigate new methods and techniques to control its spread and offer treatment. Currently, the most effective approach to containing SARS-CoV-2 (COVID-19) and minimizing its impact on education and the economy remains a vaccination control strategy, however. In this paper, a modified version of the susceptible, exposed, infectious, and recovered (SEIR) model using vaccination control with a novel construct of active disturbance rejection control (ADRC) is thus used to generate a proper vaccination control scheme by rejecting those disturbances that might possibly affect the system. For the COVID-19 system, which has a unit relative degree, a new structure for the ADRC has been introduced by embedding the tracking differentiator (TD) in the control unit to obtain an error signal and its derivative. Two further novel nonlinear controllers, the nonlinear PID and a super twisting sliding mode (STC-SM) were also used with the TD to develop a new version of the nonlinear state error feedback (NLSEF), while a new nonlinear extended state observer (NLESO) was introduced to estimate the system state and total disturbance. The final simulation results show that the proposed methods achieve excellent performance compared to conventional active disturbance rejection controls.

Background: Alzheimer’s disease (AD) is a complex neurodegenerative disease with multifactorial etiology, unsatisfactory treatment, and a necessity for broad-spectrum active substances for cure. The mucus from Helix aspersa snail is a mixture of bioactive molecules with antimicrobial, anti-inflammatory, antioxidant, and anti-apoptotic effects. So far there are no data concerning the capacity of snail extract (SE) to affect neurodegenerative disorders. Objective: The effects of SE from Helix aspersa on learning and memory deficits in Alzheimer’s type dementia (ATD) induced by scopolamine (Sco) in male Wistar rats were examined and some mechanisms of action underlying these effects were evaluated. Methods: SE (0.5 mL/100 g) was applied orally through a food tube for 16 consecutive days: 5 days before and 11 days simultaneously with Sco (2 mg/kg, intraperitoneally). At the end of Sco treatment, using behavioral methods, we evaluated memory performance. Additionally, in cortex and hippocampus the acetylcholinesterase (AChE) activity, acetylcholine and monoamines (dopamine, noradrenaline, and serotonin) content, levels of main oxidative stress markers, and expression of brain-derived neurotrophic factor (BDNF) and cAMP response element-binding protein (CREB) were determined. Results: We demonstrated that, according to all behavioral tests used, SE significantly improved the cognitive deficits induced by Sco. Furthermore, SE possessed AChE inhibitory activity, moderate antioxidant properties and the ability to modulate monoamines content in two brain structures. Moreover, multiple SE applications not only restored the depressed by Sco expression of CREB and BDNF, but significantly upregulated it. Conclusion: Summarizing results, we conclude that complex mechanisms underlie the beneficial effects of SE on impaired memory in Alzheimer’s type dementia.

In this review, we intend to present a complete literature survey on the conception and variants of the recent successful optimization algorithm, Harris Hawk optimizer (HHO), along with an updated set of applications in well-established works. For this purpose, we first present an overview of HHO, including its logic of equations and mathematical model. Next, we focus on reviewing different variants of HHO from the available well-established literature. To provide readers a deep vision and foster the application of the HHO, we review the state-of-the-art improvements of HHO, focusing mainly on fuzzy HHO and a new intuitionistic fuzzy HHO algorithm. We also review the applications of HHO in enhancing machine learning operations and in tackling engineering optimization problems. This survey can cover different aspects of HHO and its future applications to provide a basis for future research in the development of swarm intelligence paths and the use of HHO for real-world problems.

With the development of green logistics and the promotion of new energy vehicle development policies domestically and abroad, electric vehicles have been put into logistics and distribution as an alternative to traditional fuel vehicles. The Electric Vehicle Routing Problem (EVRP) has attracted widespread attention from the academic community. This paper comprehensively examines the latest research progress on electric vehicle routing models and solution algorithms in logistics and distribution. Firstly briefly introduces EVRP models considering battery losses; secondly, based on the composition of the EVRP objective function and constraints, EVRP models are classified into four types: EVRP considering load and battery life constraints, EVRP with a time window and considering charging strategies, the study of vehicle routing problems for hybrid fleets, and EVRP combined with charging/swapping station location. Then, briefly introduce exact algorithms, traditional heuristics, meta-heuristics, and hybrid algorithms for solving EVRP models. Moreover, it analyzes the main meta-heuristics that are more widely used. Finally, this review points out the development trend of EVRP theoretical methods.

Emerging systems in microfluidics are embracing bio-inspired designs in which boundaries are flexible and mimic peristaltic propulsion mechanisms encountered in nature. These devices utilize electro-kinetic body forces to manipulate very precisely ionic biofluids for a range of medical applications including. Motivated by exploring in more detail electro-hemorheological micro-pumping, in the current article, a mathematical model is developed for peristalsis propulsion of a viscoelastic biofluid in a curved microchannel with electro-osmotic effect and thermal transport under static axial electrical field and with viscous heating. The third grade Reiner-Rivlin model is deployed for blood rheology. The novelty of the current work is therefore the simultaneous consideration of electrokinetics, viscoelastic behavior with the third grade Reiner-Rivlin model and coupled flow and heat transport with viscous dissipation in peristaltic pumping in a curved micro-channel. A Poisson-Boltzmann formulation is adopted to simulate the charge number density associated with the electrical potential. Asymmetric zeta potential (25 mV) is prescribed and mobilizes an electric double layer (EDL). The governing conservation equations for mass, energy, momentum and electrical potential with associated boundary conditions are simplified using lubrication approximations and rendered dimensionless via appropriate scaling transformations. Analytical solutions are derived in the form of Bessel functions and numerical evaluations are conducted via the ND solver command in MATHEMATICA symbolic software. The simulations show that with stronger viscoelastic effect, boluses are eliminated and there is relaxation in streamlines in the core and peripheral regions of the micro-channel. Increasing Brinkman number (dissipation parameter) elevates temperatures. An increase in electrical double layer thickness initially produces a contraction in the upper bolus and an expansion (lateral) in the lower bolus in the micro-channel. With modification in zeta potential ratio parameter from positive to negative values, in the lower half of the micro-channel, axial flow deceleration is generated.

In this study, aiming at the huge amount of information in the tourism field, the pressure of ecological environment, and tedious personalized route planning, an ecotourism personalized route planning system based on the ecological footprint model is designed. In order to recommend routes that meet the time limit and the starting and ending points of the user’s choice, the tourist route recommendation problem is studied as a directional problem on the basis of comprehensively considering the popularity of scenic spots and the user’s interest preferences as the scenic spots score. The scenic spot scoring strategy is scenic spot scoring, and the iterative local search strategy is used to plan tourist routes according to the optimization goal with the largest route score, and improve the real tourist routes, food, and accommodation strategies. On the basis of launched tourist routes, they recommend tourists’ favorite food and accommodation. The model finally completes the fine arrangement of scenic spots, food, and accommodation in the whole tourist route. The system test results show that the system has obvious advantages in personalized path planning effect, excellent user feedback effect, and certain application value.

Net primary production (NPP) is an essential component of the terrestrial carbon cycle and an essential factor of ecological processes. In global change research, it was the core content to study the driving forces of NPP change. In this paper, we focused on the Southwest Karst area of China and analyzed the response mechanisms of NPP to topography, land-use types, climatic change, and human activities. Our results showed that (1) changes in elevation and slope lead to significant differences in the spatial distribution of NPP. With the increase of elevation and slope, NPP first increased and then decreased, their critical values were 2000 m and 15°, respectively. (2) NPP varied significantly among different land-use types. The average NPP of the forest was the highest, and the average NPP of cultivated land increased fastest. (3) Temperature and precipitation had the most substantial influence on NPP, both of them promoted the increase of NPP, and the effect of temperature was more obvious in the Southwest Karst area. (4) Ecological engineering significantly promoted the change of NPP, while animal husbandry significantly inhibited the change of NPP. (5) There were significant spatial differences in the driving effects and corresponding contributions of climatic change and human activities; both of them promoted the increase of NPP in the Southwest Karst area of China. Under climatic change and human activities, NPP increased by 1.24 gC·m

With the transformation of the supply chain from factor driven to investment driven and to innovation driven, supply chain innovation has attracted more and more attention. This paper obtains the Bayesian prior probability of innovation and studies the supply chain innovation path from the perspective of strategy and behavior. The model divides the overall innovation capability of the supply chain into specific node tasks and assets (Capability Set), and the innovation demand of the supply chain is expressed in the form of the conditional probability of market demand. Under the conditions of minimum risk, minimum cost, and rapid market response, it determines who should lead the supply chain innovation first and what type of supply chain innovation should be carried out first. Finally, taking the supply chain of the professional market in Zhejiang Province as an example, this paper verifies the theoretical model of supply chain innovation decision-making. The innovation of the supply chain of Zhejiang’s specialized market is decomposed into the capability set of each enterprise node of the supply chain. This paper transforms the group innovation ability of the supply chain in the professional market into the individual innovation ability of a single enterprise node and reveals the starting point and intensity of the demand innovation of products/services in the supply chain.

A field gas extraction experiment is carried out at a high-kitchen food large-scale landfill site with high leachate level. The leachate level was decreased to improve the gas extraction efficiency. Considering the heterogeneity of the municipal solid waste (MSW), the voids in the unsaturated MSW are divided into matrix pores and fractures. A transient dual-porosity model was then developed to analyze the pumping test results. The first and second boundary conditions considering the effect of cover layers of landfills was involved. The results show that the gas flow rate can be increased by 14%-25% due to the drawdown of the leachate level. Compared with the single pore model, the dual-porosity model can better predict the field results, indicating that the preferential flow in the landfill caused by the heterogeneity of MSWs is very important. As the pumping pressure increases by 5 times, the ratio of fractures to pores wf can be decreased by 4.4 times. This may be due to the fact that the fractures will be compressed when the effective stress was increased as the negative pumping pressure was applied. The pumping pressure and the anisotropy value of the MSWs have the greater influence on the well radius of influence. The proposed model can be used for effective design of the field gas pumping experiments. The obtained gas generation rate, gas permeability of the dual porosity MSWs can be useful for gas transport analysis and gas pumping well design for the high-kitchen food content landfills.

Adverse Outcome Pathways (AOPs) are increasingly used to support the integration of in vitro data in hazard assessment for chemicals. Quantitative AOPs (qAOPs) use mathematical models to describe the relationship between key events (KEs). In this paper, data obtained in three cell lines, LHUMES, HepG2 and RPTEC/TERT1, using similar experimental protocols, was used to calibrate a qAOP of mitochondrial toxicity for two chemicals, rotenone and deguelin. The objectives were to determine whether the same qAOP could be used for the three cell types, and to test chemical-independence by cross-validation with a dataset obtained on eight other chemicals in LHUMES cells. Repeating the calibration approach for both chemicals in three cell lines highlighted various practical difficulties. Even when the same readouts of KEs are measured, the mathematical functions used to describe the key event relationships may not be the same. Cross-validation in LHUMES cells was attempted by estimating chemical-specific potency at the molecular initiating events and using the rest of the calibrated qAOP to predict downstream KEs: toxicity of azoxystrobin, carboxine, mepronil and thifluzamide was underestimated. Selection of most relevant readouts and accurate characterization of the molecular initiating event for cross-validation are critical when designing in vitro experiments targeted at calibrating qAOPs. published

An enterprise’s development and growth are inextricably linked to rational and efficient resource integration and optimization. This study focuses on the reorganization and integration of industrial elements inside the firm from the standpoint of resource integration. The ideal resource integration strategy is investigated by integrating the industrial parts of a certain enterprise in order to increase the efficiency of project completion and lower enterprise expenses. The enterprise’s internal material and human resources are limited, but it is frequently necessary to execute numerous activities at the same time, and each activity must meet multiple goals. This research investigates how to properly integrate and schedule resources while attaining different goals. This research proposes using an enhanced particle swarm optimization technique (IPSO) to combine firms’ internal resources. In order to address the issue of uneven particle dispersion caused by random population initialization, IPSO incorporates chaos theory into particle population initialization. The logistic mapping sequence generates a huge number of particles, and the particles with the highest quality are chosen for initialization. This can increase particle quality, allowing particles to be spread equally during setup. In the late stage, the classic particle swarm optimization algorithm (PSO) has a slow convergence rate, causing the algorithm to readily slip into a local optimal solution. This research proposes a dynamic inertia weight update approach based on fitness value. In the later stages of the algorithm, this strategy can improve the convergence speed and quality of the global optimal solution, allowing the particles to do a global search and eventually identify the population’s ideal solution. Furthermore, IPSO creates a fitness function depending on task completion time. IPSO is used to test the performance of an enterprise’s resource integration case. Experiments show that the method utilized can swiftly locate the ideal solution, complete the integration, and optimization of enterprise resources in the shortest job completion time, and for the least amount of money.

The primary opportunities for improved conservation and sustainability outcomes are through changing human behavior. Zoos, aquariums, and other public-facing biodiversity conservation institutions offer an important space for environmental learning and facilitating proenvironmental behavior change. We have focused, in this review, on examining common behavior change models as well as the Transtheoretical Model (TTM) of Behavior Change​​​​​, a widely regarded model within the health fields and, recently, in the fields of environmental and leadership studies, with new research applying the TTM specifically in a zoo setting. We have discussed critiques of the TTM and rebuttals to those critiques. We have presented examples of TTM applications in a zoo setting. Our objective has been to explore the TTM as a possible "best fit" framework and tool for zoo and aquarium practitioners in facilitating proenvironmental behavior. Key findings include that (a) the TTM differs significantly from other proenvironmental behavior theoretical models, including those that are prevalent in the conservation psychology literature and applied by zoos and aquariums, in terms of the TTM stages of change and processes of change constructs; (b) the TTM appears to overlap significantly with the 10 interventions or treatments identified by researchers as the most effective approaches to facilitating proenvironmental behavior; and (c) there is nascent and promising application of TTM constructs in zoo and aquarium programming. We remain impressed by the potential of the TTM to address a critical question within the conservation psychology research field concerning proenvironmental behavior: what specific tools to employ and when.

This paper investigates a new approach to the rapid control of an upper limb exoskeleton actuator. We used a mathematical model and motion measurements of a human arm to estimate joint torque as a means to control the exoskeleton’s actuator. The proposed arm model is based on a two-pendulum configuration and is used to obtain instantaneous joint torques which are then passed into control law to regulate the actuator torque. Nine subjects volunteered to take part in the experimental protocol, in which inertial measurement units (IMUs) and a digital goniometer were used to measure and estimate the torque profiles. To validate the control law, a Simscape model was developed to simulate the arm model and control law in which measurement data from IMUs and a goniometer were fed into the suggested Simscape model. The arm torque profiles are key to the control approach and should be traced by torques produced by the exoskeleton actuators to provide comfort and flexibility for the subjects. A DC motor was used as an actuator for the exoskeleton, and its model was used in the physical Simscape model. To reduce the error in the driving torque compared with the reference arm torque, a PID controller was implemented. The results show the potential of our methodology for tracking and controlling the actuator’s torque, in which the mean square error was reduced to less than 0.2 - a significantly low value.

In recent years, there are many new definitions that were proposed related to fractional derivatives, and with the help of these definitions, mathematical models were established to overcome the various real-life problems. The true purpose of the current work is to develop and analyze Atangana-Baleanu (AB) with Mittag-Leffler kernel and Atangana-Toufik method (ATM) of fractional derivative model for the Smoking epidemic. Qualitative analysis has been made to `verify the steady state. Stability analysis has been made using self-mapping and Banach space as well as fractional system is analyzed locally and globally by using first derivative of Lyapunov. Also derive a unique solution for fractional-order model which is a new approach for such type of biological models. A few numerical simulations are done by using the given method of fractional order to explain and support the theoretical results.

It is the need of an era to develop efficient traffic noise prediction models with optimum accuracy. In this context, the present work tries to comprehend the performance-related potential parameters based on earlier published articles worldwide that are responsible for deviation in noise values for different traffic noise prediction models and find out critical gaps. This study reviewed the process involved in source modeling and sound propagation algorithms, applicability, limitations, and recent modification in 9 principal traffic noise prediction models adapted by different countries all around the globe. The result of this review shows that many researchers had carried out comparative analysis among various traffic noise prediction models, but no emphasis was made on the recent modifications, limitations associated with those models, and strategies involved without ignoring the propagation and attenuation mechanism in the developing phase of these models. The findings of this study revealed that the major challenge for any traffic noise prediction model to be efficient enough is the inclusion of all the factors responsible for the generation and deviation of traffic noise before reaching the receiver. These responsible factors include a factor for source emission, sound propagation and attenuation, road characteristics, and other miscellaneous factors such as absorption characteristics of building facades, honking, and dynamic behavior of traffic. This study adds to the broader domain of research and will be used as reference material for future traffic noise modeling strategies.

The quest for cost-effective and thermal efficient structural materials onto beating the high cost of construction is gaining more attention among researchers. This study focused on the blending of cement and sand with waste paper pulp into cost-effective structural materials. The composites were prepared in four mix groups with each containing a fixed amount of sand at 5, 10, 15, and 20 wt.% (by weight of pulp). Cement was varied at 10, 20, 30, and 40 wt.% in each group, and curing was done for 28 days. Properties evaluated are compressive, bending, and splitting strengths. It was observed that increasing cement and sand contents enhanced strengths; howbeit, the blend of 30 wt.% cement/15 wt.% sand resulted in a reduction in bending strength even as 30 wt.% cement/20 wt.% sand engendered a decrease in bending and splitting strength. The microstructural features showed that inherent fibers of the pulp were well bonded with hydration products and sand content yielding good performance in the composites. The optimization procedure carried out depicted a combination of 35.27% cement and 20% sand as the optimum composition. Experimental outcomes were modelled for the purpose of prediction of responses. The models were confirmed statistically fit showing how varying cement content affected strength responses at fixed sand proportion. ANOVA affirmed the significant contribution of cement and sand on the strength responses. Compliance reliability was observed to be dependent on the interactive pattern between cement and sand. Going by the standard prescription for the strength properties, cement and sand content of 35.27 and sand 20 wt.% satisfied all strength requirements for low-cost construction having a compliance reliability of 1.31.

This paper analyzes the application of MEC multiserver heuristic joint task in resource allocation of the educational resource database. After constructing the scenario of educational resource database, a mathematical model is constructed from the dimensions of local execution strategy, unloading execution, and given educational resource allocation, in order to optimize the optimal allocation of educational resources through MEC. The results show that the DOOA scheme has good performance in terms of calculation cost and timeout rate. Compared with other benchmark schemes, the DQN-based unloading scheme has better performance, can effectively balance the load, and is better than the random unloading scheme and the SNR-based unloading scheme in terms of delay and calculation cost. The results show that the total hits of all category 1 users' content requests account for the proportion of the total content requests. The images have a small downward trend at the 15000 and 30000 time slots and then continue to rise. This shows that the proposed scheme can automatically adjust the caching strategy to adapt to the changes of content popularity, which proves that the agent can correctly perceive the changing trend of content popularity when the popularity of network content is unknown and improve the caching strategy accordingly to improve the cache hit rate. Therefore, the allocation of educational resources based on the MEC multiserver heuristic joint task is more reasonable and can achieve the optimal solution.

Knowledge innovation ability is the source of value realization of high-tech enterprises, and the acquisition of high-value knowledge is important. Taking knowledge as the intermediary variable, knowledge field activity and knowledge fermentation as mediating variables, and knowledge mobilization and knowledge network position transition as moderating variables, the conceptual model and theoretical analysis framework of the impact mechanism of knowledge innovation network fragmentation fault on technology practices is constructed and the moderated mediating effect model is derived. Taking high-tech enterprises as empirical samples, 538 valid questionnaires were obtained online and offline and the nonpercentile bootstrap method based on deviation correction was used to empirically investigate the influence mechanism and transmission path of knowledge innovation network fragmentation fault on high-tech enterprises’ technological practices. The empirical results show that the main effect of knowledge innovation network fragmentation fault on high-tech enterprise technology practices is significant. Knowledge field activity and knowledge fermentation play a differential mediating role in knowledge innovation network split fault and Technology Convention. Knowledge field activity and knowledge fermentation play a partial mediating role in knowledge innovation network split fault and Technology Convention. Knowledge mobilization partially moderates the split fault of knowledge innovation network and technological practices. Knowledge mobilization positively moderates the positive effect of split fault of knowledge innovation network on technological practices and significantly positively moderates the mediating effect of knowledge field activity and knowledge fermentation, resulting in the moderated mediating effect. Knowledge network location transition plays a part of moderating role in knowledge innovation network split fault and Technology Convention. Knowledge network location transition positively moderates the positive impact of knowledge innovation network split fault on Technology Convention and significantly positively moderates the mediating role of knowledge field activity and knowledge fermentation, resulting in a moderated mediating effect. Knowledge innovation network split fault, knowledge field activity and knowledge fermentation, knowledge mobilization and knowledge network location transition, and the combination of technological practices can be the antecedents of promoting technological practices in high-tech enterprises. Through the research on the mechanism of knowledge innovation network split fault in the technological practices of high-tech enterprises, the connotation of knowledge innovation network split fault is enriched, the influencing factors of technological practices are clarified, and the value-added knowledge is promoted and has guiding and reference significance for the innovation knowledge acquisition and competitiveness improvement of high-tech enterprises.

Hypertension is reported to cause major brain disorders including Parkinson's disease (PD), apart from cardiovascular and chronic kidney disorders. Considering this, for the first time, we explored the effect of modulation of the ACE2/Ang (1-7)/MasR axis using diminazene aceturate (DIZE), an ACE2 activator, in 6-hydroxydopamine (6-OHDA) induced PD model. We found that DIZE treatment improved neuromuscular coordination and locomotor deficits in the 6-OHDA induced PD rat model. Further, the DIZE-mediated activation of ACE2 led to increased tyrosine hydroxylase (TH) and dopamine transporters (DAT) expression in the rat brain, indicating the protection of dopaminergic (DAergic) neurons from 6-OHDA induced neurotoxicity. Moreover, 6-OHDA induced activation of glial cells (astrocytes and microglia) and release of neuroinflammatory mediators were attenuated by DIZE treatment in both

Rodent infestation is a global biological problem. Rodents are widely distributed worldwide, cause harm to agriculture, forestry, and animal husbandry production and spread a variety of natural focal diseases. In this study, 10 ecological niche models were combined into an ensemble model to assess the distribution of suitable habitats for Rhombomys opimus and to predict the impact of future climate change on the distribution of R. opimus under low, medium and high socioeconomic pathway scenarios of CMIP6.In general, with the exception of extreme climates (2090-SSP585), the current and potential future ranges of R. opimus habitat are maintained at approximately 220 × 10These results help identify the impact of climate change on the potential distribution of R. opimus and provide supportive information for the development of management strategies to protect against future ecological and human health risks. © 2022 Society of Chemical Industry.

Tumor angiogenesis is the tumor’s inherent blood supply system which is crucial for the growth of tumor. Extracellular Matrix Metallo Proteinases Inducer (EMMPRIN)/Cluster of Differentiation 147 (CD147) is found in high levels on tumor surfaces. This study focuses on these elevated levels of CD147 and the effect it has on tumor angiogenesis. The present article develops a Two-Dimensional Mathematical Model of Tumor Angiogenesis taking into account the CD147 molecule. The effects of CD147 on Tumor Angiogenesis Factors (TAFs), fibronectin and Matrix Metallo Proteinases (MMPs) are also incorporated. The results have been obtained through COMSOL Multiphysics 5.4 software. The results show that CD147 is responsible for swifter angiogenesis, calling for targeting this molecule in anti-angiogenic strategies. The present model is validated with the existing theoretical and experimental results.

In the process of urbanization, a brisk building boom triggers a series of environmental problems. Construction contractors usually present environmentally fraudulent behaviors, i.e., greenwashing behaviors (GWBs), to legitimize their activities, ultimately hindering the sustainable development of the society. However, the formation mechanism of the contractors' GWBs is still unclear. Through the lens of fraud GONE theory (i.e., greed, opportunity, needs, and exposure), this study applies the multi-group structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to examine the formation mechanisms of GWBs. The results of SEM show the relationships between four fraud factors and GWBs. Additionally, the projects are grouped into three categories: government investment projects, private-public-partnership (PPP) projects, and private investment projects. The results of multi-group SEM reveal that the effects of four fraud factors differ significantly across projects with different investment characteristics. The results of fsQCA suggest that there are three typical driving mechanisms for GWBs. Furthermore, this study develops a project information transparency framework and a "greenwashing tree" to form a systematic understanding of GWBs. Finally, on these bases, this study provides targeted suggestions and policy recommendations for governing contractors' GWBs.

Many engineering and scientific models are based on the nonlinear system of equations (NSEs), and their effective solution is critical for development in these domains. NSEs can be modeled as an optimization problem. So, the goal of this paper is to propose an optimization method, to solve the NSEs, which is called a chaotic enhanced genetic algorithm (CEGA). CEGA is a chaotic noise-based genetic algorithm (GA) that improves performance. CEGA will be configured so that it uses a new definition which is chaotic noise to overcome the drawbacks of optimization methods such as lack of diversity of solutions, the imbalance between exploitation and exploration, and slow convergence of the best solution. The goal of chaotic noise is to reduce the number of repeated solutions and iterations to speed up the convergence rate. In the chaotic noise, the chaotic logistic map is utilized since it has been used by numerous researchers and has proven its efficiency in increasing the quality of solutions and providing the best performance. CEGA is tested using many well-known NSEs. The suggested algorithm's results are compared to the original GA to prove the importance of the modifications introduced in CEGA. Promising results were obtained, where CEGA’s average percentage of improvement was about 75.99, indicating that it is quite effective in solving NSEs. Finally, comparing CEGA’s results with previous studies, statistical analysis by Friedman and Wilcoxon’s tests demonstrated its superiority and ability to solve this kind of problem.

Based on the unique micro-data of newly built polluting firms for the period of 2009–2018, this paper adopts the conditional logit model to empirically evaluate the impact of environmental regulations on the location choice of polluting firms. Moreover, we extend the theoretical model by considering that the environment regulations not only influence the pollution cost but also the level of technological innovation and labor cost. The empirical results show that polluting firms tend to flow into areas with stringent environmental regulations, which supports the Porter hypothesis, but the effect of environmental regulations have a divergent impact on heavily polluting firms. Heterogeneous analysis indicates that environmental regulations have shown a positive impact on the location choice of private and foreign-funded firms but no significant impact on that of state-owned firms; the impact of environmental regulation is consistent with pollution haven hypothesis for firms in the central region but is in line with Porter hypothesis for firms in other regions. Meanwhile, the probability of air polluting firms entering areas with stricter environmental regulations is higher than that of water-polluting ones. Finally, this paper further empirically tests the conduction mechanism, that is, environmental regulations can affect the location choice of polluting firms by affecting the regional technological innovation capabilities and labor cost.