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Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

In order to study the cutting performance of the straight edge hob and reduce the cutting power consumption model of the straight edge hob, this paper takes the cutting power of the straight edge hob as the minimum goal, and establishes the mathematical model of the optimization design of the straight edge hob based on the composite optimization method. The mathematical model is solved by MATLAB software. At the same time,the mowing characteristics of a roller blade were studied by investigating the relationship between the hob and the coordination of variables such as rotational speed and roller diameter with the mowing parameters. The parameter analysis of straight edge hob before and after structural parameter optimization is generated, and a design method is proposed based on this. After defining the objective function and constraint conditions, the influence of structural parameters on the power consumption and efficiency of the hob was determined by optimising the complex method; this could significantly adjust the hob parameters to lower its power consumption. The energy consumption of the optimized design is reduced by 11.1 % compared with the original scheme, and the optimization effect is remarkable. The results show that the best working parameters of the hob are cutting speed of 1000 r/min, sliding cutting angle and grinding edge angle of 25~30°. Moreover, practical tests demonstrated the feasibility of using the proposed method to design the straight edge hob to improve mowing performance and hob stability. This study can provide parameter foundation and an optimization method for lowering chopping power consumption of the roller mower blade. [ABSTRACT FROM AUTHOR]

Because of the overly averaged element sensitivity in the topological optimization of an irregular structure with a grid independent filter, a topological optimization model was built for the structural domain. The maximization of stiffness was first taken as the goal for the topological optimization of irregular structure stiffness. Subsequently, an element size filter was proposed to address the overly averaged local element sensitivity with the grid independent filter when the designed domain element size varied dramatically. Finally, the element sensitivity of the objective function was derived under the given constraints. A case study was then conducted on a naval gun mount with the maximization of structural flexibility as the objective function and the volume of structural material as a constraint. A stiffness optimization model based on the bi-directional evolutionary structural optimization algorithm was adopted for the topological optimization of the gun mount. Structural optimization was conducted for the gun mount with different shooting angles to realize its optimal stiffness and strength under the constraint of consistent material volume. The optimization results proved that the element independent filter proposed in this paper could be effectively applied in the topological optimization of an irregular structure and used to explore the topological optimization of the supporting structure under impact. [ABSTRACT FROM AUTHOR]

This paper presents an overview of pulp and paper mills (PPM) production processes, the resulting release of wastewater effluent loaded with wide range of pollutants and associated environmental impacts. The review highlighted the different types of functional materials and their modified forms employed as coagulants for pulp and paper mills industries effluent (PPME) treatment that have been intensively studied as a promising strategy for PPM to achieve cleaner and sustainable treatments in accordance with sustainable development goals (SDGs) "6-Clean water and sanitation", "9-Industry, innovation, and infrastructure", and "12-Responsible consumption and production". Standalone coagulation treatment processes are inherently ineffective towards meeting the increasingly stringent discharge requirements, coupled with their higher energy demand, and increased operational and maintenance costs. Owing to the recalcitrant nature of PPME contaminants, this review explored the effectiveness of the coagulation processes for decontamination of PPME. Furthermore, the review provides a state-of-the-art coagulation-based hybrid systems employed for enhanced PPME treatment. The process limitations, influencing factors and optimization techniques are highlighted. The review also highlights how sustained research in the subject area impacts on achieving cleaner production. The review also discusses coagulant classifications and the synergistic, antagonistic and shock load toxic effects of hybrid coagulants on toxicant biodegradation and their associated system efficiency. Moreover, it offers a guide for the development and application of sustainable hybrid-based coagulants for PPME treatment. The findings presented herein provide a vital theoretical foundation for sustainable solutions to improve coagulation-based hybrid systems efficiency and their scale-up towards potential commercialization. [Display omitted] • Pulp and paper mills produce effluents containing diverse and emerging contaminants. • Different materials and their modified forms are used as coagulants for PPME treatment. • The synergistic and shock load toxic effects of hybrid-based coagulants could be identified. • Hybrid systems are highly efficient techniques for PPME treatment. • The review highlights process weaknesses, influencing factors and optimization techniques. [ABSTRACT FROM AUTHOR]

Paper dust is a kind of cellulosic waste that is generated by converting operations in paper mills. It was derived to a low-grade cellulose acetate in this study. Papers made from recycled fiber were then impregnated with the resultant cellulose acetate. Effects of impregnation conditions on the paper properties were statistically investigated by employing central composite design (CCD) based response surface methodology (RSM). Four response variables, namely density, burst index, smoothness, and rate of surface wettability were analyzed. Polynomial estimation model of each response was developed as functions of three independent variables, which are pressing temperature (T), dipping time (D), and concentration of cellulose acetate (C). The paper which was impregnated based on the calculated optimum condition (T: 163 °C, D: 2.8 minutes, and C: 2.7 percent), possessed a density of 0.5450 g/cm³, rate of surface wettability of 0.012°/s, burst index of 2.84 kPa m²/g, and paper smoothness of 475 mL/min. There was no significant difference between the experimental values and the predicted values calculated from estimation models. The cellulose acetate impregnated Braille papers made from recycled fibre was found to have better properties than those of commercial Braille paper in terms of rate of surface wettability and burst index. [ABSTRACT FROM AUTHOR]

In the face of rapidly evolving communication technologies and increasing user demands, traditional terrestrial networks are challenged by the need for high-quality, high-speed, and reliable communication. This paper explores the integration of heterogeneous satellite networks (HSN) with emerging technologies such as Mobile Edge Computing (MEC), in-network caching, and Software-Defined Networking (SDN) to enhance service efficiency. By leveraging dual-layer satellite networks combining Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites, the study addresses resource allocation and interference coordination challenges. This paper proposes a novel resource allocation and interference coordination strategy for dual-layer satellite networks integrating LEO and GEO satellites. We formulate a mathematical optimization problem to optimize resource allocation while minimizing co-channel interference and develop an ADMM-based distributed algorithm for efficient problem-solving. The proposed scheme enhances service efficiency by incorporating MEC, in-network caching, and SDN technologies into the satellite network. Simulation results demonstrate that our proposed algorithm significantly improves network performance by effectively managing resources and reducing interference. [ABSTRACT FROM AUTHOR]

To assess a subject with multiple factors or attributes, a comprehensive evaluation index, or say a composite indicator, is often constructed to make a holistic judgement. The key problem is to assign weights to the factors. There are various weighting methods in the literature, but a gold standard is lacking. Some weighting methods may lead to a trivial weight assignment that is one factor having a weight equal to 1 and the others all zero, while some methods generate a solution contradicting intuitive judgement, or even infeasible to calculate. This paper proposes a new model to generate weights based on the maximum entropy-minimum residual (MEMR) principle, directly estimating the relationship between factor weights and the composite indicator. The MEMR composite indicator extracts the common feature of multiple factors while preserving their diversity. This paper compares the MEMR model with other commonly used weighting methods in various case studies. The MEMR model has more robust, consistent, and interpretable results than others and is suitable for all comprehensive evaluation cases involving quantitative factors. The optimization technique of the proposed MEMR model and the related statistical tests are included as a package in the DPS (data processing system) software V21.05 for the convenience of application in all fields. [ABSTRACT FROM AUTHOR]

The offshore wind power sector has witnessed exponential growth over the past decade, with large-scale offshore wind farms grappling with the challenge of elevated construction and maintenance expenses. Given that the collector system constitutes a substantial part of the investment cost in wind farms, the design and optimization of this system are pivotal to enhancing the economic viability of offshore wind farms. A thorough examination of collector system design and optimization methodologies is essential to elucidate the critical aspects of collector system design and to assess the comparative merits and drawbacks of various optimization techniques, thereby facilitating the development of collector systems that offer superior economic performance and heightened reliability. This paper conducts a review of the evolving trends in collector system research, with a particular emphasis on topology optimization models and algorithms. It juxtaposes the economic and reliability aspects of collector systems with varying topologies and voltage levels. Building on this foundation, the paper delves into the optimization objectives and variables within optimization models. Furthermore, it provides a comprehensive overview and synthesis of AI-driven optimization algorithms employed to address the optimization challenges inherent in offshore wind farm collector systems. The paper concludes by summarizing the existing research limitations pertaining to offshore wind farm collector systems and proposes innovative directions for future investigative endeavors. The overarching goal of this paper is to enhance the comprehension of offshore wind farm collector system design and optimization through a systematic analysis, thereby fostering the continued advancement of offshore wind power technology. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose an optimized type-1 Sugeno fuzzy logic backstepping sliding mode controller (T1-SFL-BSMC) to control the altitude and rotational behaviour of a quadcopter. The proposed controller improves the performance of the system by overcoming the limitations of conventional trial-and-error techniques for BSMC gain design. Design/methodology/approach: In this study, the type-1 Sugeno fuzzy logic controller (T1-SFLC) technique is proposed which dynamically modifies the BSMC gains. In addition, the parameters of T1-SFLC are further tuned by five different optimization techniques, namely, genetic algorithm, particle swarm optimization (PSO), pattern search (PS), simulated annealing (SA) and adaptive neural network (ANN). Findings: The effectiveness of these optimization techniques in tuning the T1-SFLCs for the gain scheduled BSMC is evaluated in terms of time response metrics; rising time, settling time and root mean square error (RMSE) for altitude and rotational behaviour. The results demonstrated that PSO-optimized T1-SFL-BSMC provides better responses in terms of time response metrices with reduced rise and settling times and minimized RMSE when compared to other optimized T1-SFL-BSMC. Originality/value: This paper proposes a novel approach for controlling quadcopters by combining fuzzy logic with a BSMC. It is then improved by using many optimization strategies. A more dependable and effective approach to controller design is provided by the suggested method, which is essential for unmanned aerial vehicle applications. [ABSTRACT FROM AUTHOR]

In modern warfare, the accurate prediction of the intercept time and intercept point of the interceptor is the key to achieving penetration. Aiming at this problem, firstly, a convolutional neural network (CNN) is used to automatically extract high-level features from the data, and then these features are used as the input of support vector regression (SVR) for regression prediction. The Harris Hawk optimization (HHO) is used to optimize the hyperparameters of SVR, and the HHO-CNN-SVR algorithm is proposed. In order to verify the effectiveness of the algorithm for the prediction of the interception point and interception time, this paper constructs a dataset to test the method of simulating the missile interception maneuvering target. Compared with BP, ELM, SVR, HHO-SVR, and CNN-SVR models, the HHO-CNN-SVR model has outstanding performance in prediction accuracy and stability, especially for the interception time. The error is the smallest, and the error fluctuation is small. The MAE of the prediction result is only 0.0139 s; in the interception point prediction, the error of the range and elevation direction is significantly lower than that of the models used for comparison. The MAE in the range direction is 2.3 m, and the MAE in the elevation direction is 2.01 m, which meet the engineering requirements. The HHO-CNN-SVR model has strong prediction accuracy and stability in interception time and interception point prediction. In addition, different control strategies are used to construct a new prediction set, and noise is added to the prediction set. The HHO-CNN-SVR algorithm can maintain good prediction results. The results show that the HHO-CNN-SVR model proposed in this paper has strong generalization ability and high robustness, which can provide reliable support for penetration decision making and defense system optimization. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of the energy efficiency of screw drive and linear drive CNC machine tools in turning operations. Two CNC lathes were investigated, one equipped with screw drives and the other with linear drives, during the turning of specially prepared parts. The research examines active and reactive energy consumption, offering insights into the energy efficiency of different drive technologies. The analysis indicates that lathes with linear drives exhibited a higher reactive power consumption (8 kVar) during idle operation in comparison to those with screw drives (1.2 kVar). However, both drive systems demonstrated comparable potential for reducing reactive power consumption through implementing compensation techniques, with a reduction in reactive power consumption of nearly 70%. For both drive systems, the reduction in power use with compensation was at the level of 23–30% for screw drives and 36–47% for linear drives. The study highlights the importance of considering both active and reactive energy in evaluating the energy efficiency of machine tools. The findings contribute to a deeper understanding of energy consumption in turning processes, aiding in the selection and optimization of drive systems for improved sustainability in manufacturing. Future research should explore tool wear impacts, machine-specific energy optimization, and AI-driven solutions for real-time energy management. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we address some computational challenges arising in complex simulation-based design optimization problems. High computational cost, black-box formulation and stochasticity are some of the challenges related to optimization of design problems involving the simulation of complex mathematical models. Solving becomes even more challenging in case of multiple conflicting objectives that must be optimized simultaneously. In such cases, application of multiobjective optimization methods is necessary in order to gain an understanding of which design offers the best possible trade-off. We apply a three-stage solution process to meet the challenges mentioned above. As our case study, we consider the integrated design and control problem in paper mill design where the aim is to decrease the investment cost and enhance the quality of paper on the design level and, at the same time, guarantee the smooth performance of the production system on the operational level. In the first stage of the three-stage solution process, a set of solutions involving different trade-offs is generated with a method suited for computationally expensive multiobjective optimization problems using parallel computing. Then, based on the generated solutions an approximation method is applied to create a computationally inexpensive surrogate problem for the design problem and the surrogate problem is solved in the second stage with an interactive multiobjective optimization method. This stage involves a decision maker and her/his preferences to find the most preferred solution to the surrogate problem. In the third stage, the solution best corresponding that of stage two is found for the original problem. [Copyright &y& Elsevier]

A paper cup forming machine can produce a maximum of about 140 paper cups a minute. If the rate of production is increased to improve productivity, the contact force in the barrel cam increases, producing more vibration. Therefore, a method for reduction of cam vibration is needed. The profile of the barrel cam is optimized by using a multibody dynamics model. The objective of the optimization is to minimize the contact force that occurs between the rollers and the barrel cam. An experiment is carried out using the optimized barrel cam. The reduction of vibration at higher rates of production than the current rates validated the optimization. [ABSTRACT FROM AUTHOR]

Abstract: The systematic, transparent and accurate on-line energy information of the production process is quite the basis of mill energy management and conservation. An Energy Management System (EMS) was built according to the characteristics of the energy usage in a typical newsprint paper mill. The EMS first realized the process data acquisition and integration, second fulfilled the on-line energy information calculation from the integrated data. A water and steam properties query component based on IAPWS-IF97 was developed and plugged into the EMS to facilitate the energy calculation of water and steam. The practical performance in the paper mill showed that the integrated energy information of the whole mill was gained and the on-line energy supervisory was achieved. In the end a systematic, transparent and accurate energy usage profile is obtained and it would provide the valuable fundamental energy data for the further energy analysis and optimization in the EMS. [Copyright &y& Elsevier]

This paper deals with a particular case of Bi-Objective Optimization called Bi-Objective Discrete Optimization (BODO), where the feasible set is discrete, and the two objectives take only positive values. Since the feasible set of a BODO problem is discrete and usually finite, it can theoretically be enumerated to identify the Pareto set, which consists of all Pareto-optimal solutions representing different trade-offs between the two objectives. However, in general, this problem is challenging due to two main issues: time complexity, as the number of Pareto-optimal solutions can be exponentially large, and lack of decisiveness. From a practical point of view, the Central Decision Maker (CDM) may be interested in a reduced Pareto set that reflects the CDM's preferences, which can be obtained by a computationally efficient algorithm. In this paper, we propose a new criterion for selecting solutions within the Pareto set of BODO. For this purpose, we focus on solutions achieving proportional fairness between two objectives, called generalized Nash Fairness solutions (ρ -NF solutions). The positive parameter ρ , provided by the CDM, reflects the relative importance of the first objective compared to the second one. We first introduce the ρ -NF solution concept for BODO. We then show that the ρ -NF solution set is a subset of the Pareto set, and this inclusion can be strict. We also propose a recursive Newton-like algorithm for determining the ρ -NF solution set. Finally, an illustrative example of BODO is given. [ABSTRACT FROM AUTHOR]

Urban traffic guidance system is the key solution to modern traffic congestion, and drivers are the main participants in traffic travel, so it is particularly important to explore the mechanism of the influence of driver's driving behavior differences on the induction efficiency of traffic guidance system. According to different driving styles, this paper divides the road network drivers into three types: conservative, ordinary and aggressive. This paper studies the influence of driver's path choice behavior and vehicle driving behavior characteristics on traffic guidance efficiency, quantifies its decision error and vehicle driving efficiency on traffic guidance information. As for route selection behaviour, based on SP (Stated Preference) questionnaire data, a driver path selection model was constructed using a binary Logit model to predict the probability of different types of drivers changing paths when receiving traffic guidance information. In terms of vehicle driving behavior characteristics, according to the different driving behavior habits of different types of drivers, the corresponding driver driving characteristics model is established to simulate the actual state of vehicles driving in the road network. Finally, a traffic guidance system based on system optimization was constructed based on SUMO simulation platform and simulation experiments were conducted. The average vehicle speed, CO2 and CO emissions were used as indicators to evaluate the traffic guidance efficiency of the road network. The results show that, compared with other drivers, ordinary drivers have the best performance on the traffic guidance system. After induction, the vehicle speed increases by 10.59%, the CO2 emission decreases by 0.93%, and the CO emission decreases by 10.19%. [ABSTRACT FROM AUTHOR]

The interaction between technology and healthcare has a long history. However, recent years have witnessed the rapid growth and adoption of the Internet of Things (IoT) paradigm, the advent of miniature wearable biosensors, and research advances in big data techniques for effective manipulation of large, multiscale, multimodal, distributed, and heterogeneous data sets. These advances have generated new opportunities for personalized precision eHealth and mHealth services. IoT heralds a paradigm shift in the healthcare horizon by providing many advantages, including availability and accessibility, ability to personalize and tailor content, and cost-effective delivery. Although IoT eHealth has vastly expanded the possibilities to fulfill a number of existing healthcare needs, many challenges must still be addressed in order to develop consistent, suitable, safe, flexible, and power-efficient systems that are suitable fit for medical needs. To enable this transformation, it is necessary for a large number of significant technological advancements in the hardware and software communities to come together. This keynote paper addresses all these important aspects of novel IoT technologies for smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics. It identifies new perspectives and highlights compelling research issues and challenges, such as scalability, interoperability, device-network-human interfaces, and security, with various case studies. In addition, with the help of examples, we show how knowledge from CAD areas, such as large scale analysis and optimization techniques can be applied to the important problems of eHealth. [ABSTRACT FROM AUTHOR]

This paper brieffy overviews some recent and very fresh results on a rather new class of dynamic optimization problems governed by the so-called sweeping (Moreau) processes with controlled moving sets. Uncontrolled sweeping processes have been known in dynamical systems and applications starting from 1970s while control problems for them have drawn attention of mathematicians, applied scientists, and practitioners quite recently. We discuss here such problems and major results achieved in their theory and applications. [ABSTRACT FROM AUTHOR]

To automatic detect and characterize paper impurities with computer vision, we present a novel two parts evaluation procedure with feature representations using Alternating Direction Method of Multipliers (ADMM) sparse codes. The method is based on an offline training step to obtain sparse coefficients and codebooks via learning extracted features with ADMM optimization, followed by an online detection step to use linear SVM classifier to assess defective paper samples from no-defective ones. Our approach bridges the gap between paper impurities evaluation and sparse feature representations, taking advantages of existing ADMM algorithms to handle sparse codes problem. We compare different feature descriptors and sparse code methods to implement the procedure and experimentally validate it on a dataset of 11 paper classes. Experiment results show that the proposed method is competitive and effective in terms of evaluation accuracy and speed. [ABSTRACT FROM AUTHOR]

The aim of this article is to develop an experiment and a procedure to investigate the restoration of water-damaged paper and archival materials using freeze drying in order to allow a reproducible test and comparison of the influence of different operating conditions on drying time and restored paper quality. Firstly, a reproducible method for the preparation of soaked samples simulating water-damaged paper has been developed. Then, the samples have been freeze-dried in a laboratory-scale apparatus that allowed monitoring the temperature as well as the weight of the samples. The technique of evaporative freezing, which reduces the drying time required, has been used in this case. An innovative procedure for the visualization of the progress of the drying process has been validated, thus allowing the validation of a simple phenomenological model of the time evolution of the ice core volume; in addition, data on the residual moisture of the dried paper sheets in different zones have been given. Finally, optimization of this particular drying process by using simple or more sophisticated approaches has been discussed. [ABSTRACT FROM AUTHOR]

There are usually many sources for the supply of raw material to a pulp or paper mill in Sweden. Optimization of this supply is therefore a challenging task, and can only be managed properly if all aspects of risk are considered. In our study, these risks are related to when the weather reduces the load-bearing capacity of the ground or the roads. A stochastic and a deterministic model have been formulated, and they have been solved with mixed-integer quadratic programming and tested with data from a Swedish forest company. The results of this study show that the option value is greater than zero and that both the optimal policy and the option value change whenever the storage cost is altered. This shows that the optimal planning policy obtained from the stochastic model differs from the solution of the deterministic model. [ABSTRACT FROM AUTHOR]

The article discusses the result of an experimental study which shows that people do not follow the optimal strategy in practice and suggests two strategies for defeating human opponents in rock-paper-scissors (RPS). The study found that the probability of repeating a game on the next trial is well above one-third, while the strategy of always playing the symbol is assessed to be an effective strategy in defeating the symbol that the other player has previously played. The study also observed that the wins of each competition have increased by 26.4%, while the losses are decreased by 9.51%.

A well-designed scheduling plan that meets the practical constraints of the workshop is crucial for enhancing production efficiency in ship plane block assembly. Unlike traditional flow line scheduling problems, the scheduling optimization problem for ship plane block flow line involves dual resource constraints, including work teams and spare parts supply limitations. This can be seen as a Dual Resource Constrained Blocked Flow Shop Scheduling Problem (DRCBFSP). This paper presents a scheduling optimization method for this kind of problem to minimize the maximum completion time. To address the dual constraints, chromosomes are encoded as a two-dimensional array composed of positive integers representing the assembly order of blocks and the allocation of work teams. An improved Grey Wolf Optimization Algorithm (IGWO) is proposed to solve the problem, and the Rank Order Value (ROV) rule is used to transform the discrete scheduling solution with the continuous individual position vector. The IGWO algorithm also incorporates nonlinear search factors, dynamic inertia weight factors, and Gaussian mutation perturbation strategies to enhance its development and exploration capabilities. The experimental results suggest that the mathematical model and the IGWO algorithm established in this paper can effectively solve the DRCBFSP encountered in ship block building. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive approach to multiscale and multiphysics modelling of Solid Oxide Fuel Cells (SOFCs) by combining physics-based simulations with data-driven techniques. The modelling approach is tailored to specific end-use scenarios, ensuring that parameter selection aligns with operational requirements for accurate and efficient SOFC design. The study begins by constructing Representative Volume Elements (RVE s) from reconstructed microstructures, applying first-order homogenisation to upscale material properties, which are then incorporated into a macroscopic SOFC model. A major contribution is the structured model definition based on physical process entities, using a graphical representation of model topology. This approach simplifies complex system interactions by representing capacities (such as reservoirs, distributed systems, and interfaces) and transport processes (e.g., diffusion, convection, thermal diffusion), thereby enhancing clarity and improving the accuracy of SOFC performance simulations. A machine learning framework complements the physics-based modelling by training Artificial Neural Networks (ANNs) on simulation-generated datasets, delivering fast and reliable performance predictions. The study compares two optimisation techniques — Levenberg–Marquardt (LM) and Adam optimiser — demonstrating that LM is more effective for sparse datasets and smaller networks, whereas Adam performs better with large datasets and higher learning capacities. This hybrid modelling approach not only boosts predictive accuracy for SOFC performance but also lowers computational costs. By integrating physics-based simulations, machine learning, and a knowledge-driven simulation platform, this work advances SOFC design and optimisation, contributing to more efficient and cost-effective clean energy solutions. Additionally, the paper introduces a knowledge-driven simulation platform to enhance data management and integrate multiscale, multiphysics models. The platform leverages structured data models and ontological mappings to improve semantic interoperability, allowing for dataset reuse and validation across different simulation stages. This ensures a robust, reusable, and well-organised workflow, facilitating large-scale simulations and improving overall modelling accuracy. • Microstructure modelling links to SOFC properties. • Graphical-based modelling framework enhances clarity of complex SOFC processes. • Finite element analysis with non-isothermal fluid flow. • Machine learning evaluates SOFC performances with LM and Adam optimisers. • Semantic platform streamlines multi-scale modelling and data management. [ABSTRACT FROM AUTHOR]

With rapidly developing new energy technologies, rational energy planning has become an important area of research. Ground source heat pumps (GSHPs) have shown themselves to be highly efficient. effective in reducing building or district energy consumption and operating costs. However, when optimizing integrated energy systems, most studies simplify the GSHP model by using the rated coefficient of performance (COP) of the GSHP unit, neglecting factors such as soil, buried piping, and actual operating conditions. This simplification leads to a deviation from the actual operation of GSHPs, creating a gap between the derived operational guidelines and real-world performance. Therefore, this paper examines a hotel equipped with photovoltaic panels, a GSHP, and a hybrid energy storage unit. By constructing models of the underground pipes, GSHP units, and pumps, this paper considers the thermal exchanger between the underground pipes and the soil, the thermal pump, and the operating status of the unit. The purpose is to optimize the running expenses using an enhanced mote swarm optimization (PSO) algorithm to calculate the optimal operating strategy of system equipment. Compared to the simplified energy system optimization model, the detailed GSHP unit model shows a 21.36% increase in energy consumption, a 13.64% decrease in the mean COP of the GSHP unit, and a 44.4% rise in system running expenses. The differences in the GSHP model affect the energy consumption results of the unit by changing the relationship between the power consumption of the PV system and the GSHP at different times, which in turn affects the operation of the energy storage unit. The final discussion highlights significant differences in the calculated system operating results derived from the two models, suggesting that these may profoundly affect the architectural and enhancement processes of more complex GSHP configurations. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of the application of different optimization techniques in the design and production planning phase in the metal additive manufacturing process, specifically laser powder bed fusion (L-PBF) additive technology. This technology has a significant market share and belongs to the group of mature additive technology for the production of end-use metal parts. In the application of this technology, there is a space for additional cost/time reduction by simultaneously optimizing topology structure and part orientations. Simultaneous optimization reduces the production time and, indirectly, the cost of parts production, which is the goal of effective process planning. The novelty in this paper is the comparison of the part orientation solutions defined by the software algorithm and the experienced operator, where the optimal result was selected from the aspect of time and production costs. A feature recognition method together with symmetry operations in the part orientation process were also examined. A framework for the optimal additive manufacturing planning process has been proposed. This framework consists of design and production planning phases, within which there are several other activities: the redesign of the part, topological optimization, the creation of alternative build orientations (ABOs), and, as a final step, the selection of the optimal build orientation (OBO) using the multi-criteria decision method (MCDM). The results obtained after the MCDM hybrid method application clearly indicated that simultaneous topology optimization and part orientation has significant influence on the cost and time of the additive manufacturing process. The paper also proposed a further research direction that should take into consideration the mechanical as well as geometric, dimensioning and tolerances (GDT) characteristics of the part during the process of ABOs and OBO, as well as the uses of symmetry in these fields. [ABSTRACT FROM AUTHOR]

Scissor structures, widely used in kinetic architecture, have been researched for over six decades. However, most studies focus on the deployability of the scissor structures, often disregarding the potential transformability of scissor loops that allow changing the curvature. Three scissor units, namely, translational, polar, and angulated units, do not adhere to the trajectory of an arc suitable for a transformable canopy structure. Although the deployment route of the polar version aligns with this trajectory, it is impractical for achieving transformability due to deviations. Therefore, the paper presents a novel approach to optimize generalized scissor linkages (GSLs), consisting of symmetrical and identical scissor units. Defined by three variables, GSL is introduced to address the transformability issue, and the optimization method is developed. According to this method, the selected polar scissor is modified to synthesize the GSL version. It is demonstrated that no GSL is composed of identical and symmetrical units deploying precisely along the arc trajectory. Nevertheless, the proposed optimization method enables the output GSL to converge toward the desired route. Three basic scissor unit types can be represented by a single parameterized GSL, effectively unifying different scissor linkages under a single definition. Practical Applications: Scissor linkages find extensive applications in kinetic architecture, such as domes, canopies, tents, emergency bridges, temporary shelters, and responsive facades. Existing studies on such linkages predominantly focus on deployed and contracted configurations of these mechanical structures. This study diverges by attempting to establish intermediate phases for designing such structures that can adapt to changing environmental conditions or user needs. To achieve the objective, the scissor linkage must move within a defined range, conceptualized as an arc of a circle. For instance, by tracking the movement of the sun, one can ensure a consistent positioning of shadowed areas beneath an architectural canopy. None of the existing scissor linkages composed of basic translational, polar, and angulated units in the literature moves along this trajectory precisely. Only polar linkages approximate the desired route despite their large deviations. The generalized scissor linkage (GSL) introduced in the paper with distinct parameters demonstrates fewer deviations. Hence, an attempt is made to synthesize the GSL as the optimized version of the polar linkages. The mathematical foundation of the optimization method is introduced. It is foreseen that this method holds the potential to evolve into a versatile design tool in the future. [ABSTRACT FROM AUTHOR]

To meet the strict requirement on whole chain of vaccine production, storage, transportation, and distribution, most researches have been done on cascade refrigeration systems to achieve high operation performance, while a research gap on the performance exploration of eco‐friendly refrigeration systems still exists. In this paper, a comprehensive thermodynamic model was established to analyze the operation performance of a pre‐cooled cascade refrigeration system with eco‐friendly refrigerants propane (R290) and ethane (R170). Based on the present thermodynamic model, the performance optimization on the R290‐R170 cascade refrigerator was made with considerations of degree of subcooling, degree of superheat, evaporation temperature, condensation temperature, cascade condensation temperature, and cascade temperature difference. Variations of the coefficient of performance, exergy destruction, and total exergy efficiency of the refrigeration cycle were analyzed. Two mathematical correlations yielding the optimal cascade condensation temperature and maximized coefficient of performance were developed by multilinear regression analysis. When the evaporation temperature is − 60°C, the maximized coefficient of performance and total exergy efficiency are 1.276 and 49.51%. This paper demonstrates the potential for improving the R290‐R170 cascade refrigeration system and furnishes the basis for further exploration on ultra‐low‐temperature refrigerators. [ABSTRACT FROM AUTHOR]

The complexity of power quality (PQ) concerns is intensifying in tandem with the proliferation of inverter‐based renewable energy systems. The integration of power electronic devices within the distribution network exacerbates the complexity and introduces greater temporal variability to signal components. This paper introduces an advanced, online optimization technique for the decomposition and identification of PQ disturbances (PQDs). Initially, an improved variational mode decomposition (IVMD) method is presented, leveraging an energy ratio criterion for precise decomposition of concurrent PQDs. Subsequently, utilizing the characteristic attributes derived from IVMD, an optimized support vector machine (OSVM) algorithm is developed through the synthesis of diverse kernel functions. The OSVM strategically employs distinct kernel functions to augment the discriminability of the feature set. The synergy of IVMD and OSVM enables the detection of multiple PQDs, remarkably even with a minimal amount of training data. A series of experiments have been conducted to validate the effectiveness of the proposed methodology. The results corroborate that the formulated framework exhibits robust learning capabilities and a high degree of resistance to noise interference. Moreover, the hardware platform experiments prove that the proposed method has a satisfactory real‐time performance for its practicability. This paper introduces an advanced online optimization technique for decomposing and identifying power quality disturbances (PQDs). Leveraging an improved variational mode decomposition method and an optimized support vector machine algorithm, the framework enables the detection of multiple PQDs even with minimal training data. [ABSTRACT FROM AUTHOR]

The independent rate-distortion optimization technique for the video coding standard H. 266/VVC (versatile video coding) suffers from a loss in coding performance due to the oversight of temporal propagation effects of coding distortion in reference unit. To address this issue, this paper proposed a low-latency rate-distortion optimization algorithm based on the temporal influence of coding distortion. Firstly, leveraging the temporal propagation continuity characteristics of video images, it computed the temporal propagation influence weight of encoding distortion by considering motion compensation prediction errors and reconstruction errors. Secondly, it established a rate-distortion optimization model based on the temporal propagation impact of encoding distortion. Lastly, it utilized the temporal propagation influence weight of encoding distortion to adjust the Lagrange multipliers and quantization parameters of coding units. Experimental results demonstrate that, under low-delay P-frame and B-frame configurations, compared to the baseline algorithm, the BD-rate savings reach 1.6% and 0.9%, and the coding time decrease by 2.93% and 6.02%, respectively. The experimental results demonstrate that the algorithm proposed in this paper effectively improves coding performance without increasing encoding complexity, making it suitable for real-time encoding applications. [ABSTRACT FROM AUTHOR]

No matter whether with constraint or without constraint, most of the research about distributed optimization is studied for the kind of quadratic performance criteria that does not have an integrator; these optimization problems only concern the state value at the time of the final state, not the whole process of the system change. For this problem, this paper discusses second-order multi-agent systems with a discrete-time dynamic and a continuous-time dynamic, respectively, for distributed optimization control problems, and proposes sufficient conditions to ensure the quadratic performance criteria with an integrator is positive. Specifically, under sufficient conditions, we describe the multi-agent systems that are considered in this paper to be connected topology; all the agents can obtain the information from their neighbors. In addition, the structure of our controller only relies on the Laplace matrix of the system's topology, and the reaction coefficients in the controller are the parameters in the performance criteria. Finally, the analysis of convergence is given and verified by numerical examples and simulations. [ABSTRACT FROM AUTHOR]

In recent years, fog computing has become a promising technology that supports computationally intensive and time-sensitive applications, especially when dealing with Internet of Things (IoT) devices with limited processing capability. In this context, offloading can push resource-intensive tasks closer to the end devices at the network edge. This allows user equipment to profit from the fog computing environment by offloading their tasks to fog resources. Thus, computation offloading mechanisms can overcome the resource constraints of devices and enhance the system's performance by minimizing delay and extending the battery lifetime of devices. In this regard, designing an algorithm to decide which tasks to offload and where to execute them is crucial. Recently, there has been a growing interest in utilizing Reinforcement Learning (RL) and deep reinforcement learning (DRL), to address computation offloading mechanisms in the context of fog computing. This paper reviews the research conducted on Reinforcement learning (RL) and Deep Reinforcement Learning (DRL) based computation offloading mechanisms for IoT applications in the fog environment. We provide a comprehensive and detailed survey, analyzing and classifying the research paper in terms of RL techniques, objectives, architecture, and use cases. Then, in particular, we identify the advantages and weaknesses of each paper. After that, We systematically elaborate on open issues and future research directions that are crucial for the next decade. [ABSTRACT FROM AUTHOR]

During the five-axis flank milling process, the uncut chip area varies continuously with the cutter motion, which leads to a challenging issue for the geometric construction of the instantaneous uncut chip thickness (IUCT). In this paper, a novel approach to calculate the IUCT effectively considering the cutter runout effect is proposed. The element motion of the cutter extracted from the cutter location file (CLF) is described with one linear variable and two angular variables, which are necessary factors in the IUCT calculation of five-axis milling. The geometry relationship between cutter motion and IUCT has been analyzed, and the parametric expression of IUCT in five-axis milling can be deduced to predict the cutting force. The explicit five-axis IUCT expression facilitates a more comprehensive understanding of the key parameters in the cutting process, which could be helpful in optimizing the processing parameters. Compared to the numerical method based on true tooth trajectories, the time consumption of the proposed method is reduced greatly in the simulation. The experiment of five-axis flank milling is carried out, and the result reveals that the presented method can be well applied to the practical milling process. We believe that the IUCT model in this paper will promote the simulation and optimization system in five-axis machining. [ABSTRACT FROM AUTHOR]

Production rescheduling involves re-optimizing production schedules in response to disruptions that render the initial schedule inefficient or unfeasible. This process requires simultaneous consideration of multiple objectives to develop new schedules that are both efficient and stable. However, existing review papers have paid limited attention to the multi-objective optimization techniques employed in this context. To address this gap, this paper presents a systematic literature review on multi-objective production rescheduling, examining diverse shop-floor environments. Adhering to the PRISMA guidelines, a total of 291 papers were identified. From this pool, studies meeting the inclusion criteria were selected and analyzed to provide a comprehensive overview of the problems tackled, dynamic events managed, objectives considered, and optimization approaches discussed in the literature. This review highlights the primary multi-objective optimization methods used in relation to rescheduling strategies and the dynamic disruptive events studied. Findings reveal a growing interest in this research area, with "a priori" and "a posteriori" optimization methods being the most commonly implemented and a notable rise in the use of the latter. Hybridized algorithms have shown superior performance compared to standalone algorithms by leveraging combined strengths and mitigating individual weaknesses. Additionally, "interactive" and "Pareto pruning" methods, as well as the consideration of human factors in flexible production systems, remain under-explored. [ABSTRACT FROM AUTHOR]

This review paper provides a comprehensive analysis of radiation shielding concrete, covering its properties, design, evaluation, and applications. It begins with an introduction, stating the objective and scope. The paper explores radiation shielding basics, including ionizing radiation, shielding principles, and materials used for shielding. Concrete's properties relevant to shielding, radiation attenuation mechanisms, and factors affecting its efficiency are discussed. Different types of radiation shielding concrete are examined, along with their applications. The design and formulation of shielding concrete, including mix proportions, optimization techniques, and quality control, are presented. Evaluation methods and standards are discussed. Lastly, challenges, future directions, and emerging technologies are outlined. This review paper serves as a valuable resource for professionals involved in radiation shielding. The review on radiation shielding concrete highlighted its effectiveness in attenuating ionizing radiation, emphasizing material composition, density, and thickness as key design factors. Evaluation methods, such as gamma spectroscopy and Monte Carlo simulations, are discussed, demonstrating its versatile applications in nuclear facilities, healthcare, and space exploration. [ABSTRACT FROM AUTHOR]

The introduction of hybrid alternating current (AC)/direct current (DC) distribution networks led to several developments in smart grid and decentralized power system technology. The paper concentrates on several topics related to the operation of hybrid AC/DC networks. Such as optimization methods, control strategies, energy management, protection issues, and proposed solutions. The implementation of neural network optimization methods has great importance for the successful integration of multiple energy sources, dynamic energy management, establishment of system stability and reliability, power distribution optimization, management of energy storage, and online fault detection and diagnosis in hybrid networks like the hybrid AC–DC microgrids (MG). Taking advantage of renewable energy generation and cost-cutting through the neural network optimization technique holds the key to these progressions. Besides identifying the challenges in the operation of a hybrid system, the paper also compares this system to conventional MGs and shows the benefits of this type of system over different MG structures. This review compares the different topologies, particularly looking at the AC–DC coupled hybrid MGs, and shows the important role of the interlinking of converters that are used for efficient transmission between AC and DC MGs and generally used to implement the different control and optimization techniques. Overall, this review paper can be regarded as a reference, pointing out the pros and cons of integrating hybrid AC/DC distribution networks for future study and improvement paths in this developing area. [ABSTRACT FROM AUTHOR]

The fundamental objective of a modern power system lies in ensuring reliable and effective energy access for its customers. The assessment and determination of optimal operating conditions for power systems involve the utilization of the optimal power flow (OPF) tool. By considering critical factors such as generator power, bus voltages, and line power flow limits while satisfying the power balance equations, the OPF tool enables the identification of the most favorable configuration for efficient power system operation. Traditional optimization methods have limitations in addressing complex power system problems due to poor convergence and long computational times. As a result, computational intelligence tools have gained popularity in recent years. These tools are versatile and enable efficient solution of power system problems by effectively handling qualitative constraints. This paper presents a well-organized and comprehensive review of the algorithms used in power system optimization in the existing literature, encompassing the most recent developments in the field. Specifically, it examines the application of various population-based artificial intelligence techniques that have gained widespread adoption over the past decade (2012–2022). The aim of these techniques is to resolve an OPF problem. This paper organizes the reviewed papers into various types of population-based metaheuristic algorithms, each one implemented sequentially to deal with the OPF problem in the same chronological order in which they appeared in the literature. [ABSTRACT FROM AUTHOR]

In the Internet era, the e‐commerce industry has risen, its development scale continues to expand, cross‐border e‐commerce (CBEC) has also been born, and it is now in the stage of sustainable development. The rapid development of CBEC also needs the strong support of logistics, the two are inseparable, and today, the development scale of CBEC is constantly expanding. The existing e‐commerce logistics (ECL) model is also gradually unable to meet the increasingly diverse needs of users, and new logistics models need to be actively explored. To change this situation, this paper carried out a specific analysis of CBEC logistics model, and applied embedded technology to ECL, which also built a logistics tracking system. At the same time, combined with the ant colony algorithm, the paper carried out experimental research on the logistics package distribution route planning problem. From the experimental results, in terms of average delivery time, the algorithm's result was 25.95 hr, while the traditional algorithm was 32.53 hr; in terms of average distribution freight cost, the algorithm's result was 163.3 yuan, while the traditional algorithm was 257.7 yuan; in terms of average distribution cost, this algorithm's result was 131.53 yuan, while the traditional algorithm was 211.68 yuan. To sum up, this algorithm could effectively optimize the distribution route of logistics packages and improve the efficiency of package transportation. [ABSTRACT FROM AUTHOR]

The combination of different energy vectors like electrical energy, hydrogen, methane, and water is a crucial aspect to deal with in integrated local energy communities (ILECs). The ILEC stands for a set of active energy users that maximise benefits and minimise costs using optimisation procedures in producing and sharing energy. In particular, the proper management of different energy vectors is fundamental for achieving the best operating conditions of ILECs in terms of both energy and economic perspectives. To this end, different solutions have been developed, including advanced control and monitoring systems, distributed energy resources, and storage. Energy management planning software plays a pivotal role in developing ILECs in terms of performance evaluation and optimisation within a multi-carrier concept. In this paper, the state-of-the-art of ILECs is further enhanced by providing important details on the critical aspects related to the overall value chain for constituting an ILEC (e.g., conceptualisation, connecting technologies, barriers/limitations, control, and monitoring systems, and modelling tools for planning phases). By providing a clear understanding of the technical solutions and energy planning software, this paper can support the energy system transition towards cleaner systems by identifying the most suitable solutions and fostering the advancement of ILECs. [ABSTRACT FROM AUTHOR]

This paper presents a fault diagnosis method for a vacuum contactor using the generalized Stockwell transform (GST) of vibration signals. The objective is to solve the problem of low diagnostic performance efficiency caused by the inadequate feature extraction capability and the redundant pixels in the graph background. The proposed method is based on the time-frequency graph optimization technique and ShuffleNetV2 network. Firstly, vibration signals in different states are collected and converted into GST time-frequency graphs. Secondly, multi-resolution GST time-frequency graphs are generated to cover signal characteristics in all frequency bands by adjusting the GST Gaussian window width factor λ. The OTSU algorithm is then combined to crop the energy concentration area, and the size of these time-frequency graphs is optimized by 68.86%. Finally, considering the advantages of the channel split and channel shuffle methods, the ShuffleNetV2 network is adopted to improve the feature learning ability and identify fault categories. In this paper, the CKJ5-400/1140 vacuum contactor is taken as the test object. The fault recognition accuracy reaches 99.74%, and the single iteration time of model training is reduced by 19.42%. [ABSTRACT FROM AUTHOR]

The scientific and technical aspects of gun barrel movement for effective attack and defense are of ultimate significance. The precise control of the gun barrel is of strategic importance during targeting, especially in changing environmental conditions. Fuzzy logic control offers a powerful alternative to classical and manual solutions to deal with the complexity and uncertainties of dynamic systems, thus increasing targeting accuracy and precision. This approach provides adaptability and intuitive parameterization while simplifying system design and implementation. On the other hand, in fuzzy control management, accurate modeling and visualization of gun barrel movement is essential to achieve efficient aiming and performance. Many papers and realizations can be found on the (automatic) fuzzy control of cannon barrels. In this paper, the authors also suggest an implementation of a fuzzy-controlled cannon barrel. The novelty of this approach is the application of new defuzzification methods, resulting in an accurate solution for the problem. The article starts with a review of the theory and literature on the control of cannon barrels. It is followed by a comparison of different implementations, including simulation tests on the accuracy, and a discussion of some practical issues. [ABSTRACT FROM AUTHOR]

This paper focuses on the computation issue of portfolio optimisation with scenario-based mean-Average Value at Risk (AVaR) in credibilistic environment. The portfolio optimisation problem is designed in two cases: risk taker model and risk-averse model. The main idea is to replace the portfolio selection models with linear programming (LP) problems. Since the computing time required for solving LP greatly depends on the dimension and the structure of the problem, the conventional numerical methods are usually less effective in real-time applications. One promising approach to handle online applications is to employ recurrent neural networks based on circuit implementation. Hence, according to the convex optimisation theory and some concepts of ordinary differential equations, a neural network model for solving the LP problems related to portfolio selection problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with fuzzy returns. Some illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

The shortest path problem is fundamental in graph theory and has been studied extensively due to its practical importance. Despite this aspect, finding the shortest path between two nodes remains a significant challenge in many applications, as it often becomes complex and time consuming. This complexity becomes even more challenging when constraints make the problem non-additive, thereby increasing the difficulty of finding the optimal path. The objective of this paper is to present a broad perspective on the conventional shortest path problem. It introduces a new method to classify cost functions associated with graphs by defining distinct sets of cost functions. This classification facilitates the exploration of line graphs and an understanding of the upper bounds on the transformation sizes for these types of graphs. Based on these foundations, the paper proposes a practical methodology for solving non-additive shortest path problems. It also provides a proof of optimality and establishes an upper bound on the algorithmic cost of the proposed methodology. This study not only expands the scope of traditional shortest path problems but also highlights their computational complexity and potential solutions. [ABSTRACT FROM AUTHOR]

The document is a special issue of the Mathematics journal focusing on "Optimization Algorithms: Theory and Applications." It includes 10 selected papers from 9 countries covering topics such as arithmetic optimization, semi-parametric regression, flow shop scheduling, unconstrained optimization, metaheuristic techniques, many-objective optimization, and more. The papers present innovative algorithms and approaches that outperform existing methods in various optimization problems, showcasing the diverse research fields within the optimization area. [Extracted from the article]

Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm. [ABSTRACT FROM AUTHOR]