Search Results (5,888)

With the advancement of dual-carbon goals and the construction of new types of power systems, the proportion of electric vehicle charging stations (EVCSs) in the coupling system of power distribution and transportation networks is gradually increasing. However, the surge in charging demand not only causes voltage fluctuations and a decline in power quality but also leads to tension in the power grid load in some areas. The complexity of urban road networks further increases the challenge of charging station planning. Although laying out charging stations in areas with high traffic flow can better meet traffic demands, it may also damage power quality due to excessive grid load. In response to this problem, this paper proposes an optimized layout plan for electric vehicle charging stations considering the coupling effects of roads and electricity. By using section power flow to extract dynamic data from the power distribution network and comparing the original daily load curves of the power grid and electric vehicles, this paper plans reasonable capacity and charging/discharging schemes for EVCSs. It considers the impact of the charging and discharging characteristics of EVCSs on the power grid while satisfying the peak-shaving and valley-filling regulation benefits. Combined with the traffic road network, the optimization objectives include optimizing the voltage deviation, transmission line margin, network loss, traffic flow, and service range of charging stations. The Gray Wolf Optimizer (GWO) algorithm is used for solving, and the optimal layout plan for electric vehicle charging stations is obtained. Finally, through road–electricity coupling network simulation verification, the proposed optimal planning scheme effectively expands the charging service range of electric vehicles, with a coverage rate of 83.33%, alleviating users’ charging anxiety and minimizing the impact on the power grid, verifying the effectiveness and feasibility of the proposed scheme. Full article

This study presents a comprehensive investigation of cubic–quartic solitons within birefringent optical fibers, focusing on the effects of the Kerr law on the refractive index. The researchers have derived soliton solutions analytically using the sine-Gordon function technique. To validate their analytical results, the study employs the improved Adomian decomposition method, a numerical technique known for its efficiency and accuracy in solving nonlinear problems. This method effectively approximates solutions while minimizing computational errors, allowing for reliable numerical simulations that corroborate the analytical findings. The insights gained from this research contribute to a deeper understanding of the symmetry properties involved in nonlinear wave propagation in optical fibers. The study highlights the significant role of nonlinearities in shaping the behavior of waves within these systems. The use of proposed method not only serves as a checking mechanism for the sine-Gordon solutions but also illustrates its potential applicability to other nonlinear systems exhibiting complex symmetry behaviors. This versatility could lead to new exploration fronts in nonlinear optics and photonics, expanding the toolkit available for researchers in these rapidly evolving fields. Full article

The LBS2ITS project, titled “Curricula Enrichment Delivered through the Application of Location-Based Services to Intelligent Transport Systems”, is a collaborative initiative funded by the Erasmus+ program of the European Union. The primary objectives of the project were twofold: to develop new curricula and modernize existing programs at four universities in Sri Lanka. This effort was driven by the need to align educational offerings with the rapidly evolving fields of Location-Based Services (LBSs) and Intelligent Transport Systems (ITSs). A key feature of the LBS2ITS project is its interdisciplinary approach, which draws on expertise from a range of academic disciplines. The project has successfully developed curricula that integrate diverse fields such as geomatics, cartography, transport engineering, urban planning, environmental engineering, and computer science. By blending these perspectives, the curricula provide students with a holistic understanding of LBSs and ITSs, preparing them to address complex, real-world challenges that span multiple sectors. In this paper, the curriculum development and modernization process is detailed, with a particular focus on the two key phases: teacher training and curriculum development. The teacher training phase was crucial in equipping educators with the skills and knowledge necessary to deliver the new and updated courses. This phase also provided an opportunity for teachers to familiarize themselves with the latest trends and technologies in LBSs and ITSs, ensuring that they could effectively convey this information to students. The development phase focused on the creation of the curriculum itself, ensuring that it met both academic standards and industry needs. The curriculum was designed to be flexible and responsive to emerging technologies and methodologies, allowing for continuous improvement and adaptation. Additionally, the paper delves into the theoretical frameworks underpinning the methodologies employed in the project. These include Problem-Based Learning (PBL) and Problem-Based e-Learning (PBeL), both of which encourage active student engagement and foster critical thinking by having students tackle real-world problems. The emphasis on PBL ensures that students not only acquire theoretical knowledge but also develop practical problem-solving skills applicable to their future careers in LBSs and ITSs. Furthermore, the project incorporated rigorous quality assurance (QA) mechanisms to ensure that the teaching methods and curriculum content met high standards. This included regular feedback loops, stakeholder involvement, and iterative refinement of course materials based on evaluations from both students and industry experts. These QA measures are essential for maintaining the relevance, effectiveness, and sustainability of the curricula over time. In summary, the LBS2ITS project represents a significant effort to enrich and modernize university curricula in Sri Lanka by integrating cutting-edge technologies and interdisciplinary approaches. Through a combination of innovative teaching methodologies, comprehensive teacher training, and robust quality assurance practices, the project aims to equip students with the skills and knowledge needed to excel in the fields of LBSs and ITSs. Full article

Context: The systematic evaluation of a practitioner’s adherence to and competence in delivering psychotherapeutic interventions can be complex. This study describes the development of a fidelity assessment tool for the Reitman Centre CARERS Program (RCCP), a carer group-psychotherapy intervention with multiple didactic and clinical components. The tool’s value in informing psychotherapy training and best practices for practitioners from diverse professional settings is examined. Methods: The RCCP Fidelity Assessment Tool (RCCP-FAT) was developed following an iterative process of item writing and checking. Seven components of the RCCP—Group Structure, Dementia Education, Problem-Solving Techniques, Therapeutic Simulation, Vertical Cohesion, Horizontal Cohesion, and Overall Global Rating—were assessed, with three to eight items, and a “global score” assigned to each. Fifteen trained raters were paired up to rate 36 RCCP sessions using the RCCP-FAT. Rater agreement, correlation between itemized and global scores, and correlation between global ratings and RCCP participants’ satisfaction were calculated. Results: A total of 1188 RCCP-FAT items were rated by each of the two rater groups. Rater agreement was calculated to be 54.3% (κ = 0.32; 95% CI, 0.02681–0.3729). A positive correlation was found between the itemized and global scoring for four RCCP components evaluated (R = 0.833 to 0.929; p < 0.01). The global score and the participants’ satisfaction with “Simulation” was also positively correlated (R = 0.626, p < 0.01). Conclusions: The study provided evidence for fair rater agreement for all RCCP-FAT assessment items. More importantly, the process of developing the tool systematically crystallized the clinical elements of the RCCP and helped to standardize the training methods by creating a framework for providing feedback to learners that matches the items on the RCCP-FAT. The use of the RCCP-FAT to guide the training and mentoring of incoming group leaders is essential in the scaling and dissemination of a complex training method like the RCCP to ensure fidelity to the original evidence-based intervention. Full article

The instability of the double-diffusive convection of an Oldroyd-B fluid in a vertical Brinkman porous layer caused by temperature and solute concentration differences with the Soret effect is studied. Based on perturbation theory, an Orr–Sommerfeld eigenvalue problem is derived and numerically solved using the Chebyshev collocation method. The effects of dimensionless parameters on the neutral stability curves and the growth rate curves are examined. It is found that Lewis number Le, Darcy–Prandtl number Pr_D, and normalized porosity η have critical values: When below these thresholds, the parameters promote instability, whereas exceeding them leads to suppression of instability. In addition, for Le < Le_c2 (a critical value of Le), S_r strengthens the instability of the flow, while for Le > Le_c2, S_r suppresses it. These results highlight the complex coupling of heat and mass transfer in Oldroyd-B fluids within porous media. Full article

Surrogate-assisted evolutionary algorithms (SAEAs) are widely used in the field of high-dimensional expensive optimization. However, real-world problems are usually complex and characterized by a variety of features. Therefore, it is very challenging to choose the most appropriate surrogate. It has been shown that multiple surrogates can characterize the fitness landscape more accurately than a single surrogate. In this work, a multi-surrogate-assisted multi-tasking optimization algorithm (MSAMT) is proposed that solves high-dimensional problems by simultaneously optimizing multiple surrogates as related tasks using the generalized multi-factorial evolutionary algorithm. In the MSAMT, all exactly evaluated samples are initially grouped to form a collection of clusters. Subsequently, the search space can be divided into several areas based on the clusters, and surrogates are constructed in each region that are capable of completely describing the entire fitness landscape as a way to improve the exploration capability of the algorithm. Near the current optimal solution, a novel ensemble surrogate is adopted to achieve local search in speeding up the convergence process. In the framework of a multi-tasking optimization algorithm, several surrogates are optimized simultaneously as related tasks. As a result, several optimal solutions spread throughout disjoint regions can be found for real function evaluation. Fourteen 10- to 100-dimensional test functions and a spatial truss design problem were used to compare the proposed approach with several recently proposed SAEAs. The results show that the proposed MSAMT performs better than the comparison algorithms in most test functions and real engineering problems. Full article

Artificial intelligence (AI) is used in tasks that usually require human intelligence. The motivation behind this study is the growing interest in deploying AI in public spaces, particularly in autonomous vehicles such as flying drones, to address challenges in navigation and control. The primary challenge lies in developing a robust, cost-effective system capable of autonomous navigation in real-world environments, handling obstacles, and adapting to dynamic conditions. To tackle this, we propose a novel approach integrating machine learning (ML) algorithms, specifically, reinforcement learning (RL), with a comprehensive simulation and testing framework. Reinforcement learning machine algorithms designed to solve problems requiring optimization of the solution for the highest possible reward were used. It was assumed that the algorithms do not have to be created from scratch, but they need a well-defined training environment that will appropriately reward or punish the actions taken. This study aims to develop and implement a novel approach to autonomous drone navigation using machine learning (ML) algorithms. The primary innovation lies in the comprehensive integration of ML algorithms with a real-world drone control system, encompassing both simulations and real-world testing. A vital component of this approach is creating a multi-stage training environment that accurately replicates actual flight conditions and progressively increases the complexity of scenarios, ensuring a robust evaluation of algorithm performance. This research also introduces a new approach to optimizing system cost and accessibility. It involves using commercially available, cost-effective drones and open-source or free simulation tools, significantly reducing entry barriers for potential users. A critical aspect of this study is to assess whether affordable components can provide sufficient accuracy and stability without compromising system quality. The authors developed a system capable of autonomously determining optimal flight paths and controlling the drone, allowing it to avoid obstacles and respond to dynamic conditions in real time. The performance of the trained algorithms was confirmed through simulations and real-world flights, which allowed for assessing their usefulness in practical drone navigation scenarios. Full article

This paper introduces a novel approach for classifying multidimensional physiological and clinical data using Synolitic Graph Neural Networks (SGNNs). SGNNs are particularly good for addressing the challenges posed by high-dimensional datasets, particularly in healthcare, where traditional machine learning and Artificial Intelligence methods often struggle to find global optima due to the “curse of dimensionality”. To apply Geometric Deep Learning we propose a synolitic or ensemble graph representation of the data, a universal method that transforms any multidimensional dataset into a network, utilising only class labels from training data. The paper demonstrates the effectiveness of this approach through two classification tasks: synthetic and fMRI data from cognitive tasks. Convolutional Graph Neural Network architecture is then applied, and the results are compared with established machine learning algorithms. The findings highlight the robustness and interpretability of SGNNs in solving complex, high-dimensional classification problems. Full article

This paper investigates the integration of cognitive agents powered by Large Language Models (LLMs) within the Scaled Agile Framework (SAFe) to reinforce software project management. By deploying virtual agents in simulated software environments, this study explores their potential to fulfill fundamental roles in IT project development, thereby optimizing project outcomes through intelligent automation. Particular emphasis is placed on the adaptability of these agents to Agile methodologies and their transformative impact on decision-making, problem-solving, and collaboration dynamics. The research leverages the CogniSim ecosystem, a platform designed to simulate real-world software engineering challenges, such as aligning technical capabilities with business objectives, managing interdependencies, and maintaining project agility. Through iterative simulations, cognitive agents demonstrate advanced capabilities in task delegation, inter-agent communication, and project lifecycle management. By employing natural language processing to facilitate meaningful dialogues, these agents emulate human roles and improve the efficiency and precision of Agile practices. Key findings from this investigation highlight the ability of LLM-powered cognitive agents to deliver measurable improvements in various metrics, including task completion times, quality of deliverables, and communication coherence. These agents exhibit scalability and adaptability, ensuring their applicability across diverse and complex project environments. This study underscores the potential of integrating LLM-powered agents into Agile project management frameworks as a means of advancing software engineering practices. This integration not only refines the execution of project management tasks but also sets the stage for a paradigm shift in how teams collaborate and address emerging challenges. By integrating the capabilities of artificial intelligence with the principles of Agile, the CogniSim framework establishes a foundation for more intelligent, efficient, and adaptable software development methodologies. Full article

The Cutting Stock Problem (CSP) is an optimization challenge that involves dividing large objects into smaller components while considering various managerial objectives. The problem’s complexity can differ based on factors such as object dimensionality, the number of cutting stages required, and any technological constraints. The demand for coils of varying sizes and quantities necessitates intermediate splitting and slitting stages to produce the finished rolls. Additionally, relationships between orders are affected by dimensional variations at each stage of processing. This specific variant of the problem is known as the One-and-a-Half Dimensional Two-Stage Cutting Stock Problem (1.5-D TSCSP). To address the 1.5-D TSCSP, two algorithmic approaches were developed: the Generate-and-Solve (G&S) method and a hybrid Row-and-Column Generation (R&CG) approach. Both aim to minimize total trim loss while navigating the complexities of the problem. Inspired by existing problems in the literature for simpler versions of the problem, a set of randomly generated test cases was prepared, as detailed in this paper. An implementation of the two approaches was used to obtain solutions for the generated test campaign. The simpler G&S approach demonstrated superior performance in solving smaller instances of the problem, while the R&CG approach exhibited greater efficiency and provided superior solutions for larger instances. Full article

The application of smart materials in robots has attracted considerable research attention. This study developed an inchworm robot that integrates smart materials and a bionic design, using the unique properties of magnetorheological elastomers (MREs) to improve the performance of robots in complex environments, as well as their adaptability and movement efficiency. This research stems from solving the problem of the insufficient adaptability of traditional bionic robots on different surfaces. A robot that combines an MRE foot, an electromagnetic control system, and a bionic motion mechanism was designed and manufactured. The MRE foot was made from silicone rubber mixed with carbonyl iron particles at a specific ratio. Systematic experiments were conducted on three typical surfaces, PMMA, wood, and copper plates, to test the friction characteristics and motion performance of the robot. On all tested surfaces, the friction force of the MRE foot was reduced significantly after applying a magnetic field. For example, on the PMMA surface, the friction force of the front leg dropped from 2.09 N to 1.90 N, and that of the hind leg decreased from 3.34 N to 1.75 N. The robot movement speed increased by 1.79, 1.76, and 1.13 times on PMMA, wooden, and copper plate surfaces, respectively. The MRE-based intelligent foot design improved the environmental adaptability and movement efficiency of the inchworm robot significantly, providing new ideas for the application of intelligent materials in the field of bionic robots and solutions to movement challenges in complex environments. Full article

In geoenergy science and engineering, well placement optimization is the process of determining optimal well locations and configurations to maximize economic value while considering geological, engineering, economic, and environmental constraints. This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. This study proposes a hybrid workflow for determining the locations of production wells during primary oil recovery using a multi-modal convolutional neural network (M-CNN) integrated with an evolutionary optimization algorithm. The particle swarm optimization algorithm provides the M-CNN with full-physics reservoir simulation results as learning data correlating an arbitrary well location and its cumulative oil production. The M-CNN learns the correlation between near-wellbore spatial properties (e.g., porosity, permeability, pressure, and saturation) and cumulative oil production as inputs and output, respectively. The learned M-CNN predicts oil productivity at every candidate well location and selects qualified well placement scenarios. The prediction performance of the M-CNN for hydrocarbon-prolific regions is improved by adding qualified scenarios to the learning data and re-training the M-CNN. This iterative learning scheme enhances the suitability of the proxy for solving the problem of maximizing oil productivity. The validity of the proxy is tested with a benchmark model, UNISIM-I-D, in which four oil production wells are sequentially drilled. The M-CNN approach demonstrates remarkable consistency and alignment with full-physics reservoir simulation results. It achieves prediction accuracy within a 3% relative error margin, while significantly reducing computational costs to just 11.18% of those associated with full-physics reservoir simulations. Moreover, the M-CNN-optimized well placement strategy yields a substantial 47.40% improvement in field cumulative oil production compared to the original configuration. These findings underscore the M-CNN’s effectiveness in sequential well placement optimization, striking an optimal balance between predictive accuracy and computational efficiency. The method’s ability to dramatically reduce processing time while maintaining high accuracy makes it a valuable tool for enhancing oil field productivity and streamlining reservoir management decisions. Full article

This paper presents an innovative approach to modelling the fiber optic fusion effect using the Network Simulation Method (NSM). An analogy between the heat conduction equations and electrical circuits is developed, allowing a complex physical problem to be transformed into an equivalent electrical system. Using NGSpice, thermal interactions in an anisotropic optical fiber under high optical power conditions are simulated. The methodology addresses the distribution of the temperature in the system, considering thermal variations and temperature-dependent material characteristics. In an NSM equivalent circuit, the effect of applying the spark is modelled by a switch that switches the spark-generating source on and off. It can be seen that temperature variation with time, or temperature rise rate (K/s), depends on the applied power. In addition, the mathematical method of nondimensionalization is used to study the real influence of each parameter of the problem on the solution and the relationship between the variables. Four optical fiber cases are analysed, each characterised by different areas and refractive indices, revealing how these variables affect the propagation of the melting phenomenon. The results highlight the effectiveness of the NSM in solving nonlinear and coupled problems in thermal engineering, providing a solid framework for future research in the optimisation of optical communication systems. Full article

Separate DC-DC converters for each energy source are typically configured in fuel-cell hybrid vehicles. This results in a complex control structure of the powertrain system, low energy density of the converter, and high cost due to the large number of components. Conducting research on DC-DC converters with good energy flow management and high integration is a trend to solve such problems. Based on the analysis of the basic functional structure of the converter, this paper designs a buffering unit circuit with energy collection and distribution functions and appropriately connects it with the pulse unit circuit of the converter. Through device optimization reuse and power transmission path integration, a class of non-isolated four-port DC-DC converters is constructed, which consists of an auxiliary energy charging module, input energy source control module, braking energy feedback module and forward bootstrap boost circuit. This converter has two bi-directional ports, a uni-directional input and a bi-directional output, for separate connection to the power batteries, supercapacitors, fuel cells and DC bus. It can adapt to the fluctuation of the vehicle’s driving condition while achieving dynamic and flexible regulation of power flow and can flexibly allocate power according to the load current and voltage level of energy. It can realize a total of 14 operation modes, including six output power supply operation modes, five auxiliary power charging operation modes, and three braking energy regeneration operation modes. Furthermore, the mathematical model of this converter is constructed using the state-average method and the small-signal modeling method in order to achieve the responsiveness and stability of switching multiple operating modalities. The PI control parameters are optimized using the particle swarm optimization algorithm to achieve optimized control of the converter. The simulation system is set up using MATLAB R2024a to verify that the proposed converter topology and algorithm can dynamically allocate appropriate current paths to manipulate the power flow under various operating conditions, effectively improving the utilization rate and efficiency of energy. The converter has the characteristics of high gain and high power density, which is suitable for three-energy fuel cell hybrid electric vehicles. Full article

With the increasing number of satellites and rising user demands, the volume of satellite data transmissions is growing significantly. Existing scheduling systems suffer from unequal resource allocation and low transmission efficiency. Therefore, effectively addressing the large-scale multi-objective satellite data transmission scheduling problem (SDTSP) within a limited timeframe is crucial. Typically, swarm intelligence algorithms are used to address the SDTSP. While these methods perform well in simple task scenarios, they tend to become stuck in local optima when dealing with complex situations, failing to meet mission requirements. In this context, we propose an improved method based on the minimum angle particle swarm optimization (MAPSO) algorithm. The MAPSO algorithm is encoded as a discrete optimizer to solve discrete scheduling problems. The calculation equation of the sine function is improved according to the problem’s characteristics to deal with complex multi-objective problems. This algorithm employs a minimum angle strategy to select local and global optimal particles, enhancing solution efficiency and avoiding local optima. Additionally, the objective space and solution space exhibit symmetry, where the search within the solution space continuously improves the distribution of fitness values in the objective space. The evaluation of the objective space can guide the search within the solution space. This method can solve multi-objective SDTSPs, meeting the demands of complex scenarios, which our method significantly improves compared to the seven algorithms. Experimental results demonstrate that this algorithm effectively improves the allocation efficiency of satellite and ground station resources and shortens the transmission time of satellite data transmission tasks. Full article