Search Results (2,560)

The growing need for sustainable energy solutions has propelled the development of Hybrid Renewable Energy Systems (HRESs), which integrate diverse renewable sources like solar, wind, biomass, geothermal, hydropower and tidal. This review paper focuses on balancing economic, environmental, social and technical criteria to enhance system performance and resilience. Using comprehensive methodologies, the review examines state-of-the-art algorithms such as Multi-Objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), alongside Crow Search Algorithm (CSA), Grey Wolf Optimizer (GWO), Levy Flight-Salp Swarm Algorithm (LF-SSA), Mixed-Integer Linear Programming (MILP) and tools like HOMER Pro 3.12–3.16 and MATLAB 9.1–9.13, which have been instrumental in optimizing HRESs. Key findings highlight the growing role of advanced, multi-energy storage technologies in stabilizing HRESs and addressing the intermittency of renewable sources. Moreover, the integration of metaheuristic algorithms with machine learning has enabled dynamic adaptability and predictive optimization, paving the way for real-time energy management. HRES configurations for cost-effectiveness, environmental sustainability, and operational reliability while also emphasizing the transformative potential of emerging technologies such as quantum computing are underscored. This review provides critical insights into the evolving landscape of HRES optimization, offering actionable recommendations for future research and practical applications in achieving global energy sustainability goals. Full article

An efficient linearization method for solving a system of nonlinear equations was developed, showing good stability and convergence properties. It uses an unconventional and simple strategy to improve the performance of classic methods by a full-rank update of the Jacobian approximates. It can be considered both as a discretized Newton’s method or as a quasi-Newton method with a full-rank update of the Jacobian approximates. A solution to the secant equation presented earlier was based on the Wolfe-Popper procedure. The secant equation was splitted into two equations by introducing an auxiliary variable. A simplified algorithm is given in this paper for the full-rank update procedure.It directly solves the secant equation with the pseudoinverse of the Jacobian approximate matrix. Numerical examples are shown for demonstration purposes. The convergence and efficiency of the suggested method are discussed and compared with the convergence and efficiency of classic linearization methods. Full article

Diabetes requires effective monitoring of the blood glucose level (BGL), traditionally achieved through invasive methods. This study addresses the non-invasive estimation of BGL by utilizing heart rate variability (HRV) features extracted from photoplethysmography (PPG) signals. A systematic feature selection methodology was developed employing advanced metaheuristic algorithms, specifically the Improved Dragonfly Algorithm (IDA), Binary Grey Wolf Optimizer (bGWO), Binary Harris Hawks Optimizer (BHHO), and Genetic Algorithm (GA). These algorithms were integrated with machine learning (ML) models, including Random Forest (RF), Extra Trees Regressor (ETR), and Light Gradient Boosting Machine (LightGBM), to enhance predictive accuracy and optimize feature selection. The IDA-LightGBM combination exhibited superior performance, achieving a mean absolute error (MAE) of 13.17 mg/dL, a root mean square error (RMSE) of 15.36 mg/dL, and 94.74% of predictions falling within the clinically acceptable Clarke error grid (CEG) zone A, with none in dangerous zones. This research underscores the efficiency of utilizing HRV and PPG for non-invasive glucose monitoring, demonstrating the effectiveness of integrating metaheuristic and ML approaches for enhanced diabetes monitoring. Full article

While both psychopathology and cognitive deficits manifest in mental health disorders, the nature of their relationship remains poorly understood. Recent research suggests a potential common factor underlying both domains. Using data from the Philadelphia Neurodevelopmental Cohort (N = 9494, ages 8–21), we estimated and validated a “c” factor representing overall cerebral functioning through a structural model combining cognitive and psychopathology indicators. The model incorporated general factors of psychopathology (“p”) and cognitive ability (“g”), along with specific sub-domain factors. We evaluated the model’s criterion validity using external measures, including parent education, neighborhood socioeconomic status, global functioning, and intracranial volume, and assessed its predictive utility for longitudinal psychosis outcomes. The model demonstrated acceptable fit (CFI = 0.98, RMSEA = 0.021, SRMR = 0.030), and the “c” factor from this model showed stronger associations with parent education (r = 0.43), neighborhood SES (r = 0.47), and intracranial volume (r = 0.39) than “p” and “g” factors alone. Additionally, baseline “c” factor scores significantly predicted psychosis spectrum outcomes at follow-up (d = 0.30–0.57). These findings support the utility of a “c” factor in capturing overall cerebral function across cognitive and psychopathology domains, with potential implications for understanding brain function, improving clinical assessment, and optimally focusing interventions. Full article

Background: The purpose of this study is the evaluation of imaging findings of acute-phase cardiac CT (cCT) in stroke patients with large vessel occlusion (LVO) to identify potential cardioembolic sources (CES) in patients without intracardiac thrombi and atrial fibrillation (AF). Material and Methods: This retrospective study included 315 patients with LVO who underwent cCT imaging in the acute stroke setting. The images were analysed for 15 imaging findings following the established minor and major cardioembolic risk factors. The final stroke aetiology was determined using the TOAST classification through interdisciplinary consensus following a thorough clinical evaluation. Multivariate regression analysis was performed to identify imaging findings associated with CES. Results: A cardioembolic aetiology was identified on cardiac CT in 211 cases (70%). After adjustment for AF and intracardiac thrombi, the multivariate regression analysis revealed significant associations with left ventricular dilation (adjusted odds-ratio (AOR) 32.4; 95% CI 3.0–349; p = 0.004), visible interatrial right-to-left shunt (AOR 30.8; 95% CI 2.7–341.3; p = 0.006), valve implants (AOR 24.5; 95% CI 2.2–270.9; p = 0.009), aortic arch atheroma grade > II (AOR 6.9; 95% CI 1.5–32.8; p = 0.015) and post-ischaemic myocardial scars (AOR 6.3, 95% CI 1.2–34.1; p = 0.032) as independent risk factors for a cardioembolic aetiology. The combined model achieved an area under the ROC curve of 0.83. Conclusions: In patients with LVO without AF and intracardiac thrombi as a cause, the presence of left ventricular dilatation, interatrial right-to-left shunts, valve implants, post-ischaemic myocardial scarring and advanced aortic arch atheroma (grade > 2) in particular is significantly associated with a cardioembolic cause of stroke and should be add-on evaluated in acute-phase cCT. Further investigations are warranted to confirm these associations. Full article

In human-dominated landscapes, human activities shape prey spatial behavior, creating complex landscapes of risks. We investigated habitat selection of roe deer using resource selection functions in a human-dominated mountain system located in the southwestern Alps, characterized by a high presence of wolves and human disturbance. Our study aimed to assess how the interplay of hunting, presence of infrastructures, and recreational activities in the presence of wolves influenced roe deer spatial responses inside and outside a protected area. We documented that during the hunting period, roe deer increased selection of high-wolf-density areas, with the strongest effect observed during wild boar drive hunts, supporting the risk enhancement hypothesis, where avoiding one predator increases exposure to another, and highlighting the temporary yet significant impact of hunting on predator–prey dynamics. During the period of the wild boar drive hunt, roe deer also showed stronger selection for proximity to buildings, supporting the human shield hypothesis. Protected areas had an increased effect on roe deer avoidance of trails, where hiking and recreational activities are more concentrated. Our findings revealed the complex trade-offs that roe deer face in navigating multiple risks within human-modified landscapes, important for the development of effective conservation and human sustainability strategies. Full article

Background/Objectives: The objective of this study was to investigate the internal morphology and root canal configurations (RCCs) of maxillary central incisors (MxCIs) in a Swiss–German population by means of micro-computed tomography (µCT). Methods: RCCs, main foramina, and accessory canals of MxCIs were examined using µCT and 3D imaging software. The root canal anatomy was classified according to three classification systems by Vertucci (Ve, 1984), Weine et al. (We, 1969), and Briseño-Marroquín et al. (Br, 2015). Results: The most common RCC observed among a total of 112 investigated single-rooted maxillary central incisors was Br 1-1-1/1 (97.3%, Ve I, We I), with a small percentage showing Br 1-1-1/2 (2.7%). One main foramen existed in 87.5% of the specimens, 8% had one accessory foramen, 3.5% had two, and a rare case had four accessory foramina (0.9%). Accessory root canals were mainly located in the middle and apical regions of the roots. Conclusions: Detailed insights into the root canal morphology of MxCIs in a Swiss–German population are provided. The predominant RCC was a simple root canal (Ve I, Br 1-1-1/1). However, accessory canals were detected in the middle and apical third in over 40% of the teeth examined. These anatomical features should be considered during endodontic treatment planning and execution. Full article

In modern marine warfare, unmanned underwater vehicles (UUVs) have fast and efficient attack capabilities. However, existing research on UUV attack strategies is relatively limited, often ignoring the requirement for the effective allocation of different strategic value areas, which restricts its performance in the marine combat environment. To this end, this paper proposes an innovative UUV task allocation and saturation attack strategy. The strategy first divides the area according to the distribution density of enemy UUVs, and then reasonably allocates tasks according to the enemy’s regional value and the attack capability of our UUVs. Our UUVs then sail to the enemy area and are evenly distributed in the encirclement to ensure accurate saturation attacks. In the task allocation link, the grey wolf optimizer is improved by introducing Logistic chaos mapping and differential evolution mechanism, which improves the search efficiency and allocation accuracy. At the same time, the combination of the optimal matching algorithm and Bezier curve dynamic path control ensures the accuracy and flexibility of a coordinated attack. The simulation experimental results show that the strategy shows high attack efficiency and practicality in marine combat scenarios, providing an effective solution for UUV attack tasks in complex marine environments. Full article

The frequent occurrence of forest fires in mountainous regions has posed severe threats to both the ecological environment and human activities. This study proposed a multi-target firefighting task planning method of forest fires by multiple UAVs (Unmanned Aerial Vehicles) integrating task allocation and path planning. The forest fire environment factors such high temperatures, dense smoke, and signal shielding zones were considered as the threats. The multi-UAVs task allocation and path planning model was established with the minimum of flight time, flight angle, altitude variance, and environmental threats. In this process, the study considers only the use of fire-extinguishing balls as the fire suppressant for the UAVs. The improved multi-population grey wolf optimization (MP–GWO) algorithm and non-Dominated sorting genetic algorithm II (NSGA-II) were designed to solve the path planning and task allocation models, respectively. Both algorithms were validated compared with traditional algorithms through simulation experiments, and the sensitivity analysis of different scenarios were conducted. Results from benchmark tests and case studies indicate that the improved MP–GWO algorithm outperforms the grey wolf optimizer (GWO), pelican optimizer (POA), Harris hawks optimizer (HHO), coyote optimizer (CPO), and particle swarm optimizer (PSO) in solving more complex optimization problems, providing better average results, greater stability, and effectively reducing flight time and path cost. At the same scenario and benchmark tests, the improved NSGA-II demonstrates advantages in both solution quality and coverage compared to the original algorithm. Sensitivity analysis revealed that with the increase in UAV speed, the flight time in the completion of firefighting mission decreases, but the average number of remaining fire-extinguishing balls per UAV initially decreases and then rises with a minimum of 1.9 at 35 km/h. The increase in UAV load capacity results in a higher average of remaining fire-extinguishing balls per UAV. For example, a 20% increase in UAV load capacity can reduce the number of UAVs from 11 to 9 to complete firefighting tasks. Additionally, as the number of fire points increases, both the required number of UAVs and the total remaining fire-extinguishing balls increase. Therefore, the results in the current study can offer an effective solution for multiple UAVs firefighting task planning in forest fire scenarios. Full article

The accurate prediction of rock mass quality ahead of the tunnel face is crucial for optimizing tunnel construction strategies, enhancing safety, and reducing geological risks. This study developed three hybrid models using random forest (RF) optimized by moth-flame optimization (MFO), gray wolf optimizer (GWO), and Bayesian optimization (BO) algorithms to classify the surrounding rock in real time during tunnel boring machine (TBM) operations. A dataset with 544 TBM tunneling samples included key parameters such as thrust force per cutter (TFC), revolutions per minute (RPM), penetration rate (PR), advance rate (AR), penetration per revolution (PRev), and field penetration index (FPI), with rock classification based on the Rock Mass Rating (RMR) method. To address the class imbalance, the Borderline Synthetic Minority Over-Sampling Technique was applied. Performance assessments revealed the MFO-RF model’s superior performance, with training and testing accuracies of 0.992 and 0.927, respectively, and key predictors identified as PR, AR, and RPM. Additional validation using 91 data sets confirmed the reliability of the MFO-RF model on unseen data, achieving an accuracy of 0.879. A graphical user interface was also developed, enabling field engineers and technicians to make instant and reliable rock classification predictions, greatly supporting safe tunnel construction and operational efficiency. These models contribute valuable tools for real-time, data-driven decision-making in tunneling projects. Full article

Path planning is of great research significance as it is key to affecting the efficiency and safety of mobile robot autonomous navigation task execution. The traditional gray wolf optimization algorithm is widely used in the field of path planning due to its simple structure, few parameters, and easy implementation, but the algorithm still suffers from the disadvantages of slow convergence, ease of falling into the local optimum, and difficulty in effectively balancing exploration and exploitation in practical applications. For this reason, this paper proposes a multi-strategy improved gray wolf optimization algorithm (MSIAR-GWO) based on reinforcement learning. First, a nonlinear convergence factor is introduced, and intelligent parameter configuration is performed based on reinforcement learning to solve the problem of high randomness and over-reliance on empirical values in the parameter selection process to more effectively coordinate the balance between local and global search capabilities. Secondly, an adaptive position-update strategy based on detour foraging and dynamic weights is introduced to adjust the weights according to changes in the adaptability of the leadership roles, increasing the guiding role of the dominant individual and accelerating the overall convergence speed of the algorithm. Furthermore, an artificial rabbit optimization algorithm bypass foraging strategy, by adding Brownian motion and Levy flight perturbation, improves the convergence accuracy and global optimization-seeking ability of the algorithm when dealing with complex problems. Finally, the elimination and relocation strategy based on stochastic center-of-gravity dynamic reverse learning is introduced for the inferior individuals in the population, which effectively maintains the diversity of the population and improves the convergence speed of the algorithm while avoiding falling into the local optimal solution effectively. In order to verify the effectiveness of the MSIAR-GWO algorithm, it is compared with a variety of commonly used swarm intelligence optimization algorithms in benchmark test functions and raster maps of different complexities in comparison experiments, and the results show that the MSIAR-GWO shows excellent stability, higher solution accuracy, and faster convergence speed in the majority of the benchmark-test-function solving. In the path planning experiments, the MSIAR-GWO algorithm is able to plan shorter and smoother paths, which further proves that the algorithm has excellent optimization-seeking ability and robustness. Full article

With the rapid development of radio technology and its widespread application in the military field, the electromagnetic environment in which radar communication operates is becoming increasingly complex. Among them, human radio interference makes radar countermeasures increasingly fierce. This requires radar systems to have strong capabilities in resisting electronic interference, anti-radiation missiles, and radar detection. However, array antennas are one of the effective means to solve these problems. In recent years, array antennas have been extensively utilized in various fields, including radar, sonar, and wireless communication. Many evolutionary algorithms have been employed to optimize the size and phase of array elements, as well as adjust the spacing between them, to achieve the desired antenna pattern. The main objective is to enhance useful signals while suppressing interference signals. In this paper, we introduce the dandelion optimization (DO) algorithm, a newly developed swarm intelligence optimization algorithm that simulates the growth and reproduction of natural dandelions. To address the issues of low precision and slow convergence of the DO algorithm, we propose an improved version called the chaos exchange nonlinear dandelion optimization (CENDO) algorithm. The CENDO algorithm aims to optimize the spacing of antenna array elements in order to achieve a low sidelobe level (SLL) and deep nulls antenna pattern. In order to test the performance of the CENDO algorithm in solving the problem of comprehensive optimization of non-equidistant antenna array patterns, five experimental simulation examples are conducted. In Experiment Simulation Example 1, Experiment Simulation Example 2, and Experiment Simulation Example 3, the optimization objective is to reduce the SLL of non-equidistant arrays. The CENDO algorithm is compared with DO, particle swarm optimization (PSO), the quadratic penalty function method (QPM), based on hybrid particle swarm optimization and the gravity search algorithm (PSOGSA), the whale optimization algorithm (WOA), the grasshopper optimization algorithm (GOA), the sparrow search algorithm (SSA), the multi-objective sparrow search optimization algorithm (MSSA), the runner-root algorithm (RRA), and the cat swarm optimization (CSO) algorithms. In the three examples above, the SLLs obtained using the CENDO algorithm optimization are all the lowest. The above three examples all demonstrate that the improved CENDO algorithm performs better in reducing the SLL of non-equidistant antenna arrays. In Experiment Simulation Example 4 and In Experiment Simulation Example 5, the optimization objective is to reduce the SLL of a non-uniform array and generate some deep nulls in a specified direction. The CENDO algorithm is compared with the DO algorithm, PSO algorithm, CSO algorithm, pelican optimization algorithm (POA), and grey wolf optimizer (GWO) algorithm. In the two examples above, optimizing the antenna array using the CENDO algorithm not only results in the lowest SLL but also in the deepest zeros. The above examples both demonstrate that the improved CENDO algorithm has better optimization performance in simultaneously reducing the SLL of non-equidistant antenna arrays and reducing the null depth problem. In summary, the simulation results of five experiments show that the CENDO algorithm has better optimization ability in the comprehensive optimization problem of non-equidistant antenna array patterns than all the algorithms compared above. Therefore, it can be regarded as a strong candidate to solve problems in the field of electromagnetism. Full article

Orthodontic tooth movement (OTM) is a complex process involving bone remodeling, and is regulated by various molecular factors, including microRNAs (miRNAs). These small, non-coding RNAs are critical in post-transcriptional gene regulation and have been implicated in the modulation of osteoclast and osteoblast activity during OTM. This study aimed to explore the expression profiles of salivary exosome-derived miRNAs during OTM to identify potential biomarkers that could provide insights into the biological processes involved in orthodontic tooth movement. Saliva samples were collected from 15 patients at three time points: before treatment (Day 0), 7 days after the treatment’s onset (Day 7), and 40 days after the treatment’s onset (Day 40). The exosomes were isolated, and the miRNAs were extracted and sequenced. A differential expression analysis and gene ontology (GO) enrichment were performed to identify the miRNAs involved in osteoblast and osteoclast differentiation. Out of the 1405 detected miRNAs, 185 were analyzed. Several miRNAs were associated with bone-remodeling processes. The statistically significant finding was the downregulation of hsa-miR-4634 after 40 days of treatment. These findings contribute to the understanding of miRNA regulation in orthodontics and may have broader implications for skeletal disorders, such as osteoporosis. Full article

Carbon price forecasting and pricing are critical for stabilizing carbon markets, mitigating investment risks, and fostering economic development. This paper presents an advanced decomposition-integration framework which seamlessly integrates econometric models with machine learning techniques to enhance carbon price forecasting. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is employed to decompose carbon price data into distinct modal components, each defined by specific frequency characteristics. Then, Lempel–Ziv complexity and dispersion entropy algorithms are applied to analyze these components, facilitating the identification of their unique frequency attributes. The framework subsequently employs GARCH models for predicting high-frequency components and a gated recurrent unit (GRU) neural network optimized by the grey wolf algorithm for low-frequency components. Finally, the optimized GRU model is utilized to integrate these predictive outcomes nonlinearly, ensuring a comprehensive and precise forecast. Empirical evidence demonstrates that this framework not only accurately captures the diverse characteristics of different data components but also significantly outperforms traditional benchmark models in predictive accuracy. By optimizing the GRU model with the grey wolf optimizer (GWO) algorithm, the framework enhances both prediction stability and adaptability, while the nonlinear integration approach effectively mitigates error accumulation. This innovative framework offers a scientifically rigorous and efficient tool for carbon price forecasting, providing valuable insights for policymakers and market participants in carbon trading. Full article

Hidradenitis suppurativa (HS) is a chronic inflammatory skin condition that affects about 1% of the world’s population. It is characterized by round, painful nodules, abscesses or sinuses, often in the genital area. HS has the worst impact on quality of life (QoL) of any dermatological condition. Methods: The aim of this systematic review is to analyze how HS affects patients’ sexual quality of life (SQoL), herein defined as a person’s evaluation of their sexual relationships, including physical and mental aspects, and their response to this evaluation. Results: The systematic search yielded 41 primary results. After screening, 6 studies were selected for this review. Men with HS suffer from lower SQoL than male controls and sexual dysfunction is more common in both male and female patients than in controls. Sexual dysfunction is worse in all HS patients than in controls, and worse in female patients than in male patients. Disease severity is not related to any of the sexual concepts analysed. Conclusions: HS has a strong impact on SQoL, as patients suffer from sexual dysfunction and sexual distress more often than healthy controls, and feel that their relationships are negatively affected by the disease. Therefore, the impact of HS on SQoL should be further investigated, especially the psychological aspect of SQoL. Full article