Search Results (272)

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

Precisely predicting photovoltaic (PV) output is crucial for reliable grid integration; so far, most models rely on site-specific sensor data or treat large meteorological datasets as black boxes. This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. Starting from 27 local and plant-level variables recorded at 15 min resolution for a 1 MW array in Çanakkale region, Türkiye (1 August 2022–3 August 2024), we apply a three-stage feature-selection pipeline: (i) variance filtering, (ii) hierarchical correlation clustering with Ward linkage, and (iii) a meta-heuristic optimizer that maximizes a neural-network R² while penalizing poor or redundant inputs. The resulting subset, dominated by apparent temperature and diffuse, direct, global-tilted, and terrestrial irradiance, reduces dimensionality without significantly degrading accuracy. Feature importance is then quantified through two complementary aspects: (a) tree-based permutation scores extracted from a set of ensemble models and (b) information gain computed over random feature combinations. Both views converge on shortwave, direct, and global-tilted irradiance as the primary drivers of active power. Using only the selected features, the best model attains an average R² ≅ 0.91 on unseen data. By utilizing transparent feature-reduction techniques and explainable importance metrics, the proposed approach delivers compact, more generalized, and reliable PV forecasts that generalize to sites lacking embedded sensor networks, and it provides actionable insights for plant siting, sensor prioritization, and grid-operation strategies. Full article

Background: Soccer performance is largely dependent on high-intensity, unilateral actions such as sprints, jumps, and changes of direction. These demands can lead to strength and power differences between limbs, highlighting the importance of individualised assessment in professional players. Rotational inertial devices offer a valuable method to evaluate and train these mechanical variables separately for each leg. The aim of this study was twofold: (a) to characterise the mechanical variables derived from several lower-body strength exercises performed on rotational inertial devices, all targeting the same muscle group; and (b) to compare the mechanical variables between the dominant and non-dominant leg for each exercise. Methods: Twenty-six male professional soccer players (age = 26.3 ± 5.1 years; height = 182.3 ± 0.6 cm; weight = 75.9 ± 5.9 kg; body mass index = 22.8 ± 1.1 kg/m²; fat mass percentage = 9.1 ± 0.6%; fat-free mass = 68.8 ± 5.3 kg), all belonging to the same professional Belgian team, voluntarily participated in this study. The players completed a single assessment session consisting of six unilateral exercises (i.e., quadriceps hip, hamstring knee, adductor, quadriceps knee, hamstring hip, and abductor). For each exercise, they performed two sets of eight repetitions with each leg (i.e., dominant and non-dominant) in a randomised order. Results: The quadriceps hip exercise resulted in higher mechanical values compared to the quadriceps knee exercise in both limbs (p < 0.004). Similarly, the hamstring hip exercise produced greater values across all variables and limbs (p < 0.004), except for peak force, where the hamstring knee exercise exhibited higher values (p < 0.004). The adductor exercise showed higher peak force values for the dominant limb (p < 0.004). The between-limb comparison revealed differences only in the abductor exercise (p < 0.004). Conclusions: These findings suggest the necessity of prioritising movement selection based on targeted outcomes, although it should be considered that the differences between limb differences are very limited. Full article

This research presents a comprehensive methodology for calibrating Discrete Element Method (DEM) parameters governing rice grain interactions with biodegradable Polylactic Acid (PLA) components in agricultural bucket elevator systems. Rice grains, a critical global food staple requiring efficient post-harvest handling, were modeled as three-sphere clusters to accurately represent their physical dimensions (6.5 mm length), while the Hertz–Mindlin contact model provided the theoretical framework for particle interactions. The calibration process employed a multi-phase experimental design integrating Plackett–Burmann screening, steepest ascent method, and Face Central Composite Design to systematically identify and optimize critical micro-mechanical parameters for agricultural material handling. Statistical analysis revealed the coefficient of static friction between rice and PLA as the dominant factor, contributing 96.49% to system performance—significantly higher than previously recognized in conventional agricultural processing designs. Response Surface Methodology generated predictive models achieving over 90% correlation with experimental results from 3D-printed PLA shear box tests. Validation through comparative velocity profile analysis during bucket elevator discharge operations confirmed excellent agreement between simulated and experimental behavior despite a 20% discharge velocity variance that warrants further investigation into agricultural material-specific phenomena. The established parameter set enables accurate virtual prototyping of sustainable agricultural handling equipment, offering post-harvest processing engineers a powerful tool for optimizing bulk material handling systems with reduced environmental impact. This integrated approach bridges fundamental agricultural material properties with sustainable engineering design principles, providing a scalable framework applicable across multiple agricultural processing operations using biodegradable components. Full article

Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. To overcome these limitations, we propose a probabilistic methodology for turbine-level active power prediction and uncertainty estimation using high-frequency SCADA data and farm-wide autoregressive information. The method leverages a Stochastic Variational Gaussian Process with a Linear Model of Coregionalization, incorporating physical models like manufacturer power curves as mean functions and enabling flexible modeling of active power and its associated variance. The approach was validated on a wind farm in the Belgian North Sea comprising over 40 turbines, using only 15 days of data for training. The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. Improvements of around 5% were seen in dominant wind directions (200°–300°) using 2 and 3 Latent GPs, with similar improvements observed on the test set. The model also successfully reconstructs wake effects, with Energy Ratio estimates closely matching SCADA-derived values, and provides meaningful uncertainty estimates and posterior turbine correlations. These results demonstrate that the methodology enables interpretable, data-efficient, and uncertainty-aware turbine-level power predictions, suitable for advanced wind farm monitoring and control applications, enabling a more sensitive underperformance detection. Full article

Accurate estimation of long-term average (LTA) discharge is fundamental for water resource assessment, infrastructure planning, and hydrological modeling, yet it remains a significant challenge, particularly in data-scarce or ungauged basins. This study introduces an advanced machine learning framework to estimate long-term average discharge using globally available hydrological station metadata from the Global Runoff Data Centre (GRDC). The methodology involved comprehensive data preprocessing, extensive feature engineering, log-transformation of the target variable, and the development of multiple predictive models, including a custom deep neural network with specialized pathways and gradient boosting machines (XGBoost, LightGBM, CatBoost). Hyperparameters were optimized using Bayesian techniques, and a weighted Meta Ensemble model, which combines predictions from the best individual models, was implemented. Performance was rigorously evaluated using R², RMSE, and MAE on an independent test set. The Meta Ensemble model demonstrated superior performance, achieving a Coefficient of Determination (R²) of 0.954 on the test data, significantly surpassing baseline and individual advanced models. Model interpretability analysis using SHAP (Shapley Additive explanations) confirmed that catchment area and geographical attributes are the most dominant predictors. The resulting model provides a robust, accurate, and scalable data-driven solution for estimating long-term average discharge, enhancing water resource assessment capabilities and offering a powerful tool for large-scale hydrological analysis. Full article

The deep integration of smart grid and Internet of Things technologies has made high-speed power line carrier communication a key communication technology in energy management, industrial monitoring, and smart home applications, owing to its advantages of requiring no additional wiring and offering wide coverage. However, the inherent characteristics of power line channels, such as strong noise, multipath fading, and time-varying properties, pose challenges to traditional encryption algorithms, including low key distribution efficiency and weak anti-interference capabilities. These issues become particularly pronounced in high-speed transmission scenarios, where the conflict between data security and communication reliability is more acute. To address this problem, a secure transmission method for high-speed power line carrier communication based on generalized two-dimensional polynomial chaotic mapping is proposed. A high-speed power line carrier communication network is established using a power line carrier routing algorithm based on the minimal connected dominating set. The autoregressive moving average model is employed to determine the degree of transmission fluctuation deviation in the high-speed power line carrier communication network. Leveraging the complex dynamic behavior and anti-decoding capability of generalized two-dimensional polynomial chaotic mapping, combined with the deviation, the communication key is generated. This process yields encrypted high-speed power line carrier communication ciphertext that can resist power line noise interference and signal attenuation, thereby enhancing communication confidentiality and stability. By applying reference modulation differential chaotic shift keying and integrating the ciphertext of high-speed power line carrier communication, a secure transmission scheme is designed to achieve secure transmission in high-speed power line carrier communication. The experimental results demonstrate that this method can effectively establish a high-speed power line carrier communication network and encrypt information. The maximum error rate obtained by this method is 0.051, and the minimum error rate is 0.010, confirming its ability to ensure secure transmission in high-speed power line carrier communication while improving communication confidentiality. Full article

Background: Self-harm in young people is influenced by multiple factors, with media playing a significant role. While research has examined its harmful and protective effects, little attention has been paid to how healthcare professionals interpret and respond to media’s role in shaping young people’s experiences of self-harm. To our knowledge, no research has examined adolescent mental health professionals’ perspectives and, crucially, how these are constructed and understood. The study aimed to examine the following: (1) how mental health practitioners construct and use discourses to interpret the role of media in young people’s self-harm; and (2) how these discourses shape clinical understanding and practice. Methods: This qualitative study employed semi-structured interviews with ten clinicians from child and adolescent mental health services across England working with young people who self-harm. Data were analysed using critical discourse analysis to uncover how broader societal and institutional narratives shape clinicians’ perspectives. Results: Two dominant discourses were identified: “Media as Disruptor” and “The Hidden World of Youth”. These discourses framed media as both a risk factor and a potential intervention tool, positioning media as a powerful yet morally ambiguous force in young people’s lives. Clinicians largely framed media’s influence as negative but acknowledged its capacity for education and intervention. Conclusions: This research offers new insights into how media-related self-harm risks and benefits are framed and managed in mental health care settings. The study underscores the need for systemic changes in clinical practice, enhanced training, updated guidelines and a shift towards broader sociocultural perspectives in understanding self-harm and suicidal behaviour. Full article

Background/Objectives: Power indices may provide valuable information for performance and injury prevention in soccer players, so increasing the knowledge about them seems essential. Therefore, this study aimed to establish limb-specific normative values for flywheel-derived power indices in professional soccer players, while accounting for limb performance or ability, to explore the relationships between power indices across variables and to compare the power outcomes related to these indices between injured and non-injured players within four months post-assessment. Methods: Twenty-two male professional soccer players (age: 26.6 ± 4.6 years; competitive level: Belgian second division) were recruited from a single elite-tier club to participate in this cross-sectional diagnostic study. Participants underwent a standardized assessment protocol, executed in a rotational inertial device, comprising six unilateral exercises focused on the lower limbs: hip-dominant quadriceps (Qhip), knee-dominant quadriceps (Qknee), hip-dominant hamstrings (Hhip), knee-dominant hamstrings (Hknee), adductor (Add), and abductor (Abd). The testing session incorporated a randomized, counterbalanced design, with each exercise comprising two sets of eight maximal concentric–eccentric repetitions per limb. Leg dominance was operationally defined as the self-reported preferred limb for ball-striking tasks. Power indices were calculated from these exercises. Results: No significant differences in flywheel-derived power indices were found between limbs or between injured and non-injured players. However, significant correlations between indices were found in all power variables, with the Qhip:Qknee and Hhip:Hknee concentric ratios emerging as the most clinically actionable biomarkers for rapid screening. Conclusions: These results suggest the necessity of including more variables for injury prediction. Moreover, power indices could be considered based on the classification of limbs as “strong” or “weak”. Full article

The rapid adoption of electric vehicles (EVs) and the increasing use of photovoltaic (PV) generation have introduced new operational challenges for unbalanced power distribution systems. These include elevated power losses, voltage imbalances, and adverse environmental impacts. This study proposed a hybrid objective optimization framework to address these issues by minimizing real and reactive power losses, voltage deviations, voltage imbalance indexes, and CO₂ emissions. Nineteen simulation cases were analyzed under various configurations incorporating EV integration, PV deployment, reactive power compensation, and zonal control strategies. An improved gray wolf optimizer (IGWO) was employed to determine optimal placements and control settings. Among all cases, Case 16 yielded the lowest objective function value, representing the most effective trade-off between technical performance, voltage stability, and sustainability. The optimized configuration significantly improved the voltage balance, reduced system losses, and maintained the average voltage within acceptable limits. Additionally, all optimized scenarios achieved meaningful reductions in CO₂ emissions compared to the base case. The results were validated with an objective function $\left(F_{best}\right)$ as a reliable composite performance index and demonstrated the effectiveness of coordinated zone-based optimization. This approach provides practical insights for future smart grid planning under dynamic, renewable, rich, and EV-dominated operating conditions. Full article

This study aimed to uncover how harm is normalised in migrant communities using rationalisations, power imbalances, and emotional distancing. This research counters the dominant discourse that migrant communities are cohesive, altruistic, and protective by critically analysing the psychological and moral mechanisms of intra-community harm. Migration scholarship has long extolled the contribution of migrant networks to settlement, employment, and integration. Using a qualitative ethnographic approach, data were gathered using participant observation and semi-structured interviews with twelve purposively sampled migrants. The aim of applying a primary qualitative study was to capture a detailed, first-hand understanding of participants’ lived experiences and social relations. It permitted the in-depth examination of how people rationalise and navigate intra-community harm in the actual contexts of their lives. Thematic analysis yielded four significant findings: one, injustices in the community are frequently met with silence and inaction due to fear and moral disengagement; two, assistance is extraordinarily situational and gendered, often falling disproportionately on women or being mediated by institutions; three, internal exploitation—like rent gouging and manipulation of aid—is justified through community narratives; and four, people increasingly feel isolation, emotional burnout, and only symbolic unity at communal events. The research suggests that, although migrant networks can offer critical resources, they are not invulnerable to internal hierarchies and moral collapses. To create effectively inclusive and nurturing settings, future interventions must account for more than mere structural barriers, intra-group processes, and psychological rationalisations allowing intra-community injury. Full article

Human activity recognition using wearable accelerometer data can be a useful digital biomarker for severity assessment and the diagnosis of diseases, where the relationship between onset and patient activity is crucial. For long-term monitoring in clinical settings, the volume of data collected over time should be minimized to reduce power consumption, computational load, and communication volume. This study aimed to determine the lowest sampling frequency that maintains recognition accuracy for each activity. Thirty healthy participants wore nine-axis accelerometer sensors at five body locations and performed nine activities. Machine-learning-based activity recognition was conducted using data sampled at 100, 50, 25, 20, 10, and 1 Hz. Data from the non-dominant wrist and chest, which have previously shown high recognition accuracy, were used. Reducing the sampling frequency to 10 Hz did not significantly affect the recognition accuracy for either location. However, lowering the frequency to 1 Hz decreases the accuracy of many activities, particularly brushing teeth. Using data with a 10 Hz sampling frequency can maintain recognition accuracy while decreasing data volume, enabling long-term patient monitoring and device miniaturization for clinical applications. Full article

An enhanced snow geese algorithm (ESGA) is proposed to address the problems of the weakened population diversity and unbalanced search tendencies encountered by the snow geese algorithm (SGA) in the search process. First, an adaptive switching strategy is used to dynamically select the search strategy to balance the exploitation and exploration capabilities. Second, a dominant group guidance strategy is introduced to improve the population quality. Finally, a dominant stochastic difference search strategy is designed to enrich the population diversity and help it escape from the local optimum by co-directing effects in multiple directions. Ablation experiments were performed on the CEC2017 test set to illustrate the improvement mechanism and the degree of compatibility of their improved strategies. The proposed ESGA with a highly cited algorithm and the powerful improved algorithm are compared on the CEC2022 test suite, and the experimental results confirm that the ESGA outperforms the compared algorithms. Finally, the ability of the ESGA to solve complex problems is further highlighted by solving the robot path planning problem. Full article

With the rapid development of e-commerce, Return Freight Insurance (RFI) has emerged as a vital tool for retailers and customers to mitigate the financial burden posed by high return rates in online shopping. This paper investigates the strategic decision-making of RFI providers (insurers), examining whether they should offer fixed-compensation insurance at a lower price or full-coverage insurance at a higher price under varying market conditions. Specifically, we develop game-theoretic models to analyze insurers’ strategic decisions in both monopoly and duopoly markets, accounting for customer return cost and product mismatch probability heterogeneity. Our findings reveal that in a monopoly market, when customer return costs are relatively low and concentrated, insurers benefit from offering fixed compensation insurance (i.e., pre-set reimbursement amounts) at a lower price to attract a broader customer base. By contrast, when return costs are more dispersed and higher, full-coverage insurance (which reimburses actual freight costs) becomes more profitable, as the insurer can utilize its flexible pricing power to attract higher-paying customer segments. In a duopoly market, customer return cost heterogeneity significantly influences market equilibrium. When heterogeneity is high, full-coverage insurance dominates, as insurers can leverage precise market segmentation to justify higher premiums. Conversely, when return costs are more uniform, fixed compensation insurance is preferred for its affordability, appealing to a wider range of customers. Full article

Power demand forecasting is a critical and challenging task for modern power systems and integrated energy systems. Due to the absence of well-established theoretical frameworks and publicly available feature databases on power demand changes, the known interpretable features of power demand fluctuations are primarily derived from expert experience and remain significantly limited. This substantially hinders advancements in power demand forecasting accuracy. Emerging multimodal learning approaches have demonstrated great promise in machine learning and AI-generated content (AIGC). In this paper, we propose, for the first time, a textual-knowledge-guided numerical feature discovery (TKNFD) framework for short-term power demand forecasting by interacting text modal data—a potentially valuable yet long-overlooked resource in the field of power demand forecasting—with numerical modal data. TKNFD systematically and automatically aggregates qualitative textual knowledge, expands it into a candidate feature-type set, collects corresponding numerical data for these features, and ultimately constructs four-dimensional multivariate source-tracking databases (4DM-STDs). Subsequently, TKNFD introduces a two-stage quantitative feature identification strategy that operates independently of forecasting models. The essence of TKNFD lies in achieving reliable and comprehensive feature discovery by fully exploiting the dual relationships of synonymy and complementarity between text modal data and numerical modal data in terms of granularity, scope, and temporality. In this study, TKNFD identifies 38–50 features while further interpreting their contributions and dependency correlations. Benchmark experiments conducted in Maine, Texas, and New South Wales demonstrate that the forecasting accuracy using TKNFD-identified features consistently surpasses that of state-of-the-art feature schemes by up to 36.37% MAPE. Notably, driven by multimodal interaction, TKNFD can discover previously unknown interpretable features without relying on prior empirical knowledge. This study reveals 10–16 previously unknown interpretable features, particularly several dominant features in integrated energy and astronomical dimensions. These discoveries enhance our understanding of the origins of strong randomness and non-linearity in power demand fluctuations. Additionally, the 4DM-STDs developed for these three regions can serve as public baseline databases for future research. Full article