Search Results (3,146)

With the increasing penetration of renewable energy sources and power-electronic devices, modern power systems exhibit pronounced wideband oscillation characteristics with large frequency spans, strong modal coupling, and significant time-varying behaviors. Accurate identification and classification of wideband oscillation patterns have therefore become critical challenges for ensuring the secure and stable operation of “dual-high” power systems. Existing methods based on signal processing or single-modality deep-learning models often fail to fully exploit the complementary information embedded in heterogeneous data representations, resulting in limited performance when dealing with complex oscillation patterns.To address these challenges, this paper proposes a multimodal attention-based fusion network for wideband oscillation classification. A dual-branch deep-learning architecture is developed to process Gramian Angular Difference Field images and raw time-series signals in parallel, enabling collaborative extraction of global structural features and local temporal dynamics. An improved Inception module is employed in the image branch to enhance multi-scale spatial feature representation, while a gated recurrent unit network is utilized in the time-series branch to model dynamic evolution characteristics. Furthermore, an attention-based fusion mechanism is introduced to adaptively learn the relative importance of different modalities and perform dynamic feature aggregation. Extensive experiments are conducted using a dataset constructed from mathematical models and engineering-oriented simulations. Comparative studies and ablation studies demonstrate that the proposed method significantly outperforms conventional signal-processing-based approaches and single-modality deep-learning models in terms of classification accuracy, robustness, and generalization capability. The results confirm the effectiveness of multimodal feature fusion and attention mechanisms for accurate wideband oscillation classification, providing a promising solution for advanced power system monitoring and analysis. Full article

Photovoltaic–thermal (PVT) collectors often experience limited heat extraction under laminar cooling conditions, and the influence of controlled flow pulsation on full-scale PVT performance has not been clearly established. This study experimentally investigates a water-cooled PVT system operated under pulsating flow using an indoor solar simulator to quantify its thermal and electrical response. Flow pulsations were generated using a solenoid valve at frequencies of 0.25, 0.5, 1, and 2 Hz across inlet flow rates of 1–4 L/min, with average irradiance maintained between 700 and 800 W/m². System performance was benchmarked against uncooled and continuous-flow reference cases. Pulsating operation reduced the PVT surface temperature and produced a clear enhancement in thermal performance relative to continuous flow, while electrical efficiency exhibited a smaller but consistent improvement that followed the same thermal trend. A pulsation frequency of 0.5 Hz yielded the most favorable results, achieving thermal efficiencies exceeding 50% at higher flow rates without any measurable increase in average pressure drop. Electrical efficiency stabilized at approximately 9.82%, slightly higher than that obtained under continuous-flow operation. The results indicate that low-frequency pulsating flow can significantly improve thermal energy extraction in PVT systems under controlled conditions, with modest associated electrical gains, and provide a basis for further investigation of flow-modulation strategies for thermally driven PVT applications. Full article

This study investigates the relationship between energy consumption and oil production in petroleum extraction using a Generalized Linear Model (GLM). By analyzing actual production data from a Daqing Oilfield production plant (2021–2024), key energy consumption features such as electricity usage, mechanical extraction, water injection, and transportation were selected to construct a GLM predictive model. Comparisons with Generalized Least Squares (GLS) and linear regression models demonstrated the superior performance of GLM, achieving a coefficient of determination ($R^2$) of 0.884, with mean absolute error (MAE) and mean squared error (MSE) of 0.095 and 0.012, respectively. The study highlights the nonlinear impacts of energy consumption on oil production, offering theoretical insights for optimizing energy use and supporting low-carbon transitions in oilfield operations. Future research will explore interaction effects to enhance model generalizability. Full article

This research presents a comprehensive evaluation of semi-elastic polyurethane adhesives used for bonding wooden flooring, with a particular focus on both domestic (oak) and exotic hardwood species (teak, iroko, wenge, merbau). Given the increasing interest in sustainable construction practices and the growing use of diverse wood species in flooring systems, this study aimed to assess the mechanical, morphological, and surface properties of adhesive joints under both standard laboratory and thermally aged conditions. Mechanical testing was conducted according to PN-EN ISO 17178 standards and included shear and tensile strength measurements on wood–wood and wood–concrete assemblies. Specimens were evaluated in multiple aging conditions, simulating real-world application environments. Shear strength increased post-aging, with the most notable improvement observed in wenge (21.2%). Tensile strength between wooden lamellas and concrete substrates remained stable or slightly decreased (up to 18.8% in wenge), yet all values stayed above the 1 MPa minimum requirement, confirming structural reliability. Surface properties of the wood species were characterized through contact angle measurements and 3D optical roughness analysis. Teak exhibited the highest contact angle (74.9°) and the greatest surface roughness, contributing to mechanical interlocking despite its low surface energy. Oak and iroko showed high wettability and balanced roughness, supporting strong adhesion. Scanning electron microscopy (SEM) revealed stable adhesive penetration across all species and aging conditions, with no signs of delamination or interfacial failure. The study confirms the suitability of polyurethane adhesives for durable, long-lasting bonding in engineered and solid wood flooring systems, even when using extractive-rich or dimensionally sensitive tropical species. The results emphasize the critical role of surface morphology, wood anatomy, and adhesive compatibility in achieving optimal bond performance. These findings contribute to improved material selection and application strategies in flooring technology. Future research should focus on bio-based adhesive alternatives, chemical surface modification techniques, and in-service performance under cyclic loading and humidity variations to support the development of eco-efficient and resilient flooring systems. Full article

Background: Caloric restriction (CR) is a dietary intervention based on limiting calories relative to the basic energy needs of the organism, which changes the intensity of metabolism, causes changes in the functioning of the endocrine and sympathetic systems, and influences the expression of genes in muscle, heart, and brain cells. During the use of CR, there is a transition from carbohydrate supply to increased fat metabolism. Fatty acids are more or less susceptible to free radicals, depending on their molecular structure. Oxidation (peroxidation) contributes to the production of metabolites (including hydroxyeicosatetraenoic acid and hydroxyoctadecadienoic acid), some of which are involved in inflammation. Methods: The aim of this study was to evaluate the effects of long-term caloric restriction on the tissue levels of selected fatty acids and fatty acid-derived lipid mediators with pro-inflammatory or anti-inflammatory properties in skeletal muscle and intestinal tissues. The study was carried out on C57BL/6 mice. During the 8-month experiment, the mice in the study group were fed a 30% calorie restricted diet—according to the Every-Other-Day Diet concept. Analyses were performed on intestinal and muscle tissues collected from animals. Fatty acid derivatives were isolated using solid-phase extraction (C-18 columns) columns, and isolation of fatty acids was performed using a modified Folch method. The compounds were analyzed by liquid and gas chromatography. Results: CR induced detectable alterations in both fatty acid profiles and lipid mediator concentrations in a tissue-specific manner. However, most of these changes did not remain statistically significant after multiple testing correction. Conclusions: These findings suggest potential effects of long-term CR on lipid signaling pathways, although the current dataset lacks the statistical power required to draw definitive conclusions. This study highlights the need for further research using larger sample sizes and integrated multiomic approaches to elucidate the molecular mechanisms underlying lipidomic adaptations to prolonged caloric restriction. Full article

Background/Objectives: Dental caries remains one of the most prevalent chronic diseases worldwide, making enamel remineralization a key objective in minimally invasive dentistry. This in vitro study compared the remineralization efficacy of five therapeutic toothpastes containing fluoride, NovaMin, CPP-ACP, nano-hydroxyapatite, arginine, and xylitol. Methods: Sixty enamel specimens were prepared from extracted human posterior teeth and artificially demineralized. Samples were randomly allocated into six groups (n = 10): one negative control (C1) stored in artificial saliva and five treatment groups (P1–P5). A 28-day remineralization protocol with twice-daily applications was performed. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) were used to assess surface morphology and elemental composition (Ca, P, F, Na, O, Ca/P ratio) at days 1, 14, and 28. Vickers microhardness testing was used to evaluate changes in mechanical properties. Statistical analysis included one-way ANOVA, repeated measures ANOVA, Tukey’s post hoc test, and Kruskal–Wallis where appropriate (α = 0.05). Results: All therapeutic toothpastes produced some increase in mineral content compared to the demineralized control. At day 28, significant intergroup differences were observed for calcium, phosphorus, and fluoride (p < 0.001). The arginine–fluoride formulation (P4) and the NovaMin-based formulation (P3) showed the most consistent increases in Ca and P, with SEM revealing the formation of a continuous, compact surface layer and marked reduction in prismatic porosities. Fluoride-containing toothpastes (P1, P3, P4) showed significant fluoride incorporation (p < 0.001 vs. control). The nano-hydroxyapatite/xylitol prototype (P5) produced a delayed but progressive increase in Ca and P, with partial filling of prismatic spaces. The CPP-ACP-based toothpaste (P2) led to limited changes, with only slight differences vs. control at day 28. Vickers microhardness values increased significantly in groups P1, P3, P4, and P5 (p < 0.05), in agreement with the higher mineral levels found in these samples. Conclusions: Under the present in vitro conditions, toothpastes containing fluoride in combination with NovaMin or arginine, as well as nano-hydroxyapatite/xylitol, demonstrated the highest remineralization potential under the present in vitro conditions, both chemically and mechanically. Xylitol-based formulations without a direct mineral supply showed limited effects. The pH and active composition of the toothpaste strongly influenced enamel remineralization outcomes. Full article

This study aims to develop an electrochemical sensor based on a glassy carbon electrode (GCE) modified with Fe₃O₄ and graphene for the detection of myristicin as a characteristic compound in nutmeg plants. Electrode modification materials were prepared from a combination of graphene and magnetite, synthesized via a hydrothermal method, and further characterized using X-ray diffraction (XRD), scanning electron microscope–energy dispersive spectroscopy (SEM-EDS), and transmission electron microscopy (TEM). The two modifying materials were then optimized, and the optimum conditions were obtained at a w/w ratio of 1:2, which was applied to the GCE surface using the drop-casting technique. The electrochemical performance of the Fe₃O₄/graphene-modified electrode was evaluated under optimum experimental conditions using a Britton–Robinson buffer solution at pH 5. The scan-rate analysis of the electrode to evaluate its electrochemical performance showed an increase in surface area from 0.101 cm² for the bare GCE to 0.534 cm² for the GCE/Fe₃O₄–graphene. Electroanalytical performance was evaluated using differential pulse voltammetry (DPV), which showed a linear response over the concentration range of 1–100 µM, with a limit of detection of 0.19 µM and a limit of quantitation of 0.58 µM. The developed electrode was applied successfully to detect myristicin in nutmeg seed extract samples, and its calculated concentrations were not significantly different from those obtained with the GC-MS method. These results suggest that the developed sensor may have further potential as an alternative detection tool for characterizing electroactive compounds in nutmeg plants. Full article

Lithium-ion batteries are extensively employed in new energy vehicles, where accurate State of Charge (SOC) estimation is fundamental for optimal battery management. However, existing methods often rely on single-model approaches and fail to leverage the complementary advantages of multiple models. This study proposes an innovative hybrid estimation model integrating a Temporal Convolutional Network (TCN) that efficiently captures long-range temporal dependencies via dilated convolution and residual blocks, with a Bidirectional Long Short-Term Memory Network (BiLSTM) that extracts bidirectional context information to enhance the accuracy of SOC estimation. First, the Panasonic datasets are utilized, with current, voltage, and cell temperature selected as input features. Subsequently, the proposed model is evaluated under various temperature conditions and driving cycles, demonstrating high accuracy and robustness. Finally, comparative experiments are conducted against traditional methods, such as standalone TCN and Long Short-Term Memory (LSTM) networks, under both 10 °C and −10 °C operating conditions. The results show that the hybrid model achieves superior performance in error metrics. Specifically, based on a second-order resistor-capacitor network, at −10 °C, the Root Mean Squared Error is reduced by 0.948%, and at 10 °C, it decreases by 0.398%. Additionally, the Maximum Absolute Error is lowered by 2.751% at −10 °C and by 2.192% at 10 °C. These improvements highlight the model’s significant potential as an effective solution for SOC estimation in lithium-ion batteries. Full article

Feature extraction and description are fundamental components of visual perception systems used in applications such as visual odometry, Simultaneous Localization and Mapping (SLAM), and autonomous navigation. In resource-constrained platforms, such as Unmanned Aerial Vehicles (UAVs), achieving real-time hardware acceleration on Field-Programmable Gate Arrays (FPGAs) is challenging. This work demonstrates an FPGA-based implementation of an adaptive ORB (Oriented FAST and Rotated BRIEF) feature extraction pipeline designed for high-throughput and energy-efficient embedded vision. The proposed architecture is a completely new design for the main algorithmic blocks of ORB, including the FAST (Features from Accelerated Segment Test) feature detector, Gaussian image filtering, moment computation, and descriptor generation. Adaptive mechanisms are introduced to dynamically adjust thresholds and filtering behavior, improving robustness under varying illumination conditions. The design is developed using a High-Level Synthesis (HLS) approach, where all processing modules are implemented as reusable hardware IP cores and integrated at the system level. The architecture is deployed and evaluated on two FPGA platforms, PYNQ-Z2 and KRIA KR260, and its performance is compared against CPU and GPU implementations using a dedicated C++ testbench based on OpenCV. Experimental results demonstrate significant improvements in throughput and energy efficiency while maintaining stable and scalable performance, making the proposed solution suitable for real-time embedded vision applications on UAVs and similar platforms. Notably, the FPGA implementation increases DSP utilization from 11% to 29% compared to the previous designs implemented by other researchers, effectively offloading computational tasks from general purpose logic (LUTs and FFs), reducing LUT usage by 6% and FF usage by 13%, while maintaining overall design stability, scalability, and acceptable thermal margins at 2.387 W. This work establishes a robust foundation for integrating the optimized ORB pipeline into larger drone systems and opens the door for future system-level enhancements. Full article

Although interest in tidal energy has increased in recent years, its development remains significantly behind that of other renewable sources such as solar and wind energy. This delay is primarily caused by the complex and harsh ocean environment, which imposes significant constraints on operational systems. This paper proposes a new approach to the design and control of a marine current turbine (MCT) emulator without a pitch mechanism, operating in real time below the rated marine current speed.The emulator control strategy integrates two approaches: predictive control for regulating the speed of the DC machine, and a Linear Quadratic Regulator (LQR) control scheme for maximizing power extraction from the marine current. Our experimental results demonstrate the effectiveness of the proposed hybrid control strategy, which allows precise tracking of reference signals and stable regulation of the direct current machine (DCM) speed, thereby ensuring synchronization with the turbine’s rotational speed. This approach ensures optimal and robust performance over the entire range of marine current variations. Full article

Background: Osteoporosis (OP) is characterized by reduced bone mineral density and increased fracture risk. Many spinal surgery patients have undiagnosed OP due to the lack of preoperative screening, leading to postoperative complications. Magnetic resonance imaging (MRI), a routine, non-invasive tool for spinal assessment, offers potential for opportunistic OP detection. This study aimed to develop deep learning models to identify OP using lumbar MRI. Methods: We retrospectively enrolled 218 patients (≥50 years) who underwent both lumbar MRI and dual-energy X-ray absorptiometry (DXA). After segmentation of vertebral bodies from T1- and T2-weighted MRI images, 738 images per sequence were extracted. Separate convolutional neural network (CNN) models were trained for each sequence. Model performance was evaluated using receiver operating characteristic curves and area under the curve (AUC). Results: Among tested classifiers, EfficientNet b4 showed the best performance. For the T1-weighted model, it achieved an AUC of 82%, with a sensitivity of 85% and specificity of 79%. For the T2-weighted model, the AUC was 83%, with a sensitivity of 86% and specificity of 80%. These results were superior to those of InceptionResNet v2 and ResNet-50 for both sequences. Conclusions: The AI models provided reliable OP classification without additional imaging or radiation. AI-based analysis of standard lumbar MRI sequences can accurately identify OP. These models may assist in early detection of undiagnosed OP in surgical candidates, enabling timely treatment and perioperative strategies to improve outcomes and reduce healthcare burden. Full article

The Croton genus includes a diverse group of plants with remarkable potential in natural products research, particularly due to their bioactive compounds with hypoglycemic and phytochemical significance. This study examines Croton guatemalensis Lotsy, focusing on its chemical composition and its biological efficacy as a glucose-6-phosphatase inhibitor. Phytochemical analysis led to the isolation and structural elucidation of eleven compounds (1–11), including three new ent−clerodane diterpenes, designated crotoguatenoic acids C (9), D (10), and E (11). The absolute configurations of compounds 9–11 were determined by electronic circular dichroism (ECD) as (5R,8R,9R,10S)-configured ent–clerodanes. High-performance liquid chromatography–mass spectrometry (HPLC–MS/MS) revealed 25 peaks tentatively assigned to terpenoids, flavonoids, and alkaloids, highlighting the species’ chemical diversity. In vitro assays using ethanol–water extract (EWE) and isolated compounds with rat liver microsomes demonstrated inhibitory activity against glucose-6-phosphatase (G6Pase), particularly among ent–clerodane diterpenes (73–96%), with EWE and compounds 1, 4, and 11 showing the highest inhibition. Molecular docking analysis revealed strong interactions between these diterpenoids and the G6PC1 binding pocket, with binding energies comparable to chlorogenic acid (positive control). These findings position C. guatemalensis as a valuable source of bioactive diterpenoids and support the potential of ent-clerodane derivatives as natural G6Pase inhibitors for hyperglycemia management. Full article

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives. Full article

In Colombia, two main palm varieties, Elaeis guineensis and Elaeis oleifera, are cultivated for the production of crude palm oil (CPO). During the CPO extraction process, several residues are generated, including empty fruit bunches (EFB), nut fiber, palm kernel cake, and Palm Oil Mill Effluent (POME), among others. These residues are commonly used for biochar and compost production to improve soil quality, for biogas generation, and for energy production through biomass combustion. Because the rachis is rich in lignocellulosic material and exhibits physicochemical properties suitable for thermochemical processes, it is proposed as a feedstock for hydrogen synthesis through gasification. In this study, a techno-economic analysis and an FP²O resilience assessment were conducted for a hydrogen production process based on the utilization of palm rachis generated in María la Baja, northern Colombia. The economic evaluation results indicate that the capital investment required for plant installation is USD 10,111,255.23. The economic indicators show favorable performance with a Return on Investment (ROI) of 58.83%, a Net Present Value (NPV) of USD 25.01 million, a B/C ratio of 3.29, and a Discounted Payback Period (DPBP) of 4.54 years. Regarding techno-economic resilience, critical values for processing capacity, selling price, and feedstock cost were identified through parameter variation. The findings suggest that the process has opportunities for improvement, since small changes in these variables could significantly reduce its resilience. Finally, an On-Stream efficiency of 39.65% at the break-even point was obtained, indicating that the process can operate at less than 50% of its maximum capacity while still generating significant profits. Full article

Self-powered perovskite photodiodes provide an attractive platform for low-power and high-sensitivity photodetection; however, their performance capabilities are often constrained by inefficient interfacial charge extraction and noise suppression. Here, we report a polymer-mediated interfacial engineering strategy for methylammonium lead iodide (MAPbI₃) photodiodes by integrating thermally optimized nickel oxide (NiO_x) hole-transport layers (HTLs) with a nonionic polymeric surfactant, poly(oxyethylene)(10) tridecyl ether (PTE). NiO_x films annealed at 300 °C establish a favorable energetic baseline for hole extraction, while the ppm-level incorporation of PTE into the MAPbI₃ precursor enables the molecular-scale modulation of the NiO_x/MAPbI₃ interface without forming an additional interlayer. The external quantum efficiency at 640 nm increases from 78.7% for pristine MAPbI₃ to 84.1% and 84.6% for devices incorporating 30 and 60 ppm PTE, corresponding to enhanced responsivities of 406, 434, and 437 mA/W. These improvements translate into reduced noise-equivalent power and an increase in the noise-limited detectivity from 2.50 × 10¹² to 2.76 × 10¹² Jones under zero-bias operation. Importantly, enhanced sensitivity is achieved without compromising the dynamic performance, as all devices retain fast temporal responses and kilohertz-level bandwidths. These results establish polymeric-surfactant-assisted interfacial engineering as a scalable and effective platform for low-noise, high-sensitivity self-powered perovskite photodiodes for renewable-energy-integrated systems. Full article