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This paper presents a novel approach to designing a CMOS inverter using the Mayfly Optimization Algorithm (MA). The MA is utilized in this paper to obtain symmetrical switching of the inverter, which is crucial in many digital electronic circuits. The MA method is found to have a fast convergence rate compared to other optimization methods, such as the Symbiotic Organisms Search (SOS), Particle Swarm Optimization (PSO), and Differential Evolution (DE). A total of eight different sets of design parameters and criteria were analyzed in Case I, and the results confirmed compatibility between the MA and Spice techniques. The maximum discrepancy in fall time across all design sets was found to be 2.075711 ns. In Case II, the objective was to create a symmetrical inverter with identical fall and rise times. The difference in fall and rise times was minimized based on Spice simulations, with the maximum difference measuring 0.9784731 ns. In Case III, the CMOS inverter was designed to achieve symmetrical fall and rise times as well as propagation delays. The Spice simulation results demonstrated that symmetry had been successfully achieved, with the minimum difference measuring 0.312893 ns and the maximum difference measuring 1.076540 ns. These Spice simulation results are consistent with the MA results. The results conclude that the MA is a reliable and simple optimization technique and can be used in similar electronic topologies. [ABSTRACT FROM AUTHOR]

Research on reconfiguration focused on the final configuration that reported the lowest losses. However, loads are growing rapidly, which is an important aspect that was not considered. This paper discusses an effective loadability approach to obtain the maximum load that could be added to any network. The main goal of this paper is to minimize power losses, enhance voltage index and achieve the maximum network loadability through simultaneous optimal size and location of distributed-generations (DGs), network reconfiguration and tab changer adjustment. A multi-objective decision approach integrated with weight factors that consider the maximum network loadability with the minimum loss and best voltage profile is the methodology adopted. Firefly (FA) was the selected optimization technique. The weight factors of the optimization function are determined using analytic hierarchy process. To assess the capabilities of the proposed method, simulations using MATLAB software are carried out on different networks. The obtained outcomes prove that integrating optimal placement and sizing of DGs in the system and network reconfiguration incorporating tap changer adjustment simultaneously yields a remarkable enhancement in voltage profile index, the minimum losses and the maximum bus loadability. The active and reactive power loss reductions for scenario 5 are 72.40% and 66.59% for IEEE 33-bus networks. Part of the output results is compared with other published works, and the proposed approach obtains a better result. The results report a high-power loss reduction of 71.55% for IEEE 33-bus systems. The proposed method also produces a better voltage profile compared with other published works. [ABSTRACT FROM AUTHOR]

Various strategies for optimizing the renewable energy system size exist, and they vary depending on the system design. In this paper, a standalone hybrid renewable energy system composed of a photovoltaic power source, wind turbines, storage, and diesel generators was considered. Linear programming was used to optimize the system in terms of multiple objectives. Bender's decomposition, lexicographic optimization, and epsilon constraint are among the linear programming techniques employed. The main objective function is to keep excess power to a minimum while also keeping unsatisfied demand to a minimum. This paper considers a full-year time span, optimizes household self-consumption, reduces curtailed power, maximizes local use of renewable generated power, lowers operational costs, lowers CO2 emissions, considers multiple renewable energy sources, considers system sizing as an objective, and incorporates the net present worth value of the components into the sizing. The aforementioned objectives and considerations have never been brought together yet in the literature. Furthermore, using a 365-day span is more comprehensive than using a 24-h span, which is typically used to limit the size of the linear programming issue. Therefore, in terms of integrated objectives, cost analysis, considerations, and timeframe, the proposed method is considered one of the most comprehensive. The ability to adjust the feed in limit to the grid and the change in electricity price during the day are also considered in this work. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to describe the new method for optimizing the topology of the control system frame for a canard missile to create its efficient model. Determining the minimum volume of the part risked losing some of the mechanical interfaces and functionality required of the frame. The proposed method must cope with these requirements and include a validation loop of the improved solution proposed by the software. The processing of the mathematical model to a printable form must take into account manufacturing technologies limitations and appropriate curvature continuities to avoid stress concentrations. Design/methodology/approach: Real examples from the aerospace industry are presented and the process of determining a prototype is described. The optimization assumed leaving the largest volume of the domain. Strength analyses were performed on both the assembly fasteners and the robust prototype. Once all boundary conditions were validated, topological optimization was performed in the ANSYS environment. The algorithm of the optimization was presented. Findings: Obtained fatigues showed the vast potential of topology optimization, efficient method of weight reduction in specific situations. It can be considered as an innovative approach to the manufacturing of products with a structure focused on the best possible correlation of weight and strength, for example of a canard rocket. Originality/value: The paper introduces precise manufacturing technology of the inner frame for the missile's control system, which ensures sufficient properties of the material, known as EBM. [ABSTRACT FROM AUTHOR]

Oilfield wastewater with high moisture content can be efficiently treated by supercritical water gasification (SCWG) technology to produce hydrogen. This paper conducted a thermodynamic and environmental analysis of the oilfield wastewater SCWG system to provide optimization strategies. The waste heat utilization technology can enhance the heat integration of the system to reduce the exergy destruction and increase the H 2 yield. Sensitivity analysis showed that a ratio of preheating water to emulsion 3 can promote water-sensitive reactions to produce H 2 and save the H 2 needed to maintain the energetically self-sufficient system. Low system pressures, high feedstock preheating temperatures, and low oxidation reactor temperatures can reduce system energy destruction to obtain high H 2 yield and system efficiency. Meantime, the developed SCWG system has the potential to achieve natural enrichment of greenhouse gases after the H 2 separation has been completed. The use of waste heat utilization technology and carbon capture and storage (CCS) technology is expected to increase system efficiency to 53.43 % and reduce global warming potential (GWP) of 5.52 kg CO 2 -eq/kg H 2. This work would be of great value to the process design and optimization for the SCWG system of oilfield wastewater. [Display omitted] • The process of supercritical water gasification of oilfield wastewater was simulated. • Optimization strategies were given by thermodynamic and environmental analysis. • Operational details were revealed by comprehensive sensitivity analysis. • The exergy efficiency of 53.43 % were obtained. • Global Warming Potential decreased to 5.52 kg CO 2 -eq/kg H 2 with CCS. [ABSTRACT FROM AUTHOR]

This paper formulates multi-objective (MO) generation scheduling problem of an integrated energy system (IES). The integrated energy system comprises combined heat and power (CHP), hydroelectric, thermal and heat units. The interdependency of heat and power in CHP units, water transport delay among various multi-chain hydroelectric units and valve point loading effect of thermal units makes the generation scheduling problem a complex, constrained, discontinuous, non-differentiable and multimodal optimization problem. The main motive of this research work is to concurrently reduce the overall generation cost and pollutant emissions emitted by various generating units. The generation scheduling problem is treated as a MO problem owing to the contradictory nature of these objectives. Thus, a very proficient hybrid optimization approach, i.e., quantum-based cuckoo search algorithm (QCSA) with mutation operators, is proposed for searching for the optimal generation schedule of the MO-integrated energy system generation scheduling (IESGS) problem. The proposed technique significantly improves the solutions obtained by QCSA by utilizing three mutation operators, i.e., Cauchy, Gaussian, and opposition-based mutation. The proposed strategy has been implemented to MO-hydroelectric-thermal (HT) generation scheduling and MO-IESGS problems to demonstrate the efficacy of the proposed method. The cardinal priority method is utilized for finding the most suitable non-dominated solution for both problems. The obtained results are comparatively better than the published results. The proposed approach's robustness compared to the QCSA has been further verified using the t-test. Based on comparisons and statistical analysis, the proposed technique is a promising approach for handling complex, multi-dimensional and non-convex generation scheduling optimization problems. [ABSTRACT FROM AUTHOR]

The textile industry produces high volumes of colored effluents that require multiple treatments to remove non-adsorbed dyes, which could be recalcitrant due to their complex chemical structure. Most of the studies have dealt with the biodegradation of mono or diazo dyes but rarely with poly-azo dyes. Therefore, the aim of this paper was to study the biodegradation of a four azo-bond dye (Sirius grey) and to optimize its decolorization conditions. Laccase-containing cell-free supernatant from the culture of a newly isolated fungal strain, Coriolopsis gallica strain BS9 was used in the presence of 1-hydroxybenzotriazol (HBT) to optimize the dye decolorization conditions. A Box–Benken design with four factors, namely pH, enzyme concentration, HBT concentration, and dye concentration, was performed to determine optimal conditions for the decolorization of Sirius grey. The optimal conditions were pH 5, 1 U/mL of laccase, 1 mM of HBT, and 50 mg/L of initial dye concentration, ensuring a decolorization yield and rate of 87.56% and 2.95%/min, respectively. The decolorized dye solution showed a decrease in its phytotoxicity (Germination index GI = 80%) compared to the non-treated solution (GI = 29%). This study suggests that the laccase-mediator system could be a promising alternative for dye removal from textile wastewater. [ABSTRACT FROM AUTHOR]

Modern rail/road transportation systems are critical to global travel and commercial transportation. The improvement of transport systems that are needed for efficient cargo movements possesses further challenges. For instance, diesel-powered trucks and goods trains are widely used in long-haul unimodal transportation of heavy cargo in most landlocked and developing countries, a situation that leads to concerns of greenhouse gases (GHGs) such as carbon dioxide coming from diesel fuel combustion. In this context, it is critical to understand aspects such as the use of some parameters, variables and constraints in the formulation of mathematical models, optimization techniques and algorithms that directly contribute to sustainable transportation solutions. In seeking sustainable solutions to the bulk cargo long-haul transportation problems in Zambia, we conduct a systematic review of various transportation modes and related mathematical models, and optimization approaches. In this paper, we provide an updated survey of various transport models for bulk cargo and their associated optimized combinations. We identify key research challenges and notable issues to be considered for further studies in transport system optimization, especially when dealing with long-haul unimodal or single-mode heavy cargo movement in countries that are yet to implement intermodal and multimodal systems. [ABSTRACT FROM AUTHOR]

Energy costs are now one of the leading criteria when procuring new computing hardware. Until recently, developers and users focused only on pure performance in terms of time-to-solution. Recent advances in energy-aware runtime systems render the optimization of both runtime and energy-to-solution possible by including hardware tuning depending on the application's workload. This work presents the impact that energy-sensitive tuning strategies have on a state-of-the-art high-performance computing code based on the lattice Boltzmann approach called waLBerla. We evaluate both CPU-only and GPU-accelerated supercomputers. This paper demonstrates that, with little user intervention, when using the energy-efficient runtime system called MERIC, it is possible to save a significant amount of energy while maintaining performance. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) play a major role in the wide variety of applications like underground pipeline and leaks monitoring, temperature distribution monitoring in industrial cyber systems, military, forest life monitoring, and environmental and geographical monitoring. Sensors are widely used in these different applications. The number of sensors and the application concerned mainly decides the energy consumption, network lifetime. In this process relay nodes may help the sensors as backbone to connect with sink node or base stations. In this paper, we introduce a new approach for relay node selection in WSN to minimize the energy consumption of the network. It uses channel aware relay selection technique using game theory optimization and act as a virtual backbone in connecting to the base station. However, the relay nodes are varied to check the optimal number of relays required for the small, medium and large number of nodes deployed in the network. Simulations are carried out using Network Simulator NS-2.35 and network is analyzed in wide variety of scenarios. Results show that the proposed relay node selection algorithm reduces energy consumption, improves lifetime, throughput of the network. [ABSTRACT FROM AUTHOR]

Large amounts of geographical and property rights information are contained in 3D cadastral property units, although the amount of cadastral information expressed from various perspectives varies. Therefore, it is crucial to choose a perspective that is compatible with human visual perception and conveys the most data as the perspective changes. This paper proposes an optimization method for generating the optimal view of 3D cadastral property units to address the above issues. The construction of candidate perspectives is the first step, followed by the calculation of feature values, including visibility of 3D boundary points under each candidate perspective, visible area ratio, and visual comfort. Then, an improved particle swarm optimization technique is used to determine the optimal view of 3D cadastral property units and the ideal viewpoint for the expected input model. The research results show that the selected optimal viewpoint fully considers human visual comfort, can accommodate a large amount of cadastral information, and can be used as a mapping drawing in social applications of 3D cadastral systems. This viewpoint can effectively supplement front views, side views, and axis side views, enabling more visual information to be transmitted in 3D religion maps and 3D property right maps and improving readability. [ABSTRACT FROM AUTHOR]

This article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual‐queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density‐based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV‐assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective decision model is formulated utilizing both single-layer and three-layer radon reduction layer materials by considering radon emanation rate and cost as decision objectives. Through a multi-objective non-dominated ranking method, the optimal solution is identified from a range of feasible alternatives. In the case of the example uranium tailings pond, the optimized three-layer radon reduction layer materials reduce the comprehensive cost by 47.8% compared with the single-layer radon reduction layer materials while meeting the radon emanation rate limit. Furthermore, the utilization of non-dominated multi-objective optimization techniques demonstrates higher efficiency in optimizing each radon reduction layer schemes. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present an improvement to the power quality of the grid by using a colliding body optimization (CBO) based proportional-integral (PI) compensated design for a grid-connected solar photovoltaic-fed brushless DC motor (BLDC)-driven water pumping system with a bidirectional power flow control. The system with bidirectional power flow allows driving the pump at full proportions uninterruptedly irrespective of the weather conditions and feeding a grid when water pumping is not required. Design/methodology/approach: Here, power quality issue is taken care of by the optimal generation of the duty cycle of the voltage source converter. The duty cycle is optimally generated by optimal selection of the gains of the current controller (i.e. PI), with the CBO technique resulting in a nearly unity power factor as well as lower total harmonic distortion (THD) of input current. In the CBO technique, the gains of the PI controller are considered as agents and collide with each other to obtain the best value. The system is simulated using MATLAB/Simulink and validated in real time with OPAL RT simulator, OP5700. Findings: It was found that the power quality of grid using the CBO technique has improved much better than the particle swarm optimization and Zeigler–Nichols approach. The bidirectional flow of control of VSC allowed for optimum resource utilization and full capacity of water pumping whatever may be weather conditions. Originality/value: Improved power quality of grid by optimally generation of the duty cycle for the proposed system. A unit vector tamplate generation technique is used for bidirectional power transfer. [ABSTRACT FROM AUTHOR]

In this paper, we have researched implementing convolutional neural network (CNN) models for devices with limited resources, such as smartphones and embedded computers. To optimize the number of parameters of these models, we studied various popular methods that would allow them to operate more efficiently. Specifically, our research focused on the ResNet-101 and VGG-19 architectures, which we modified using techniques specific to model optimization. We aimed to determine which approach would work best for particular requirements for a maximum accepted accuracy drop. Our contribution lies in the comprehensive ablation study, which presents the impact of different approaches on the final results, specifically in terms of reducing model parameters, FLOPS, and the potential decline in accuracy. We explored the feasibility of implementing architecture compression methods that can influence the model’s structure. Additionally, we delved into post-training methods, such as pruning and quantization, at various model sparsity levels. This study builds upon our prior research to provide a more comprehensive understanding of the subject matter at hand. [ABSTRACT FROM AUTHOR]

This paper deals with the proposition of a kinetic model for the direct synthesis of DME via CO2 hydrogenation in view of the necessary optimization of the catalytic system, reactor design, and process strategy. Despite the fact that DME synthesis is typically treated as a mere combination of two separated catalytic steps (i.e., methanol synthesis and methanol dehydration), the model analysis is now proposed by taking into account the improvements related to the process running over a hybrid catalyst in a rational integration of the two catalytic steps, with boundary conditions properly assumed from the thermodynamics of direct DME synthesis. Specifically, the CO2 activation step at the metal–oxide interface in the presence of ZrO2 has been described for the first time through the introduction of an ad hoc mechanism based on solid assumptions from inherent studies in the literature. The kinetic modeling was investigated in a tubular fixed-bed reactor operating from 200 to 260 °C between 1 and 50 bar as a function of a gas hourly space velocity ranging from 2500 to 60,000 NL/kgcat/h, in a stoichiometric CO2/H2 feed mixture of 1:3 v/v. A well-detailed elementary mechanism was used to predict the CO2 conversion rate and identify the key reaction pathways, starting with the analysis of the implicated reactions and corresponding kinetic mechanisms and expressions, and finally estimating the main parameters based on an appropriate modeling of test conditions. [ABSTRACT FROM AUTHOR]

This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present context of cellular networks, device-to-device (D2D) communication stands out as a promising technique to enhance the spectrum efficiency. Simultaneously, RIS have gained considerable attention due to their ability to enhance the quality of dynamic wireless networks by maximizing the spectrum efficiency without increasing the power consumption. However, prevalent centralized D2D transmission schemes require global information, leading to a significant signaling overhead. Conversely, existing distributed schemes, while avoiding the need for global information, often demand frequent information exchange among D2D users, falling short of achieving global optimization. This paper introduces a framework comprising an outer loop and inner loop. In the outer loop, decentralized dynamic resource allocation optimization has been developed for self-organizing network communication aided by RIS. This is accomplished through the application of a multi-player multi-armed bandit approach, completing strategies for RIS and resource block selection. Notably, these strategies operate without requiring signal interaction during execution. Meanwhile, in the inner loop, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm has been adopted for cooperative learning with neural networks (NNs) to obtain optimal transmit power control and RIS phase shift control for multiple users, with a specified RIS and resource block selection policy from the outer loop. Through the utilization of optimization theory, distributed optimal resource allocation can be attained as the outer and inner reinforcement learning algorithms converge over time. Finally, a series of numerical simulations are presented to validate and illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents a novel Fourier spectral method that utilizes optimization techniques to ensure the positivity and conservation of moments in the space of trigonometric polynomials. We rigorously analyze the accuracy of the new method and prove that it maintains spectral accuracy. To solve the optimization problem, we propose an efficient Newton solver that has a quadratic convergence rate. Numerical examples are provided to demonstrate the high accuracy of the proposed method. Our method is also integrated into the spectral solver of the Boltzmann equation, showing the benefit of our approach in applications. [ABSTRACT FROM AUTHOR]

Introduction/purpose: Destabilization of critical resources (CRs) or critical infrastructure (CI) important for the stability of the state can be dangerous for society, economy, and especially national security. Disruption of one CI object or one of its parts often affects and causes disruption of other dependent CI, because the modern society has become a "networked society". The paper proposes a model for quantifying and defining the interdependence between different CIs and their priorities, based on statements of experts. Methods 9 : The proposed methods that combine the Laboratory for Testing and Evaluation of Decision Making (DEMATEL) and the Analytical Network Process (ANP) have been successfully modified by fuzzy logic theory in this work. Results: Integrating multiple methods into a unique input data analysis model significantly affects the change in ranking. Conclusion: The work contributes to military science in making strategic decisions related to national security management through increasing the resilience of CRs and the societies that rely on them. [ABSTRACT FROM AUTHOR]

In order to mitigate the impact of temperature and irradiation variations on photovoltaic (PV) panels, it is crucial to employ the maximum power point tracking (MPPT) technique. This paper introduces a novel MPPT approach that provides a reference current, denoted as Iref, to extract the maximum power from the PV array. The difference between the input current Ipv, and Iref is then fed into a PID controller, which adjusts the duty cycle accordingly to control the Quadratic Boost Converter (QBC). The proposed MPPT approach is based on the analysis of measurement data obtained from PV panels. From this, a linear mathematical function between Iref and solar irradiation is achieved. To assess the performance of this approach, a comparative study is being carried out not only with conventional MPPT techniques such as P&O and INC-COND but also with modern techniques such as ANN, PSO, FLC, and improved P&O (IMP-P&O). The simulation results clearly demonstrate enhanced tracking performance under various test conditions, including the Step & Ropp test, as well as real profiles for both sunny and cloudy days. Certainly, the novel approach shows its ability to outperform both conventional and modern methods with a faster convergence speed (less than 2 milliseconds), the lowest steady-state oscillations (less than 0.25 Watts), and the highest tracking efficiency (greater than 99.79%). [ABSTRACT FROM AUTHOR]

Motivated by Ahmadi-Javid (Journal of Optimization Theory Applications, 155(3), 2012, 1105-1123) and Ahmadi-Javid and Pichler (Mathematics and Financial Economics, 11, 2017, 527-550), the concept of Tsallis Value-at-Risk (TsVaR) based on Tsallis entropy is introduced in this paper. TsVaR corresponds to the tightest possible upper bound obtained from the Chernoff inequality for the Value-at-Risk. The main properties and analogous dual representation of TsVaR are investigated. These results partially generalize the Entropic Value-at-Risk by involving Tsallis entropies. Three spaces, called the primal, dual, and bidual Tsallis spaces, corresponding to TsVaR are fully studied. It is shown that these spaces equipped with the norm induced by TsVaR are Banach spaces. The Tsallis spaces are related to the Lp spaces, as well as specific Orlicz hearts and Orlicz spaces. Finally, we derive explicit formula for the dual TsVaR norm. [ABSTRACT FROM AUTHOR]

Systems design methods should aim for systems creating value. The decision-making processes in system engineering struggle to optimize this objective; however, even though the traditional concept of system value as a purely economic metric is recognized as deficient, a well-defined and standard conceptualization of comprehensive system value is still lacking. This study set out to facilitate different stakeholders, involved in developing systems, with a broad perspective on value. We define the system value as the system's holistic impact, encompassing the multi-domain effects on processes, environments, and stakeholders. This inclusive view, to be used by practitioners designing systems and policies, is expected to update existing practices and enhance resulting systems. This paper renders an extensive review of value references in multiple domains, both in system engineering and external, non-engineering, disciplines, and sets the foundation for a revised framing of value in systems engineering. To enable future applications for systems optimization, system value is thoroughly characterized, including its dependency on internal and external factors. This research lays the groundwork for problem formulation of a system value measure, its application in system engineering methods, and further analysis of the subject, both for engineered and non-technical systems. [ABSTRACT FROM AUTHOR]

Severe droughts, water-use conflicts, and minimum outflow requirements have caused infeasibilities in modeling large-scale complex hydrothermal systems. This paper presents and discusses a new formulation of objective function and constraints to better represent multiple water uses and deal with infeasibilities in the HIDROTERM model, a nonlinear programming (NLP) model developed to optimize the operation of the Brazilian interconnected hydrothermal system. The system is one of the largest in the world. In the last 10 years, approximately 72% of electricity consumed in Brazil has been supplied by hydropower plants, but with increasing shares from thermal, wind, and, more recently, solar power generation. Because of hydrological changes in recent years and the additional operational constraints imposed by environmental agencies, the use of models for decision making, such as HIDROTERM, frequently encounters the problem of infeasibility. To avoid this problem, a new objective function is proposed in HIDROTERM that incorporates economic penalties when the preset requirements are not met. Based on economic considerations, some hard constraints are removed from the constraint set and added to the objective function as penalty terms. These constraints are tightly linked with multiple water uses, including environmental protection, flood control, consumptive uses, and navigation purposes. The proposed change in the objective function and constraints avoids the problem of infeasibilities and makes it possible to apply the model to extreme conditions. Additionally, this formulation helps identify and minimize the risk, duration, and intensity of unavoidable non-compliance occurrences related to multiple water uses. The presented new version of the HIDROTERM model can be used to study and support decision making in the management and operation of large-scale hydrothermal systems. These systems can be formed by a set of hydraulically connected individual hydropower plants and reservoirs, located within a set of electrically connected subsystems. Each subsystem contains demands and other power plants, including thermal, wind, nuclear, and solar plants, and exchanges. The model has been developed with continued improvements since 2008 and applied to large systems such as the Brazilian Interconnected Power System (BIPS) with over 150 reservoirs. A graphical user interface is provided. The objective function is to minimize the total costs of additional thermal dispatch and exchanges, as well as avoid or minimize deficits in energy supply in subsystems, minimum releases, consumptive uses, and minimum storages in reservoirs. The decision variables are the power and nonpower releases for each hydropower plant and the thermal dispatches and exchanges for each subsystem. Some typical applications include (1) monthly planning operation of the system, considering a planning horizon from 3 to 5 years; (2) diagnostic studies about reliability and resilience of existing systems, processing the same system configuration to a set of different hydrological or energy demand scenarios; (3) impact analysis of changes in operational rules and constraints related to multiple uses of water; and (4) planning and evaluation of expansion alternatives. The model can be applied to a complete hydrothermal system or subsets of it, including subsystems, basins, cascades, or a single hydropower plant. [ABSTRACT FROM AUTHOR]

Sixth-generation wireless systems not only have more demanding communication requirements, they are also expected to have high-precision sensing capabilities and sufficient computing power. Integrated sensing, communication, and computation (ISCC) can meet the above system requirements and save spectrum resources. In this paper, we build a resource allocation and offloading decision problem in an ISCC scenario that makes considerations for user mobility and partial offloading policies. The established problem minimizes the average task cost when given constraints such as the typical sensing failure rate and task completion delay. We use Lyapunov optimization theory to transform the proposed problem and propose a two-level optimization algorithm based on matching theory to offer a solution for the transformed problem. The inner layer obtains the task offloading ratio through theoretical derivation, and the outer layer determines the base station access and channel assignment based on the inner layer results. The simulation results show that the average task cost can be effectively reduced while also guaranteeing high-quality sensing performance. [ABSTRACT FROM AUTHOR]

Mathematical optimization is a widespread method in order to improve, for instance, the efficiency of energy systems. A simulation approach based on partial differential equations can typically not be formulated as an optimization problem, thus requiring interfacing to an external optimization environment. This is, among others, also true for the programming language Modelica. Because of high computation time, such coupled approaches are often limited to small-scale optimization problems. Since simulation models tend to get more complex, simulation time and, in turn, associated optimization time rise significantly. To enable proper sampling of the search space, individual optimization runs need to be solved in acceptable times. This paper addresses the search for a proper optimization approach and tool to couple with Modelica/Dymola. The optimization is carried out on an exemplary power plant model from the ClaRa-Library using an evolutionary algorithm (SPEA2-based) with Ansys optiSlang. To verify and evaluate the results, a comparison with the standard Dymola optimization library is performed. Both parallelization and indirect optimization with surrogate models achieved a significant runtime reduction by a factor of up to 5.4. The use of meta models is particularly advantageous for repetitive optimization runs of the same optimization problem but may lead to deviations due to the calculated approximations. [ABSTRACT FROM AUTHOR]

There has been a notable increase in the frequency and severity of extreme events, such as hurricanes, floods, wildfires, and storms. These events, although infrequent, have a significant disruptive effect, causing prolonged outages and compromising essential services, thereby severely affecting customers' safety. As a result, there is an urgent requirement to enhance the resilience of distribution networks by quickly restoring the loads during and after a disaster. In this regard, this paper reviews the existing studies on black-start service restoration in active distribution systems and microgrids. A comprehensive review is conducted for each aspect of the restoration problem, encompassing various proposed methods and modeling techniques found in the existing literature. The aim of this review is to consolidate the knowledge and findings from previous studies, providing a valuable resource for researchers and practitioners in the field. Also, some key research directions for the future work in this field are recommended for developing more practical and reliable methods. [ABSTRACT FROM AUTHOR]

Water distribution networks (WDNs) require large capital investment and ongoing operational costs, resulting in their optimisation being a highly researched field. Despite the benefits tanks bring to networks, most optimisation models omit them as decision variables due to the complexity they can introduce to heuristic approaches. This paper addresses the least-cost optimisation of WDN design and operation through the development and application of the Small-network Configurator for Optimising Pump Energy consumption (SCOPE). The SCOPE algorithm incorporates pipes, pumps and tanks as decision variables and solves the optimisation problem through an iterative approach that pairs EPANET simulation results with subsequent hydraulic calculations to converge on the pumping and storage configuration which yields the lowest energy consumption. The SCOPE output can then be used to seed further optimisation techniques. This approach has been tested on the synthetic Nametown and real-life Dili networks and demonstrated annual cost savings of 10% and 25%, respectively. [ABSTRACT FROM AUTHOR]

Abnormal cells in the human body that keep on mutating are termed to be cancer in medical terms. There are multiple types of cancer identified in human beings. It is very much essential to identify and classify the type of cancer in its earlier stage. This objective can be satisfied by artificial intelligence which has a subfield of machine learning to create a generalized model that could identify and classify cancer with increased performance. To perform the identification and classification of various cancer types, in this paper, two techniques are adopted. The optimized feature set computation was done using the Kernel-Induced Matriarch path tracking Elephant Herding Optimization (KIM-EHO) and the classification for the given samples was done using the Support Vector Machines (SVM). The proposed techniques are implemented with the benchmark datasets and the results proved that the proposed methodologies outperformed the existing methods in terms of accuracy, specificity, sensitivity and time complexity. [ABSTRACT FROM AUTHOR]

The convergence rate has been widely accepted as a performance measure for choosing a better metaheuristic algorithm. So, we propose a novel technique to improve the performance of the existing Grey Wolf Optimization (GWO) algorithm in terms of its convergence rate. The proposed approach also prioritizes the test cases that are obtained after executing the input benchmark programs. This paper has three technical contributions. In our first contribution, we generate test cases for the input benchmark programs. Our second contribution prioritizes test cases using an improved version of the existing GWO algorithm (CGWO). Our third contribution analyzes the obtained result and compares it with state-of-the-art metaheuristic techniques. This work is validated after running the proposed model on six benchmark programs. The obtained results show that our proposed approach has achieved 48% better APFD score for the prioritized order of test cases than the non-prioritized order. We also achieved a better convergence rate, which takes around 4000 fewer iterations, when compared with the existing methods on the same platform. [ABSTRACT FROM AUTHOR]

In recent years, convolutional neural networks (CNNs) have gained widespread adoption in remote sensing image processing. Deploying CNN-based algorithms on satellite edge devices can alleviate the strain on data downlinks. However, CNN algorithms present challenges due to their large parameter count and high computational requirements, which conflict with the satellite platforms' low power consumption and high real-time requirements. Moreover, remote sensing image processing tasks are diverse, requiring the platform to accommodate various network structures. To address these issues, this paper proposes an algorithm–hardware co-optimization and deployment method for FPGA-based CNN remote sensing image processing. Firstly, a series of hardware-centric model optimization techniques are proposed, including operator fusion and depth-first mapping technology, to minimize the resource overhead of CNN models. Furthermore, a versatile hardware accelerator is proposed to accelerate a wide range of commonly used CNN models after optimization. The accelerator architecture mainly consists of a parallel configurable network processing unit and a multi-level storage structure, enabling the processing of optimized networks with high throughput and low power consumption. To verify the superiority of our method, the introduced accelerator was deployed on an AMD-Xilinx VC709 evaluation board, on which the improved YOLOv2, VGG-16, and ResNet-34 networks were deployed. Experiments show that the power consumption of the accelerator is 14.97 W, and the throughput of the three networks reaches 386.74 giga operations per second (GOPS), 344.44 GOPS, and 182.34 GOPS, respectively. Comparison with related work demonstrates that the co-optimization and deployment method can accelerate remote sensing image processing CNN models and is suitable for applications in satellite edge devices. [ABSTRACT FROM AUTHOR]

In many service systems, the service rates of the servers or the machines degrade over time due to a variety of reasons like fatigue, deficiency (due to manufacturing defect or energy loss, deformation, or excessive heat), and lack of proper maintenance. To make a service system beneficial for both customers and management points of view, it is imperative that models are developed to understand the impact of degradation. In this paper, we study M A P / P H / 1 - type queueing models incorporating degradation, failures/breakdowns and repairs. The degradation and to restore to normalcy in the service rate we look at two scenarios. In the first one, the service is restored to normalcy immediately after the server becomes idle or a fixed number of services is offered. In the second one, the restoration takes a random time that is modeled using a phase type distribution. Also, there are a variety of reasons that lead to failures/breakdowns of the server. We model these using (possibly) different phase type distributions. Both the models are studied analytically using matrix-analytic methods and illustrative numerical examples bringing out the qualitative behavior of the impact of degrading services are discussed. Two cost optimization problems whose solutions are obtained using particle swarm optimization technique along with a few illustrative examples are presented. [ABSTRACT FROM AUTHOR]

Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it popular among researchers. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the Dice coefficient, specificity, sensitivity, and F1 score values obtained were 0.93, 0.99, 0.94, and 0.94, respectively. In addition, McNemar's statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperformed all other models, resulting in improved breast lesion segmentation. [ABSTRACT FROM AUTHOR]

Circuit design plays a pivotal role in engineering, ensuring the creation of efficient, reliable, and cost-effective electronic devices. The complexity of modern circuit design problems has led to the exploration of multi-objective optimization techniques for circuit design optimization, as traditional optimization tools fall short in handling such problems. While metaheuristic algorithms, especially genetic algorithms, have demonstrated promise, their susceptibility to premature convergence poses challenges. This paper proposes a pioneering approach, the chaotic multi-objective Runge–Kutta algorithm (CMRUN), for circuit design optimization, building upon the Runge–Kutta optimization algorithm. By infusing chaos into the core RUN structure, a refined balance between exploration and exploitation is obtained, critical for addressing complex optimization landscapes, enabling the algorithm to navigate nonlinear and nonconvex optimization challenges effectively. This approach is extended to accommodate multiple objectives, ultimately generating Pareto Fronts for the multiple circuit design goals. The performance of CMRUN is rigorously evaluated against 11 multiobjective algorithms, encompassing 15 benchmark test functions and practical circuit design scenarios. The findings of this study underscore the efficiency and real-world applicability of CMRUN, offering valuable insights for tailoring optimization algorithms to the real-world circuit design challenges. [ABSTRACT FROM AUTHOR]

Distributed optimization is an important direction of research in modern optimization theory. Its applications include large scale machine learning, distributed signal processing and many others. The paper studies decentralized min-max optimization for saddle point problems. Saddle point problems arise in training adversarial networks and in robust machine learning. The focus of the work is optimization over (slowly) time-varying networks. The topology of the network changes from time to time, and the velocity of changes is limited. We show that, analogically to decentralized optimization, it is sufficient to change only two edges per iteration in order to slow down convergence to the arbitrary time-varying case. At the same time, we investigate several classes of time-varying graphs for which the communication complexity can be reduced. [ABSTRACT FROM AUTHOR]

In large-scale multi-objective optimization problems (LSMOPs), multiple conflicting objectives and hundreds even thousands of decision variables are contained. Therefore, it is a great challenge to address LSMOPs due to the curse of dimensionality. To tackle LSMOPs, this paper proposes a resource allocation-based multi-objective optimization evolutionary algorithm. In the proposed algorithm, decision variables are firstly divided into convergence-related variables and diversity-related variables by the proposed layer thickness-based variable classification (LTVC) method. Then, a resource allocation-based convergence optimization strategy is introduced for the convergence-related variables, which can allocate more computational resource to the sub-component with the best contribution. For the diversity-related variables, diversity optimization technique is adopted. Finally, the experimental results verify that the proposed algorithm has a competitive performance compared with some state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Narrow escape and narrow capture problems which describe the average times required to stop the motion of a randomly travelling particle within a domain have applications in various areas of science. While for general domains, it is known how the escape time decreases with the increase of the trap sizes, for some specific 2D and 3D domains, higher-order asymptotic formulas have been established, providing the dependence of the escape time on the sizes and locations of the traps. Such results allow the use of global optimisation to seek trap arrangements that minimise average escape times. In a recent paper (Iyaniwura (2021) SIAM Rev. 63 (3), 525–555), an explicit size- and trap location-dependent expansion of the average mean first passage time (MFPT) in a 2D elliptic domain was derived. The goal of this work is to systematically seek global minima of MFPT for $1\leq N\leq 50$ traps in elliptic domains using global optimisation techniques and compare the corresponding putative optimal trap arrangements for different values of the domain eccentricity. Further, an asymptotic formula for the average MFPT in elliptic domains with N circular traps of arbitrary sizes is derived, and sample optimal configurations involving non-equal traps are computed. [ABSTRACT FROM AUTHOR]

Tandem solar cells (TSCs) perform a better adaptation of the incident photons in different-energy-level bandgap materials, and overcome the Shockley–Queisser limit, but they require advanced control over the management of light for optimum performance. Nanomaterials and nanostructures offer a vastly improved control over the management of light. Through different optimization techniques, researchers can gain valuable insights regarding the optimization of various parameters of nano-optical designs. Over the past years, the number of studies on this topic has been continuously increasing. The present study reviews various current state-of-the-art optical designs, and provides an overview of the optimization techniques and numerical modeling of TSCs. This paper collected and analyzed different studies published within the years 2015–2022, using systematic literature review techniques, such as specific protocol screening and a search strategy. Seven different optical designs were extracted, along with their advanced local and global optimization methods, which offer a solution to the optical limitations of TSCs. [ABSTRACT FROM AUTHOR]

To enhance the stability of power system, a power system stabilizer (PSS) is one of the control mechanisms installed in the power plants. A suitable PSS parameters set can help in reducing the power swing. In general, the design of PSS parameters is performed based on a nominal operating condition of the power system. In this paper, a multi‐objective optimization algorithm based on pole placement technique is proposed to find a robust PSS parameters set. The proposed strategy searches for the optimal PSS parameters under multiple operating conditions of the generator. Preliminary simulation results on a single machine infinite bus (SMIB) model show that the proposed PSS parameters design method can further improve the robustness of the overall power systems performance under different types of disturbance. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Electric vehicle systems are growing in popularity as a promising solution to the problems associated with traditional transportation systems, such as environmental pollution. Nonetheless, the task of devising control algorithms that effectively control electric vehicle systems continues to persist as a complex realm of research. This is due to the ongoing pursuit of refining the dynamic attributes of the system, a task that is riddled with challenges. The inherent instability exhibited by the system and the presence of undesirable behaviors further compound the intricacy of this endeavor, demanding careful attention and comprehensive analysis of the system's unstable roots. In this paper, an advanced control algorithm is proposed for controlling the speed of the electric vehicle system based on the lead compensation. In addition, the control algorithm is designed to include the utilization of model reference control to improve the time response properties of the closed-loop system. Then, the Zebra Optimization Algorithm (ZOA) is used to optimally define the compensator parameters such that the reference model and system outputs be asymptotically identical with desirable steady-state error. Eventually, the simulation results show that the proposed optimal model reference lead compensator ensures stable and high-performance control of the electric vehicle system while maintaining simplicity and acceptable control action compared to other control approaches. This is substantiated by the noteworthy reductions it brings about: a staggering 90% decrease in rise time, a substantial 51% decrease in overshoot, and an impressive 99% reduction in cost value. In addition, the intervention of the proposed method completely eradicates steady-state errors, thus addressing a critical aspect of control system performance. [ABSTRACT FROM AUTHOR]

Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied. [ABSTRACT FROM AUTHOR]

Radial basis function networks are widely used in a multitude of applications in various scientific areas in both classification and data fitting problems. These networks deal with the above problems by adjusting their parameters through various optimization techniques. However, an important issue to address is the need to locate a satisfactory interval for the parameters of a network before adjusting these parameters. This paper proposes a two-stage method. In the first stage, via the incorporation of grammatical evolution, rules are generated to create the optimal value interval of the network parameters. During the second stage of the technique, the mentioned parameters are fine-tuned with a genetic algorithm. The current work was tested on a number of datasets from the recent literature and found to reduce the classification or data fitting error by over 40% on most datasets. In addition, the proposed method appears in the experiments to be robust, as the fluctuation of the number of network parameters does not significantly affect its performance. [ABSTRACT FROM AUTHOR]

In the design of antenna arrays which require fast and robust flat‐top beam synthesis, computationally efficient methods are preferred. This feature is usually met by analytical techniques or simple optimisation procedures. On the other hand, in the flat‐top beam synthesis, a common requirement is the ability to control beamwidth or sidelobe level. However, this can result in a high dynamic range ratio (DRR) of array's excitation coefficients. In this paper, a straightforward method for the design of symmetrical flat‐top arrays with controllable sidelobe level or DRR is proposed. The method is based on quadratic and cubic transforms of Gaussian excitations. In addition, the method utilises zero coefficients whose positions are used to control the DRR, including the ability to achieve its minimum. Compared to other flat‐top arrays with analytically shaped beams, the proposed arrays have lower DRRs for the same sidelobe level. [ABSTRACT FROM AUTHOR]

A helicopter is a multivariable, nonlinear, higher order and strongly coupled system. The helicopter dynamics is subjected to unknown external disturbances and sensitive to parametric uncertainties. Such complications require a sophisticated control approach which may handle all these difficulties. The paper presents a trajectory control approach using an improved teaching learning based optimization (iTLBO) method, which optimizes the parameters of proportional integral derivative controller. This control approach is validated by minimizing the yaw and pitch errors of two degrees of freedom helicopter system. It is observed that the experimental results in a laboratory setup does not stand true when subjected to weather changes during the helicopter flight. To overcome this situation, a real time like conditions are created in the laboratory using the two external fans creating strong wind disturbance and the proposed controller is implemented to eliminate the resulting external disturbances created due to high speed wind of external fans. Initially, modelling of helicopter system is achieved using the Lagrangian approach. Then, the TLBO is tuned to improve the controller performance using MATLAB platform. The effectiveness and superiority of iTLBO is proved over other optimization techniques available in literature like GA, PSO and TLBO by using linear and nonlinear benchmark functions. The optimized parameters hence obtained are implemented on helicopter system under the presence of external air disturbance and disturbance less results are obtained. Results obtained are validated using time response analysis on the MATLAB/Simulation platform. [ABSTRACT FROM AUTHOR]

The rapid growth of technology and uneven demographic population distribution has led to uneven distribution of electrical load demand across the three phases of the electrical distribution network. It has resulted in a steep rise in voltage unbalance scenarios. Such occurrences may have a lasting negative impact on the distribution system in the form of energy loss and extra costs for generation. This paper proposes a Rao optimization-based demand-side management (DSM) model to help the distribution utilities mitigate these issues. The proposed methodology has been designed to effectively distribute the load demand pattern in each node connected across each phase. The proposed DSM methodology has been designed keeping in view to discard any possibility of the need for lateral/load re-phasing. A day ahead load scheduling format has been adopted to design this proposed methodology. It has been implemented on IEEE European 906bus low voltage distribution network. Further, to verify the effectiveness of the proposed methodology, Gauss implicit Zbus load-flow method is performed upon the test system. The test system considered is IEEE European 906bus low voltage distribution system. The entire methodology is designed in the MATLAB 2018a platform. The implementation of the proposed methodology has reduced the phase voltage unbalance rate in the original test system by ~ 50%. Following the application of the suggested technique, the peak to average ratio in the original test system reduced by 6.22%, 38.7%, 3.22%, and 26.46% in each of the three phases, including the total load consumption of the end users. [ABSTRACT FROM AUTHOR]

In this paper, different methods are utilized to solve the coordination issue involving directional overcurrent relays (DOCRs) and distance relays. The proper coordination of DOCRs and distance relays is a critical issue for system security in electrical networks. Finding the DOCRs setting, pickup current(Ip) and timed dial setting (TDS), and operating time for zone-2 of distance relays is the primary objective of solving the coordination problem. The constant parameters A & B of the directional overcurrent relay that are responsible to control the form of the relay´s characteristics as well as the Ip and TDS have been regarded as variables in this problem. The optimal value for these four DOCR settings has been determined using different optimization techniques. The primary and backup relays must operate sequentially and without any violations, and this must be guaranteed by optimization techniques. In order to determine the operation time for zone-2 and DOCRs setting, optimization methods are examined utilizing the 8-bus and IEEE 30-bus networks. Different optimization algorithms, including recent and traditional techniques, are compared. The obtained results show the superiority of the genetic algorithm (GA) in solving the coordination problem of distance relays and DOCRs. Also, the obtained results prove the ability of the GA method compared to the particle swarm algorithm (PSO), grey wolf optimization (GWO), water cycle technique (WCA), equilibrium optimizer (EO), African vultures optimization algorithm (AVOA), flow direction algorithm (FDA), and gorilla troops optimizer (GTO) techniques. [ABSTRACT FROM AUTHOR]

The wind abandonment phenomenon of cogeneration units in regional integrated energy systems (RIES) under the operation mode of "heat for electricity" and the improvement in the operation efficiency of the energy storage system under a low-temperature environment are problems that need to be solved urgently. To this end, a regional integrated energy system optimization scheduling method based on fine energy storage and wind power consumption is proposed in the paper. First, a fine energy storage model more adapted to a low-temperature environment is established on the power side to accurately simulate the actual working state of the energy storage components and quantify the uncertainty of the wind power output using the conditional value-at-risk (CVaR) theory. Then, a combined heat and power demand response mechanism is introduced on the load side to reduce the peak-to-valley difference in the heat and power loads, it is realized to promote the system's consumption of wind power without increasing the transmission power of the contact line. Finally, the example is solved on the MATLAB platform with the objective of minimizing the total cost of the RIES optimal dispatch. The simulation results show that the proposed model is not only more adaptable to a low-temperature environment compared with the traditional model but also reduces the overall cost of the system by 2.58% while realizing the complete consumption of wind power. This innovative study provides a feasible and efficient solution to improve the performance of integrated energy systems, especially the operation capability in extreme environments. [ABSTRACT FROM AUTHOR]

The need for reliable wireless communication in remote areas has led to the adoption of unmanned aerial vehicles (UAVs) as flying base stations (FlyBSs). FlyBSs hover over a designated area to ensure continuous communication coverage for mobile users on the ground. Moreover, rate-splitting multiple access (RSMA) has emerged as a promising interference management scheme in multi-user communication systems. In this paper, we investigate an RSMA-enhanced FlyBS downlink communication system and formulate an optimization problem to maximize the sum-rate of users, taking into account the three-dimensional FlyBS trajectory and RSMA parameters. To address this continuous non-convex optimization problem, we propose a TD3-RFBS optimization framework based on the twin-delayed deep deterministic policy gradient (TD3). This framework overcomes the limitations associated with the overestimation issue encountered in the deep deterministic policy gradient (DDPG), a well-known deep reinforcement learning method. Our simulation results demonstrate that TD3-RFBS outperforms existing solutions for FlyBS downlink communication systems, indicating its potential as a solution for future wireless networks. [ABSTRACT FROM AUTHOR]

In the realm of bioinformatics, Multiple Sequence Alignment (MSA) is a pivotal technique used to optimize the alignment of multiple biological sequences, guided by specific scoring criteria. Existing approaches addressing the MSA challenge tend to specialize in distinct biological features, leading to variability in alignment outcomes for the same set of sequences. Consequently, this paper proposes an enhanced evolutionary-based approach that simplifies the sequence alignment problem without considering the sequences in the non-dominated solution. Our method employs a multi-objective optimization technique that uniquely excludes non-dominated solution sets, effectively mitigating computational complexities. Utilizing the Sum of Pairs and the Total Conserved Column as primary objective functions, our approach offers a novel perspective. We adopt an integer coding approach to enhance the computational efficiency, representing chromosomes with sets of integers during the alignment process. Using the SABmark and BAliBASE datasets, extensive experimentation is conducted to compare our method with existing ones. The results affirm the superior solution quality achieved by our approach compared to its predecessors. Furthermore, via the Wilcoxon signed-rank test, a statistical analysis underscores the statistical significance of our model's improvement (p < 0.05). This comprehensive approach holds promise for advancing Multiple Sequence Alignment in bioinformatics. [ABSTRACT FROM AUTHOR]

In the backdrop of the ongoing reforms within the electricity market and the escalating integration of renewable energy sources, power service providers encounter substantial trading risks stemming from the inherent uncertainties surrounding market prices and load demands. This paper endeavors to address these challenges by proposing a credibility theory-based information gap decision theory (CTbIGDT) to improve robustness of electricity trading under uncertainties. To begin, we establish credibility theory as a foundational risk assessment methodology for uncertain price and load, incorporating both necessity and randomness measures. Subsequently, we advance the concept by developing the CTbIGDT optimization model, grounded in the consideration of expected costs, with the primary aim of fortifying the robustness of electricity trading practices. The ensuing model is then transformed into an equivalent form and solved using established standard optimization techniques. To validate the efficacy and robustness of our proposed methodology, a case study is conducted utilizing a modified IEEE 33-node distribution network system. The results of this study serve to underscore the viability and potency of the CTbIGDT model in enhancing the effectiveness of electricity trading strategies in an uncertain environment. [ABSTRACT FROM AUTHOR]