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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]

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]

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]

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]

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]

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]

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]

The electric vehicle battery state prediction in real time is an important issue to avoid the risks of burning the battery due to over-charging or dead batteries that are caused by aging. Based on the past works, it is found that the State of Charge (SOC) can be predicted, while predicting the State of Health (SOH) is a difficult challenge. Usually, the SOH is predicted after the end of the driving or the charging cycle under constant conditions; this method is practically impossible because the battery can reach the end of the battery life before achieving the prediction process. In this paper, a SOH prediction method is proposed based on SOC prediction because there is a relation between the SOC and the SOH as indicated by deriving a mathematical model. The prediction process of battery age is achieved during the beginning of the battery charging process under constant conditions of charging, in which a SOC estimation has been implemented by the nonlinear auto-regressive with exogenous input neural network (NARX) with two initial values of SOC, the default value (0%) and practical value (10%) and two charging current rates (0.5C and 1C). The proposed method has been simulated by MATLAB, which several scenarios have been achieved to validate the proposed method. The root-mean-square error (RMSE) values are very promising for both predicting SOC and SOH that are 0.5% and 0.018%, respectively. [ABSTRACT FROM AUTHOR]

In the present paper, an attempt is made to construct a deterministic mathematical simulation for population systems, by which their temporal (equation of motion) and spatiotemporal (equation of distribution) behaviour can be deduced, as solutions of the constitutional differential equations of the system. The generic formulation of the constitutional equations gives the simulation the possibility to expand to several populations, but also to parameters of different nature (say economic), by applying proper transformations according to the inner properties of each parameter. The introduction of the topographical features of such a system can be reduced to a boundary conditions problem, applied to the constitutional differential equations. Two initial applications are analyzed herein, namely a one-dimensional inertial population system, and a one-dimensional dynamic population system, where the external force corresponds to a space of constant curvature. The theoretically predicted behaviors of the population distribution of these systems are compared qualitatively to actual field data, collected from cities around the World. [ABSTRACT FROM AUTHOR]

With the development of the realistic manufacturing process, the distributed scheduling, machine velocity, and resource constraints have attracted much attention. This paper addresses the distributed hybrid flowshop scheduling problem (DHFSP) with machine velocity and resource constraints to minimize the makespan and total energy consumption simultaneously. A mathematical model of the problem is formulated. To solve the proposed problem, a collaboration-based multi-objective algorithm (CBMA) is developed. First, a machine velocity adjustment rule considering resource constraints is proposed by analyzing the characteristics of the problem. In the proposed algorithm, each solution is represented by a well-designed three-dimensional vector. Then, an objective-balanced machine selection strategy is employed to balance the quality and diversity of the initial population. Next, a Pareto knowledge-based collaborative search mechanism enhances the global search ability in each iteration. To improve the convergence of the algorithm, a distributed machine velocity adjustment rule is embedded into the local search. Finally, a set of instances based on realistic industrial processes are tested. The effective performance of the proposed algorithm is verified through computational comparisons. [ABSTRACT FROM AUTHOR]

To summarize the effects of spiritual intelligence (SI) training in several contexts and to identify the most consensual patterns in SI intervention design. The "adaptive application" of spirituality in life is called SI, the ability to use spirituality in everyday problem-solving and it is proven to relate to better clinical and spiritual care (SC) competency in healthcare professionals. Interventions aiming to increase SI have been tested in different settings with benefits that can have a significant impact on the way healthcare professionals approach SC. It included any quantitative studies that used reproducible methodology and reported on the implementation of interventions aiming to increase SI. Text, proceedings, conference or opinion papers, abstracts, reviews, mixed methods and qualitative studies were excluded from this scoping review. Scoping review of quantitative studies on "spiritual intelligence" (query term) that include SI intervention programs (inclusion criteria) conducted on PubMed Central, Scopus, Web Of Science and PsycInfo databases, using the Joanna Briggs Institute methodology. Studies published until the 1st january 2022 were included. The studies' selection, extraction and synthesis of data was carried out by two independent reviewers. From the 10 articles/studies included, six were quasi-experimental and three experimental. Most (n=9) were conducted in Iran. The most common target samples of the studies were nurses (4 studies) and students (4 studies). SI training protocols, although based in group sessions, varied in their content between the different studies. SI interventions reported significant increase of SI levels, improvement of communications skills and reduction of anxiety, stress and depression levels. Despite the consensus among studies regarding the benefits of spiritual intelligence programs, more studies are needed to gauge long-term outcomes. There is also a need to standardize training protocols in spiritual intelligence. [ABSTRACT FROM AUTHOR]

The classical manufacturing systems assume that all produced items are of perfect quality. They also need to consider the rework process in manufacturing operations. Moreover, most of the previous literature considers single-stage production-inventory systems and does not consider multi-stage options. However, in real-world production-inventory systems, defective items are inevitable, and a fraction of the produced items may be defective. In addition, to avoid extra costs and consider environmental issues, organizations tend to rework activities. We propose single and multi-stage production-inventory systems in manufacturing operations where the process is defective, rework is possible, and a percentage of items are scrapped. A main assumption in the current paper is that the defective rate is assumed to be an uncertain parameter. The grey systems theory, as a mathematical tool to address uncertain information in real-world situations, is utilized to model the random defective rate via a grey nonlinear programming problem. The proposed issues are investigated via numerical examples to assess the impact of grey parameters on optimal solutions. [ABSTRACT FROM AUTHOR]

The massive Internet of Things (IoT) comprises different gateways (GW) covering a given region of a massive number of connected devices with sensors. In IoT networks, transmission interference is observed when different sensor devices (SD) try to send information to a single GW. This is mitigated by allotting various channels to adjoining GWs. Furthermore, SDs are permitted to associate with anyGWin a network, naturally choosing the one with a higher received signal strength indicator (RSSI), regardless of whether it is the ideal choice for network execution. Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs. Recently, in remote IoT networks, the utilization of machine learning (ML) strategies has arisen as a viable answer to determine the effect of various models in the system, and reinforcement learning (RL) is one of these ML techniques. Therefore, this paper proposes the use of an RL algorithm for GW determination and association in IoT networks. For this purpose, this study allows GWs and SDs with intelligence, through executing the multi-armed bandit (MAB) calculation, to investigate and determine the optimal GW with which to associate. In this paper, rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations, which include different IoT network topologies. The evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art (RSSI-based) and related research approaches. [ABSTRACT FROM AUTHOR]

The optimization of train formation plan is a large-scale combinatorial optimization problem, which is difficult to solve. This paper mainly studies the optimization algorithm of the single-block train formation plan. The corresponding mathematical model is established by consulting relevant literature, and a positive feedback search algorithm based on absolute conditions is proposed. First of all, the wagon flow that meets the absolute conditions directly runs through train flow without making other choices. For the wagon flow that does not meet the absolute conditions, select the target station to reach directly according to the probability. The probability of wagon flow selecting a station is calculated according to the pheromone of the wagon flow at the station. At the same time, a pheromone update strategy with positive feedback mechanism is proposed to make the search process converge. Finally, the feasibility of the algorithm and the necessity of introducing absolute conditions into the algorithm are verified by taking eight technical stations in the linear direction of the road network as examples. [ABSTRACT FROM AUTHOR]

This paper introduces a novel pseudo-random sequence generator, applicable across all uses of pseudo noise (PN)-sequence. The proposed generator, coined as the circular pseudo-random signal generator, embodies a unique fusion of graphical representation and mathematical modeling. The cornerstone of this method is its capability to offer variable configurations in pseudo-random sequence generation, enabling the adaptive operation of the pseudo-random sequence between the transmitter and the receiver. Uniquely, the circular pseudo-random Sequence Generator can generate pseudo-random sequences of varying lengths, with practical implementation feasible through multiple methodologies, including microcontrollers or field-programmable gate array (FPGA) technology. Consequently, the paper endeavors to elucidate the mathematical model of generation, supplemented with illustrative examples, and demonstrate the real-world implementation using FPGA technology. With broad applicability, this sequence generator is well-suited to all applications requiring such a generator, notably in security applications and pilot generations. [ABSTRACT FROM AUTHOR]

Membrane-based purification of therapeutic agents has recently attracted global attention as a promising replacement for conventional techniques like distillation and pervaporation. Despite the conduction of different investigations, development of more research about the operational feasibility of using polymeric membranes to separate the detrimental impurities of molecular entities is of great importance. The focus of this paper is to develop a numerical strategy based on multiple machine learning methods to predict the concentration distribution of solute through a membrane-based separation process. Two inputs are being analyzed in this study, specifically r and z. Furthermore, the single target output is C , and the number of data points exceeds 8000. To analyze and model the data for this study, we used the Adaboost (Adaptive Boosting) model over three different base learners (K-Nearest Neighbors (KNN), Linear Regression (LR), and Gaussian Process Regression (GPR)). In the process of hyper-parameter optimization for models, the BA optimization algorithm applied on the adaptive boosted models. Finally, Boosted KNN, Boosted LR, and Boosted GPR have scores of 0.9853, 0.8751, and 0.9793 in terms of R2 metric. Based on the recent fact and other analyses, boosted KNN model is introduced as the most appropriate model of this research. The error rates for this model are 2.073 × 101 and 1.06 × 10−2 in terms of MAE and MAPE metrics. • Predicting concentration distribution of solute through a membrane-based process. • Machine learning model development using CFD data. • Optimization of hyper-parameters using BA algorithm. [ABSTRACT FROM AUTHOR]

This study focuses on an artery with multiple stenoses, emphasizing the electro-osmotic effects. The artery's walls are porous, and slip boundary effects are present. Blood ow problems are better modeled with a slip and porous border. It is examined extensively due to the wide range of applications in the medical field, especially in diagnosing drug delivery and handling cellular irregularities. In this paper, we have visualized the non-Newtonian behavior of blood by using viscoelastic fluids as Williamson's fluid model. A mathematical model for an incompressible fluid is created, and the mathematical issue is then transformed into its dimensionless form by applying limitations in the case of mild multiple stenoses. The partial differential equations for the velocity and temperature profiles can be found when the problem is put into a dimensionless form. Analytical solutions of the resulting system are calculated with the help of the Homotopy Perturbation Method (HPM). The visual representation of analytically obtained solutions is investigated for both symmetric and non-symmetric geometries of stenosis. For varied values of ow rate Q and electro-osmotic parameter m, the streamlines are examined in detail. [ABSTRACT FROM AUTHOR]

Oil waste is one of the most important pollutants in the oil and gas industry. Since the oil owing in the wells contains significant amount of saltwater, the effluent amount rises upon increasing the oil reservoir extraction. Separating the saltwater from the extracted oil before starting the refinery process plays an essential role in reducing the oil costs and benefiting from the transfer capacity as well. This paper presents a new Chapman-Kolmogorov Equation-Based (CKEB) method to evaluate the availability of a desalination system with three-state equipment and weighted-k-out-of-n configuration. In this system, the equipment is repairable, and each repair facility can repair all equipment types of different sub-systems (pump stations). All failures and repairs were considered to have a constant rate (with exponential distribution) and use the Chapman-Kolmogorov equations to drive the system availability. Then, the presented method was validated using a simulation technique. Finally, the elapsed times of solving both techniques were calculated and compared. The results confirmed the superiority of the CKEB technique in terms of computational time. Compared with the simulation technique, the computational time ratio for the CKEB method was in the range of 0:0002%0:0058% for the small-size problems, 0:05%0:94% for the medium-size problems, and 1.31%-5.39% for the large-size problems. [ABSTRACT FROM AUTHOR]

The recent outbreak of rabies virus has affected numerous individuals in the community, highlighting the importance of studying the disease mathematically in epidemiology. In this paper, we develop a mathematical model for the spread of rabies disease using the harmonic mean incidence rate and determine the reproduction number R 0 using the next generation matrix approach. The fractional dynamics of the model incorporate the interaction between infected individuals and environmental factors. We analyze the model using both Atangana-Baleanu-Caputo (ABC) and Caputo-Fabrizio (CF) techniques, drawing on both modern and classical approaches. For qualitative investigation using fractional operators, we utilize the Banach fixed-point theory and derive the Hyers-Ulam stability concept. We assign values to the model parameters and utilize the Newton interpolation technique to obtain a numerical scheme. Additionally, we perform sensitivity analysis on the model. In conclusion, our findings provide important insights for the study of epidemics. [ABSTRACT FROM AUTHOR]

This paper argues that a greater understanding of the role of emotions in experiencing leadership, the impact of role models and cultures of the workplace and profession, may enable further development for effective leadership development at undergraduate level and beyond. Leadership has gained prominence as a necessary skill in nursing literature and policy, linking its importance to patient safety, working cultures, resilience and emotional labour globally. Viewed as essential in many undergraduate nursing programmes and a requirement by professional regulators, there is a globally agreed acceptance of its importance. Despite this, the focus on evaluating and researching the effectiveness of leadership learning and through experiences of students in contexts of learning remain limited. This paper presents a discussion on the importance of experiences of leadership, exploring the emotional impact and how enabling reflexivity and critical analysis can be integrated in education. An approach is proposed for nursing education where the emotional impact of experiencing leadership is given significance. Experiences of leadership in practice and educational learning in higher education should allow students to reflect and conceptualise experience, aligning educational contexts of learning. Acknowledgement of emotional experience and pragmatism provides opportunity to strengthen evidence and knowledge and establish leadership as a concept of value in the profession from an early stage. A critical theoretical discussion paper Based on a narrative inquiry study, drawing on theory and philosophies of emotions in education and leadership from 1907 to 2023 Acknowledgement of emotional experience and pragmatism provides opportunity to strengthen evidence and establish leadership as a concept of value in the profession from an early stage. Placing value on the experience of leadership by reflexivity and pragmatic, experiential approaches to learning can align educational contexts of learning and focus on effective leadership learning for the nursing profession. Pragmatic approaches and reflexivity rationalise emotional experiences of leadership and encourage critical thinking and learning [ABSTRACT FROM AUTHOR]

Copyright of Paradigma is the property of Universidad Pedagogica Experimental Libertador and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the stabilization and set point tracking of the utility boiler is achieved via the Proportional-Integral-Derivative (PID) controller, tuned using the versions of metaheuristic Particle Swarm Optimization (PSO) algorithms. The utility boiler model dynamics are based on the Bell and Astrom boiler turbine system. Initially, the non-linear mathematical model is obtained using the first principles data from the plant model. Further, Taylor series approximation is applied to linearize the non-linear equations around the equilibrium point to implement a stable controller for the plant. Finally, the PID controller gains for the plant model are determined using the PSO algorithms to achieve the desired response. For tuning the PID controller, three PSO algorithms namely standard PSO, Adaptive PSO (APSO) and Improved Adaptive PSO (IAPSO) are proposed. The performance of the proposed PSO based PID control algorithms are evaluated using error metrics such as Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) and Integral Square Error (ISE). The simulation results show that the proposed APSO and IAPSO algorithms have improved performance when compared to regular PSO algorithms and are capable of optimizing the control parameters. [ABSTRACT FROM AUTHOR]

The distributed heterogeneous permutation flowshop scheduling problem with lot-streaming (DHPFSPLS) is a provocative scheduling and optimization problem confronting both industry and engineering. However, no result is available in investigating the DHPFSPLS with variable number of sublots. This paper presents a many-objective mathematical model of this problem with the objectives of makespan, idle time of machines, total production cost and total flow time, considering the transfer time and sequence-independent setup time. Based on this model, a modified adaptive switching-based many-objective evolutionary algorithm is proposed, in which each solution is coded using a three-vector-based solution representation, i.e., a factory assignment vector, a lot-splitting vector and a job permutation vector. Then, a novel two-population collaborative search strategy based on a learning mechanism is designed, which can enhance exploitation abilities and make effective use of optimization knowledge from the population. Moreover, an adaptive switching strategy-based environmental selection is implemented to ensure the convergence and diversity of the solution set. Through a variety of computational tests and comparisons, the effectiveness of the proposed algorithm in solving the many-objective DHPFSPLS is demonstrated. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual framework that combines technology-based instruction and mathematical modeling in a STEAM-oriented approach to enhance the English language acquisition of Malaysian students. The proposed framework consists of a six-month English as a Foreign Language program that integrates technology and mathematical simulation in a STEAMoriented methodology. The exercises are designed to enhance linguistic competence, with a focus on improving listening, speaking, reading, and writing skills. The framework aims to foster a positive learning environment that encourages self-determination and promotes sociocultural interaction. The integration of technology-enabled instruction and mathematical modeling offers a viable strategy for enhancing the language competency of non-native English speakers. However, further research and empirical analysis are necessary to evaluate the impact of this framework on academic performance. Despite this limitation, the proposed framework offers a promising approach to address the challenges faced by less proficient Malaysian students in acquiring English language skills. In conclusion, this paper presents a conceptual framework that integrates technology-based instruction and mathematical modeling in a STEAM-oriented approach to enhance the English language acquisition of less proficient Malaysian students. The framework is grounded in sociocultural and self-determination theoretical perspectives and aims to create a positive learning environment that promotes linguistic competence. [ABSTRACT FROM AUTHOR]

In this digital, internet-based world, it is not new to face cyber attacks from time to time. A number of heavy viruses have been made by hackers, and they have successfully given big losses to our systems. In the family of these viruses, the Stuxnet virus is a well-known name. Stuxnet is a very dangerous virus that probably targets the control systems of our industry. The main source of this virus can be an infected USB drive or flash drive. In this research paper, we study a mathematical model to define the dynamical structure or the effects of the Stuxnet virus on our computer systems. To study the given dynamics, we use a modified version of the Caputo-type fractional derivative, which can be used as an old Caputo derivative by fixing some slight changes, which is an advantage of this study. We demonstrate that the given fractional Caputo-type dynamical model has a unique solution using fixed point theory. We derive the solution of the proposed non-linear non-classical model with the application of a recent version of the Predictor–Corrector scheme. We analyze various graphs at different values of the arrival rate of new computers, damage rate, virus transmission rate, and natural removal rate. In the graphical interpretations, we verify the values of fractional orders and simulate 2-D and 3-D graphics to understand the dynamics clearly. The major novelty of this study is that we formulate the optimal control problem and its important consequences both theoretically and mathematically, which can be further extended graphically. The main contribution of this research work is to provide some novel results on the Stuxnet virus dynamics and explore the uses of fractional derivatives in computer science. The given methodology is effective, fully novel, and very easy to understand. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model depicting the transmission dynamics of Chickenpox is developed by incorporating a new parameter denoting the rate of precautionary measures. The influence and the importance of following precautionary measures are showed by applying the real data collected at Phuket province, Thailand. The model analysis such as positivity and boundedness of the solutions are provided. The rate of precaution for the spread the of chickenpox was a factor that influenced the basic reproductive number, which was calculated using the next-generation matrix approach. The model's equilibrium points are identified, and the condition for the disease-free equilibrium's local and global asymptotic stability is established. The model also shows forward bifurcation. Numerical simulation is carried out to show the importance of considering the precautionary measures while controlling the disease spread and the influence of those introduced parameters are depicted graphically. Though our results, we concluded that the rate of precautionary measures plays an vital role at the same time it reduces the chance of getting infected by Chickenpox virus. • The Mathematical model is developed to understand the control of Chickenpox. • Diseases spread among people with and without complications were studied. • Stability of equilibrium points were examined by Routh and Castillo theorems. • While studying the influence of transmission rate, we ended up with forward bifurcation. • In our study of transmission rate, we discovered forward bifurcation. • We proved that, Multiple precautions = lower infection risk, reduced R 1. [ABSTRACT FROM AUTHOR]

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This paper considers the acculturation process and the ways in which it can unfold. The main focus is directed towards the possibility of creating novel ways of acting and living as a form of acculturation. Although this possibility has been acknowledged by researchers, it has been mostly integrated into typologies of biculturalism based on John Berry's bidimensional acculturation model, and considered to belong to integration strategy. It is argued that such an approach poses logical and methodological problems, and that the form of acculturation, where something novel is created, goes beyond the bidimensional conceptualization of acculturation. A new model extending Berry's model is proposed. It includes a third dimension which refers to the possibility of creating new ways of acting and living to the process which will be subsequently referred to as cultural creation. Distinctions and relationships with other tridimensional models and/or models including cultural creation process are discussed. Subsequently, another layer to the model is introduced which relates to the understanding of culture providing foundation for the model. Familiarity, unfamiliarity and novelty are proposed as reference points for the model's dimensions, enabling the model to move away from the systemic conception of culture and integrate the constructivist understanding of culture. The potential mechanisms responsible for the creation of novelty within the acculturation process are discussed in context of two theoretical approaches: the creative cognition approach and the practice approach. [ABSTRACT FROM AUTHOR]

Copyright of Revista Interuniversitaria de Formación del Profesorado is the property of Asociacion Universitaria de Formacion del Profesorado (AUFOP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An integrated decision in supply chain is a significant principle for proper competition in today's market. This paper proposes a novel mathematical model in a two-stage supply chain scheduling to make a coordination between procurement and manufacturing activities. The supply chain scheduling along with the production approach of cellular manufacturing under demand, processing time, and transportation time uncertainties makes business environment sustainably responsive to the changing needs of customers. Uncertainties are formulated by queuing theory. In this paper, a new mixed-integer nonlinear programming formulation is used to determine types of vehicles to carry raw materials, suppliers to procure, priority of each part in order to process, and cell formation to configure work centers. The goal is to minimize total tardiness. A linearization method is used to ease tractability of the model. A genetic algorithm is developed due to the NP-hard nature of the problem. The parameters of the genetic algorithm are set and estimated by Taguchi's experimental design. Numerous test problems are employed to validate the effectiveness of the modeling and the efficiency of solution approaches. Finally, a real case study and a sensitivity analysis are discussed to provide significant managerial insights and assess the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

This paper discusses a particular situation in project management in which an analyst attempts to prioritize several independent activities to handle all of them one by one in such a way that there would be no precedence relationships over the activities. The novelty of this research is that the structure of prioritized activities is linear in arrangement which can be considered as a combinatorial optimization problem. The paper formulates a mathematical model and applies it to two real cases in the oil and gas industry. In addition, a row generation procedure is developed to solve largescale problems and the computational results for the problem instances of size up to 300 activities are reported. The results demonstrate the applicability and efficiency of the proposed methodology. [ABSTRACT FROM AUTHOR]

Assessing uncertainty in decision-making is a major challenge for DecisionMakers (DMs), and the g-Rung Orthopair Fuzzy Set (q-ROFS) as the direct extension of Intuitionistic Fuzzy Set (IFS) and Pythagorean Fuzzy Set (PFS) play a crucial role in this aspect. The Complex g-Run Orthopair Fuzzy Set (Cq-ROFS) is a strong tool to deal with imprecision, vagueness, and fuzziness by expanding the scope of Membership Degree (MD) and Non-Membership Degree (NMD) of q-ROFS from real to complex unit disc. In this paper, we develop some new Cq-ROF Hamacher Aggregation Operators (AOs), i.e., the Cq-ROF Hamacher Weighted Averaging (Cq-ROFHWA) operator, the Cq- ROFH Weighted Geometric (Cq-ROFHWG) operator, the Cq-ROFH Ordered Weighted Averaging (Cq-ROFHOWA) operator, and the Cq-ROFH Ordered Weighted Geometric (Cq-ROFHOWG) operator. Subsequently, we establish a novel Cq-ROF graph framework based on the Hamacher operator called Cq-ROFH Graphs (Cq-ROFHGs) and evaluate its energy and Randic energy. In particular, we compute the energy of a splitting Cq-ROFHG and shadow Cq-ROFHG. Further, we describe the notions of Cq-ROFH digraphs (Cq- ROFHDGs). Moreover, an algorithm is given to solve Multiple Attribute Group DecisionMaking (MAGDM) problems and the main steps are discussed clearly. Finally, a numerical instance related to the Facade Clothing Systems (FCS) selection is presented to show the effectiveness of the developed concepts in decision-making circumstances. In order to verify the effectiveness of our proposed scheme, a comparative analysis with previous approaches is provided. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective mathematical model to optimize and harmonize a supply chain in order to reduce costs, improve quality, and gain a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to enhance both quality and customer satisfaction and reduce the production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was first validated. The parameters of the proposed algorithm were then adjusted to solve the model using Multi-Objective Simulated Annealing (MOSA) algorithm. To validate the performance of the designed algorithm, some examples were solved based on General Algebraic Modeling System (GAMS). The MOSA algorithm achieved average errors of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in the average less than one minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS failed to reach an optimal solution for large problems in a reasonable computational time. The average error of the designed algorithm was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this paper. [ABSTRACT FROM AUTHOR]

This paper addresses disassembly line scheduling problems (DLSP) to minimize the smoothing index with the workstation number threshold. First, a mathematical model is developed to formulate the concerned problems. Second, seven novel neighborhood structures are designed based on the feature of the DLSP and the corresponding local search operators are designed. Third, a novel Q-Learning based variable neighborhood iterative search (Q-VNIS) algorithm is first proposed to solve the DLSP. Q-learning is employed to select the premium local search operator in each iteration. Finally, the effectiveness of Q-learning in the proposed Q-VNIS is verified. To test the performance of the proposed Q-VNIS, 20 cases with different scales are solved and the Friedman test is executed. The experimental results and discussions show that the proposed Q-VNIS competes strongly for solving the DLSP. [ABSTRACT FROM AUTHOR]

Robust and transparent formal benefit-risk (BR) analyses for medicinal products represent a means to better understand the appropriate use of medicinal products, and to maximize their value to prescribers and patients. Despite regulatory and social imperatives to conduct structured BR (sBR) assessments, and the availability of a plethora of methodological tools, there exists large variability in the uptake and execution of sBR assessments among pharmaceutical companies. As such, in this paper we present an sBR assessment framework developed and implemented within a large global pharmaceutical company that aims to guide the systematic assessment of BR across the continuum of drug development activities, from first-time-in-human studies through to regulatory submission. We define and emphasize the concepts of Key Clinical Benefits and Key Safety Risks as the foundation for BR analysis. Furthermore, we define and foundationally employ the concepts of sBR and a Core Company BR position as the key elements for our BR framework. We outline 3 simple stages for how to perform the fundamentals of an sBR analysis, along with an emphasis on the weighting of Key Clinical Benefits and Key Safety Risks, and a focus on any surrounding uncertainties. Additionally, we clarify existing definitions to differentiate descriptive, semi-quantitative, and fully quantitative BR methodologies. By presenting our framework, we wish to stimulate productive conversation between industry peers and health authorities regarding best practice in the BR field. This paper may also help facilitate the pragmatic implementation of sBR methodologies for organizations without an established framework for such assessments. [ABSTRACT FROM AUTHOR]

Magnetic localization techniques are used in in vivo therapy, drug delivery, and surgical localization widely due to their ability to locate in an unobstructed environment precisely. The Levenberg-Marquardt (L-M) algorithm is one of the most common magnetic localization algorithms due to its fast optimization speed and high precision, but it requires high initial values such that improper initial values can easily lead to local optimization. In this paper, the performance of ten commonly used intelligent optimization algorithms is compared based on the mathematical model of the permanent magnet. The final position of the magnet is obtained by a fusion algorithm that uses the result of the intelligent optimization algorithm as the initial value of L-M. The real-time localization accuracy of this fusion algorithm is verified on a hardware platform and experiments demonstrate that the integration of the TSA algorithm and L-M algorithm fusion yields higher performance of the magnetic positioning system. [ABSTRACT FROM AUTHOR]

This paper studies a distributed flowshop group robust scheduling problem with uncertain processing times, which has great significance in actual production activities. First, we formulate the problem and establish a robust mathematical model with an expect-risk rule. Second, we develop an effective cooperative iterated greedy (CIG) algorithm to address the studied problem. In the CIG, we propose a heuristic method with two modified sequence rules to obtain an initial solution. We present a dummy scenario method to reduce the complexity of the multi-scenario environment and speed up the process of convergence. We utilize different iterated greedy processes to optimize the family scheduling sub-problem and job scheduling sub-problem respectively. In each iterated greedy process, we design the corresponding operators based on the problem-specific characteristics. We also propose a cooperation mechanism to link the iterated greedy processes to emphasize the coupling relationship between the two sub-problems. Finally, we conduct comparative and comprehensive evaluation experiments by comparing the CIG with six high-performing algorithms in the literature. The results indicate that the proposed CIG significantly outperforms the other competitors from the average relative deviation index. [ABSTRACT FROM AUTHOR]

Mitogen-activated protein kinase (MEK) inhibitors and oncolytic virotherapy are identified as promising cancer therapies that can enhance the efficacy of other cancer treatments. A few studies demonstrate that cancer cells proliferate when exposed to virotherapy with MEK inhibitors in an integer order model or without them in a fractional order model. None of them are intended to investigate tumor cell growth under the combined treatment strategy of chemo-virotherapy with a MEK inhibitor in a fractional order model. In this paper, a mathematical model based on fractional order ordinary differential equations (ODEs) is developed for the mutual interactions among tumor cells, as well as a therapeutic combination of chemotherapy, oncolytic viruses and the functional consequence of MEK inhibitor, to investigate how virotherapy could enhance chemotherapy under the action of MEK inhibitor. The numerical results show that virus burst size and MEK inhibitors have a noticeable impact on regulating the trend of tumor cell proliferation. While virotherapy responses to tumor cell proliferation are undoubtedly quicker than chemotherapeutic treatment responses, MEK intensity clearly affects the success of the treatment regimen. The results of this study can contribute to the development of a therapeutic strategy that combines MEK inhibitor functional monitoring with tumor cell growth control. [ABSTRACT FROM AUTHOR]

Traditional linear active disturbance rejection control (LADRC) may have difficulty to achieve a rapid precise disturbance rejection for a permanent magnet synchronous linear motor (PMSLM). By making use of model information, a model predictive and compensated LADRC (MPLADRC) method is proposed in this paper. In this method, a model compensated extended state observer (MESO) is designed to transform the controlled object into an established mathematical model through total disturbance compensation. Meanwhile, considering the delay problem of MESO, a phase advance module is designed to improve the estimation speed of MESO for system disturbance and state, thus the MESO can rapidly compensate various uncertainty disturbances to the controlled object in real time. The model predictive controller (MPC) is then designed based on the mathematical model, and its optimal control law is then obtained through a quadratic objective function to further suppress the disturbance unobserved by the designed MESO. The proposed method can thus realize a dual-degree-of-freedom disturbance rejection through the MESO and MPC. The simulation and experimental results validate the effectiveness of the proposed MPLADRC in rapid anti-disturbance and fast positioning for the motion control of the PMSLM. • An MPLADRC method for achieving real-time disturbance rejection is proposed. • A model compensation ESO is designed to accurately estimate the total disturbance. • A phase advance module is designed to solve the ESO delay problem. • A predictive controller is designed to further suppress the disturbance. [ABSTRACT FROM AUTHOR]

In this paper, harmonic resonance analysis of the grid comprising Wind Power Plants (WPPs) with Permanent Magnet Synchronous Generators (PMSGs) is presented. Usually, Norton equivalent circuit is used as the Wind Turbine (WT) model for harmonic resonance studies, in which the WTs are simply modeled as ideal current and voltage sources and the impacts of converters controls on the WTs output impedances are neglected. This paper deals with the harmonic resonance analysis of the WPP system by considering the impacts of converters controls and current and voltage measurement filters. In doing so, harmonic impedance models of the WTs are presented for simple and detailed cases. Then, the WT harmonic impedance is extracted for the mentioned cases. Next, the results of frequency scan and harmonic mode resonance analyses for different cases are given and compared. Then, the effects of the current and voltage measurement filters and WT converter control on harmonic resonance analysis of the study WPP system are presented. It is shown that increasing the bandwidth of the grid-side converter current control loop cannot shift the resonant frequencies, but it certainly enhances the system damping and, consequently, reduces the driving point impedances in the resonant frequencies. [ABSTRACT FROM AUTHOR]

This research paper proposes a hybrid Whale Optimization Algorithm (WOA) variant based on Equilibrium Optimizer (EO), named Equilibrium Whale Optimization Algorithm (EWOA). The major finding lies in an efficient hybridization of bio-inspired (WOA) and physics-based (EO) metaheuristic algorithms. Upon mathematical modelling, EWOA proposes a main architecture that combines WOA's encircling and net-bubble attacking mechanisms via EO's weight balance strategy. The proposed algorithm was tested on 23 classical, 28 constrained CEC 2017, 30 unconstrained CEC 2017, 10 CEC 2019, and 10 CEC 2020 benchmark problems, in comparison with six recently proposed state-of-the-art algorithms (including WOA and EO). EWOA outperforms other algorithms with the best statistical mean performance on 46 out of 101 functions and the most promising clustering data in the graph, respectively. The fact that EWOA could achieve best statistical SD performance on 2 of the total 5 benchmark sets proves that EWOA is competitively robust. EWOA can converge to the optimum before 50% iterations of most benchmark functions, achieving the fastest convergence rate compared to other algorithms. The major contribution thereby lies in the successful development of this hybrid algorithm, which yields better optimization efficiency than the original and other state-of-the-art algorithms in terms of statistics, convergence and clustering. [ABSTRACT FROM AUTHOR]

Copyright of Chemical Industry / Hemijska Industrija is the property of Association of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)