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The red palm weevil is one of the most dangerous pests affecting date palm trees in the Gulf region and the Middle East. This paper investigates the control of the red palm weevil (RPW) in date palms using several techniques, namely stem injection, sex pheromone traps, and the Sterile Insect Technique. A mathematical model describing the spread of the red palm weevil is presented, and the boundedness of the solution for the RPW system is studied. The conditions for local stability, forward bifurcation, and Hopf bifurcation are also obtained. Additionally, this research highlights the significance of optimal control in managing RPW and simulates the model using the Forward-Backward Sweep numerical method. It is observed that the parameters of the sterile insect technique, the pheromone trap, and the mechanical injection play important roles in controlling the RPW. Mechanical Injection may be more effective in controlling the palm weevil compared to the other two methods. [ABSTRACT FROM AUTHOR]

The significance of mental health inequities globally is illustrated by higher rates of anxiety and depression amongst racial and ethnic minority populations as well as individuals of lower socioeconomic status. The COVID-19 pandemic has further exacerbated these pre-existing mental health inequities. With rising mental health concerns, arts engagement offers an accessible, equitable opportunity to combat mental health inequities and impact upstream determinants of health. As the field of public health continues to shift its focus toward social ecological strategies, the social ecological model of health offers an approach that prioritizes social and structural determinants of health. To capture the impacts of arts engagement, this paper creates an applied social ecological model of health while aiming to advocate that engaging in the arts is a protective and rehabilitative behavior for mental health. [ABSTRACT FROM AUTHOR]

Community health workers (CHWs) and promotores de salud are frontline health workers who typically come from the communities they serve. Despite providing crucial services, they are not institutionalized (or integrated) within much of the U.S. health care system. Many work, either officially or unofficially, as medical interpreters--restricting their full impact as CHWs/promotores. In this paper, we detail the misemployment and its effects among a subsample of CHWs/promotores in two geographically distinct, exploratory projects. We encourage that collaborative research with CHWs/promotores continue and that fidelity to the CHW model be ensured to realize their true potential. [ABSTRACT FROM AUTHOR]

The article analyses the evolution of mathematical models used in life cycle estimation. A comparison between the different mathematical models and their areas of applicability is proposed. This analysis is generally done in terms of costs. The importance of the article lies in the proposal of a new mathematical model for estimating life cycle of power substations, compatible with current and future requirements. The study findings reveal that an optimization model can be established using the life cycle assessment method to obtain a longer lifespan, but also to minimize costs (maintenance, repairs, replacements, etc.). In order to establish a mathematical model of the life cycle of electrical substations, it is necessary to study several important design parameters and decision factors for each stage of the life cycle. Even if the paper does not provide empirical results, it must prove that it is necessary to develop a multi-algorithm optimization framework to find an adequate solution. The life cycle must be modelled according to: equipment availability technological evolution, maintenance activities, risk management, environmental protection, human error, etc. The study helps further research to build, implement and validate a robust model with development possibilities depending on the needs of the researchers. [ABSTRACT FROM AUTHOR]

Emerging Industry 5.0 pushes advanced automation towards resilient solutions with enhanced human role. Resilience as an ability to sustain processes in the face of disruptions and adversarial attacks requires careful modelling and simulation. Cellular automata are efficient mathematical models used to simulate the behavior of complex systems, which change their state based on a set of predefined rules. In this paper, we suggest several updates to cellular automata (particularly Conway's "Game of Life") to address resilience. These include "Life and Creation", "War and Peace", and their hybrid "War and Creation" capable of addressing the important components of resilience, such as controllable creativity and adversarial interactions. Inherited in these updates and known advantages of cellular automata, such as simplicity, emergent behavior, parallelism, and adaptability, makes it a powerful simulation tool for a wide range of Industry 5.0 systems that involve humans, smart infrastructure, their complex and adversarial interactions, safety, and resilience. [ABSTRACT FROM AUTHOR]

With the rapid growth of Internet applications, massive demand for cloud services has brought challenges to data center networks (DCNs). Traditional wired DCNs adopt multilayer structure, leading to the congestion in high-layer switches. Furthermore, complex cabling in wired DCNs affects the flow of cold air, reduces the cooling efficiency, and increases energy consumption. To solve these problems, in this paper, we introduce an architecture for intra-DCN based on free space optic (FSO) wireless communication. The proposed scheme exploits spatial position of FSO transceivers at different heights to realize obstruction-free transmission for inter-rack requests. We formulate the transceiver arrangement problem into a mathematical model while satisfying the requirement for direct line-of-sight communication between any pair of transceiver nodes. Moreover, we evaluate the network performance of the proposed scheme via simulation in terms of flow completion time, server throughput, and power consumption. Compared with traditional ones, our scheme can considerably decrease power consumption while obtaining better network performance. [ABSTRACT FROM AUTHOR]

Industrial clusters are one of the most current development models. Aggregation of firms in a geographical area has many advantages, such as cost reduction, better supply, and knowledge emission with linkage together. The linkage result will be created the networks. The industrial clusters without co-operation networking will not be developed. That must be noticed to severe changes of business environment parameters. Therefore, this paper develops an uncertain mathematical model under sustainable and dynamic conditions. The model contains four objectives, namely profit, transportation cost, employment, and environment appraisal of the cluster. The outcome of this research is to find the best/optimal solution for firms’ arrangements with/within networks that maximize the profit, employment, and environment score and so minimize the transportation cost. The assignment patterns show horizontal and vertical cooperation with/within networks. The efficiency of model clustering in sub-clusters is followed by the neighbor clustering efficiency and the one’s clustering efficiency methods. [ABSTRACT FROM AUTHOR]

This research paper focuses on the development of advanced mathematical models for disease diagnosis and prediction, and the subsequent creation of automated systems based on these models. These systems leverage a range of mathematical models and incorporate cutting-edge information technology achievements to provide medical professionals with valuable decision-making support. By amalgamating mathematical rigor and technological innovation, this research endeavors to enhance the accuracy and efficiency of medical diagnoses, thereby improving patient care and healthcare outcomes. This study delves into the persistent need for contemporary information systems, where information plays a crucial role in decision-making. It aims to provide an objective approach to addressing pressing medical challenges, particularly in disease diagnosis and prediction, enhancing the effectiveness of these critical tasks. Automated medical information systems, built on advanced mathematical models, significantly empower physicians. Machine diagnostics, relying on deterministic logic, the phase interval method, and information-probabilistic logic, bolster diagnostic capabilities. Functional entropy enables individuals to handle vague information, aiding decision-making. Assessing imprecision and uncertainty computationally diminishes subjectivity, while employing fuzzy set theory enhances diagnostic modeling. Mathematical models assess diagnostic indicators, and linguistic variables quantify resemblance. The diagnostic model for primary biliary cirrhosis and active hepatitis utilizes a diagnostic table and gradient projection. This comprehensive study advances medical diagnostics through mathematical models and automated systems, addressing critical healthcare challenges. [ABSTRACT FROM AUTHOR]

This paper studies a cascaded flowshop joint scheduling problem that has critical applications in the electronic information equipment manufacturing industry but has received limited attention in the scheduling field. The cascaded flowshop joint scheduling problem encompasses both a distributed permutation flowshop scheduling problem and a hybrid flowshop scheduling problem. This paper investigates the efficient scheduling of a set of jobs in two heterogeneous flowshops to minimize the makespan. We present a mixed integer linear programming mathematical model and a shifting iterated greedy algorithm, which constantly changes its search space to explore different solution spaces. Based on the specific characteristics of the problem, a hybrid scheduling approach that combines forward and backward scheduling, a step-by-step destruction and reconstruction operator, and three adaptive reconstructive methods that combine coarse-tuning and fine-tuning are proposed to explore the near-optimal solution. Through comprehensive computational comparison and statistical analysis, the results demonstrate that the proposed shifting iterated greedy algorithm performs significantly better in relative deviation index values at the same CPU running time. [ABSTRACT FROM AUTHOR]

Tourist trip design is a fast-growing area of research. Tourist interest, budget, travel style, safety, and the existence of travelers with special needs (for example, wheelchair accessibility) are some of the elements to consider for obtaining personalized routes. Including these elements in a single mathematical model can considerably complicate the solution process. Also, route decision-making is affected by the context (health, social, political, economic, etc.) in which decisions are made. In this paper, the first aim is to propose a three-step methodology to obtain contextualized solutions to a tourist trip design problem (TTDP) with time-dependent recommendation factors. The methodology consists of (1) providing a basic TTDP model that avoids the complexity of including contextual information, (2) obtaining a set of solutions to the problem using a Modeling to Generate Alternatives (MGA) approach, and (3) using a recently developed a posteriori method to include the contextual information through fuzzy propositions. The second aim of the paper is to evaluate three algorithmic strategies for the MGA step. Lastly, considering a context for people with mobility impairments, an example is solved using real data. The results show the usefulness of the proposed methodology in solving the TTDP with contextual information. • A tourist trip design problem with time-dependent recommendations factors is solved. • A methodology to obtain contextualized solutions is developed. • An accessibility context is modeled with fuzzy propositions. • Solutions are obtained by using the modeling-to-generate-alternatives approach. • Best solutions are suitable for the accessibility context. [ABSTRACT FROM AUTHOR]

Governments worldwide have promulgated greenness-based electric vehicle subsidy (GEVS) policies to encourage electric vehicle (EV) manufacturers to develop products with higher greenness (i.e., energy saving and emission reduction performance). Normally a GEVS policy would set a subsidy threshold to ensure that only EVs whose greenness meets the subsidy threshold requirement receive the subsidy. By considering consumers' environmental preferences (CEPs), this paper develops game-theoretical models to investigate the impacts of the GEVS policy on EV manufacturers' product development strategies and profits, as well as on the environment. The findings show that the product development strategies highly depend on subsidy thresholds, and three equilibrium product development strategies are obtained in equilibrium. Besides, it is intuitive to find that, in the absence of CEP, a low subsidy threshold can increase both EV manufacturers' profits and reduce the environmental impact of EVs simultaneously. However, the opposite results emerge when consumers have strong environmental preferences; that is, a low subsidy threshold would hamper both EV manufacturers' profits, and meanwhile increase EVs' environmental impacts. Surprisingly, as CEP increases, the GEVS policy is more likely to reduce EV manufacturers' profits and increase EVs' environmental impacts. • The product development strategies of electric vehicle manufacturers highly depend on subsidy thresholds. • Three equilibrium product development strategies are obtained in equilibrium. • A low subsidy threshold can make the environment better off without consumers' environmental preferences. • A low subsidy threshold can hamper profits and environment with consumers' environmental preferences. • A high subsidy threshold is not effective in increasing profits and protecting environment. [ABSTRACT FROM AUTHOR]