Search Results (5,665)

Hybrid supercapacitors (HSCs) are a novel type of supercapacitor composed of battery-type electrodes and capacitor-type electrodes, which have directly transformed the global energy landscape. On one hand, they can replace clean energy sources that are heavily dependent on climatic conditions in specific regions, thereby enhancing the effective utilization of intermittent energy sources. On the other hand, with their high energy density akin to secondary batteries and the long lifespan and high power density characteristic of supercapacitors, they perfectly bridge the gap between secondary batteries and supercapacitors. This article reviews the fundamental energy storage principles of HSCs and highlights the latest optimization strategies for HSCs based on transition metal oxides (TMOs) and carbon over the past two years. These strategies include heteroatom doping, heterostructured materials, nanocomposites, and metal–organic frameworks (MOF). Finally, prospects on future research directions of HSCs are discussed. Full article

Traditional pest and disease management methods are inefficient, relying on agricultural experts or static resources, making it difficult to respond quickly to large-scale outbreaks and meet local needs. Although deep learning technologies have been applied in pest and disease management, challenges remain, such as the dependence on large amounts of manually labeled data and the limitations of dynamic reasoning. To address these challenges, this study proposes IPM-AgriGPT (Integrated Pest Management—Agricultural Generative Pre-Trained Transformer), a Chinese large language model specifically designed for pest and disease knowledge. The proposed Generation-Evaluation Adversarial (G-EA) framework is used to generate high-quality question–answer corpora and combined with Agricultural Contextual Reasoning Chain-of-Thought Distillation (ACR-CoTD) and low-rank adaptation (LoRA) techniques further optimizes the base model to build IPM-AgriGPT. During the evaluation phase, this study designed a specialized benchmark for the agricultural pest and disease domain, comprehensively assessing the performance of IPM-AgriGPT in pest management tasks. Experimental results show that IPM-AgriGPT achieved excellent evaluation scores in multiple tasks, demonstrating its great potential in agricultural intelligence and pest management. Full article

In the era of transformative technologies, generative artificial intelligence (genAI) offers profound opportunities and challenges for education. This study explores the development and execution of an interactive workshop designed to equip educators with foundational genAI literacy. Using a design-based research (DBR) framework, the workshop leverages interactivity and contextual relevance to introduce genAI concepts, prompting strategies and ethical considerations. Participants engaged in a scripted learning workshop design, comparing human and AI responses, exploring genAI’s probabilistic foundations, context dependency, and vulnerability to manipulation. Conducted across 12 workshops with 191 participants in Austria, this study revealed significant improvements in self-perceived genAI understanding, with 70% of participants reporting better grades in post-assessment evaluations. Feedback emphasized the workshop’s strengths in interactivity and relevance, alongside recommendations for deeper school-specific applications. Scalability analysis showed that workshop duration remained consistent regardless of group size, suggesting potential for broader implementation. The findings highlight the effectiveness of scripted learning workshop design in fostering critical AI literacy, preparing educators to critically evaluate and ethically integrate genAI into pedagogical practices. This adaptable model contributes to the discourse on professional development in AI-enhanced education. Full article

Raw meteorological satellite images often suffer from defects such as noise points and lines due to atmospheric interference and instrument errors. Current solutions typically rely on manual visual inspection to identify these defects. However, manual inspection is labor-intensive, lacks uniform standards, and is prone to both false positives and missed detections. To address these challenges, we propose DeepDR, a two-level deep defect recognition framework for meteorological satellite images. DeepDR consists of two modules: a transformer-based noise image classification module for the first level and a noise region segmentation module based on a pseudo-label training strategy for the second level. This framework enables the automatic identification of defective cloud images and the detection of noise points and lines, thereby significantly improving the accuracy of defect recognition. To evaluate the effectiveness of DeepDR, we have collected and released two satellite cloud image datasets from the FengYun-1 satellite, which include noise points and lines. Subsequently, we conducted comprehensive experiments to demonstrate the superior performance of our approach in addressing the satellite cloud image defect recognition problem. Full article

This paper develops novel results in the harmonic analysis of Bessel operators, extending their theory to higher-dimensional and non-Euclidean spaces. We present a refined framework for Hardy spaces associated with Bessel operators, emphasizing atomic decompositions, dual spaces, and connections to Sobolev and Besov spaces. The spectral theory of families of boundary-interpolating operators is also expanded, offering precise eigenvalue estimates and functional calculus applications. Furthermore, we explore Bessel operators under non-standard measures, such as fractal and weighted geometries, uncovering new analytical phenomena. Key implications include advanced insights into singular integrals, heat kernel behavior, and the boundedness of Riesz transforms, with potential applications in fractal geometry, constrained wave propagation, and mathematical physics. Full article

An integrated micro- and nano-grid with net-zero renewable energy is a sophisticated energy system framework aimed at attaining optimal efficiency and sustainability. This survey paper examines several contemporary research works in this domain. This document summarizes the latest papers selected for analysis to comprehend the current state-of-the-art, integration process, methodology, and research gaps. The objective of this review is to identify existing trends and ongoing transformations in this domain. At the conclusion of the study, emerging technologies for smart grid integration are offered, emphasizing Transactive Control, Blockchain Technology, and Quantum Cryptography, based on existing research gaps. Microgrids and nano-grids are localized energy systems capable of functioning alone or in tandem with larger power grids, offering resilience and adaptability. By incorporating renewable energy sources like solar, wind, and storage devices, these networks can produce and regulate energy locally, guaranteeing that the generated energy meets or surpasses the energy used. The incorporation of intelligent technology and control systems facilitates optimized energy distribution, real-time monitoring, and load balancing, advancing the objective of net-zero energy use. This strategy not only bolsters energy security but also markedly decreases carbon emissions, rendering it an essential element in the shift towards a sustainable and resilient energy future. The worldwide implementation of interconnected micro- and nano-grids utilizing net-zero renewable energy signifies a pivotal transition towards a sustainable and resilient energy future. These localized energy systems can function independently or in conjunction with conventional power grids, utilizing renewable energy sources like solar, wind, and advanced storage technology. Integrating these resources with intelligent control systems enables micro- and nano-grids to optimize energy production, distribution, and consumption at a detailed level, ensuring that communities and companies globally can attain net-zero energy usage. This method not only diminishes greenhouse gas emissions and reliance on fossil fuels but also improves energy security and grid stability in various places. These technologies, when implemented globally, provide a scalable answer to the issues of energy access, environmental sustainability, and climate change mitigation, facilitating a cleaner and more equal energy landscape worldwide. Full article

Tomato cultivation is a vital agricultural practice worldwide, yet it faces significant challenges due to various diseases that adversely affect crop yield and quality. This paper presents a novel tomato disease detection system within an operational framework that leverages an innovative deep learning-based classifier, specifically a Vision Transformer (ViT) integrated with cascaded group attention (CGA) and a modified Focaler-CIoU (Complete Intersection over Union) loss function. The proposed method aims to enhance the accuracy and robustness of disease detection by effectively capturing both local and global contextual information while addressing the challenges of sample imbalance in the dataset. To improve interpretability, we integrate Explainable Artificial Intelligence (XAI) techniques, enabling users to understand the rationale behind the model’s classifications. Additionally, we incorporate a large language model (LLM) to generate comprehensive, context-aware explanations and recommendations based on the identified diseases and other relevant factors, thus bridging the gap between technical analysis and user comprehension. Our evaluation against state-of-the-art deep learning methods, including convolutional neural networks (CNNs) and other transformer-based models, demonstrates that the ViT-CGA model significantly outperforms existing techniques, achieving an overall accuracy of 96.5%, an average precision of 93.9%, an average recall of 96.7%, and an average F1-score of 94.2% for tomato leaf disease classification. The integration of CGA and Focaler-CIoU loss not only contributes to improved model interpretability and stability but also empowers farmers and agricultural stakeholders with actionable insights, fostering informed decision making in disease management. This research advances the field of automated disease detection in crops and provides a practical framework for deploying deep learning solutions in agricultural settings, ultimately supporting sustainable farming practices and enhancing food security. Full article

Sustainable agriculture has been proposed by the United Nations as a key indicator of the Sustainable Development Goals (SDGs). It presents diverse features and rich connotations in the transformation towards modernization. However, for a long time in China, transformations of agricultural modernization have not been the concern of spatial planning, nor the emerging land use requirements of agricultural modernization under the trends of urban–rural integration, such as the application of modern technologies for the mechanization of agricultural production, the coexistence of multiple forms of business entities with agricultural enterprises as the main body, the extension of the industrial chain from the primary to the secondary and the tertiary, and the concentration of industrial spaces, as well as specific land use due to those transformations. This paper constructs an analytical framework of “business entity, business model, production technology, and production space” based on the literature studies and selects eight representative agricultural cases from Beijing, Zhejiang, and Yunnan to conduct field investigations and case studies to reveal the transformative aspects of agricultural modernization in China and its land use requirements, enriching the understanding of modern agriculture from the perspective of spatial planning. This study finds that the transformation of agricultural modernization has generated new requirements for the construction of necessary production facilities, but these requirements cannot be met in terms of land use due to the constraints imposed by China’s current land use regulations. The paper advocates for the development of agricultural parks, the optimization of land use regulations, and the allocation of agricultural land use in spatial planning in line with the trends of agricultural modernization, thus supporting the sustainable development of agriculture. Full article

Deep-learning-based ship detection methods in Synthetic Aperture Radar (SAR) imagery are a current research hotspot. However, these methods rely on high-quality images as input, and in practical applications, SAR images are interfered with by speckle noise, leading to a decrease in image quality and thus affecting detection accuracy. To address this problem, we propose a unified framework for ship detection that incorporates a despeckling module into the object detection network. This integration is designed to enhance the detection performance, even with low-quality SAR images that are affected by speckle noise. Secondly, we propose a Multi-Scale Window Swin Transformer module. This module is adept at improving image quality by effectively capturing both global and local features of the SAR images. Additionally, recognizing the challenges associated with the scarcity of labeled data in practical scenarios, we employ an unlabeled distillation learning method to train our despeckling module. This technique avoids the need for extensive manual labeling and making efficient use of unlabeled data. We have tested the robustness of our method using public SAR datasets, including SSDD and HRSID, as well as a newly constructed dataset, the RSSDD. The results demonstrate that our method not only achieves a state-of-the-art performance but also excels in conditions with low signal-to-noise ratios. Full article

As energy and carbon markets evolve, it has emerged as a prevalent trend for multiple virtual power plants (VPPs) to engage in market trading through coordinated operation. Given that these VPPs belong to diverse stakeholders, a competitive dynamic is shaping up. To strike a balance between the interests of the distribution system operator(DSO) and VPPs, this paper introduces a bi-level energy–carbon coordination model based on the Stackelberg game framework, which consists of an upper-level optimal pricing model for the DSO and a lower-level optimal energy scheduling model for each VPP. Subsequently, the Karush-Kuhn-Tucker (KKT) conditions and the duality theorem of linear programming are applied to transform the bi-level Stackelberg game model into a mixed-integer linear program, allowing for the computation of the model’s global optimal solution using commercial solvers. Finally, a case study is conducted to demonstrate the effectiveness of the proposed model. The simulation results show that the proposed game model effectively optimizes energy and carbon pricing, encourages the active participation of VPPs in electricity and carbon allowance sharing, increases the profitability of DSOs, and reduces the operational costs of VPPs. Full article

Diagnosing bipolar disorder (BD) and schizophrenia (SCH) presents significant challenges due to overlapping symptoms, reliance on subjective assessments, and the late-stage manifestation of many symptoms. Current methods using structural magnetic resonance imaging (sMRI) as input data often fail to provide the objectivity and sensitivity needed for early and accurate diagnosis. sMRI is well known to be capable of detecting anatomical changes, such as reduced gray matter volume in SCH or cortical thickness alterations in BD. However, advanced techniques are required to capture subtle neuroanatomical patterns critical for distinguishing these disorders in sMRI. Deep learning (DL) has emerged as a transformative tool in neuroimaging analysis, offering the ability to automatically extract intricate features from large datasets. Building on its success in other domains, including autism spectrum disorder and Alzheimer’s disease, DL models have demonstrated the potential to detect subtle structural changes in BD and SCH. Recent advancements suggest that DL can outperform traditional statistical methods, offering higher classification accuracy and enabling the differentiation of complex psychiatric disorders. In this context, this study introduces a novel deep learning framework for distinguishing BD and SCH using sMRI data. The model is specifically designed to address subtle neuroanatomical differences, offering three key contributions: (1) a tailored DL model that leverages explainability to extract features that boost psychiatric MRI analysis performance, (2) a comprehensive evaluation of the model’s performance in classifying BD and SCH using both spatial and morphological analysis together with classification metrics, and (3) detailed insights, which are derived from both quantitative (performance metrics) and qualitative analyses (visual observations), into key brain regions most relevant for differentiating these disorders. The results have achieved an accuracy of 78.84%, an area under the curve (AUC) of 83.35%, and a Matthews correlation coefficient (MCC) of 59.10% using the proposed framework. These metrics significantly outperform traditional machine learning models. Furthermore, the proposed method demonstrated superior precision and recall for both BD and SCH, with notable improvements in identifying subtle neuroanatomical patterns. Depending on the acquired result, it can be said that the proposed method enhances the application of DL in psychiatry, paving the way for more objective, non-invasive diagnostic tools with the potential to improve early detection and personalized treatment. Full article

Palm oil mill effluent (POME) is a major contributor to industrial oily wastewater in Malaysia, demanding effective treatment solutions. This study explores the potential of esterified kenaf core (EKC) fiber as an oil adsorbent for oil removal from POME, optimized using a full central composite design (CCD) within the response surface methodology (RSM) framework. The optimum conditions achieved 76% oil removal efficiency, with a 1:0.5 ratio of mercerized kenaf core to stearic acid (MKC:SA), 15 wt% of catalyst, and 1 h reflux time during the esterification process. The regression model exhibited strong predictive capability, with a significant quadratic correlation and an R² value of 0.94. The Fourier transform infrared (FTIR) spectroscopy revealed the existence of ester functional groups characterized by significant hydrophobicity and a decrease in hydroxyl groups, indicating the chemical changes of EKC. Moreover, the scanning electron microscopy (SEM) research demonstrated structural alterations in EKC, including heightened surface roughness, fibrillation, and pore development, which improved oil adhesion relative to raw kenaf core (RKC). These findings indicate that EKC provides an effective, environmentally sustainable solution for managing oil wastewater issues in the palm oil sector, facilitating enhanced ecological sustainability and resource management. Full article

Islam in the history and modernization of the Maldives demonstrates an intrinsic tension, serving both as the foundational cornerstone of national identity and as the source of social conflict and political division. On the one hand, the narrative of being a “100% Muslim nation” has shaped a highly unified national identity, achieving legalization and institutionalization within the power structure and becoming a critical pillar of state legitimacy and social integration. On the other hand, the politicization and homogenization of religion have weakened social inclusivity, exacerbating religious extremism and social tensions in the face of globalization. The Maldivian experience not only reveals how religion undergoes self-transformation through power negotiation and legal reform but also reflects how the tension between traditional religion and the modern state shapes the dynamic framework of national governance. As a microcosm of global religious renaissance and geopolitical interaction, the Maldives offers a vital theoretical perspective and practical insights for understanding the complex interplay among religion, power, and national identity. Full article

The characterization of territory in Peru’s Andean regions faces significant challenges due to a lack of comprehensive methodologies capable of addressing the complexity of these contexts. This research aims to bridge that gap by developing a methodology that integrates the CFS-RAI Principles with the Working with People (WWP) model to provide a detailed, contextualized framework for territorial analysis. The framework leverages the CFS-RAI Principles’ focus on sustainable agriculture and the WWP model’s proven effectiveness in fostering social transformation in Aymara communities in southern Peru. The research centers on Paucar del Sara Sara, a province in Ayacucho, Peru, characterized by a Human Development Index (HDI) of 0.42 and significant development potential rooted in opportunities for organic agriculture and collaboration with the mining sector. Employing geographic analysis and qualitative methods, this study draws comparisons with existing literature and presents insights from the case study to develop a matrix of key variables for territorial analysis in Andean regions. Additionally, the research introduces a methodology for defining mining companies’ areas of influence while addressing prevalent socioeconomic challenges in these territories. Full article

Although image captioning has gained remarkable interest, privacy concerns are raised because it relies heavily on images, and there is a risk of exposing sensitive information in the image data. In this study, a privacy-preserving image captioning framework that leverages partial encryption using Double Random Phase Encoding (DRPE) and deep learning is proposed to address privacy concerns. Unlike previous methods that rely on full encryption or masking, our approach involves encrypting sensitive regions of the image while preserving the image’s overall structure and context. Partial encryption ensures that the sensitive regions’ information is preserved instead of lost by masking it with a black or gray box. It also allows the model to process both encrypted and unencrypted regions, which could be problematic for models with fully encrypted images. Our framework follows an encoder–decoder architecture where a dual-stream encoder based on ResNet50 extracts features from the partially encrypted images, and a transformer architecture is employed in the decoder to generate captions from these features. We utilize the Flickr8k dataset and encrypt the sensitive regions using DRPE. The partially encrypted images are then fed to the dual-stream encoder, which processes the real and imaginary parts of the encrypted regions separately for effective feature extraction. Our model is evaluated using standard metrics and compared with models trained on the original images. Our results demonstrate that our method achieves comparable performance to models trained on original and masked images and outperforms models trained on fully encrypted data, thus verifying the feasibility of partial encryption in privacy-preserving image captioning. Full article