Search Results (8,009)

As the proportion of renewable power continues to increase, its inherent intermittency and volatility pose serious challenges to the security and stability of power systems. Scenario generation technology serves as a key tool supporting decision-making methods such as stochastic optimization and risk analysis. By generating representative power output scenarios, it can effectively characterize the uncertainty of renewable power output. This paper systematically reviews mainstream methods for the scenario generation of renewable power output, categorizing them into two major classes: sampling-based methods and model-based methods. Among them, sampling-based methods include Monte Carlo sampling, Latin hypercube sampling (LHS), Markov chains (MCs), and Copula functions. Model-based methods encompass artificial neural networks (ANNs), long short-term memory networks (LSTMs), autoregressive moving average models (ARMAs), generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models and transformer-based models. This paper elaborates on the principles and characteristics of each type of method. Moreover, scenario quality is evaluated from three dimensions: output-based metrics for numerical accuracy, distribution-based metrics for statistical consistency, and event-based metrics for key operational event representation. The current research challenges and future research directions are also summarized to provide a reference for modeling the uncertainty of renewable output. Full article

Background: Obesity is a chronic metabolic disease that has emerged as a major global public health concern. Obesity complications refer to a range of metabolic, neurological and behavioral disorders. Complex interaction mechanisms exist between obesity and the brain, including neuroendocrine regulation, center inflammatory responses, the gut–brain axis, and obesity-related cognitive impairment. Polyphenols are naturally occurring bioactive compounds widely found in plants. Recent research indicates that polyphenols may modulate the brain through multiple pathways, thereby ameliorating obesity complications. However, no data set available to summarize neuroregulatory effects of dietary polyphenols on obesity complication. Methods: The latest data available were collected to review research progress focusing on neuroregulatory roles of polyphenols on obesity complication. Results: This review summarizes the interaction between obesity and the brain and further explores the effects of polyphenols on obesity-related neurological disorders, with particular emphasis on their roles in appetite regulation, central neuroinflammation, brain leptin and insulin resistance, gut–brain axis modulation, and cognitive improvement. Finally, future perspectives are discussed. Conclusions: This paper may provide a new theoretical support and research direction for the potential of polyphenols against obesity-related neurological complications. Full article

Ultrasound (US) imaging is a widely used diagnostic method in clinics. Real-time-generated US images are used for rapid diagnosis without harm to patients. The quality of US imaging highly depends on the skill of the physician due to the differences among physicians. Techniques for autonomous robotic ultrasound (AU-RUS) acquisitions are expected to become an effective means to improve the level of US diagnosis, reduce the workload of physicians, and improve the standardization of US imaging quality. This paper aims to summarize the current research status of techniques for AU-RUS acquisitions, and to discuss the research trends and challenges regarding related technologies. Firstly, the techniques for AU-RUS acquisitions and systems are outlined. The techniques for teleoperated or autonomous US acquisitions are briefly discussed. Representative RUS acquisition systems are introduced. Then, the current research status of AU-RUS acquisitions is reviewed from four research directions: force sensitivity and control, scanning path-planning and positioning, US treatment guidance, and US image processing technology and quality assessment optimization. This review provides a decision-oriented autonomy perspective by mapping typical methods to workflow components across the stages of perception, decision-making, and execution. We identify major deployment bottlenecks, including safety-verifiable autonomy and failure recovery, motion compensation under deformation, and the lack of standardized, clinically meaningful US image quality metrics. Finally, the shortcomings of current research are summarized and analyzed, and the research trends and challenges for AU-RUS acquisitions are prospected. Full article

The digital transformation of oil and gas pipeline networks has generated substantial volumes of unstructured maintenance documentation from communication systems, creating an urgent need for automated summarization to improve operational efficiency. However, domain-specific text summarization for pipeline communication maintenance remains challenging due to scarce labeled data and the high computational cost of fine-tuning large pretrained models. Parameter-efficient fine-tuning alleviates this issue, but its effectiveness strongly depends on appropriate hyperparameter selection. This paper proposes a unified framework that combines weight-decomposed low-rank adaptation with an enhanced Artificial Bee Colony algorithm for automated hyperparameter optimization. The enhanced algorithm addresses two specific limitations of the standard Artificial Bee Colony algorithm: uninformed random initialization that ignores promising regions, and premature abandonment of stagnated solutions that discards partially useful search directions. These two components represent principled design choices, each targeting a distinct bottleneck in applying swarm intelligence search to high-dimensional mixed-type hyperparameter spaces. The method introduces a hybrid initialization strategy to exploit prior knowledge and a stagnation-guided local search mechanism to refine stagnated solutions instead of discarding them, achieving a better balance between exploration and exploitation. Experimental results on a public Chinese summarization benchmark and an industrial oil and gas pipeline communication maintenance corpus show that the proposed approach consistently outperforms full fine-tuning, manually tuned parameter-efficient methods, and several evolutionary optimization baselines in terms of ROUGE metrics. The automated search introduces modest additional computational overhead compared to manual tuning while eliminating expert-dependent hyperparameter configuration and achieving consistent performance gains across both datasets. Overall, the proposed framework provides an efficient and robust solution for adapting large language models to specialized summarization tasks in the context of pipeline communication system maintenance. Full article

The FAO Irrigation and Drainage Paper 56, which was first published in 1998, has been widely recognized as a comprehensive guidebook for estimating crop evapotranspiration and calculating crop water requirements under various conditions, supporting the efficient management of water resources in agriculture. Over the past twenty-eight years, science and technology have significantly evolved in agricultural productivity and water resource mobilization, use, and management, as well as in research advances, data availability and management, and modeling capabilities and uses. However, these improvements have come against a backdrop of increasingly pressing challenges, especially those posed by climate change and water scarcity. Thus, considering all recent advances in knowledge, an updated version (FAO56 Rev.1) of that guidebook was recently released. The current article summarizes and highlights the main features and innovations that the revision has incorporated. Full article

Dental implants are a popular clinical procedure for the rehabilitation of fully and partially ede ntulous patients. There is long-term evidence that implant-supported dental prostheses represent a predictable treatment for replacing missing teeth. However, several types of complications may arise, which can compromise implant treatment outcome. Peri-implant disease is a growing biological complication, consisting of a progressive loss of supporting bone, associated with microbial biofilm and clinical inflammation. It represents a concern for clinicians and patients, having a negative impact on quality of life. This narrative review aimed at summarize the current knowledge on etiology, epidemiology, risk factors, and pathogenesis of peri-implant disease. It also focused on the diagnostic potential of active matrix metalloproteinase-8 (aMMP-8) in peri-implant sulcular fluid for assessing the status of peri-implant tissues and the risk of developing peri-implantitis. A literature search was conducted in PubMed and Scopus databases using search terms like: peri-implantitis, peri-implant biomarkers, aMMP-8, implant maintenance, risk assessment. Clinical studies, systematic reviews, meta-analysis and consensus papers published up to June 2025 were considered. Finally, based on the main factors involved in the onset and progression of peri-implant disease, a new protocol was conceived for determining the optimal implant maintenance scheduling for individual patients. The Biomarker-Guided Implant Maintenance (BGIM) protocol considers a few key parameters, among which aMMP-8 level, and proposes three categories associated with different levels of risk for peri-implantitis. The higher the risk, the more frequently a patient should undergo professional maintenance, to prevent peri-implant disease, with potential favorable effects on implant longevity. The proposed BGIM protocol, that requires prospective validation, represents a structured and clinically applicable biomarker-driven framework for individualizing implant maintenance scheduling by integrating real-time chairside quantification of aMMP-8 with established patient-related risk factors. Full article

The ability to autonomously navigate and explore complex 3D environments in a purposeful manner, while integrating visual perception with natural language interaction in a human-like way, represents a longstanding research objective in Artificial Intelligence (AI) and embodied cognition. Vision-Language Navigation (VLN) has evolved from geometry-driven to semantics-driven and, more recently, knowledge-driven approaches. With the introduction of Large Language Models (LLMs) and Vision-Language Models (VLMs), recent methods have achieved substantial improvements in instruction interpretation, cross-modal alignment, and reasoning-based planning. However, existing surveys primarily focus on traditional VLN settings and offer limited coverage of LLM-based VLN, particularly in relation to Sim2Real transfer and edge-oriented deployment. This paper presents a structured review of LLM-enabled VLN, covering four core components: instruction understanding, environment perception, high-level planning, and low-level control. Edge deployment and implementation requirements, datasets, and evaluation protocols are summarized, along with an analysis of task evolution from path-following to goal-oriented and demand-driven navigation. Key challenges, including reasoning complexity, spatial cognition, real-time efficiency, robustness, and Sim2Real adaptation, are examined. Future research directions, such as knowledge-enhanced navigation, multimodal integration, and world-model-based frameworks, are discussed. Overall, LLM-driven VLN is progressing toward deeper cognitive integration, supporting the development of more explainable, generalizable, and deployable embodied navigation systems. Full article

In this paper, we present an extensive evaluation of tabular data distillation methods for downstream classification and regression tasks. Our analysis considers multiple distillation approaches that are problem-type independent (i.e., unsupervised). For downstream learners, we focus on non-neural models such as Random Forest, XGBoost, and Support Vector Machines, as our goal is to evaluate the quality of the distilled data independently of the learner. The evaluation is conducted on 17 classification and nine regression problems. Our findings can be summarized as follows: (1) in all cases, applying a distillation method leads to a decrease in performance compared to the baseline; (2) overall, coreset-based methods are the most effective, with performance losses that are minimal—ranging from around 3% in classification accuracy or regression correlation to, in some cases, being negligible; (3) performance loss is moderately correlated with dataset tailness, measured as the proportion of outliers; (4) all distillation methods alter dataset consistency, narrowing the range of hyperparameter values that yield good performance; and (5) the Coreset Leverage Score remains fast, regardless of the size of the original set and of the distilled set. Full article

Background/Objectives: Paediatric renal tumours, particularly Wilms tumours, have good survival outcomes following multimodal therapy; however, long-term morbidity related to nephrectomy and adjuvant treatment remains a major concern. As treatment paradigms increasingly prioritize nephron preservation and minimization of late effects, there is growing interest in technologies that can enhance intraoperative precision. Methods: A scoping review following the PRISMA guidelines was performed. We analysed articles on fluorescence for childhood renal tumours on 1 November 2025. Case reports, opinion articles, and narrative reviews were excluded. An Ovid Medline search with search terms “Kidney neoplasm” AND “Fluorescent Dyes”, along with a Cochrane trials registry search for “kidney” AND “neoplasm” AND “Fluorescent Dye”, was performed, along with a hand search of citations. Results: The Ovid Medline search yielded 21 results, and the Cochrane trials search gave 4 results. Following review, five papers were included, of which one was an ex vivo study and one was a randomised, controlled trial that is currently recruiting. Conclusions: There is a lack of evidence around the use of near-infrared fluorescence in paediatric renal tumour surgery. This review summarizes the key current findings and future perspectives. Full article

It is of great significance in real time to accurately monitor the internal state parameters of lithium-ion batteries toy ensure the safety, reliability and lasting efficiency of battery energy storage systems. The battery management system can monitor the working state, prevent overcharge or overdischarge, and make the working process more safe and reliable. The state of charge (SOC) is one of the most important indicators to monitor a working battery, and its accurate estimation is the most important work at present. SOC cannot be measured directly, so the state estimation problem of batteries is transformed into a state estimation problem of time-varying nonlinear systems, the core of which is how to obtain a more accurate and reasonable state estimation value in real time. This paper introduces the definition of battery charge state, summarizes common estimation methods and disadvantages of the ampere-hour integration method and open-circuit voltage method, and finally points out the future development direction of battery charge state estimation methods. Full article

Econophysics is an interdisciplinary field that applies physics concepts to economic and financial systems. By utilizing tools such as statistical physics, including fractal analysis and entropy measures, econophysics helps model the complex and non-linear dynamics of equity markets. This paper examines the intrinsic dynamics and regularity in information content in green finance markets (carbon, clean energy, and sustainability markets) by means of range scale analysis (R/S), detrended fluctuation analysis (DFA), fractionally integrated generalized auto-regressive conditionally heteroskedastic (FIGARCH) process, and Shannon entropy (SE). The empirical results can be summarized as follows. First, prices in all markets are persistent; however, returns are likely random as estimated Hurst exponents are close to 0.5. Second, the FIGARCH process shows that volatility series in carbon and sustainability markets are persistent, whilst volatility in clean energy is anti-persistent. Third, in carbon and sustainability markets, entropy is high in prices compared to returns and volatility series. On the contrary, the clean energy market shows lower entropy for prices than for returns and volatility. In sum, it is concluded that price and volatility series are predictable, whilst return series are not. Finally, based on a rolling window framework, it is concluded that the COVID-19 pandemic and the Russia–Ukraine war have altered long memory and randomness in all three green finance markets. Full article

Recent breakthroughs in large language models have made it feasible to effectively summarize cross-lingual dialogue information, proving essential for the global communication context. However, existing methodologies encounter difficulties in maintaining factual consistency across multiple dialogue exchanges and lack clear explanations of the summarization process. This paper presents a novel factually consistent prompting technology with large language models to address these challenges in cross-lingual dialogue summarization. First, we propose a factual replacement mechanism to enhance information analysis by incorporating noise information into summarization candidates. We adopt a self-guidance framework to enforce factual consistency, enhancing information flow tracking in cross-lingual hybrid dialogue scenarios with the assistance of GPT-based models. Furthermore, we introduce a view-aware chain-of-thought-driven architecture to improve the interpretability and transparency of the cross-lingual dialogue summarization process. Comprehensive experimental evaluations on cross-lingual summarization tasks, spanning English, French, Spanish, Russian, Chinese, and Arabic, and hybrid cross-lingual tasks substantiate that the proposed model achieves superior performance relative to state-of-the-art baselines. Full article

Combine harvesters are core modern grain production equipment with high reliability, critical for food security. Yet their metal parts suffer severe grain-induced wear during operation, directly reducing efficiency, increasing grain loss, and raising maintenance costs and environmental burdens. This paper clarifies the grain-induced wear source characteristics and the dominant mechanisms and hazards for combine harvester metal surfaces, as well as summarizes the research progress of four key protection strategies: wear-resistant materials, surface engineering, structural and parameter optimization, and maintenance and remanufacturing. Based on the latest research data, the working principles, performance advantages and application scenarios of various protective technologies were analyzed. Current research faces several challenges: insufficient systematic wear data for multiple crops, unclear multi-factor coupled wear mechanisms, limited low-cost and long-lasting protective technologies, and the absence of online wear monitoring techniques. Finally, the directions for future research focus, such as the systematic research on the wear characteristics of multiple crops, the deepening of the wear mechanism of multi-factor coupling, the development of green, low-cost and long-term protection technologies, and the development of online wear monitoring and active control systems, are explored, providing theoretical support and technical reference for the transformation of wear control in combine harvesters, from passive maintenance to active protection throughout the entire life cycle. Such future work supports the high-quality development of agricultural mechanization and ensures food security. Full article

Background/Objectives: Adequate cardiopulmonary resuscitation (CPR), defibrillation, and treatment of reversible causes are essential for improving the survival of patients suffering from out-of-hospital cardiac arrests (OHCAs). The Advanced Life Support (ALS) algorithm includes reversible causes for cardiac arrest. This study aimed to develop an interactive mobile checklist to identify reversible causes of OHCA (REBECCA) and evaluate their usability and usefulness among emergency physicians. Methods: This mixed-methods study was conducted at the Emergency Medical Service Vienna, Austria. All participants were emergency physicians from the Medical University of Vienna. An interactive mobile checklist was developed using a participatory design approach involving a focus group of 10 emergency physicians. Usability and applicability were assessed using structured questionnaires. Descriptive statistics were used to summarize participant characteristics and evaluation outcomes. Results: Among the included participants, 70% were specialists with a median prehospital experience of 2.0 (1.0–4.3) years. Although most participants were confident about their level of professional experience with OHCA, 85% still found the checklist to be helpful. The majority of the participants preferred the digital checklist over the paper-based checklist and appreciated its integration with the point-of-care ultrasound (POCUS) application. Although the participants did not communicate a significant need for further details on most causes, a small majority favored more information on intoxication and electrolyte disorders. Conclusions: The majority of the included emergency physicians found the REBECCA checklist helpful regardless of training level, whereas almost no physician needed further detailed information on the reversible causes. Our findings underscore the potential importance of future investigations aiming to reduce the cognitive load of emergency physicians during OHCA scenarios. Full article

Eye movement research has emerged as a powerful tool in architectural and environmental design, offering insights into how people visually engage with built and natural surroundings. Eye tracking technology enables the study of visual attention, user engagement, and navigation patterns, thereby informing user-centered design. This paper reviews a wide and vast body of research that demonstrates eye tracking’s capacity to inform architectural and environmental design decisions by providing objective, data-driven insights into human perception and interaction with the built world. Key methodologies are discussed, including desktop, mobile, and VR-based systems, as well as recent advances in software analytics and artificial intelligence. Beyond summarizing the existing literature, this review critically evaluates methodological approaches, identifies key challenges, and outlines future research directions. The key findings indicate increased integration of immersive technologies, diversification of analytical paradigms, and expanded application in sustainable and user-centered design. However, methodological heterogeneity, limited ecological validation, and insufficient integration with design optimization frameworks remain significant limitations. This review provides a structured foundation for advancing interdisciplinary research and enhancing evidence-based architectural design. The paper concludes by outlining a forward-looking research agenda for creating more responsive, intuitive, and human-centered environments. Full article