Search Results (277)

The development of renewable energy in rural areas presents significant potential. Integrating renewable energy sources, such as wind power and photovoltaics, into microgrids as distributed generation systems offers a viable approach for local energy utilization. In recent years, the rapid advancement of agriculture, forestry, animal husbandry, and fisheries has led to an increasing demand for electricity in these regions. However, the existing power infrastructure remains underdeveloped, resulting in a pronounced imbalance between supply and demand. This paper investigates the optimization of rural microgrid configurations by incorporating demand response strategies and the synergistic interactions among wind turbines, photovoltaic systems, batteries, and loads. A multi-objective optimization model is developed to maximize annual profits and environmental externality (namely, the proposed microgrid achieves equivalent carbon dioxide emissions reductions by replacing thermal power generation through either selling green electricity to the main grid or meeting rural load demands), which is subsequently transformed into a single-objective formulation using the Shapley value method and solved via a global harmonic search algorithm. Simulation results validate the applicability of the proposed solution method and demonstrate the economic performance, development potential, and environmental benefits of the optimized microgrid configurations. Full article

Reservoir operation under climate change poses significant challenges for hydropower-dependent countries, particularly in cascade reservoir systems. This study aims to derive optimal future operating rule curves for the Nam Khan 2 and Nam Khan 3 cascade reservoirs in Lao PDR to maximize hydropower generation under climate change. Genetic Algorithm (GA), Invasive Weed Optimization (IWO), and Harmony Search (HS) were integrated with a reservoir simulation model to optimize monthly upper and lower rule curves. Future reservoir inflows were generated using climate projections from the INM-CM5-0 climate model’s SSP245 scenario for 2025–2050. The aim was to maximize average annual electricity generation for the entire cascade system while ensuring practicable reservoir operation. The optimized rule curves obtained from all three algorithms exhibited similar seasonal patterns, reflecting regional hydrological characteristics. The proposed rule curves significantly improved hydropower performance compared to the existing operating policies. For Nam Khan 2, average annual electricity generation increased from 324.089 GWh under current operations to 788.246, 787.100, and 786.561 GWh using GA, IWO, and HS. Similarly, Nam Khan 3 achieved substantial improvements, with average annual generation increasing from 156.029 GWh to 270.049, 266.840, and 266.547 GWh. The optimized rule curves also contributed to better storage regulation and reduced variability in energy production. The findings demonstrate that integrating metaheuristic optimization techniques with reservoir simulation models provides an effective framework for adaptive hydropower-oriented reservoir operation under future climate uncertainty. Full article

Artificial Intelligence (AI) has introduced transformative possibilities in orthodontics by enhancing diagnostic precision, treatment planning, and aesthetic outcomes. In face-driven orthodontics, treatment objectives extend beyond achieving proper occlusion to optimizing facial balance and harmony. With the growing patient demand for aesthetic improvements, AI technologies enable clinicians to integrate facial analysis and dynamic soft-tissue evaluation into personalized treatment approaches. Research in this scoping review analyzed current applications of AI in face-driven orthodontics, focusing on diagnosis, soft-tissue assessment, and individualized treatment planning. A comprehensive search was conducted in PubMed and Scopus for studies published between 2021 and 2025. The review followed the PRISMA-ScR guidelines. Of 54 initially identified studies, 24 met the inclusion criteria after title, abstract, and full-text screening. Extracted data were organized according to the main application areas of AI in face-driven orthodontics. Most studies focused on AI-assisted facial analysis, 3D reconstruction, and treatment simulation. Deep learning models demonstrated high performance in soft-tissue prediction, aesthetic evaluation, and diagnostic accuracy. However, heterogeneity in datasets, a lack of standardized validation protocols, limited external validation across included studies and limited clinical applicability were identified as key limitations. AI-based facial analysis supports a shift toward individualized, aesthetics-oriented orthodontic planning. Although current evidence highlights its potential for improving diagnostic precision and treatment outcomes, further validation through large-scale clinical studies is essential for broader implementation in everyday practice. Full article

The optimization of seismic isolation parameters is essential for balancing displacement demand and acceleration control in base-isolated structures. While numerous studies have applied metaheuristic algorithms to isolator tuning, the influence of objective-function weighting on optimal design outcomes remains insufficiently explored. This study investigates the effects of displacement and acceleration on control performance in a multi-objective optimization function. Thus, acceleration can be reduced economically by limiting the isolator displacement capacity. In the study, the effective values of the acceleration and displacement coefficients in the objective function of the problem are changed for the design optimization of seismic base isolators, and the determination of the most appropriate weights in the equation and their effects on the control are investigated. In the optimization process, the adaptive harmony search algorithm, which is obtained by adapting the parameters of the harmony search algorithm inspired by the search for the best harmony, is used. The results demonstrate that increased emphasis on acceleration minimization leads to longer effective isolation periods and higher damping ratios, whereas displacement-dominated weighting results in stiffer isolation systems with reduced mobility. Full article

Background: Psychological well-being is crucial for the development and performance of young athletes. This systematic review aims to synthesize the available scientific evidence on the relationship between passion (harmonious and obsessive), basic psychological needs (BPNs), motivation, affect (positive and negative), and life satisfaction in young football (soccer) players. Methods: A systematic literature review was performed, following the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The search was conducted in the Web of Science, Scopus, ERIC, and SportDiscus databases, using a comprehensive strategy that combined keywords related to football, youth, passion, motivation, and well-being. Two independent reviewers performed article screening, eligibility assessment, and data extraction. The methodological quality of the included studies was determined using two different tools. Results: Nine studies met the inclusion criteria and were analyzed in detail. The results consistently indicate that harmonious passion is associated with greater fulfillment of BPNs, positive affect, and overall life satisfaction. In contrast, obsessive passion was linked to negative outcomes such as burnout and emotional dysregulation. The available evidence suggests a positive association of harmonious passion with motivation and well-being, and an association of obsessive passion with psychological distress. Conclusions: Within the delimited scope, the evidence suggests that harmonious passion is an important construct positively associated with the well-being and motivation of young footballers, while obsessive passion is associated with adverse outcomes. Research in this area is scarce, showing methodological diversity and heterogeneous samples, which limits the generalizability of the findings. Future research should prioritize longitudinal designs and interventions to promote harmonious passion and the satisfaction of BPNs. Full article

This narrative review addresses the complexities of managing the vertical dimension of occlusion (VDO) in restorative dentistry, focusing on predictability in prosthetic reconstructions. Altering VDO impacts biological, biomechanical, esthetic, and functional aspects, making it a controversial topic. While VDO naturally evolves throughout life, interventions require careful consideration due to potential complications. Various techniques guide VDO determination, including facial proportions, physiological methods, phonetics, and cephalometric analysis. Clinicians must understand these principles and adapt them to individual patient needs. Materials and Methods: A narrative literature review was conducted using PubMed, Scopus, Google Scholar, and the Cochrane Library, searching keywords like “vertical dimension of occlusion”, “dental”, “diagnosis”, “management” and “complications”. In addition to the literature review, two case reports with extensive prosthodontic restorations were included to illustrate the diagnostic challenges and treatment considerations in a clinical setting. Results: Increasing VDO aids restorative treatments, re-establishing morphology, and facilitating additive procedures. Minimally invasive approaches, provisional restorations, and fixed restorations with functional contours are favored. Individualized, patient-centered care is critical, recognizing unique anatomical and functional needs. This approach optimizes stomatognathic system rehabilitation while preventing adverse effects on body posture and airway dimensions. Conclusions: To ensure predictable results and minimize risks, changes in VDO should be kept to a minimum to achieve dentofacial aesthetic harmony and secure adequate space for the planned restorations The two case reports presented, with different clinical approaches, underline the importance of understanding the potential risks and benefits of VDO alteration which is crucial for achieving predictable and successful outcomes in complex restorative cases. Full article

Metaheuristic algorithms are widely utilized as effective tools for solving complex optimization problems. Among them, the Harmony Search (HS) algorithm has garnered significant attention for its simple structure and excellent performance. The efficacy of the HS algorithm is heavily dependent on the configuration of its internal parameters, with the Harmony Memory Considering Rate (HMCR) and Pitch Adjusting Rate (PAR) playing pivotal roles. These parameters determine the probabilities of using the Random Generation (RG), Harmony Memory Consideration (HMC), and Pitch Adjustment (PA) operators, thereby controlling the balance between exploration and exploitation. However, a systematic empirical analysis of the interaction between these parameters and the characteristics of the problem at hand remains insufficient. Thus, this study conducts a comprehensive empirical analysis of the performance sensitivity of the HS algorithm to variations in HMCR and PAR values. The analysis is performed on a suite of 23 benchmark functions, encompassing diverse characteristics such as unimodality/multimodality and separability/non-separability, along with 5 real-world optimization problems. Through extensive experimentation, the performance for each parameter combination was evaluated on a rank-based system and visualized using heatmaps. The results experimentally demonstrate that the algorithm’s performance is most sensitive to the HMCR value across all function types, establishing that setting a high HMCR value (≥0.9) is a prerequisite for securing stable performance. Conversely, the optimal PAR value showed a direct correlation with the topographical features of the problem landscape. For unimodal problems, a low PAR value between 0.1 and 0.3 was more effective, whereas for complex multimodal problems with numerous local optima, a relatively higher PAR value between 0.3 and 0.5 proved more efficient in searching for the global optimum. This research provides a guideline into the parameter settings of the HS algorithm and contributes to enhancing its practical applicability by proposing a systematic parameter tuning strategy based on problem characteristics. Full article

This study focuses on the mathematical method developed by integrating the surrogate model as constraints for wall stability into the heuristic optimization algorithm to gain the optimum cost and ${\mathrm{CO}}_2$ emission value of the gabion retaining wall (GRW). This study also includes the comparison of optimum GRW results with optimum cantilever retaining wall (CRW) designs for different design cases. The Harmony Search Algorithm (HSA), which efficiently explores the design space and robustly reaches the optimum result in solving optimization problems, was used as the heuristic optimization algorithm. The primary construction scenario was considered as an optimization problem, which involved excavating the slope, constructing the wall, and compacting the backfill soil to minimize the cost and CO${}_2$ emissions for separate objective functions of GRW and CRW designs. Comparative results show that GRWs outperform CRWs in terms of sustainability and cost-efficiency, achieving 55% lower cost and 78% lower CO${}_2$ emissions on average, while the HSA–surrogate model provides a fast and accurate solution for geotechnical design problems. The surrogate models for sliding, overturning, and slope stability safety factors of GRW exhibited exceptional accuracy, characterized by minimal error values (MSE, RMSE, MAE, MAPE) and robust determination coefficients ($R^2\approx 0.99$), hence affirming their dependability in safety factor assessment. By integrating the surrogate model based on the statistical method into the optimization algorithm, a quick examination of the wall’s stability was performed, reducing the required computational power. Full article

Background and Objectives: Music therapy has a long tradition in palliative care, and recent studies have investigated its Neuro-Psycho-Endocrine-Immunological (NPEI) effects in terminally ill patients. Despite numerous published articles, there is a lack of a compendium connecting the physiological basis of music therapy with the specific musical elements most effective in end-of-life settings. This narrative review aims to synthesize current evidence on the physiological mechanisms underlying responses to music, with a focus on terminal patients and implications for nursing practice. Materials and Methods: For quality and possible reproducibility, a narrative review was conducted in accordance with Scale for the Assessment of Narrative Review Articles (SANRA) guidelines. The review targeted articles from the past five years indexed in PubMed, CINAHL, Cochrane Library, Embase, Scopus, Web of Science, and PsycInfo, supplemented by additional relevant references identified through manual searching. The PICOS framework was performed to structure the search strategy and study selection, focusing on studies relevant to the biological effects of music therapy in end-of-life care and their practical implications for nursing care. Results: The neurophysiology of music perception in terminal patients is complex, involving a wide array of clinical and cultural factors. Key musical elements—such as rhythm, melody, harmony, tempo, and mode—can influence physiological and psycho-emotional responses. Music therapy interventions, when tailored to the individual’s preferences and cultural background, may modulate parameters like heart rate, blood pressure, stress hormone levels, and pain perception. Evidence supports the need for individualized approaches and highlights the NPEI rationale for integrating music therapy into end-of-life care. Conclusions: A deeper understanding of the scientific mechanisms discussed in this narrative review can enhance the effectiveness of music therapy interventions in end-of-life settings. Nursing practice can benefit by integrating evidence-based selection of musical pieces and personalizing interventions to the clinical and cultural profile of each patient. Further interdisciplinary research is needed to establish standardized criteria for music therapy in palliative care and to optimize outcomes for terminally ill patients. Full article

Dynamic effects such as wind, traffic, and earthquakes can cause loss of life and property. Since tall buildings are more sensitive to these vibrations, vibration control is an important issue in civil engineering. In this study, the Adaptive Harmony Search (AHS) was used to determine the optimum TMDI parameters. AHS shares similar steps with the classic Harmony Search (HS), which simulates the process of musicians creating new harmonies. However, unlike HS, it uses harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) values that are updated at each search step, rather than fixed HMCR and PAR values. The aim of the optimization is to minimize the maximum displacement of the upper floor in a 10-story shear building against different earthquake records. To evaluate the performance of the TMDI system, displacement and total acceleration under seismic loading were analyzed. As a result, the TMDI reduced displacement by 35% and 13.33% for non-pulse and pulse, respectively, for near-fault earthquake records. These reductions indicate that the structure’s resistance to dynamic loads can be enhanced using control systems. Full article

With the increasing deployment of unmanned surface vessels (USVs) in complex marine operations such as ocean monitoring, search and rescue, and military reconnaissance, precise heading control under environmental disturbances and system delays has become a critical challenge. This paper presents an advanced robust heading control strategy for USVs operating under these demanding conditions. The proposed approach integrates three key innovations: (1) an enhanced Smith predictor for accurate time-delay compensation, (2) a variable-universe fuzzy PID controller with self-adaptive scaling domains that dynamically adjust to error magnitude and rate of change, and (3) a hybrid metaheuristic optimization algorithm combining beetle antennae search, harmony search, and genetic algorithm (BAS-HSA-GA) for optimal parameter tuning. Through comprehensive simulations using a Nomoto first-order time-delay model under combined white noise and second-order wave disturbances, the system demonstrates superior performance with over 90% reduction in steady-state heading error and ≈30% faster settling time compared to conventional PID and single-optimization fuzzy PID methods. Field trials under sea-state 4 conditions confirm 15–25% lower tracking error in realistic operating scenarios. The controller’s stability is rigorously verified through Lyapunov analysis, while comparative studies show significant improvements in S-shaped path tracking performance, achieving better IAE/ITAE metrics than DRL, ANFC, and ACO approaches. This work provides a comprehensive solution for high-precision, delay-resilient USV heading control in dynamic marine environments. Full article

Distributed permutation flowshop scheduling is an NP-hard problem that has become a hot research topic in the fields of optimization and manufacturing in recent years. Multimodal optimization finds multiple global and local optimal solutions of a function. This study proposes a harmony search algorithm with iterative optimization operators to solve the NP-hard problem for multimodal optimization with the objective of makespan minimization. First, the initial solution set is constructed by using a distributed NEH operator. Second, after generating new candidate solutions, efficient iterative optimization operations are applied to optimize these solutions, and the worst solutions in the harmony memory (HM) are replaced. Finally, the solutions that satisfy multimodal optimization of the harmony memory are obtained when the stopping condition of the algorithm is met. The constructed algorithm is compared with three meta-heuristics: the iterative greedy meta-heuristic algorithm with a bounded search strategy, the improved Jaya algorithm, and the novel evolutionary algorithm, on 600 newly generated datasets. The results show that the proposed method outperforms the three compared algorithms and is applicable to solving distributed permutation flowshop scheduling problems in practice. Full article

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

The cross-sectional dimensions of structural elements in a structure are design elements that need to be carefully designed and are related to the stiffness of the structure. Various optimization processes are applied to determine the optimum cross-sectional dimensions of beams or columns in structures. By repeating the optimization processes for multiple load scenarios, it is possible to create a data set that shows the optimum design section properties. However, this step means repeating the same processes to produce the optimum cross-sectional dimensions. Artificial intelligence technology offers a short-cut solution to this by providing the opportunity to train itself with previously generated optimum cross-sectional dimensions and infer new cross-sectional dimensions. By processing the data, the artificial neural network can generate models that predict the cross-section for a new structural element. In this study, an optimization process is applied to a simple tubular column and an I-section beam, and the results are compiled to create a data set that presents the optimum section dimensions as a class. The harmony search (HS) algorithm, which is a metaheuristic method, was used in optimization. An artificial neural network (ANN) was created to predict the cross-sectional dimensions of the sample structural elements. The neural architecture search (NAS) method, which incorporates many metaheuristic algorithms designed to search for the best artificial neural network architecture, was applied. In this method, the best values of various parameters of the neural network, such as activation function, number of layers, and neurons, are searched for in the model with a tool called HyperNetExplorer. Model metrics were calculated to evaluate the prediction success of the developed model. An effective neural network architecture for column and beam elements is obtained. Full article

Background/Objectives: Accurate classification of COVID-19 from chest X-rays is critical but remains limited by overlapping features with other lung diseases and the suboptimal performance of current methods. This study addresses the diagnostic gap by introducing a novel hybrid framework for precise segmentation and classification of lung conditions. Methods: The approach combines multi-level thresholding with the advanced metaheuristic optimization algorithms animal migration optimization (AMO), electromagnetism-like optimization (EMO), and the harmony search algorithm (HSA) to enhance image segmentation. A convolutional neural network (CNN) is then employed to classify segmented images into COVID-19, viral pneumonia, or normal categories. Results: The proposed method achieved high diagnostic performance, with 99% accuracy, 99% sensitivity, and 99.5% specificity, confirming its robustness and effectiveness in clinical image classification tasks. Conclusions: This study offers a novel and technically integrated solution for the automated diagnosis of COVID-19 and related lung conditions. The method’s high accuracy and computational efficiency demonstrate its potential for real-world deployment in medical diagnostics. Full article