Search Results (36,025)

As the core refrigeration equipment in cooling systems, the operational state of chiller units is crucial for ship support, equipment cooling, and mission stability. However, because of their sensitivity and the complexity of operating environments, obtaining large volumes of complete, fault-labeled data is difficult in practical engineering appli-cations. This limitation makes it challenging for traditional data-driven approaches to deliver accurate fault diagnoses. Furthermore, data collected from different devices or under varying operating conditions often differ significantly in both feature dimensions and distributions, i.e., data heterogeneity, which further complicates model transfer. To address these challenges, this study proposes a deep transfer learning–based fault di-agnosis method designed to leverage abundant knowledge from the source domain while adaptively learning features of the target domain. Given the persistent difficulties in collecting sufficient high-quality labeled fault data, traditional data-driven models continue to face restricted diagnostic performance on target equipment. At the same time, data heterogeneity across devices or operating conditions intensifies the challenge of cross-domain knowledge transfer. To overcome these issues, this study develops a heterogeneous transfer learning method that integrates a dual-channel autoencoder, domain adversarial training, and pseudo-label self-training. This combination enables precise small-sample knowledge transfer from the source to the target domain. Specifi-cally, the dual-channel autoencoder is first applied to align heterogeneous feature di-mensions. Then, a Gradient Reversal Layer (GRL) and a domain discriminator are in-troduced to extract domain-invariant features. In parallel, high-confidence pseu-do-labeled samples from the target domain are incorporated into joint training to im-prove generalization and robustness. Experimental results confirm that the method achieves high fault diagnosis accuracy in typical industrial application scenarios, ena-bling effective identification of common faults in various types of chiller units under conventional operating conditions, the proposed method achieves higher accuracy and F1-scores in multi-class fault diagnosis tasks compared with both traditional approaches and existing transfer learning methods. These findings provide a novel perspective for advancing the intelligent operation and maintenance of chiller units. Full article

Three-dimensional (3D) ocean physical fields are essential for understanding ocean dynamics, but reconstructing them solely from sea-surface remote sensing remains challenging. We present 3DV-Unet, an end-to-end deep learning framework that reconstructs eddy-resolving three-dimensional essential ocean variables (temperature, salinity, and currents) from multi-source satellite data. The model employs a 3D Vision Transformer bottleneck to capture cross-depth and cross-variable dependencies, ensuring physically consistent reconstruction. Trained on 2011–2019 reanalysis and satellite data, 3DV-Unet achieves RMSEs of ~0.30 °C for temperature, 0.11 psu for salinity, and 0.05 m/s for currents, with all R² values above 0.93. Error analyses further indicate higher reconstruction errors in dynamically complex regions such as the Kuroshio Extension, while spectral analysis indicates good agreement at 100 km+ but systematic deviation in the 20–100 km band. Independent validation against 6113 Argo profiles confirms its ability to reproduce realistic vertical thermohaline structures. Moreover, the reconstructed 3D fields capture mesoscale eddy structures and their life cycle, offering a valuable basis for investigating ocean circulation, energy transport, and regional variability. These results demonstrate the potential of end-to-end volumetric deep learning for advancing high-resolution 3D ocean reconstruction and supporting physical oceanography and climate studies. Full article

The proliferation of renewable energy sources and distributed generation systems interfaced to the grid by power electronics systems is forcing us to better understand the issues arising due to the quality of electrical signals generated through these devices. Understanding and monitoring these harmonics is crucial to ensure the smooth and seamless operation of these networks, as well as to protect and manage the renewable energy sources-based power system. In this paper, we propose an advanced method of dynamic modal decomposition, called Higher-Order Dynamic Mode Decomposition (HODMD), one of the recently proposed data-driven methods used to estimate the frequency/amplitude and phase with high resolution, to identify the harmonic spectrum in power systems dominated by renewable energy generation. In the proposed method, several time-shifted copies of the measured signals are integrated to create the initial data matrices. A hard thresholding technique based on singular value decomposition is applied to eliminate ambiguities in the measured signal. The proposed method is validated and compared to Synchrosqueezing Transform based on Short-Time Fourier Transform (SST-STFT) and the Concentration of Frequency and Time via Short-Time Fourier Transform (ConceFT-STFT) using synthetic signals and real measurements, demonstrating its practical effectiveness in identifying harmonics in emerging power networks. Finally, the effectiveness of the proposed methodology is analyzed on the energy storage-based laboratory-scale microgrid setup using an Opal-RT-based real-time simulator. Full article

With the rapid economic and social development and the increasingly severe water shortage situation, the sustainable utilization of unconventional water resources is of great significance. As one of the “second water sources”, the full utilization of highly mineralized mine water (HMMW) is a key strategy for promoting sustainable development in water-scarce regions. It has obvious resource, environmental, and economic benefits that are central to sustainability. However, the mechanism of the impact of HMMW utilization on water utilization, the environment, and the economy is still unclear, making it difficult to evaluate its overall sustainability performance and to provide scientific data support to promote HMMW utilization. Therefore, this paper develops a novel sustainability-oriented accounting framework to assess the environmental–economic sustainability of HMMW utilization. Firstly, this paper proposes the method of calculating the HMMW utilization environmental benefits, proposes a novel integrated environmental–economic input–output accounting framework, which refines the HMMW sector from the traditional water industry and integrates the environmental benefits into a balanced input–output table. Secondly, taking Ningdong Energy Chemical Industry Base (NECI Base) as an example, this paper conducts applied research on the integrated environmental–economic accounting of HMMW utilization: (I) The HMMW environmental benefits of NECI Base are calculated, the utilization of 22.69 million m³ of HMMW generated environmental benefits, valued at 233.69 million CNY, demonstrating its substantial contribution to environmental sustainability. The compiled environmental–economic input–output table passed the balance verification, confirming the robustness and practicality of the accounting method. Full article

Dietary flavonoids are emerging as multifunctional bioactive compounds with significant implications for the prevention and management of chronic diseases. Integrating the latest experimental, clinical, and epidemiological evidence, this review provides a comprehensive synthesis of flavonoid classification, chemistry, dietary sources, and bioavailability, with special attention to their structural diversity and core mechanisms. Mechanistic advances related to antioxidant, anti-inflammatory, antimicrobial, anti-obesity, neuroprotective, cardioprotective, and anticancer activities are highlighted, focusing on the modulation of critical cellular pathways such as PI3K/Akt/mTOR, NF-κB, and AMPK. Evidence from in vitro and in vivo models, supported by clinical data, demonstrates flavonoids’ capacity to regulate oxidative stress, inflammation, metabolic syndrome, adipogenesis, cell proliferation, apoptosis, autophagy, and angiogenesis. An inverse correlation between flavonoid-rich dietary patterns and the risk of obesity, cancer, cardiovascular, and neurodegenerative diseases is substantiated. However, translational challenges persist, including bioavailability and the optimization of delivery strategies. In conclusion, a varied dietary intake of flavonoids constitutes a scientifically grounded approach to non-communicable disease prevention, though further research is warranted to refine clinical applications and elucidate molecular mechanisms. Full article

Cyber threats are becoming increasingly sophisticated, especially in distributed infrastructures where systems are deeply interconnected. To address this, we developed a framework that automates how organizations discover their digital assets and assess which ones are the most at risk. The approach integrates diverse public information sources, including WHOIS records, DNS data, and SSL certificates, into a unified analysis pipeline without relying on intrusive probing. For risk scoring we applied Gradient Boosted Decision Trees, which proved more robust with messy real-world data than other models we tested. DBSCAN clustering was used to detect unusual exposure patterns across assets. In validation on organizational data, the framework achieved 93.3% accuracy in detecting known vulnerabilities and an F1-score of 0.92 for asset classification. More importantly, security teams spent about 58% less time on manual triage and false alarm handling. The system also demonstrated reasonable scalability, indicating that automated OSINT analysis can provide a practical and resource-efficient way for organizations to maintain visibility over their attack surface. Full article

Cultural organizations have traditionally been viewed as resistant to change, often bound by legacy structures, public dependency, and non-commercial missions. However, recent advances in digital technologies—ranging from AI and VR to IoT and big data—are reshaping the operational and strategic landscape of these institutions. Despite this shift, academic literature has yet to comprehensively map how technological innovation transforms cultural organizations into practice. This paper addresses this gap by introducing the concept of the Cultural Organizational System (COS)—a holistic framework that captures the multi-component structure of cultural entities, including space, tools, performance, management, and networks. Using a PRISMA-based scoping review methodology, we analyze over 90 sources to identify the types, functions, and strategic roles of technological innovations across COS components. The findings reveal a taxonomy of innovation use cases, a mapping to Oslo innovation categories, and a quadrant model of enablers and barriers unique to the cultural sector. By offering an integrated view of digital transformation in cultural settings, this study advances innovation theory and provides practical guidance for cultural leaders and policymakers seeking to balance mission-driven goals with sustainability and modernization imperatives. Full article

Background/Objectives: Machine learning has been suggested as a way to improve how we predict anesthesia-related complications after surgery. However, many studies report overly optimistic results due to issues like data leakage and not fully using information from clinical notes. This study provides a transparent comparison of different machine learning models using both structured data and preoperative notes, with a focus on avoiding data leakage and involving clinicians throughout. We show how high reported metrics in the literature can result from methodological pitfalls and may not be clinically meaningful. Methods: We used a dataset containing both structured patient and surgery information and preoperative clinical notes. To avoid data leakage, we excluded any variables that could directly reveal the outcome. The data was cleaned and processed, and information from clinical notes was summarized into features suitable for modeling. We tested a range of machine learning methods, including simple, tree-based, and modern language-based models. Models were evaluated using a standard split of the data and cross-validation, and we addressed class imbalance with sampling techniques. Results: All models showed only modest ability to distinguish between patients with and without complications. The best performance was achieved by a simple model using both structured and summarized text features, with an area under the curve of 0.644 and accuracy of 60%. Other models, including those using advanced language techniques, performed similarly or slightly worse. Adding information from clinical notes gave small improvements, but no single type of data dominated. Overall, the results did not reach the high levels reported in some previous studies. Conclusions: In this analysis, machine learning models using both structured and unstructured preoperative data achieved only modest predictive performance for postoperative complications. These findings highlight the importance of transparent methodology and clinical oversight to avoid data leakage and inflated results. Future progress will require better control of data leakage, richer data sources, and external validation to develop clinically useful prediction tools. Full article

Background/Objectives: We aimed to evaluate the demographic characteristics, clinical findings, and survival outcomes of patients diagnosed with orbital metastasis, considering primary tumor type, age, and gender variables. Methods: In this observational study, demographic data, tumor localization, histopathological diagnoses, and survival times of 83 patients followed for secondary orbital metastasis at Çukurova University Ophthalmology Department between 2003 and 2023 were retrospectively reviewed. Subgroup analyses were performed according to age (<18 and ≥19), gender, and primary tumor groups. Results: The study included 83 patients (51 (61.4%) females and 32 (38.6%) males). The mean age at diagnosis was found to be 40.8 ± 24.6 years. A total of 24.1% of the cases were in the pediatric age group (mean age 5.9 years), and the most common tumor metastasizing to the orbit in this group was neuroblastoma (80%). In adult patients, the two most frequent tumors metastasizing to the orbit were breast cancer (33.3%) and lung cancer (14.3%). The most common clinical findings were proptosis (32.5%) and blurred vision (26.2%). Orbital metastases were observed more frequently in females than in males (61.4% vs. 38.6%). This ratio was similar in the pediatric age group (65.0% vs. 35.0%). The mean survival time after metastasis was calculated as 316.7 ± 68.6 days. Female patients had a significantly longer survival time after metastasis compared to males (mean 400.4 vs. 165.4 days; p = 0.037). The median survival after metastasis was 86 days for patients with breast cancer and 204 days for patients with neuroblastoma. Conclusions: The most common source of orbital metastases in females is breast cancer, while neuroblastoma is prominent in pediatric patients. Despite all available treatment options, the prognosis after orbital metastasis is poor; this highlights the importance of early diagnosis and a multidisciplinary approach. Full article

Smart crop technologies offer promising solutions for enhancing agricultural productivity and sustainability, particularly in the face of global challenges such as resource scarcity and climate variability. However, their deployment in infrastructure-limited regions, especially across Africa, faces persistent barriers, including unreliable power supply, intermittent internet connectivity, and limited access to technical expertise. This study presents a PRISMA-guided systematic review of literature published between 2015 and 2025, sourced from the Scopus database including indexed content from ScienceDirect and IEEE Xplore. It focuses on key technological components including multimodal sensing, data fusion, IoT resource management, edge-cloud integration, and adaptive network design. The analysis of these references reveals a clear trend of increasing research volume and a major shift in focus from foundational unimodal sensing and cloud computing to more complex solutions involving machine learning post-2019. This review identifies critical gaps in existing research, particularly the lack of integrated frameworks for effective multimodal sensing, data fusion, and real-time decision support in low-resource agricultural contexts. To address this, we categorize multimodal sensing approaches and then provide a structured taxonomy of multimodal data fusion approaches for real-time monitoring and decision support. The review also evaluates the role of IoT virtualization as a pathway to scalable, adaptive sensing systems, and analyzes strategies for overcoming infrastructure constraints. This study contributes a comprehensive overview of smart crop technologies suited to infrastructure-limited agricultural contexts and offers strategic recommendations for deploying resilient smart agriculture solutions under connectivity and power constraints. These findings provide actionable insights for researchers, technologists, and policymakers aiming to develop sustainable and context-aware agricultural innovations in underserved regions. Full article

The integration of artificial intelligence plays a critical role in human resource management in local governments by ensuring smooth, essential HR operations, including recruitment, performance management, and workforce planning. The current study is a systematic review focused on determining the performance management factors that should be considered when using artificial intelligence in the local government sector. Although artificial intelligence (AI) is becoming increasingly integrated into the governance and administrative systems of local governments around the world, this study raises critical questions about how performance should be managed, measured, and improved. Articles were screened based on their title, abstract, and keywords, following which the inclusion and exclusion criteria were applied. A comprehensive search was conducted in the EBSCOhost, Emerald Insight, Taylor & Francis, Scopus, and SpringerLink databases. These databases were chosen because they are prominent sources that publish various materials related to the social sciences. This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines and included 22 peer-reviewed empirical studies published between 2015 and 2025. Analysis of the identified 22 peer-reviewed articles revealed that the successful application of AI in local government performance management depends on six critical performance management factors: data quality and accessibility; strategic alignment with performance goals; evaluation criteria and metrics; ethical and legal oversight; institutional capacity and leadership; and change management and stakeholder engagement. These factors are interdependent and represent both technical and organisational dimensions of public administration. This study highlights that AI entails more than innovation; it reshapes the foundations of performance governance, requiring new capabilities, values, and institutional practices. Full article

Lipid-based formulations (LBFs) play a crucial role in enhancing the oral bioavailability of poorly water-soluble drugs by leveraging lipid digestion and solubilization processes. However, developing robust in vitro–in vivo correlations (IVIVCs) for LBFs presents unique challenges due to the complex interplay of digestion, permeation, and dynamic solubilization. This article reviews the construction of IVIVC in the context of LBFs, highlighting the limitations of traditional methods and the need for tailored approaches. It examines the in vitro tools commonly employed for LBF characterization, such as USP dissolution tests, lipolysis assays, and combined models, and discusses their relevance to in vivo performance prediction. The review also explores the sources of in vivo data essential for validating IVIVC and describes the most popular in silico tools for predicting in vivo performance, focusing on lipid-based formulations. This work aims to pave the way for more effective and adaptable IVIVC methodologies for lipid-based drug delivery systems. Full article

Achieving decarbonization targets in the aviation sector requires transformative approaches to sustainable aviation fuel (SAF) production. In this pursuit, feedstock innovation has emerged as a critical challenge. This research uses the U.S. SAF Grand Challenge as a case study, focusing on its feedstock innovation workstream, to investigate how Industry 4.0 technologies can fulfill that workstream’s objectives. An integrative literature review, drawing on academic, industry, and policy sources, is used to evaluate the Technology Readiness Levels (TRLs) of Industry 4.0 technology applications across the SAF biomass supply chain. The analysis identifies several key technologies as essential for improving yield prediction, optimizing resource allocation, and linking stochastic models to techno-economic analyses (TEAs): IoT-enabled sensor networks, probabilistic/precision forecasting, and automated quality monitoring. Results reveal an uneven maturity landscape, with some applications demonstrating near-commercial readiness, while others remain in early research or pilot stages, particularly in areas such as logistics, interoperability, and forecasting. The study contributes a structured TRL-based assessment that not only maps maturity but also highlights critical gaps and corresponding policy implications, including data governance, standardization frameworks, and cross-sector collaboration. By aligning digital innovation pathways with SAF deployment priorities, the findings offer both theoretical insights and practical guidance for advancing sustainable aviation fuel adoption and accelerating progress toward net-zero aviation. Full article

With the characteristic of “decentralized distribution and local power supply”, small hydropower (SHP) in China has become a core means of solving the problem of insufficient power supply in rural and remote mountainous areas, effectively promoting the improvement of local livelihoods. However, for a long time, SHP has had many problems, such as irrational development, old equipment, and poor economic efficiency, resulting in some rivers with connectivity loss and reduced biodiversity, etc. The Chishui River Watershed is an ecologically valuable river in the upper reaches of the Yangtze River. As an important habitat for rare fish in the upper reaches of the Yangtze River and the only large-scale tributary that maintains a natural flow pattern, the SHP plants’ dismantling and ecological restoration practices in the Chishui River Watershed can set a model for regional sustainable development. This paper adopts the methods of literature review, field research, and case study analysis, combined with the comparison of ecological conditions before and after the dismantling, to systematically analyze the effectiveness and challenges of SHP rectification in the Chishui River Watershed. The study found that after dismantling 88.2% of SHP plants in ecologically sensitive areas, the number of fish species upstream and downstream of the original dam site increased by about 6.67% and 70%, respectively; the natural hydrological connectivity has been restored to the downstream of the Tongzi River, the Gulin River and other rivers, but there are short-term problems such as sediment underflow, increased economic pressure, and the gap of alternative energy sources; the retained power stations have achieved the success and challenges of power generation and ecological management ecological flow control and comprehensive utilization, achieving a balance between power generation and ecological protection. Based on the above findings, the author proposes dynamic monitoring and interdisciplinary tracking research to fill the gap of systematic data support and long-term effect research in the SHP exit mechanism, and the results can provide a reference for the green transition of SHP. Full article

Accurately predicting aircraft fuel consumption is vital for aviation safety, operational efficiency, and resource optimization, yet existing models face key limitations. Traditional physical models rely on prior assumptions, while mainstream deep learning models use fixed architectures and time-slice tokens—failing to adapt to distinct flight phases and losing long-range temporal features critical for cross-phase dependency capture. This paper proposes Gate-iInformer, an adaptive framework centered on iInformer with a gating network. It treats flight parameters as independent tokens, integrates Informer to handle long-range dependencies, and uses the gating network to dynamically select pre-trained phase-specific sub-models. Validated on 21,000 Air China 2023 medium-aircraft flights, it reduces MAE and RMSE by up to 53.38% and 44.51%, achieves 0.068 MAE in landing, and outperforms benchmarks. Its prediction latency is under 0.5 s, meeting ADS-B needs. Future work will expand data sources to enhance generalization, boosting aviation intelligent operation. Full article