Search Results (3,956)

This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem. Full article

As sustainability and Corporate Social Responsibility gained increasing importance in agriculture, several impact assessment methodologies have been proposed. Social Return on Investment (SROI), a methodology used for understanding, measuring, and reporting the social, economic, and environmental value created by an organization, emerged as a promising approach to quantify and monetize social and environmental impacts. However, research on SROI application within the wine industry remains limited, despite the sector’s global relevance and unique economic, social, and cultural dimensions. This study addresses this gap by evaluating the potential and limitations of SROI in assessing the social impact of a wine cellar’s products, services, and activities on its stakeholders. Indeed, we find confirmation that, as in other sectors, this methodology can support sustainability reporting and strategic decision-making. Applying the SROI methodology, stakeholder outcomes were analyzed, and the results indicate that for every EUR 1 invested, approximately EUR 1.44 of social value is generated, demonstrating SROI’s effectiveness in capturing social contributions beyond financial metrics. This study highlights SROI’s advantages, while also acknowledging challenges. Findings suggest that, despite some limitations, SROI can enhance wineries’ sustainability strategies and offers a robust framework to guide wineries—and potentially other agricultural sectors—toward socially responsible and sustainable practices. Future research should focus on developing industry-specific proxies and integrating SROI with other sustainability assessment tools, particularly in support of ESG reporting. This study contributes to academic discourse on impact evaluation methodologies and provides practical implications that aim to balance economic performance with social responsibility. Full article

Electrical grids are undergoing a transformation driven by the increasing integration of renewable energy sources on the consumer side. This shift, alongside the electrification of consumption—particularly in areas such as electric mobility—has the potential to significantly reduce CO₂ emissions. However, it is also contributing to a rise in electricity prices due to growing demand and infrastructure costs. Paradoxically, these higher prices serve as a catalyst for further investment in renewable energy technologies by reducing the payback periods of such systems. Recent European legislation has accelerated this transformation by mandating the liberalization of energy markets. This regulatory shift enables the emergence of prosumers—consumers who are also producers of energy—by granting them the right to generate, store, and trade electricity using the existing distribution grid. In this new landscape, photovoltaic systems represent a viable and increasingly attractive investment option for both households and businesses. This study presents an economic evaluation of photovoltaic system investments across different European countries, focusing on key indicators such as payback periods and the impact of local solar irradiation on the resulting electricity price. The analysis provides insight into the varying economic feasibility of distributed solar energy deployment, offering a comparative perspective that supports both policymakers and potential investors in making informed decisions about renewable energy adoption. Full article

Imaging techniques such as positron emission tomography/computed tomography (PET/CT) and positron emission tomography/magnetic resonance imaging (PET/MR) have emerged as powerful and versatile tools for the comprehensive assessment of coronary artery disease (CAD). By combining anatomical and functional information in a single examination, these modalities offer complementary insights that significantly enhance diagnostic accuracy and support clinical decision-making. This is particularly relevant in complex clinical scenarios, such as multivessel disease, balanced ischemia, or suspected microvascular dysfunction, where conventional imaging may be inconclusive. This review aims to provide clinicians with an up-to-date summary of the principles, technical considerations, and clinical applications of hybrid PET/CT and PET/MR in CAD. Here, we describe how these techniques can improve the evaluation of myocardial perfusion, coronary plaque characteristics, and ischemic burden. Advantages such as improved sensitivity, spatial resolution, and quantification capabilities are discussed alongside limitations including cost, radiation exposure, availability, and workflow challenges. A dedicated focus is given to the emerging role of artificial intelligence (AI), which is increasingly being integrated to optimize image acquisition, fusion processes, and interpretation. AI has the potential to streamline hybrid imaging and promote a more personalized and efficient management of CAD. Finally, we outline future directions in the field, including novel radiotracers, automated quantitative tools, and the expanding use of hybrid imaging to guide patient selection and therapeutic decisions, particularly in revascularization strategies. Full article

Collaboration is essential in today’s technology-driven world, where IT professionals work in teams to solve complex problems. To mirror industry practices, universities have increasingly adopted project-based learning approaches, requiring students to work collaboratively on tasks such as software development. However, while considerable research has examined group project outcomes, little is known about the decision-making processes students use to select their partners, particularly in software development. This study, therefore, explores the factors influencing IT students’ choices of group project partners and how these choices reflect broader learning priorities. A qualitative approach was employed, collecting open-ended responses from 103 software development students through individual interviews conducted via MS Teams. Thematic analysis was used to identify recurring patterns in the data. Five main themes emerged: Personal Relationships & Familiarity, Work Ethic & Dedication, Communication & Teamwork, Reliability & Accountability, and Technical Skills & Competence. The findings indicate that students prioritise interpersonal trust, reliability, and communication skills over technical ability when selecting partners. This suggests that students view effective collaboration as grounded more in work ethic and relational qualities than in coding proficiency alone. Full article

Against the backdrop of escalating atmospheric carbon dioxide concentrations, carbon emission trading systems (ETS) have emerged as pivotal policy instruments, with China’s ETS playing a prominent role globally. The carbon price, central to ETS functionality, guides resource allocation and corporate strategies. Due to unexpected events, political conflicts, limited access to data information, and insufficient cognitive levels of market participants, there are epistemic uncertainties in the fluctuations of carbon and energy prices. Existing studies often lack effective handling of these epistemic uncertainties in energy prices and carbon prices. Therefore, the core objective of this study is to reveal the dynamic linkage patterns between energy prices and carbon prices, and to quantify the impact mechanism of epistemic uncertainties on their relationship with the help of uncertain differential equations. Methodologically, a dynamic model of carbon and energy prices was constructed, and analytical solutions were derived and their mathematical properties were analyzed to characterize the linkage between carbon and energy prices. Furthermore, based on the observation data of coal prices in Qinhuangdao Port and national carbon prices, the unknown parameters of the proposed model were estimated, and uncertain hypothesis tests were conducted to verify the rationality of the proposed model. Results showed that the mean squared error of the established model for fitting the linkage relationship between carbon and energy prices was 0.76, with the fitting error controlled within $3.72\%$. Moreover, the prediction error was 1.$88\%$. Meanwhile, the $5\%$ value at risk (VaR) of the logarithmic return rate of carbon prices was predicted to be $-0.0369$. The research indicates that this methodology provides a feasible framework for capturing the uncertain interactions in the carbon-energy market. The price linkage mechanism revealed by it helps market participants optimize their risk management strategies and provides more accurate decision-making references for policymakers. Full article

Traditional volumetric parameters fall short of capturing the complex, phasic nature of atrial function. In contrast, atrial strain has become recognized as a sensitive, non-invasive imaging marker that enables earlier detection of myocardial dysfunction, refined risk stratification, and individualized therapeutic decision-making across a wide range of cardiovascular diseases. Cardiovascular magnetic resonance feature tracking (CMR-FT) has emerged as a robust imaging technique for evaluating atrial strain, offering high spatial resolution, high reproducibility, and independence from acoustic window limitations. Despite its promise, the routine clinical adoption of CMR-FT atrial strain remains limited. Key barriers include intervendor variability in strain values, the absence of standardized post-processing protocols, the lengthy acquisition times inherent to CMR studies, and the time required for post-processing atrial strain analysis. Overcoming these barriers is crucial to facilitate the integration of atrial strain assessment into routine clinical CMR protocols, particularly in patients with heart failure, valvular disease, or cardiomyopathy who undergo imaging for diagnostic or prognostic evaluation. Full article

The increasing elderly population presents major challenges to traditional healthcare due to the need for continuous care, a shortage of skilled professionals, and increasing medical costs. To address this, smart elderly care homes where multiple residents live with the support of caregivers and IoT-based assistive technologies have emerged as a promising solution. For their effective operation, a reliable high speed network like 5G is essential, along with intelligent resource allocation to ensure efficient service delivery. This study proposes a deep reinforcement learning (DRL)-based resource management framework for smart elderly homes, formulated as a Markov decision process. The framework dynamically allocates computing and network resources in response to real-time application demands and system constraints. We implement and compare two DRL algorithms, emphasizing their strengths in optimizing edge utilization and throughput. System performance is evaluated across balanced, high-demand, and resource-constrained scenarios. The results demonstrate that the proposed DRL approach effectively learns adaptive resource management policies, making it a promising solution for next-generation intelligent elderly care environments. Full article

Background: Ovarian cancer is often diagnosed at advanced stages due to the absence of specific early symptoms, resulting in high mortality rates. This study aims to develop a robust and interpretable machine learning (ML) model for the early detection of ovarian cancer, enhancing its transparency through the use of the Contrastive Explanation Method (CEM), an advanced technique within the field of explainable artificial intelligence (XAI). Methods: An open-access dataset of 349 patients with ovarian cancer or benign ovarian tumors was used. To improve reliability, the dataset was augmented via bootstrap resampling. A three-layer deep neural network was trained on normalized demographic, biochemical, and tumor marker features. Model performance was measured using accuracy, sensitivity, specificity, F1-score, and the Matthews correlation coefficient. CEM was used to explain the model’s classification results, showing which factors push the model toward “Cancer” or “No Cancer” decisions. Results: The model achieved high diagnostic performance, with an accuracy of 95%, sensitivity of 96.2%, and specificity of 93.5%. CEM analysis identified lymphocyte count (CEM value: 1.36), red blood cell count (1.18), plateletcrit (0.036), and platelet count (0.384) as the strongest positive contributors to the “Cancer” classification, with lymphocyte count demonstrating the highest positive relevance, underscoring its critical role in cancer detection. In contrast, age (change from −0.13 to +0.23) and HE4 (change from −0.43 to −0.05) emerged as key factors in reversing classifications, requiring substantial hypothetical increases to shift classification toward the “No Cancer” class. Among benign cases, a significant reduction in RBC count emerged as the strongest determinant driving a shift in classification. Overall, CEM effectively explained both the primary features influencing the model’s classification results and the magnitude of changes necessary to alter its outputs. Conclusions: Using CEM with ML allowed clear and trustworthy detection of early ovarian cancer. This combined approach shows the promise of XAI in assisting clinicians in making decisions in gynecologic oncology. Full article

Phenology prediction is critical for optimizing the timing of rice crop management operations such as fertilization and irrigation, particularly in the face of increasing climate variability. This study aimed to estimate three key developmental stages in the temperate irrigated rice systems of Australia: panicle initiation (PI), flowering, and harvest maturity. Extensive and diverse field observations ($n\approx 302$) were collected over four consecutive seasons (2022–2025) from the rice-growing regions of the Murrumbidgee and Murray Valleys in southern New South Wales, encompassing six varieties and three sowing methods. The extent of data available allowed a number of traditional and emerging machine learning (ML) models to be directly compared to determine the most robust strategies to predict Australian rice crop phenology. Among all models, Tabular Prior-data Fitted Network (TabPFN), a pre-trained transformer model trained on large synthetic datasets, achieved the highest precision for PI and flowering predictions, with root mean square errors (RMSEs) of 4.9 and 6.5 days, respectively. Meanwhile, long short-term memory (LSTM) excelled in predicting harvest maturity with an RMSE of 5.9 days. Notably, TabPFN achieved strong results without the need for hyperparameter tuning, consistently outperforming other ML approaches. Across all stages, models that integrated remote sensing (RS) and weather variables consistently outperformed those relying on single-source input. These findings underscore the value of hybrid data fusion and modern time series modeling techniques for accurate and scalable phenology prediction, ultimately enabling more informed and adaptive agronomic decision-making. Full article

Reactive oxygen species (ROS) are increasingly recognized as decisive actors in photocatalytic redox chemistry, dictating both the selectivity and efficiency of target reactions, while most photocatalytic systems generate a mixture of ROS under illumination. Recent studies have revealed that tailoring the generation of specific ROS, rather than maximizing the overall ROS yield, holds the key to unlocking high-performance and application-specific catalysis. In this context, the selective production of specific ROS has emerged as a critical requirement for achieving target-oriented and sustainable photocatalytic transformations. Among the various photocatalytic materials, polymeric carbon nitride (PCN) has garnered considerable attention due to its metal-free composition, visible-light response, tunable structure, and chemical robustness. More importantly, the tunable band structure, surface chemistry, and interfacial environment of PCN collectively make it an excellent scaffold for the controlled generation of specific ROS. In recent years, numerous strategies including molecular doping, defect engineering, heterojunction construction, and co-catalyst integration have been developed to precisely tailor the ROS profile derived from PCN-based systems. This review provides a comprehensive overview of ROS regulation in PCN-based photocatalysis, with a focus on type-specific strategies. By classifying the discussion according to the major ROS types, we highlight the mechanisms of their formation and the design principles that govern their selective generation. In addition, we discuss representative applications in which particular ROS play dominant roles and emphasize the potential of PCN systems in achieving tunable and efficient photocatalytic performance. Finally, we outline key challenges and future directions for developing next-generation ROS-regulated PCN photocatalysts, particularly in the context of reaction selectivity, dynamic behavior, and practical implementation. Full article

Background: Mesenteric cystic lymphatic malformations (MCLMs) are rare benign lymphatic malformations predominantly diagnosed in children. Adult cases are exceptional and typically discovered incidentally during imaging or surgery for unrelated conditions. Their intraoperative identification, particularly in emergency settings, poses diagnostic and surgical challenges due to anatomical complexity and potential vascular involvement. Methods: A literature review was performed in PubMed and Scopus to contextualize this case, focusing on intraoperative management strategies, recurrence risk, and surgical decision-making in mesenteric lymphatic malformations. Case reports, case series, and reviews in English with relevant clinical and surgical data were included, while duplicates, non-English publications, abstracts without full text, and studies lacking essential information were excluded. Case Presentation: We report a 45-year-old male who presented with acute right lower quadrant pain, clinically and radiologically consistent with acute appendicitis. Contrast-enhanced CT incidentally identified a mesenteric cystic lesion near the terminal ileum. Intraoperative findings confirmed phlegmonous appendicitis coexisting with a large MCLM, requiring segmental enterectomy and appendectomy. Histopathology confirmed the diagnosis of MCLMs. Conclusions: This case highlights the rare coexistence of acute appendicitis and mesenteric lymphatic malformations in an adult, illustrating the surgical challenges of unexpected lymphatic lesions in emergency settings. Emphasizing real-time intraoperative decision-making, we propose an anatomy-driven algorithm that balances complete excision with safer, conservative options based on lesion features, surgical risk, and multidisciplinary input. Full article

Introduction: Acute appendicitis (AA) is a common cause of abdominal pain that can lead to complications like perforation and intra-abdominal abscesses, increasing morbidity and mortality, often requiring emergency surgery. Nevertheless, appendectomy is performed in up to 95% of uncomplicated cases, while complications like perforation and intra-abdominal abscesses increase morbidity and mortality. The current study compares the accuracy of GPT-4.5, DeepSeek R1, and machine learning in assisting with surgical decision-making for patients presenting with lower abdominal pain at the Emergency Department. Methods: In this multicenter retrospective study, 63 histopathologically confirmed appendicitis patients and 50 control patients with right abdominal pain presenting at the Emergency Department at two German hospitals between October 2022 and October 2023 were included. Using each patient’s clinical, laboratory, and radiological findings, DeepSeek (with and without Retrieval-Augmented Generation using 2020 Jerusalem guidelines) was compared in terms of accuracy with GPT-4.5 and a random forest-based machine-learning model, with a board-certified surgeon (reference standard) to determine the optimal treatment approach (laparoscopic exploration/appendectomy versus conservative antibiotic therapy). Results: Accuracy of agreement with board-certified surgeons in the decision-making of appendectomy versus conservative therapy increased non-significantly from 80.5% to 83.2% with DeepSeek and from 70.8 to 76.1% when GPT-4.5 was provided with the World Journal of Emergency Surgery 2020 Jerusalem guidelines on the diagnosis and treatment of acute appendicitis. The estimated machine-learning model training accuracy was 84.3%, while the validation accuracy for the model was 85.0%. Discussion: GPT-4.5 and DeepSeek R1, as well as the machine-learning model, demonstrate promise in aiding surgical decision-making for appendicitis, particularly in resource-constrained settings. Ongoing training and validation are required to optimize the performance of such models. Full article

The integration of deep learning (DL) and nanozyme-based biosensing has emerged as a transformative strategy for next-generation medical diagnostics. This review explores how DL architectures enhance nanozyme design, functional optimization, and predictive modeling by elucidating catalytic mechanisms such as dual-atom active sites and substrate-surface interactions. Key applications include disease biomarker detection, medical imaging enhancement, and point-of-care diagnostics aligned with the ASSURED criteria. In clinical contexts, advances such as wearable biosensors and smart diagnostic platforms leverage DL for real-time signal processing, pattern recognition, and adaptive decision-making. Despite significant progress, challenges remain—particularly the need for standardized biomedical datasets, improved model robustness across diverse populations, and the clinical translation of artificial intelligence (AI)-enhanced nanozyme systems. Future directions include integration with the Internet of Medical Things, personalized medicine frameworks, and sustainable sensor development. The convergence of nanozymes and DL offers unprecedented opportunities to advance intelligent biosensing and reshape precision diagnostics in healthcare. Full article

This study presents a comprehensive multi-criteria decision-making (MCDM) framework for evaluating and selecting optimal non-destructive testing (NDT) techniques for oil and gas facility maintenance operations. This research used a Fuzzy Analytic Hierarchy Process (FAHP) integrated with multiple MCDM methods to assess eight NDT techniques including radiographic testing, ultrasonic testing, and thermographic testing. The evaluation framework incorporated seven technical criteria and seven economic criteria. The FAHP results revealed spatial resolution (0.175) as the most critical technical criterion, followed by depth penetration (0.155) and defect characterization (0.143). For economic criteria, downtime costs (0.210) and operational costs (0.190) emerged as the most significant factors. This study used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment of Evaluations), and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods to rank NDT techniques, with results consolidated using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method. The final techno-economic analysis identified radiographic testing as the most suitable NDT method with a score of 0.665, followed by acoustic emission testing at 0.537. Visual testing ranked lowest with a score of 0.214. This research demonstrates the effectiveness of combining fuzzy logic with multiple MCDM approaches for NDT method selection in offshore welding operations. Full article