Programming Method Research Articles

Concept of flexible no-code automation for complex sample preparation procedures

Driven by demographic changes and dwindling Science Technology Engineering Mathematics enrolments, our research introduces no-code automation as a strategic response, aimed at mitigating labor shortages while enhancing productivity and safety in the laboratory environment. Employing a user-friendly, no-code software platform, we automated a complex HPTLC assay, enabling laboratory personnel to configure and modify workflows without requiring specialized programming skills. The manuscript outlines the deployment of a collaborative robot (cobot), a programmable logic controller (PLC), and the utilization of self-developed open-source hardware components to establish automated stations for sample handling, incubation, spraying, detection, and storage within the assay process. The research addresses challenges such as the handling of fragile HPTLC plates and the seamless integration of automated stations, solved through innovative design solutions and adaptive programming methods. This investigation demonstrates the feasibility and efficiency of no-code automation in overcoming skilled labor deficits.

Neural network optimal control for tripartite UAV confrontation systems based on fuzzy differential game

The neural network optimal control strategy based on a fuzzy differential game is proposed for the tripartite UAV confrontation systems consisting of the attackers, defenders, and targets. Firstly, the tripartite UAV mutual confrontation model is constructed and a nonlinear differential control system is established. Secondly, combining the fuzzy evaluation method and differential game theory, the tripartite UAV are divided into two parts of the confrontation game: attackers-defenders and attackers-targets. The optimal control strategies for the attackers, defenders and targets parties are derived separately. Then, the tripartite UAV game model is considered to be difficult to solve directly. The evaluation neural network is introduced to approximate the optimal value function using an adaptive dynamic programming method. The convergence of the evaluation neural network weights and the stability of the nonlinear differential control system are proved by using Lyapunov stability theory. Finally, the effectiveness of the tripartite UAV confrontation game control strategy designed in this paper is verified by simulation.

Efficient Pump Scheduling for Large‐Scale Multiproduct Pipelines Using Deep Reinforcement Learning

ABSTRACTOptimizing pump scheduling in multiproduct pipelines can significantly reduce energy consumption and carbon emissions. For pump scheduling in multiproduct pipelines, to describe hydraulic losses more accurately, the model needs to adopt shorter discrete time intervals, which will lead to longer decision‐making time. It combined with the large solution space of large‐scale pipelines, will lead to low solution efficiency with dynamic programming methods and poor solution quality with heuristic optimization algorithms. Given that, this article develops a multiproduct refined oil transmission simulation system and employs the enhanced Proximal Policy Optimization (PPO) algorithm with action space shaping trick to optimize pump scheduling for large‐scale multiproduct pipelines. The method of converting discrete action space to multi‐discrete action space through action shaping can address PPO's low convergence efficiency issue resulting from the large discrete action space challenge common in large‐scale multiproduct pipelines. The experimental results indicate that the proposed method, that is, PPO algorithm with multidiscrete action space exhibits significant advantages in terms of efficiency and robustness in large‐scale pipelines compared to mainstream methods for pump scheduling such as dynamic programming (DP), genetic algorithms (GA), and ant colony optimization (ACO). Furthermore, we demonstrate the effectiveness of action space shaping in large‐scale pipelines from the perspectives of exploration and exploitation.

ASSESSMENT OF KNOWLEDGE, ATTITUDES AND PRACTICES ON RHEUMATIC HEART DISEASE AMONG CARDIAC PATIENTS IN AMRAN CITY, YEMEN

Background: All three layers of the heart—the pericardium, myocardium, and endocardium (including the valves)—may sustain irreversible damage as a result of rheumatic heart disease (RHD). Rheumatic heart disease is the result of valvular damage caused by an abnormal immune response to group a streptococcal infection, usually during childhood. The study aims to achieve the assessment of Knowledge, Attitudes and Practices (KAP) on rheumatic heart disease among cardiac patients in Amran city, Yemen. Method: The descriptive analytical method was used. The questionnaire was used as a tool for collecting data the research sample was (121) of those accompanying rheumatic heart patients in clinics and medical centers in the city of Amran, and they were selected in a simple random method. The data was analyzed and processed statistically using the Statistical Package for the Social Sciences program (SPSS) and many statistical methods, and after conducting the data analysis process and testing the study hypotheses. Result: The study found that heart patients in Amran have knowledge about rheumatic heart disease causes and symptoms, but lack knowledge about risk factors and disease history. Most suffer from arthritis and do not practice regular exercise. There were no significant differences in opinions about rheumatic heart disease and risk factors among heart patients due to variables like gender, age, education, and living conditions. Rural residents showed statistically significant differences. Conclusions: The study reveals that patients with rheumatic heart disease have the highest knowledge about the causes and symptoms, but also the risk factors associated with the disease. The knowledge of the disease's history is the most common, followed by rheumatic fever. Arthritis is the most common risk factor. The study found no significant differences in opinions among heart patients in Amran, but significant differences were observed in rural residents' opinions due to factors like gender, age, and living conditions. Peer Review History: Received 13 May 2024; Reviewed 14 July 2024; Accepted 22 August; Available online 15 September 2024 Academic Editor: Dr. Sally A. El-Zahaby, Pharos University in Alexandria, Egypt, sally.elzahaby@yahoo.com Average Peer review marks at initial stage: 6.0/10 Average Peer review marks at publication stage: 7.0/10 Reviewers: Dina Abd Elfattah Eldakhs, Pharos university (PUA), Egypt, dina_eldakhs@yahoo.com Prof. Hassan A.H. Al-Shamahy, Sana'a University, Yemen, shmahe@yemen.net.ye

An exact constraint programming method for the multi-manned assembly line balancing problem with assignment restrictions

This paper proposes an exact Constraint Programming (CP) method with an extensive focus on real-world constraints for the Multi-manned Assembly Line Balancing Problem with Assignment Restrictions (MALBP-AR). We perform an in-depth literature review to gather examples from real assembly lines and organize the AR regarding tasks, stations, workers, and mounting positions. Our study classifies the AR related to transformed resources and provides a general and unified model. We explore the concept of variable workplaces to dynamically assign workers to mounting positions and aggregate no overlap restrictions to avoid interference between workers. The classic MALBP model is extended by gradually incrementing the number of restrictions. The model variant found in the literature is herein called Partial MALBP-AR. Compared to the previous state-of-the-art Tabu Search Algorithm (TSA) for this problem, the Partial MALBP-AR found twelve additional optimality proofs. Besides the relevant results regarding solution quality, the CP method also has a satisfactory CPU performance. We also propose an entirely new set of AR and test these practical conditions with the so-called Extended MALBP-AR. Such an extended model, which covers all the AR presented here, reached optimality within the computational time limit for 36 out of 38 instances. The worst-case gap for an open instance is 8.20%. The results show a trade-off between the number of deemed restrictions and the computational performance. However, considering the detailed set of AR, we can obtain more representative solutions regarding the final balancing implementation compared to theoretical cases. The method can be used to design experiments, turning certain constraints on and off and allowing managers to evaluate different resource allocation scenarios.

Curriculum, competency development, and assessment methods of MSc and PhD pharmacy programs: a scoping review

Background/objectivesWe aim to systematically review and evaluate the current landscape of postgraduate pharmacy education to a) identify current evidence, best practices, challenges, recommendations, and solutions; and b) develop a framework to optimize postgraduate pharmacy programs.MethodsA scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Electronic databases, including PubMed, Scopus, EMBASE, ProQuest, Web of Science, and Google Scholar were utilized. The search covered studies published from January 2011 to September 2023. Following the principles of Arksey and O'Malley's framework, data charting and extraction were performed using a pre-designed data collection tool, followed by the synthesis and grouping of studies based on common themes.ResultsOf the 5542 articles found, the review included 36 eligible ones focusing on pharmacy postgraduate education (PhD and MSc), grouped into three themes: 1) courses and curriculum; 2) training and skills development; 3) assessment and mentorship methods. Utilized methodologies included descriptive analyses, questionnaires, surveys, trials, and focus groups/interviews. The studies underscored the need for competency-based curricula with regular evaluations, career planning, and diverse course offerings. Identified key skills and competencies in the studies included soft skills, communication, research, desperate skills (e.g., leadership and management), and critical thinking. The studies also emphasized the value of comprehensive evaluation and peer review methods. Challenges included balancing academic and real-world requirements, training, limited resources, time constraints, and faculty workload.ConclusionEvidence-based suggestions to improve postgraduate pharmacy education include the implementation of practice-oriented courses, value of tailored/or comprehensive assessments, focus on real-world skills, effectiveness of advanced teaching methods, and mentorship role. The proposed framework can guide program enhancement and highlight the need to improve programs holistically, entailing the three themes.

CO2 emissions of fuel-cell battery hybrid system for large ships

ABSTRACT The needs of solid oxide fuel cells (SOFC) and batteries in large ships are analyzed by estimating the amount of fuel consumption and CO2 emissions. Three types of systems are proposed: 1) fuel cells-battery-generator engine system, 2) battery-generator engine system, and 3) generator engine system. This study is based on the time-varying electric power demand and adopted the sequential quadratic programming (SQP) method to find the optimal power distribution of mixed power sources. CO2 emissions in the SOFC hybrid system are 11.6% lower than in the conventional generator engine system, and those in the battery-generator engine system are 5.1% lower than in the conventional system. The reduction of CO2 emissions come from three factors: the operation of the generator engine at a relatively high load rate, the battery sharing the operation of the generator engine, and the high efficiency of the SOFC running. The SOFC hybrid system can be a sustainable technology in large vessels.

Dynamic Vaccine Allocation for Control of Human-Transmissible Disease.

During pandemics, such as COVID-19, supplies of vaccines can be insufficient for meeting all needs, particularly when vaccines first become available. Our study develops a dynamic methodology for vaccine allocation, segmented by region, age, and timeframe, using a time-sensitive, age-structured compartmental model. Based on the objective of minimizing a weighted sum of deaths and cases, we used the Sequential Least Squares Quadratic Programming method to search for a locally optimal COVID-19 vaccine allocation for the United States, for the period from 16 December 2020 to 30 June 2021, where regions corresponded to the 50 states in the United States (U.S.). We also compared our solution to actual allocations of vaccines. From our model, we estimate that approximately 1.8 million cases and 9 thousand deaths could have been averted in the U.S. with an improved allocation. When case reduction is prioritized over death reduction, we found that young people (17 and younger) should receive priority over old people due to their potential to expose others. However, if death reduction is prioritized over case reduction, we found that more vaccines should be allocated to older people, due to their propensity for severe disease. While we have applied our methodology to COVID-19, our approach generalizes to other human-transmissible diseases, with potential application to future epidemics.

Failure prediction of geared mechanism at multiple gearshift configuration by utilizing the experimental design and integer programming method

Failure prediction of geared mechanism at multiple gearshift configuration by utilizing the experimental design and integer programming method

Bi-Level Optimal Configuration of Electric Thermal Storage Boilers in Thermal–Electrical Integrated Energy System

Electric thermal storage boilers (ETSBs) are important devices in enhancing the electric–thermal decoupling ability and spatiotemporal transfer of integrated energy system (IES), which is beneficial for improving system flexibility and energy utilization efficiency. In order to obtain more accurate and comprehensive results, a bi-level optimal model is proposed to study the site selection and capacity configuration of ETSB in IES based on the established mathematical model of ETSB. The objective of upper-level optimization of the model is obtaining the lowest energy supply cost when configuring the location and capacity of ETSB, while the lower-level model optimizes the operation scheduling with the goal of obtaining the lowest operational cost. The mixed-integer linear programming method and the genetic algorithm method are selected to obtain the optimal model. To illustrate the effectiveness and advantages of the proposed method, case studies are carried out. The optimal configuration scheme for an ETSB is obtained by comparing the lowest energy supply cost under different configuration parameters. Furthermore, the impact of an ETSB on the system is also analyzed based on the variations in energy balance, abandoned energy, and energy allocation before and after configuring the ETSB.

MILP Based Differential Cryptanalysis on IVLBC and Eslice 64

Lightweight block ciphers provide security to resource-limited devices. However, many of these ciphers lack security analysis against basic attacks. This paper provides a detailed security analysis of two lightweight block ciphers, IVLBC and Eslice-64, against differential attack. The designers of IVLBC and Eslice-64 claimed that their ciphers were secure against differential attack. In this paper, to substantiate existing cryptanalysis’s claims, we perform differential attack on these two ciphers using the mixed-integer linear programming (MILP) method. We incorporate the difference distribution table (DDT) probabilities into MILP models. We discover differential distinguishers up to seven and 15 rounds for IVLBC and Eslice-64, respectively. We improve the known distinguishers for Eslice-64 by one round. Further, we mount the key recovery attack on an eight-round IVLBC and a 16-round Eslice-64 with data/memory/time complexities of 249/ 250.59/249 and 263/212.58/263 respectively.

Выбор элемента(ов) прототипа модели медицинской организации в целях оптимизации процессов регионального проекта с использованием методов динамического программирования.

Changes in federal legislation regulating the provision of patients suffering from cardiovascular diseases initiate a revision of the regional legislation, and inevitably raises questions of practical adaptation and optimization of the processes of ongoing projects, taking into account resources in the established management model in medical organizations. Finding elements of a prototype model of a medical organization implementing a regional project as objects of influence for theoretical proof of hypotheses using the dynamic programming method (callback function, recursion) is the task of optimizing the project processes considered in this article. The purposeof the research question was to optimize the processes of a regional project using the dynamic programming method, which is focused on finding the objective function F(i) – the number of ways to return the function. The subgoal was to justify the use of dynamic programming methods. Materials and methods. The information base for the study was federal and regional regulatory legal acts published in the legislative reference and legal systems of the Russian Federation, and electronic resources. The study was carried out using methods of dynamic programming, optimization, recursion, and modeling. Results. We did not find similar studies in the available literature. This study was carried out using the dynamic programming method, which is designed to solve the problem of finding the target function(s) F(i) of regional projects. The authors attempted to find a prototype of an operating procedure at the level of project executors, which is indivisible and elementary for subsequent optimization of the variable (non-constant) elements of the prototype of a medical organization model from the found objective function. Finding F(i) by categories of patients (object of influence) registered at the dispensary, obtained using the callback function F(0), F(1), F(2), F(3), …F(k); F (0) ←F (1) ←F (2) ← F (3) ……F(n‑1) ← Fn (recursion) consists of decomposing the problem into smaller subproblems, finding the optimal solution to the subproblems recursively and using the resulting solution subtasks for constructing a solution to the original problem – optimizing an element of a prototype model of a medical organization, a regional project as a whole. Conclusion. The main tasks associated with project optimization are the tasks of minimizing the cost of the project for a given deadline for its implementation and minimizing (maximizing) the project completion period for a given marginal cost of its implementation. In search of a solution to optimization issues, the authors proposed finding F(i) by categories of patients (object of influence) registered as “D”, obtained using the callback function F (0) ←F (1) ←F (2) ← F (3) ……F(n‑1) ← Fn (recursion).

Stage stochastic incremental data envelopment analysis models and applications

Data envelopment analysis (DEA) is a mathematical programming method that can evaluate the relative efficiency of multiple inputs and multiple outputs of a decision-making unit (DMU). The classical DEA model assumes that inputs and outputs are determined. However, there are some applications where the inputs–outputs are stochastic. In practice, it is important to evaluate stage performance. It is essential to eliminate the effect of preceding stage inputs (outputs) on stage performance in order to accurately assess stage performance. In this paper, we propose stage stochastic incremental DEA models that integrate two different kinds of inputs and outputs. The first kind of model takes into account the assessment of stage efficiency when determinate incremental inputs and stochastic incremental outputs are applied at the beginning and end of the stage. The second kind of model uses stochastic incremental inputs–outputs to evaluate stage efficiency. To verify the efficacy of the suggested models, the first kind of model is applied to assess the stage financing efficiency of 15 energy-saving and environmental protection clean enterprises (ESEPCEs). The second kind of model is applied in assessing the stage investment efficiency of 15 ESEPCEs. The empirical results show that the proposed models not only eliminate the effect of prior performance but also more accurately assess stage efficiency in a stochastic environment.

ADP-based online compensation hierarchical sliding-mode control for partially unknown switched nonlinear systems with actuator failures

This article investigates an adaptive dynamic programming-based online compensation hierarchical sliding-mode control problem for a class of partially unknown switched nonlinear systems with actuator failures and uncertain perturbations under an identifier-critic neural networks architecture. Firstly, by introducing a cost function related to hierarchical sliding-mode surfaces for the nominal system, the original control problem is equivalently converted into an optimal control problem. To obtain this optimal control policy, the Hamilton–Jacobi–Bellman equation is solved through an adaptive dynamic programming method. Compared with conventional adaptive dynamic programming methods, the identifier-critic network architecture not only overcomes the limitation on the unknown internal dynamic but also eliminates the approximation error arising from the actor network. The weights in the critic network are tuned via the gradient descent approach and the experience replay technology, such that the persistence of excitation condition can be relaxed. Then, a compensation term containing hierarchical sliding-mode surfaces is used to offset uncertain actuator failures without the fault detection and isolation unit. Based on the Lyapunov stability theory, all states of the closed-loop nonlinear system are stable in the sense of uniformly ultimately boundedness. Finally, numerical and practical examples are given to demonstrate the effectiveness of our presented online compensation control strategy.

Assessing the thermoeconomic performance of a solar-powered trigeneration system with an upgraded transcritical carbon dioxide unit

With the intervention of the industry in every aspect of human lifestyle on the one hand, and the prompt reduction in fossil fuel and their harms to the environment, including global warming, on the flip side, the importance of renewable sources of energy has been revealed more than ever. The presented study attempted to investigate a new configuration for a trigeneration configuration based on a solar renewable source. The proposed system comprises a parabolic trough solar collector segment to drive a transcritical carbon dioxide power and refrigeration subsystem, a dual-pressure organic Rankine cycle, and a thermal vapor compression process combined with a multi-effect desalination unit. The suggested configuration is carefully inspected from thermodynamic and economic perspectives, encompassing an analysis of a certain condition and a detailed parametric evaluation. Four decision parameters are employed for the parametric evaluation, in conjunction with two scenarios from a multi-objective particle swarm optimization combined with a linear programming method for multidimensional preference analysis as a decision-maker. The examination outputs bring out a net output power of 12.147 MW, a 7.707 MW cooling load, a 4.448 kg/s freshwater, and energetic and exergetic efficiencies of 15.286 % and 10.192 %, respectively. Moreover, examining the system's performance from an economic perspective reveals a total product cost rate of 954.249 $/h, leading to a 4.694-year payback period. This study optimizes energy usage and minimizes waste, offering industrial applications such as reducing fossil fuel dependence and lowering greenhouse gas emissions. It supports sustainable development and facilitates the global transition to cleaner energy sources.

Image based information hiding via minimization of entropy and randomization

In this paper, a new approach that can effectively and securely hide information into color images with significantly improved security and hiding capacity is proposed. The proposed approach performs information hiding in three major steps. As the first step, two binary sequences are constructed from the least significant bits in the pixels of a cover image and the information that needs to be embedded, the information entropies of both sequences are minimized with a dynamic programming method. In the second step, the resulting sequences are randomly reshuffled into randomized sequences with mappings based on a set of one-dimensional chaotic systems, a single binary sequence can be obtained by a matching operation performed between the two randomized sequences. Finally, an inverse mapping is applied to the sequence obtained in the second step, and the transformed sequence is embedded into the least significant bits in the pixels of the cover image. Both analysis and experiments show that the proposed approach can achieve guaranteed performance in both security and capacity for long binary sequences. In addition, a comparison with other state-of-the-art methods for image-based information hiding suggests that the proposed approach can achieve significantly improved performance and is promising for practical applications.

Reinsurance contracts under Stackelberg game and market equilibrium

In this paper, we investigate the robust reinsurance contracts under Stackelberg game and market equilibrium. Each reinsurance contract contains two decision makers, one insurer and one reinsurer. The insurer is ambiguity-neutral and adopts a loss-dependent premium principle to collect premium. The reinsurer is ambiguity-averse and is a Bayesian learner. By using the stochastic dynamic programming method and the inverse method, the analytical expressions of the optimal risk allocation proportion and reinsurance price are derived for the two types of reinsurance contracts. We shows that the loss-dependent premium principle has the penalty-reward nature. Both the reinsurance price and demand decrease as the extrapolative intensity increases. Learning has important significance and always puts down the reinsurance price and puts up the reinsurance demand. On the contrary, the reinsurer’s ambiguity aversion raises the reinsurance price and decreases the reinsurance demand. Finally, numerical analysis reveals that the reinsurance price is greater under the Stackelberg game than that under the market equilibrium.

Dynamic Event-Triggered Safe Control for Nonlinear Game Systems With Asymmetric Input Saturation.

This article focuses on the Pareto optimal issues of nonlinear game systems with asymmetric input saturation under dynamic event-triggered mechanism (DETM). First, the safe control is guaranteed by transforming the system with safety constraints into the one without state constraints utilizing barrier function. The united cost function integrating nonquadratic utility function is constructed to provide the foundation to achieve the Pareto optimal solutions. Then, the adaptive dynamic programming method with concurrent learning is proposed to approximate the Pareto optimal strategies wherein both current and historical data are utilized. To further lessen the consumptions of computation/communication resources, the DETM is integrated into the adaptive algorithm framework which can avoid Zeno phenomena. All the signals of the closed-loop system are proved to be uniformly ultimately bounded. Finally, the simulation results are given to validate the effectiveness of the proposed method from several aspects.

A fast smoothing newton method for bilevel hyperparameter optimization for SVC with logistic loss

Support vector classification (SVC) with logistic loss has excellent theoretical properties in classification problems where the label values are not continuous. In this paper, we reformulate the hyperparameter selection for SVC with logistic loss as a bilevel optimization problem in which the upper-level problem and the lower-level problem are both based on logistic loss. The resulting bilevel optimization model is converted to a single-level nonlinear programming (NLP) based on the KKT conditions of the lower-level problem. Such NLP contains a set of nonlinear equality constraints and a simple lower-bound constraint. The second-order sufficient condition is characterized, which guarantees that the strict local optimizers are obtained. To solve such NLP, we apply the smoothing Newton method proposed in [Liang L, Sun D., Toh KC. A squared smoothing Newton method for semidefinite programming, 2023] to solve the KKT conditions, which contain one pair of complementarity constraints. We show that the smoothing Newton method has a superlinear convergence rate. Extensive numerical results verify the efficiency of the proposed approach and strict local minimizers can be achieved both numerically and theoretically. In particular, compared with other methods, our algorithm can achieve competitive results while consuming less time than other methods.

Current Trends of Exercise Programs for Improving Cognitive Function in Older Adults

PURPOSE: Limited studies have analyzed exercise protocols for cognitive improvement in older adults. Therefore, we closely examined the composition, content, and operation methods of these exercise programs to identify current trends.METHODS: We searched the Web of Science, PubMed, and Google Scholar databases for studies published between 2019 and 2024. The search terms used included “older adults”, “cognitive function”, “mild cognitive impairment”, “dementia”, “exercise”, “physical activity”, and other related terms, in both Korean and English.RESULTS: Aerobic exercises primarily comprised step-based exercises (walking and stepping) and dance routines, typically lasting 15-60 minutes per session, 1-3 times per week for 6-24 weeks, regardless of the individual’s cognitive level. Assistant instructors often participated, and in cases of severe cognitive impairment, patients’ caregivers also participated. Resistance exercises were most often performed using body weight and elastic bands, along with other tools and equipment, such as balls, water bottles, or specialized equipment. The exercises commonly involved 8-12 repetitions, 2-3 sets, with 1-minute rest between sets. Several programs included functional exercises to improve daily living performance. Other activities that were incorporated into the exercise programs included exergames and balance, mind-body, peripheral, and eye exercises.CONCLUSIONS: Recent cognitive exercise programs for older adults typically last three months and use a variety of tools or digital technologies within the scope of older adult exercise guidelines. Exercises for people with severe cognitive impairment tend to be more individualized, gradual, and flexible in their approach.