Search Results (214)

As distributed generators (DGs) and flexible adjustable loads (FALs) further penetrate distribution networks (DNs), to reduce regulation complexity compared with traditional centralized control frameworks, DGs and FALs in DNs should be packed in several clusters to enable their dispatch to become standard in the industry. To mitigate the negative influence of DGs’ and FALs’ spatiotemporal distribution and uncertain output characteristics on dispatch, this paper proposes an intelligent dynamic cluster partitioning strategy for DNs, from which the DN’s resources and loads can be intelligently aggregated, organized, and regulated in a dynamic and optimal way with relatively high implementation efficiency. An environmental model based on the Markov decision process (MDP) technique is first developed for DN cluster partitioning, in which a continuous state space, a discrete action space, and a dispatching performance-oriented reward are designed. Then, a novel random forest Q-learning network (RF-QN) is developed to implement dynamic cluster partitioning by interacting with the proposed environmental model, from which the generalization and robust capability to estimate the Q-function can be improved by taking advantage of combining deep learning and decision trees. Finally, a modified IEEE-33-node system is adopted to verify the effectiveness of the proposed intelligent dynamic cluster partitioning and regulation strategy; the results also indicate that the proposed RF-QN is superior to the traditional deep Q-learning (DQN) model in terms of renewable energy accommodation rate, training efficiency, and portioning and regulation performance. Full article

We investigate the multifractal geometry of irregular sets arising from weighted averages of random variables, where the weights $\left(w_n\right)$ form a positive sequence with exponential growth. Our analysis applies in particular to sequences generated by linear recurrence relations of Fibonacci type, including higher-order generalizations such as the Tetranacci sequence $\left({\mathsf{T}}_n\right)$. Using a Cantor-type construction built from alternating free and forced blocks, we show that the associated exceptional sets may attain full Hausdorff and packing dimension, independently of the precise form of the recurrence. We further develop a probabilistic interpretation of $\left({\mathsf{T}}_n\right)$ through an appropriate Markov representation that encodes its combinatorial evolution and yields sharp asymptotic behavior. Finally, given $n+1$ consecutive terms of a Fibonacci-type sequence, one may construct a polynomial $P_n\left(x\right)$ of degree at most n via Lagrange interpolation; we show that this polynomial admits an implicit recursive representation consistent with the underlying recurrence. Full article

The construction industry remains one of the main contributors to environmental degradation due to its high material consumption and massive waste generation. This study introduces Granizzo, a hybrid methodological framework that integrates artificial intelligence (AI), parametric design, and digital fabrication to transform construction and demolition waste (CDW) into sustainable architectural mosaics. The workflow involves material selection, AI-driven classification of fragments, generative design algorithms for pattern optimization, and CNC-based experimental prototyping. A dataset comprising brick, cement, marble, glass, and stone fragments was analyzed using a Random Forest classifier, achieving an average accuracy above 90%. Parametric design algorithms based on circle packing and tessellation achieved up to 92% surface coverage, reducing voids and optimizing formal diversity compared to manually assembled mosaics. Prototypes fabricated with CNC molds exhibited 35% shorter assembly times and 20% fewer voids, confirming the technical feasibility of the proposed process. A preliminary Life Cycle Assessment (LCA) revealed measurable environmental benefits in energy savings and CO₂ reduction. The findings suggest that Granizzo constitutes a replicable methodological platform that merges digital precision and sustainable materiality, enabling a circular approach to architectural production and aligning with contemporary challenges of design innovation, material reuse, and computational creativity. Full article

Background/Objectives: Obesity is a major global public health and economic challenge. Governments worldwide have implemented nutrition-focused policies such as sugar-sweetened beverage taxes, front-of-pack labeling, food assistance reforms, and school nutrition standards to improve diet quality and reduce obesity. Because large-scale randomized controlled trials are often infeasible and conventional epidemiologic methods overlook population heterogeneity and behavioral feedback, microsimulation modeling has become a key tool for evaluating long-term and distributional policy impacts. This scoping review examined the application of microsimulation to obesity-related nutrition policies, focusing on model structure, behavioral parameterization, and integration of economic and equity analyses. Methods: Following PRISMA guidelines (PROSPERO CRD42024599769), five databases were searched for peer-reviewed studies. Data were extracted on policy mechanisms, model design, parameterization, and equity analysis. Study quality was assessed using a customized 21-item checklist adapted from CHEERS and NIH tools. Results: Twenty-nine studies met the inclusion criteria, with most policy settings based in the United States. Most employed dynamic, stochastic, individual-level microsimulation models with diverse behavioral assumptions, obesity equations, and calibration approaches. While most studies stratified outcomes by socioeconomic or demographic group, only one used a formal quantitative equity metric. Conclusions: Microsimulation modeling provides valuable evidence on the long-term health, economic, and distributional impacts of nutrition policies. Future work should strengthen methodological transparency, standardize equity assessment, and expand application beyond high-income settings to improve the comparability, credibility, and policy relevance of simulation-based nutrition policy research. Full article

The increasing demand for sustainable construction materials has motivated the optimization of mortar mix designs to reduce cement consumption and its environmental impact while maintaining adequate mechanical performance. This study develops a machine learning (ML) model for optimizing mortar mixtures used in concrete roofing tiles by integrating aggregate particle packing techniques with non-linear regression algorithms, using an industry-grade dataset generated in the Central Laboratory of Wienerberger Ltd. Unlike most previous studies, which mainly focus on compressive strength, this research targets the transverse strength of industrial roof tile mortar. The proposed approach combines Tarantula Curve gradation limits, experimentally derived packing density ($\eta$), and ML regression within a unified and application-oriented workflow, representing a research direction rarely explored in the literature for optimizing concrete mix transverse strength. Fine concrete aggregates were characterized through a sand sieve analysis and subsequently adjusted according to the Tarantula Curve method to optimize packing density and minimize void content. Physical properties of cements and fine aggregates were assessed, and granulometric mixtures were evaluated using computational methods to calculate fineness modulus summation (FMS) and packing density. Mortar samples were tested for transverse strength at 1, 7, and 28 days using a three-point bending test, generating a robust dataset for modeling training. Three ML models—Random Forest Regressor (RFR), XG-Boost Regressor (XGBR), and Support Vector Regressor (SVR)—were evaluated, confirming their ability to capture nonlinear relationships between mix parameters and transverse strength. The analysis of input variables, which consistently ranked as the highest contributors according to impurity-based and permutation-based importance metrics, revealed that the duration of curing, density, and the summation of the fineness modulus significantly influenced the estimated transverse strength derived from the models. The integration of particle size distribution optimization and ML demonstrates a viable pathway for reducing cement content, lowering costs, and achieving sustainable mortar mix designs in the tile manufacturing industry. Full article

Efficient packing of irregular mechanical parts in limited container space is essential for reducing transportation and storage costs in automated manufacturing. This study focuses on eccentric-shaped parts characterized by geometric asymmetry, multiple orientations, and local irregularities, and proposes a two-stage three-dimensional packing strategy. In the first stage, an optimal single-layer layout is generated using a heuristic algorithm that combines grid scanning with a gravity-drop principle and tabu search to optimize part positions and orientations. In the second stage, the optimized layer template is vertically replicated with buffer layers to enhance stacking stability, ensuring feasible and non-overlapping arrangements. Comparative experiments with Best Fit, BLF, LHL, and Random methods show that the proposed approach increases average space utilization by 8.3%, 8.8%, 8.1%, and 15.5%, respectively, while maintaining high stability and reasonable computation time. The results demonstrate that this method achieves dense and stable packing, offering an effective solution for intelligent packing and automated production of irregular parts. Full article

Wireless charging for drones is significant for solving problems such as the frequent manual plugging and unplugging of cables. A large number of densely packed transmitting coils and fully independent on-off control can precisely track the receiver with random access location. To balance the excitation area of the transmitter, additional hardware cost, and receiving voltage fluctuation, the wireless charging system of drones based on a D3-type transmitter is proposed in this article. The circuit model considering states of multiple switches is developed for three excitation modes. The dual-coil excitation mode is selected after comparative analysis. The transmitter reconfiguration method with low hardware cost and high receiving-excitation area ratio is proposed based on one detection sensor of DC current and one relay furtherly. Finally, an experimental prototype is built to verify the theoretical analysis and proposed method. When the output voltage fluctuation is limited to ±10%, the ratios of the maximum misalignment value in the x-axis and y-axis directions to the side length of the receiver reach 66.7% and 46.7%, respectively. The receiving-excitation area ratio of 37.5% is achieved, significantly reducing the excitation area not covered by the receiver. The maximum receiving power is 289.44 W, while the DC-DC efficiency exceeds 87.05%. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

Electric vehicle (EV) battery pack cases (BPCs) must withstand mechanical loads such as impact, compression, and vibration to ensure structural integrity and passenger safety. This study evaluates the mechanical durability of a full-scale aluminum BPC using combined experimental testing and finite element analysis (FEA). A bottom impact test, 200 kN compression test, and power spectral density (PSD)-based random vibration test were conducted to simulate representative operating and handling conditions. The numerical model replicated boundary conditions and load profiles identical to the experiments, enabling a direct comparison of stress distribution and deformation characteristics. The results demonstrated that stress and displacement trends predicted by FEA closely matched experimental observations, with stress concentrations appearing at corner and frame junction regions and less than 1 mm deformation recorded under peak compression loading. Vibration responses were most pronounced in the vertical direction, without bolt loosening or structural damage. These results verify the reliability of the proposed BPC design and provide quantitative evidence supporting simulation-driven lightweight battery enclosure development. Full article

Temperature and humidity excursions during transport accelerate quality loss in fresh produce. This study develops and validates a self-contained Internet of Things (IoT) platform for in-transit monitoring and energy-aware operation. The battery-powered device operates independently of vehicle power and continuously logs temperature, relative humidity, GPS position, and onboard power draw. Power budgeting combines firmware-level deep-sleep scheduling with a LiFePO₄ battery pack, enabling uninterrupted operation for up to four days. Using ∼10,000 time-stamped observations collected over four consecutive days in a standard dry truck (non-commercial validation), we trained and compared Gradient Boosting Machine (GBM), Random Forest (RF), and Linear Regression (LR) models to predict energy consumption under varying environmental and routing conditions. GBM and LR achieved high explanatory power ($R^2\approx 0.88$) with a mean absolute error of 0.77 A·h, while RF provided interpretable feature importance data, identifying temperature as the dominant driver, followed by trip duration and humidity. The end-to-end system integrates an EMQX MQTT broker, a Laravel web application, MongoDB storage, and Node-RED flows for real-time dashboards and multi-day historical analytics. The proposed platform supports proactive decision-making in perishable logistics, with the AI analysis validating that the collected time-aligned on-route data can configure sampling/transmit cadence to preserve autonomy under stressful conditions. Full article

We study the multifractal structure of irregular sets arising from Fibonacci-weighted sums of sequences of random variables. Focusing on Cantor-type subsets $K_{\epsilon }$ of the unit interval, we construct sequences of free and forced blocks, where the free blocks allow full binary branching and the forced blocks fix the digits, controlling the weighted averages. We prove that these sets can attain full Hausdorff and packing dimension while their Hausdorff measure can vanish. We prove that the packing measure of $K_{ϵ}$ depends sensitively on the growth of the forced blocks. Our construction illustrates the mechanism by which Fibonacci-type weights induce irregularity, providing a probabilistic counterpart to classical multifractal phenomena in dynamical systems. Full article

In this study, both experimental and simulation approaches were employed to investigate the removal efficiency of gaseous pollutants using two different types of packing materials—random and structured packings—under varying gas flow rates and column diameters. A synthetic gas mixture containing 2200 ppm H₂S and 26.75% CO₂ was used to evaluate the performance of the system. Simulation studies were conducted using Aspen Plus^TM V9, and the results were validated with experimental data. H₂S removal efficiencies were found to range between 79% and 98%, while CO₂ removal ranged from 6% to 20%. Comparative analyses revealed that an increase in gas flow rate and column diameter led to a decrease in pollutant removal efficiency for both types of packings. A previously unobserved packing-dependent scaling effect was revealed: increasing column diameter decreases removal efficiency for random packings but enhances it (up to a threshold) for structured packings, offering new scale-up guidelines. Most notably, a previously unobserved trend was identified: increasing column diameter exerts opposing effects on removal efficiency depending on packing type—a packing-dependent scaling behavior with significant implications for industrial column design. The findings provide valuable insights into the design and optimization of industrial-scale gas treatment systems, demonstrating that simulation data can effectively support the selection of appropriate column dimensions, gas flow rates, and packing types for varying pollutant concentrations. A mechanistic analysis revealed that the superior H₂S removal over CO₂ arises from its higher solubility, instantaneous reaction with OH⁻, and greater enhancement factor, with structured packings mitigating maldistribution effects at larger column diameters—offering new scale-up insights supported by the literature. Full article

Background: Non-melanoma skin cancer (NMSC) is the most frequent cancer in fair-skinned populations and represents a growing public health concern due to its impact in terms of morbidity and treatment costs. While some meta-analyses have investigated cigarette smoking as a risk factor for NMSC, less is known about its prognostic implications in patients with NMSC. This systematic review and meta-analysis aims to fill this gap by assessing the association between smoking habits and survival in patients with NMSC. Methods: A systematic search was conducted in PubMed and EMBASE up to 25 February 2025, to identify prospective studies of patients with histologically confirmed NMSC that evaluated the association between smoking habits and survival. Study-specific hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were pooled using random effects meta-analysis models. Results: A total of five studies published between 2015 and 2022 were included. The meta-analysis revealed that being a current or ever smoker at diagnosis was associated with a worse overall survival (summary HR 2.42, 95% CI 1.91–3.06). A similar result was observed when smoking exposure was assessed in terms of pack-years or number of cigarettes per day (summary HR 2.44, 95% CI 2.02–2.93). Conclusions: Our findings indicate that cigarette smoking is a negative prognostic factor in these patients, despite the generally excellent prognosis of NMSC. It is reasonable to assume that this unfavourable effect is largely due to the increased risk of developing other life-threatening conditions, in which smoking plays a causal role. These results underscore the clinical relevance of systematically integrating smoking cessation counselling into the routine management of patients with NMSC. Full article

This study evaluated the effects of low-dose electron beam irradiation (0, 1, 2, and 3 kGy) on storage stability and quality properties of chicken and duck breast meat. Five foodborne pathogens (Salmonella typhimurium, Listeria monocytogenes, Staphylococcus aureus, Bacillus cereus, and Escherichia coli) were inoculated into the samples and subjected to irradiation under vacuum packaging. The irradiated samples were vacuum-packed and stored at 4 °C. Microbial recovery, lipid and protein oxidation, physicochemical characteristics, and meat color were analyzed over 0, 1, and 2 weeks. A completely randomized design was used with five biological replicates (n = 5) per treatment, and each measurement was performed in triplicate (technical replicates). Electron beam treatment effectively reduced microbial counts, achieving complete inactivation of all pathogens except Bacillus cereus at 3 kGy. Irradiation resulted in significant reductions in pH and water-holding capacity (p < 0.05) while increasing thiobarbituric acid-reactive substances (TBARS) and volatile basic nitrogen (VBN) values, particularly in duck and chicken, respectively. Color parameters such as L* and b* decreased, while a*, chroma, and redness increased, with hue angle showing a decreasing trend. These changes were associated with myoglobin transformation and protein oxidation caused by irradiation-induced reactive oxygen species. Despite minor variations, proximate composition remained unaffected by irradiation. Overall, electron beam irradiation at doses up to 3 kGy effectively enhanced microbial safety without compromising nutritional quality, indicating its potential as a non-thermal preservation method for raw poultry meat products. Full article

Packed granular materials absorb sound. In previous studies, granular materials sized a few millimeters and samples of grain size as a powder were studied; however, the grain sizes in between have not been addressed. In this study, the sound absorption coefficients of materials ranging from granular materials with a grain size d = 4 mm to powder materials with d = 0.05 mm were analyzed theoretically and experimentally. In addition, five packing types were studied: four types of regular packing and random packing. For these packing structures, the propagation constants and characteristic impedances were substituted within a one-dimensional transfer matrix for sound wave propagation, from which the normal-incidence sound absorption coefficient was calculated. Furthermore, our analysis accounted for particle longitudinal vibrations due to sound pressure. According to analyses of cross-sectional CT images considering tortuosity, the theoretical values for random packing tended to be close to the experimental values for d = 0.8 mm and smaller. For random packing structures with d = 0.3 mm or smaller, the experimental values were closer to the theoretical values for simple cubic lattice than the theoretical values for random packing. Full article