Search Results (4,015)

The accurate forecasting of electricity sales volumes constitutes a critical task for power system planning and operational management. Nevertheless, subject to meteorological perturbations, holiday effects, exogenous economic conditions, and endogenous grid operational metrics, sales data frequently exhibit pronounced volatility, marked nonlinearities, and intricate interdependencies. This inherent complexity compounds modeling challenges and constrains forecasting efficacy when conventional methodologies are applied to such datasets. To address these challenges, this paper proposes a novel decomposition–integration forecasting framework. The methodology first applies Variational Mode Decomposition (VMD) combined with the Zebra Optimization Algorithm (ZOA) to adaptively decompose the original data into multiple Intrinsic Mode Functions (IMFs). These IMF components, each capturing specific frequency characteristics, demonstrate enhanced stationarity and clearer structural patterns compared to the raw sequence, thus providing more representative inputs for subsequent modeling. Subsequently, an improved RevInformer model is employed to separately model and forecast each IMF component, with the final prediction obtained by aggregating all component forecasts. Empirical verification on an annual electricity sales dataset from a commercial building demonstrates the proposed method’s effectiveness and superiority, achieving Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Percentage Error (MSPE) values of 0.044783, 0.211621, and 0.074951, respectively—significantly outperforming benchmark approaches. Full article

The study examines the influence of diverse flue gas compositions on the operational parameters and efficiency of MCFCs (molten carbonate fuel cells) as CO₂ separation devices to provide foundational knowledge on MCFC operation under various industrial conditions. MCFCs inherently rely on the presence of CO₂ at the cathode, where it combines with oxygen to form carbonate ions that migrate through the electrolyte; thus, CO₂ acts as a carrier species rather than a fuel, enabling simultaneous electricity generation and CO₂ separation. The findings indicate that MCFCs are most effective when operated with CO₂-rich flue gases, such as those from coal and lignite-fired power plants with CO₂ contents of roughly 12–15 vol.% and O₂ contents of 2–6 vol.%. In these cases, CO₂ reduction rates of up to 80% can be achieved while maintaining favorable cell voltages. Under such conditions, relevant also for the cement industry (CO₂ between 15 and 35 vol.%), the Nernst voltage can reach about 1.18 V. In contrast, flue gases from gas turbines, which typically contain only 4–6 vol.% CO₂ and 11–13 vol.% O₂, result in lower Nernst voltages (0.6–0.7 V) and a decrease in efficiency. To address this issue, potential modifications to the MCFC electrolyte are suggested to enhance oxygen-ion conductivity and improve performance. By quantifying the operational window and CO₂-reduction potential for different sectors at 650 °C and 1 atm using a reduced-order model, the paper provides a technology assessment that supports sustainable industrial operation and the design of CCS (carbon capture and sequestration) strategies in line with climate goals. Full article

This review critically examines photoresponsive supercapacitors based on TiO₂/graphene hybrids, with a particular focus on their emerging dual role as energy-storage devices and environmental sensors. We first provide a concise overview of the electronic structure of TiO₂ and the key attributes of graphene and related nanocarbons that enable efficient charge separation, transport, and interfacial engineering. We then summarize and compare reported device architectures and electrode designs, highlighting how morphology, graphene integration strategies, and illumination conditions govern specific capacitance, cycling stability, rate capability, and light-induced enhancement in performance. Particular attention is given to the underlying mechanisms of photo-induced capacitance enhancement—including photocarrier generation, interfacial polarization, and photodoping—and to how these processes can be exploited to embed sensing functionality in working supercapacitors. We review representative studies in which TiO₂/graphene systems operate as capacitive sensors for humidity, gases, and volatile organic compounds, emphasizing quantitative figures of merit such as sensitivity, response/recovery times, and stability under repeated cycling. Finally, we outline current challenges in materials integration, device reliability, and benchmarking, and propose future research directions toward scalable, multifunctional TiO₂/graphene platforms for self-powered and environmentally aware electronics. This work is intended as a state-of-the-art summary and critical guide for researchers developing next-generation photoresponsive supercapacitors with integrated sensing capability. Full article

Timely and accurate fault diagnosis in power transmission systems is critical to ensuring grid stability, operational safety, and minimal service disruption. This study presents a unified deep learning framework that simultaneously performs fault identification, fault type classification, and fault location estimation using a multi-task learning (MTL) approach. Using the IEEE 39–Bus network, a comprehensive data set was generated under various load conditions, fault types, resistances, and location scenarios to reflect real-world variability. The proposed model integrates a shared representation layer and task-specific output heads, enhanced with an attention mechanism to dynamically prioritize salient input features. To further optimize the model architecture, Optuna was employed for hyperparameter tuning, enabling systematic exploration of design parameters such as neuron counts, dropout rates, activation functions, and learning rates. Experimental results demonstrate that the proposed Optimized Multi-Task Learning Attention Network (MTL-AttentionNet) achieves high accuracy across all three tasks, outperforming traditional models such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), which require separate training for each task. The attention mechanism contributes to both interpretability and robustness, while the MTL design reduces computational redundancy. Overall, the proposed framework provides a unified and efficient solution for real-time fault diagnosis on the IEEE 39–bus transmission system, with promising implications for intelligent substation automation and smart grid resilience. Full article

This paper develops a compact and scalable mathematical model of an AC circuit with an uncontrolled diode bridge rectifier, formulated using dimensionless variables. The model captures the joint influence of supply inductance, resistance, and load parameters on current waveform distortion, harmonic content, and reactive power exchange, which are often simplified or addressed separately in existing studies. Experimental validation confirms the applicability of the model over a wide range of operating conditions and grid strengths. The results provide quantitative characteristics that support the interpretation of power quality measurements and the assessment of how nonlinear loads interact with non-stiff supply sources. The proposed formulation offers an efficient analytical tool for harmonic analysis in distribution networks, particularly where a balance between modelling accuracy and computational effort is required. Full article

Legal regulation and practical implementation of artificial intelligence (AI) in Kazakhstan’s criminal procedure are considered within the context of judicial digital transformation. Risks arise for fundamental procedural principles, including the presumption of innocence, adversarial process, and protection of individual rights and freedoms. Legislative mechanisms ensuring lawful and rights-based application of AI in criminal proceedings are required to maintain procedural balance. Comparative legal analysis, formal legal research, and a systemic approach reveal gaps in existing legislation: absence of clear definitions, insufficient regulation, and lack of accountability for AI use. Legal recognition of AI and the establishment of procedural safeguards are essential. The novelty of the study lies in the development of concrete approaches to the introduction of artificial intelligence technologies into criminal procedure, taking into account Kazakhstan’s practical experience with the digitalization of criminal case management. Unlike existing research, which examines AI in the legal profession primarily from a theoretical perspective, this work proposes detailed mechanisms for integrating models and algorithms into the processing of criminal cases. The implementation of AI in criminal justice enhances the efficiency, transparency, and accuracy of case handling by automating document preparation, data analysis, and monitoring compliance with procedural deadlines. At the same time, several constraints persist, including dependence on the quality of training datasets, the impossibility of fully replacing human legal judgment, and the need to uphold the principles of the presumption of innocence, the right to privacy, and algorithmic transparency. The findings of the study underscore the potential of AI, provided that procedural safeguards are strictly observed and competent authorities exercise appropriate oversight. Two potential approaches are outlined: selective amendments to the Criminal Procedure Code concerning rights protection, privacy, and judicial powers; or adoption of a separate provision on digital technologies and AI. Implementation of these measures would create a balanced legal framework that enables effective use of AI while preserving core procedural guarantees. Full article

Developing photoelectrochemical systems that couple pollutant removal with resource recovery is of great significance for sustainable wastewater treatment. In this study, a dual-function photocatalytic fuel cell (PFC) was developed using a TiO₂ nanotube photoanode modified with an amorphous CoOx cocatalyst, which markedly enhances charge separation and interfacial reaction kinetics. The optimized TiO₂@CoOx electrode achieves a twofold enhancement in photocurrent compared to pristine TiO₂. When applied to Cu²⁺-containing wastewater, the PFC achieved 91% Cu²⁺ removal under N₂-purged conditions, with metallic Cu identified as the sole reduction product. Although dissolved oxygen reduced metal recovery efficiency through competitive electron consumption, it simultaneously increased power generation and improved anodic organic degradation. Overall, the results demonstrate that amorphous-CoOx-modified TiO₂ photoanodes offer an effective platform for integrating sustainable energy production with wastewater remediation and valuable copper recovery. Full article

To address challenges in small object detection within drone aerial imagery—such as sparse feature information, intense background interference, and drastic scale variations—this paper proposes YOLO-SR, a lightweight detection algorithm based on attention enhancement and feature reuse mechanisms. First, we designed the lightweight feature extraction module C2f-SA, which incorporates Shuffle Attention. By integrating channel shuffling and grouped spatial attention mechanisms, this module dynamically enhances edge and texture feature responses for small objects, effectively improving the discriminative power of shallow-level features. Second, the Spatial Pyramid Pooling Attention (SPPC) module captures multi-scale contextual information through spatial pyramid pooling. Combined with dual-path (channel and spatial) attention mechanisms, it optimizes feature representation while significantly suppressing complex background interference. Finally, the detection head employs a decoupled architecture separating classification and regression tasks, supplemented by a dynamic loss weighting strategy to mitigate small object localization inaccuracies. Experimental results on the RGBT-Tiny dataset demonstrate that compared to the baseline model YOLOv5s, our algorithm achieves a 5.3% improvement in precision, a 13.1% increase in recall, and respective gains of 11.5% and 22.3% in mAP_0.5 and mAP_0.75, simultaneously reducing the number of parameters by 42.9% (from 7.0 × 10⁶ to 4.0 × 10⁶) and computational cost by 37.2% (from 60.0 GFLOPs to 37.7 GFLOPs). The comprehensive improvement across multiple metrics validates the superiority of the proposed algorithm in both accuracy and efficiency. Full article

This essay examines how Shakespeare’s King Lear and Tarkovsky’s Nostalghia employ fool figures to articulate truths inaccessible through rational discourse. The Fool in King Lear speaks through riddles, songs, and prophecies, revealing uncomfortable realities about power and identity that direct statement cannot safely convey. His performed madness contrasts with Lear’s genuine descent into insanity, yet both states access knowledge unavailable to those maintaining social position and sanity. Tarkovsky’s Domenico embodies the Russian Orthodox tradition of yurodstvo (holy foolishness), performing sacred madness through impossible rituals and apocalyptic prophecy. His mathematical impossibility—“1 + 1 = 1”—expresses spiritual unity that logic cannot grasp. Both figures draw on Plato’s distinction in the Phaedrus between divine madness and human pathology, where four forms of god-sent mania provide superior insight into rational thought. Through Erasmus’s humanist satire and Foucault’s analysis of reason’s violent separation from unreason, the essay traces how Western culture moved from integrating fool-wisdom to confining it as pathology. The protective mechanisms enabling fool-speech—performance frames, liminal positioning, sacred authorization—reveal society’s ambivalent need for dangerous truths. As contemporary culture increasingly medicalizes cognitive deviation, these masterworks preserve essential epistemological functions, demonstrating why certain truths require the fool’s disruptive voice. Full article

Genomic organization and its comparative analysis throughout all major kingdoms of life are extensively studied across multiple scales, ranging from individual gene-level analyses to system-wide investigations. This work introduces a novel framework for characterizing genetic architecture through a new integral genomic parameter. We propose the concept of a multidimensional Gene Space to enable holistic quantification of genome organization principles. Gene Space—a multidimensional space based on the frequencies of nucleotide tokens, such as individual nucleotides, codons, or codon pairs. We demonstrate that in this space, genes from each of the studied microorganism species occupy a limited region, and individual genes from different species can be effectively separated with more than 95% accuracy. Consequently, a specific Genome Subspace can be defined for each species, which constrains the organism’s evolutionary pathways, thereby determining the constraints on gene optimization for these species. Further in-depth analysis is required to test if it is true for other organisms as well. The Gene Space framework offers a novel and powerful approach for genome analysis at the most basic levels, with promising applications in comparative genomics, evolutionary biology, and gene optimization. Full article

The presented research is focused on identifying a cheap and environmentally friendly solution for recovering useful non-ferrous metals contained in used Ni-MH batteries—more specifically, in batteries that power medical equipment, i.e., portable defibrillators. The cathodic paste of Ni-MH batteries contains Ni(OH)₂ as an active material to which Zn, Co and Mn can be added. The paste is impregnated into a support mesh made of nickel. The anodic paste of Ni-MH batteries contains mixtures of rare earths capable of storing the released hydrogen. The paste is mixed with a binder and pressed onto a metal grid made of nickel alloy. After manual disassembly, the components of the Ni-MH batteries were analyzed by X-ray Fluorescence Spectroscopy (XRF) before and after the separation/recovery operation. To separate the cathode and anode paste from the metal supports (grids, metal meshes), an ultrasonic bath with appropriate solutions was used, and the optimal working parameters were established. The recovery of the anode paste was achieved by completely passing the rare earths into the citric acid solution used for ultrasonication; the nickel mesh was cleaned of the Ni(OH)₂ paste using water as the ultrasonication medium. After separation from the metal supports, the anode and cathode pastes were analyzed and characterized by XRF, optical and electron microscopy (SEM, EDX). The results obtained are of real interest for those who study the recycling of Ni-MH batteries; the use of ultrasound in a low-concentration citric acid environment for the purpose of recovering rare earths can be an economic and ecological alternative for battery recycling. Full article

Background: Artificial intelligence (AI) emerges as a powerful tool to assist breast cancer screening; however, its integration into different mammographic modalities remains insufficiently explored. Digital Mammography (DM) is widely accessible but presents limitations in dense breast tissue, whereas Contrast-Enhanced Spectral Mammography (CESM) provides functional information that enhances lesion visualization. Understanding how deep learning models behave across these modalities, and determining whether their decision-making patterns remain consistent, is essential for equitable clinical adoption. Methods: This study evaluates three convolutional neural network (CNN) architectures, ResNet-18, DenseNet-121, and EfficientNet-B0, for binary classification of breast lesions using DM and CESM images from the public CDD-CESM dataset (2006 images, three diagnostic classes). The models are trained separately on DM and CESM using three classification tasks: Normal vs. Benign, Benign vs. Malignant, and Normal vs. Malignant. A 3-fold cross-validation scheme and an independent test set are employed. Training uses transfer learning with ImageNet weights, weighted binary cross-entropy (BCE) loss, and SHapley Additive exPlanations (SHAP) analysis to visualize pixel-level relevance of model decisions. Results: CESM yields higher performance in the Normal vs. Benign and Benign vs. Malignant tasks, whereas DM achieves the highest discriminative ability in the Normal vs. Malignant comparison (EfficientNet-B0: AUC = 97%, Accuracy = 93.15%), surpassing the corresponding CESM results (AUC = 93%, Accuracy = 85.66%). SHAP attribution maps reveal anatomically coherent decision patterns in both modalities, with CESM producing sharper and more localized relevance regions due to contrast uptake, while DM exhibits broader yet spatially aligned attention. Across architectures, EfficientNet-B0 demonstrates the most stable performance and interpretability. Conclusions: CESM enhances subtle lesion discrimination through functional contrast, whereas DM, despite its simpler acquisition and wider availability, provides highly accurate and explainable outcomes when combined with modern CNNs. The consistent SHAP-based relevance observed across modalities indicates that both preserve clinically meaningful information. To the best of our knowledge, this study is the first to directly compare DM and CESM under identical preprocessing, training, and evaluation conditions using explainable deep learning models. Full article

The implementation of photoelectric conversion in photoelectric integrated systems requires the design of photodetectors (PDs) with quick response times and low power consumption. In this work, the self-powered photodetector was prepared by antimony selenide (Sb₂Se₃) microwires (MW)/Se microtube (MT) heterojunction by coating Ag nanowires (NW). The incorporation of Ag-NW involves dual enhancement mechanisms. First, the surface plasmon resonance (SPR) effect amplifies the light absorption across UV–vis–NIR spectra, and the conductive networks facilitate the rapid carrier transport. Second, the type-II band alignment between Sb₂Se₃ and Se synergistically separates photogenerated carriers, while the Ag-NW further suppress the recombination through built-in electric field modulation. The optimized device achieves remarkable responsivity of 122 mA W⁻¹ at 368 nm under zero bias, with a response/recovery time of 8/10 ms, outperforming most reported Sb₂Se₃-based detectors. The heterostructure provides an effective strategy for developing self-powered photodetectors with broadband spectral adaptability. The switching ratio, responsivity, and detectivity of the Sb₂Se₃-MW/Se-MT/Ag-NW device increased by 260%, 810%, and 849% at 368 nm over the Sb₂Se₃-MW/Se-MT device, respectively. These results show that the addition of Ag-NW effectively improves the photoelectric performance of the Sb₂Se₃-MW/Se-MT heterojunction, providing new possibilities for the application of self-powered optoelectronic devices. Full article

Modern power systems face growing challenges in stability assessment due to large-scale renewable energy integration and rapidly changing operating conditions. Data-driven approaches have emerged as promising solutions for real-time stability assessment, yet their performance often degrades under network topology reconfigurations. To address this limitation, the Spatiotemporal Contrastive Graph Convolutional Network (STCGCN) is proposed for the joint task prediction of voltage and transient stability across known and unknown topologies. The framework integrates a graph convolutional network (GCN) encoder to capture spatial dependencies and a temporal convolutional network to model electromechanical dynamics. It also employs supervised contrastive learning to extract discriminative features due to the grid topology variation, enhance stability class separability, and mitigate class imbalance under varying operating conditions, such as fluctuating loads and renewable integration. Case studies on the IEEE 39-bus system demonstrate that STCGCN achieves 89.66% accuracy on in-sample datasets from known topologies and 87.73% on out-of-sample datasets from unknown topologies, outperforming single-task learning approaches. These results highlight the method’s robustness to topology variations and its strong generalization across configurations, providing a topology-aware and resilient solution for real-time joint voltage and transient stability assessment in power systems. Full article

Artifacts remain a major challenge in electroencephalogram (EEG) recordings, often degrading the accuracy of clinical diagnosis, brain computer interface (BCI) systems, and cognitive research. Although recent deep learning approaches have advanced EEG denoising, most still struggle to model long-range dependencies, maintain computational efficiency, and generalize to unseen artifact types. To address these challenges, this study proposes MDSC-VA, an efficient denoising framework that integrates multi-scale (M) depth-wise separable convolution (DSConv), variational autoencoder-based (VAE) latent encoding, and a multi-head self-attention mechanism. This unified architecture effectively balances denoising accuracy and model complexity while enhancing generalization to unseen artifact types. Comprehensive evaluations on three open-source EEG datasets, including EEGdenoiseNet, a Motion Artifact Contaminated Multichannel EEG dataset, and the PhysioNet EEG Motor Movement/Imagery dataset, demonstrate that MDSC-VA consistently outperforms state-of-the-art methods, achieving a higher signal-to-noise ratio (SNR), lower relative root mean square error (RRMSE), and stronger correlation coefficient (CC) values. Moreover, the model preserved over 99% of the dominant neural frequency band power, validating its ability to retain physiologically relevant rhythms. These results highlight the potential of MDSC-VA for reliable clinical EEG interpretation, real-time BCI systems, and advancement towards sustainable healthcare technologies in line with SDG-3 (Good Health and Well-Being). Full article