Search Results (1,790)

Low-voltage distribution area is the “last kilometer” connecting the distribution network and users, and the traditional distribution system is difficult to digitally manage in the low-voltage area, resulting in untimely and imprecise handling of voltage overruns, short-circuit outages, and other abnormal problems. With the deployment of smart meters, new sensors, smart gateways, and other devices in distribution areas, digital intelligent monitoring and management based on digital twins in LV distribution areas has gradually become the focus of distribution network research. In view of the profound changes that are taking place in the low-voltage distribution area, this paper first summarizes the characteristics and shortcomings of the existing digital twin research in the low-voltage distribution area, then puts forward the ‘6C’ development goals for the digital transformation of the low-voltage distribution area, introduces the practice work of Guangxi Power Grid Corporation around the ‘6C’ development goals in the low-voltage distribution area. Finally, the future research work of the ‘6C’ development goals for the digital transformation of the low-voltage distribution area is promising. Full article

The growing importance of microgrids—linking buildings with distributed energy resources and storage—is driving the evolution of Smart Local Energy Systems (SLESs). These systems require advanced modeling and simulations to address growing complexity, decentralization, and interoperability. This review presents an analysis of commonly used environments and methods applied in the design and operation of SLESs. Particular emphasis is placed on their capabilities for multi-domain integration, predictive control, and smart automation. A novel contribution is the identification of Closed Ecological Systems (CES) and Life Support Systems (LSSs)—fully or semi-isolated environments designed to sustain human life through autonomous recycling of air, water, and other resources—as promising new application domains for SLES technologies. This review explores how concepts developed for building and energy systems, such as demand-side management, IoT-based monitoring, and edge computing, can be adapted to CES/LSS contexts, which demand isolation, autonomy, and high reliability. Challenges related to model integration, simulation scalability, and the bidirectional transfer of technologies and modeling between Earth-based and space systems are discussed. This paper concludes with a SWOT analysis and a roadmap for future research. This work lays the foundation for developing sustainable, intelligent, and autonomous energy infrastructures—both terrestrial and extraterrestrial. Full article

Named Data Networking (NDN) is identified as a significant shift within the information-centric networking (ICN) perspective that avoids our current IP-based infrastructures by retrieving data based on its name rather than where the host is placed. This shift in paradigm is especially beneficial in Internet of Things (IoT) settings because information sharing is a critical challenge, as millions of IoT items create enormous traffic. Content caching in the network is another key characteristic of NDN used in IoT, which enables data storing within the network and provides IoT devices with the opportunity to address nearby caching nodes to gain the intended content, which, in its turn, will minimize latency as well as bandwidth consumption. However, effective caching solutions must be developed since cache management is made difficult by the constant shifting of IoT networks and the constrained capabilities of IoT devices. This paper gives an overview of cache strategies in NDN-based IoT systems. It emphasizes six strategy types: popularity-based, freshness-aware, collaborative, hybrid, probabilistic, and machine learning-based, evaluating their performances in terms of demands like content preference, cache update, and power consumption. By analyzing various caching policies and their performance characteristics, this paper provides valuable insights for researchers and practitioners developing caching strategies in NDN-based IoT networks. Full article

Students with autism are more likely to be victims of bullying. There are many factors that play a critical role in this. One of these is the school context. This study explores the experiences of bullying among students with autism based on school type. Semi-structured interviews were conducted with children diagnosed with ASD from different regions of Greece. The 16 children who participated in the study were aged between 12 and 15. Four attended special schools, two attended mainstream schools with educational support, and the remainder attended mainstream schools with support from special classes. The interviews were analysed using thematic analysis, revealing five main themes: (1) school experience, (2) friendship, (3) school bullying management and reactions, (4) where school bullying takes place, and (5) emotions arising from school bullying. The findings suggest that a safe and protective school environment can reduce bullying in children with ASD. These findings could contribute to the development of policies and intervention programmes aimed at addressing bullying in students with ASD. Full article

Deployment of Unmanned Aerial Vehicles (UAVs) continues to expand rapidly across a wide range of applications, including environmental monitoring, precision agriculture, and disaster response. Despite their increasing ubiquity, UAVs remain inherently vulnerable to security threats due to resource-constrained hardware, energy limitations, and reliance on open wireless communication channels. These factors render traditional cryptographic solutions impractical, thereby necessitating the development of lightweight, UAV-specific security mechanisms. This review article presents a comprehensive analysis of lightweight encryption techniques and key management strategies designed for energy-efficient and secure UAV communication. Special emphasis is placed on recent cryptographic advancements, including the adoption of the ASCON family of ciphers and the emergence of post-quantum algorithms that can secure UAV networks against future quantum threats. Key management techniques such as blockchain-based decentralized key exchange, Physical Unclonable Function (PUF)-based authentication, and hierarchical clustering schemes are evaluated for their performance and scalability. To ensure comprehensive protection, this review introduces a multilayer security framework addressing vulnerabilities from the physical to the application layer. Comparative analysis of lightweight cryptographic algorithms and multiple key distribution approaches is conducted based on energy consumption, latency, memory usage, and deployment feasibility in dynamic aerial environments. Unlike design- or implementation-focused studies, this work synthesizes existing literature across six interconnected security dimensions to provide an integrative foundation. Our review also identifies key research challenges, including secure and efficient rekeying during flight, resilience to cross-layer attacks, and the need for standardized frameworks supporting post-quantum cryptography in UAV swarms. By highlighting current advancements and research gaps, this study aims to guide future efforts in developing secure communication architectures tailored to the unique operational constraints of UAV networks. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

Background: Melanoma is one of the most aggressive types of skin cancer. Its diagnosis appears to be challenging due to morphological similarities to benign melanocytic lesions. Even though histopathological evaluation is the diagnostic gold standard, immunohistochemistry (IHC) proves to be useful in challenging cases. Preferentially Expressed Antigen in Melanoma (PRAME) has emerged as a promising diagnostic, prognostic, and therapeutic marker in melanoma. Methods: This review critically examines the role of PRAME across clinical domains. It presents an evaluation of PRAME’s diagnostic utility in differentiating melanomas from benign nevi, its prognostic significance across melanoma subtypes, and therapeutic applications in emerging immunotherapy strategies. An extensive analysis of the current literature was conducted, with a focus on PRAME expression patterns in melanocytic lesions and various malignancies, along with its integration into IHC protocols and investigational therapies. Results: PRAME demonstrates high specificity and sensitivity in distinguishing melanoma from benign melanocytic proliferations, particularly in challenging subtypes such as acral, mucosal, and spitzoid lesions. Its overexpression correlates with poor prognosis in numerous malignancies. Therapeutically, PRAME’s HLA class I presentation enables T-cell-based targeting. Early-phase trials show promising results using PRAME-directed TCR therapies and bispecific ImmTAC agents. However, immune evasion mechanisms (i.e., heterogeneous antigen expression, immune suppression in the tumor microenvironment, and HLA downregulation) pose significant challenges to therapy. Conclusions: PRAME is a valuable biomarker for melanoma diagnosis and a promising target for immunotherapy. Its selective expression in malignancies supports its clinical utility in diagnostic precision, prognostic assessment, and precision oncology. Ongoing research aimed at overcoming immunological barriers will be essential for optimizing PRAME-directed therapies and establishing their place in the personalized management of melanoma. Full article

Customer segmentation is a foundation of Customer Relationship Management (CRM) and is widely regarded as a key to business development success. As the principles of sustainable development become increasingly central to business strategy, it is necessary that social, environmental, and economic considerations be incorporated into customer segmentation—even in regulated markets such as the postal market. The article develops and applies a three-dimensional (3D) segmentation model of business customers in the Slovak postal market, utilizing cluster analysis within STATISTICA analytical software for operationalization of the segmentation criteria. The 3D model reacts to the three pillars of sustainable development and is verified under real conditions at Slovak Post, plc. By adopting a systems perspective, the research places customer segmentation as an integral component of the entire socio-technical system, emphasizing the interrelatedness of organizational, social, and environmental considerations. The study illustrates how a systems-based approach to segmentation enables postal operators to uncover key customer segments, optimize resource allocation, and support competitiveness and sustainability goals. The practical applicability of the model is illustrated by its potential for application in other regulated service industries, providing a solid framework for sustainable customer management and strategic decision-making in complex environments. The research underscores the critical role of systems thinking in addressing the complex challenges of sustainable development in regulated industries. Full article

From the 17th century, European leisure travellers sought novel experiences, places and landscapes; they explored them within the context of contemporary, but temporally changing, social norms. Amongst travellers’ earliest motivations were reportage, curiosity and recuperation in managed landscapes. From the late 18th century, images in art galleries and then guidebooks directed leisure travellers into ‘wild’ places. Supporting and part-driving these developments were travel and antiquarian publications. That normalisation of ‘wild places’ exploration coincided with natural history’s popularisation. From the early 19th century, geosites were recognised, scientifically described, and popularised through a range of publications; this marked the beginning of geotourism. This can be contextualised within the rise in resort-based coastal tourism. These various themes are explored in relation to ‘Coastal Southern England’, an important tourism region from the early-18th century. By the Great War’s (1914–1918) close, its tourism patterns and nature, recognisable in present-day offerings, were established. Its development as a geotourism region can be conceptualised through the ‘travellers’ gaze’ and ‘adapted comfort zone’ models. Early geotourism literature and artistic representations, along with their creators’ biographies, could underpin modern geo-interpretation, of which some exemplars are given. General conclusions are drawn and future research suggested. Full article

The vulnerability of older adults in rural mountainous regions presents a critical challenge for sustainable development, particularly in the context of smart city and digital town construction. In this study, we develop a comprehensive analytical framework and evaluation index to assess Vulnerability to Elderly Poverty (VEP) and adaptive capacity, with a focus on its integration with smart infrastructure and age-friendly rural built environment strategies. Using Shizhu County in Chongqing, China, as a case study, we explore spatial disparities in VEP and apply quantile regression to identify the driving factors of adaptability. Our findings indicate that subsidy-dependent, middle-aged, and empty-nest older adults are the most vulnerable groups, with limited capacity to adapt to changing environments. A geographically alternating “high–low–high–low” VEP pattern reflects uneven development in infrastructure, accessibility, and public service construction. These disparities highlight the need for targeted planning and building interventions in rural settings. The key factors influencing adaptability include individual attributes, intergenerational support, and macro-level conditions such as policy design and digital infrastructure deployment. The integration of aging-friendly building strategies, smart infrastructure, and digital tools significantly enhances older adults’ resilience and social inclusion. Based on our results, we propose four adaptation models for aging populations in rural areas, emphasizing the construction of inclusive digital infrastructure, aging-sensitive building design, and community-based support systems. Strategic recommendations include promoting digital literacy through built environment interventions, enhancing intergenerational living arrangements, and embedding elderly-responsive features into smart construction planning. This research offers new insights into construction management practices that support aging in place and poverty alleviation through inclusive and resilient built environments. Full article

There are still relatively few applications that serve the function of a traditional tactile map, allowing visually impaired individuals to explore a digital map by sliding their fingers across it. Moreover, existing technological solutions either lack a spatial learning mode or provide only limited functionality, focusing primarily on navigating to a selected destination. To address these gaps, the authors have proposed an original concept for an indoor mobile application that enables map exploration by sliding a finger across the smartphone screen, using audio spatial descriptions as the primary medium for conveying information. The spatial descriptions are hierarchical and contextual, focusing on anchoring them in space and indicating their extent of influence. The basis for data management and analysis is GIS technology. The application is designed to support spatial orientation during user interaction with the digital map. The research emphasis was on creating an effective cartographic communication message, utilizing voice-based delivery of spatial information stored in a virtual building model (within a database) and tags placed in real-world buildings. Techniques such as Text-to-Speech, TalkBack, QRCode technologies were employed to achieve this. Preliminary tests conducted with both blind and sighted people demonstrated the usefulness of the proposed concept. The proposed solution supporting people with disabilities can also be useful and attractive to all users of navigation applications and may affect the development of such applications. Full article

The outlook for biobased plastics in packaging applications is increasingly promising, driven by a combination of environmental advantages, technological innovation, and shifting market dynamics. Derived from renewable biological resources, these materials offer compelling benefits over conventional fossil-based plastics. They can substantially reduce greenhouse gas emissions, are often recyclable or biodegradable, and, in some cases, require less energy to produce. These characteristics position biobased plastics as a key solution to urgent environmental challenges, particularly those related to climate change and resource scarcity. Biobased plastics also demonstrate remarkable versatility. Their applications range from high-performance barrier layers in multilayer packaging to thermoformed containers, textile fibers, and lightweight plastic bags. Notably, all major fossil-based packaging applications can be substituted with biobased alternatives. This adaptability enhances their commercial viability across diverse sectors, including food and beverage, pharmaceutical, cosmetics, agriculture, textiles, and consumer goods. Several factors are accelerating growth in this sector. These include the increasing urgency of climate action, the innovation potential of biobased materials, and expanding government support through funding and regulatory initiatives. At the same time, consumer demand is shifting toward sustainable products, and companies are aligning their strategies with environmental, social, and governance (ESG) goals—further boosting market momentum. However, significant challenges remain. High production costs, limited economies of scale, and the capital-intensive nature of scaling biobased processes present economic hurdles. The absence of harmonized policies and standards across regions, along with underdeveloped end-of-life infrastructure, impedes effective waste management and recycling. Additionally, consumer confusion around the disposal of biobased plastics—particularly those labeled as biodegradable or compostable—can lead to contamination in recycling streams. Overcoming these barriers will require a coordinated, multifaceted approach. Key actions include investing in infrastructure, advancing technological innovation, supporting research and development, and establishing clear, consistent regulatory frameworks. Public procurement policies, eco-labeling schemes, and incentives for low-carbon products can also play a pivotal role in accelerating adoption. With the right support mechanisms in place, biobased plastics have the potential to become a cornerstone of a sustainable, circular economy. Full article

This study introduces a novel deep learning architecture for vessel trajectory prediction based on Automatic Identification System (AIS) data. The motivation stems from the increasing importance of maritime transport and the need for intelligent solutions to enhance safety and efficiency in congested waterways—particularly with respect to collision avoidance and real-time traffic management. Special emphasis is placed on river navigation scenarios that limit maneuverability with the demand of higher forecasting precision than open-sea navigation. To address these challenges, we propose a Principal Component Analysis (PCA)-driven layered attention mechanism integrated within an encoder–decoder model to reduce redundancy and enhance the representation of spatiotemporal features, allowing the layered attention modules to focus more effectively on salient positional and movement patterns across multiple time steps. This dual-level integration offers a deeper contextual understanding of vessel dynamics. A carefully designed evaluation framework with statistical hypothesis testing demonstrates the superiority of the proposed approach. The model achieved a mean positional error of 0.0171 nautical miles (SD: 0.0035), with a minimum error of 0.0006 nautical miles, outperforming existing benchmarks. These results confirm that our PCA-enhanced attention mechanism significantly reduces prediction errors, offering a promising pathway toward safer and smarter maritime navigation, particularly in traffic-critical riverine systems. While the current evaluation focuses on short-term horizons in a single river section, the methodology can be extended to complex environments such as congested ports or multi-ship interactions and to medium-term or long-term forecasting to further enhance operational applicability and generalizability. Full article

Background/Objectives: Accurate prediction of pediatric appendicitis diagnosis, management, and severity is critical for clinical decision-making. We aimed to evaluate the predictive performance of a wide range of machine learning models, combined with various feature selection techniques, on a pediatric appendicitis dataset. A particular focus was placed on the role of ultrasound (US) image-descriptive features in model performance and explainability. Methods: We conducted a retrospective cohort study on a dataset of 781 pediatric patients aged 0–18 presenting to Children’s Hospital St. Hedwig in Regensburg, Germany, between January 2016 and February 2023. We developed and validated predictive models; machine learning algorithms included the random forest, logistic regression, stochastic gradient descent, and the light gradient boosting machine (LGBM). These were paired exhaustively with feature selection methods spanning filter-based (association and prediction), embedded (LGBM and linear), and a novel redundancy-aware step-up wrapper approach. We employed a machine learning benchmarking study design where AI models were trained to predict diagnosis, management, and severity outcomes, both with and without US image-descriptive features, and evaluated on held-out testing samples. Model performance was assessed using overall accuracy and area under the receiver operating characteristic curve (AUROC). A deep learner optimized for tabular data, GANDALF, was also evaluated in these applications. Results: US features significantly improved diagnostic accuracy, supporting their use in reducing model bias. However, they were not essential for maximizing accuracy in predicting management or severity. In summary, our best-performing models were, for diagnosis, the random forest with embedded LGBM feature selection (98.1% accuracy, AUROC: 0.993), for management, the random forest without feature selection (93.9% accuracy, AUROC: 0.980), and for severity, the LGBM with filter-based association feature selection (90.1% accuracy, AUROC: 0.931). Conclusions: Our results demonstrate that high-performing, interpretable machine learning models can predict key clinical outcomes in pediatric appendicitis. US image features improve diagnostic accuracy but are not critical for predicting management or severity. Full article

This study introduces the concept of symmetry as a fundamental theoretical perspective for understanding the linguistic structure of cyberbullying texts. It posits that such texts often exhibit symmetry breaking between surface-level language forms and underlying semantic intent. This structural-semantic asymmetry increases the complexity of the recognition task and places higher demands on the semantic modeling capabilities of detection systems. With the rapid growth of social media, the covert and harmful nature of cyberbullying speech has become increasingly prominent, posing serious challenges to public opinion management and public safety. While mainstream approaches to cyberbullying detection—typically based on traditional deep learning models or pre-trained language models—have achieved some progress, they still struggle with low accuracy, poor generalization, and weak interpretability when handling implicit, semantically complex, or borderline expressions. To address these challenges, this paper proposes a cyberbullying detection method that combines LoRA-based fine-tuning with Small-Scale Hard-Sample Adaptive Training (S-HAT), leveraging a large language model framework based on Meta-Llama-3-8B-Instruct. The method employs prompt-based techniques to identify inference failures and integrates model-generated reasoning paths for lightweight fine-tuning. This enhances the model’s ability to capture and represent semantic asymmetry in cyberbullying texts. Experiments conducted on the ToxiCN dataset demonstrate that the S-HAT approach achieves a precision of 84.1% using only 24 hard samples—significantly outperforming baseline models such as BERT and RoBERTa. The proposed method not only improves recognition accuracy but also enhances model interpretability and deployment efficiency, offering a practical and intelligent solution for cyberbullying mitigation. Full article