Search Results (399)

Offshore wind turbines are rapidly scaling in size, which amplifies the need for credible integrated load analyses that consistently resolve the coupled dynamics among rotor–nacelle–tower systems and their support substructures. This study presents a comprehensive ultimate limit state (ULS) load assessment for a fixed jack-up-type substructure (hereafter referred to as K-wind) coupled with the IEA 15 MW reference wind turbine. Unlike conventional monopile or jacket configurations, the K-wind concept adopts a self-installable triangular jack-up foundation with spudcan anchorage, enabling efficient transport, rapid deployment, and structural reusability. Yet such a configuration has never been systematically analyzed through full aero-hydro-servo-elastic coupling before. Hence, this work represents the first integrated load analysis ever reported for a jack-up-type offshore wind substructure, addressing both its unique load-transfer behavior and its viability for multi-MW-class turbines. To ensure numerical robustness and cross-code reproducibility, steady-state verifications were performed under constant-wind benchmarks, followed by time-domain simulations of standard prescribed Design Load Case (DLC), encompassing power-producing extreme turbulence, coherent gusts with directional change, and parked/idling directional sweeps. The analyses were independently executed using two industry-validated solvers (Deeplines Wind v5.8.5 and OrcaFlex v11.5e), allowing direct solver-to-solver comparison and establishing confidence in the obtained dynamic responses. Loads were extracted at the transition-piece reference point in a global coordinate frame, and six key components (Fx, Fy, Fz, Mx, My, and Mz) were processed into seed-averaged signed envelopes for systematic ULS evaluation. Beyond its methodological completeness, the present study introduces a validated framework for analyzing next-generation jack-up-type foundations for offshore wind turbines, establishing a new reference point for integrated load assessments that can accelerate the industrial adoption of modular and re-deployable support structures such as K-wind. Full article

The aerodynamic pressure of a car is crucial for its shape design. To overcome the time-consuming and costly bottleneck of wind tunnel tests and computational fluid dynamics (CFD) simulations, deep learning-based surrogate models have emerged as highly promising alternatives. However, existing methods that only predict on the surface of objects only learn the mapping of pressure. In contrast, a physically realistic field has values and gradients that are structurally unified and self-consistent. Therefore, existing methods ignore the crucial differential structure and intrinsic continuity of the physical field as a whole. This oversight leads to their predictions, even if locally numerically close, often showing unrealistic gradient distributions and high-frequency oscillations macroscopically, greatly limiting their reliability and practicality in engineering decisions. To address this, this study proposes the Geo-PhysNet model, a graph neural network framework specifically designed for complex surface manifolds with strong physical constraints. This framework learns a differential representation, and its network architecture is designed to simultaneously predict the pressure scalar field and its tangential gradient vector field on the surface manifold within a unified framework. By making the gradient an explicit learning target, we force the network to understand the local mechanical causes leading to pressure changes, thereby mathematically ensuring the self-consistency of the field’s intrinsic structure, rather than merely learning the numerical mapping of pressure. Finally, to solve the common noise problem in the predictions of existing methods, we introduce a physical regularization term based on the surface Laplacian operator to penalize non-smooth solutions, ensuring the physical rationality of the final output field. Experimental verification results show that Geo-PhysNet not only outperforms existing benchmark models in numerical accuracy but, more importantly, demonstrates superior advantages in the physical authenticity, field continuity, and gradient smoothness of the generated pressure fields. Full article

For clinical decision support to work, medical knowledge needs to be easy to find quickly and accurately. Retrieval-Augmented Generation (RAG) systems use big language models and document retrieval to help with diagnostic reasoning, but they could cause hallucinations and have strict privacy rules in healthcare. We tested twelve different types of RAG, such as dense, sparse, hybrid, graph-based, multimodal, self-reflective, adaptive, and security-focused pipelines, on 250 de-identified patient vignettes. We used Precision@5, Mean Reciprocal Rank, nDCG@10, hallucination rate, and latency to see how well the system worked. The best retrieval accuracy (P@5 ≥ 0.68, nDCG@10 ≥ 0.67) was achieved by a Haystack pipeline (DPR + BM25 + cross-encoder) and hybrid fusion (RRF). Self-reflective RAG, on the other hand, lowered hallucinations to 5.8%. Sparse retrieval gave the fastest response (120 ms), but it was not as accurate. We also suggest a single framework for reducing hallucinations that includes retrieval confidence thresholds, chain-of-thought verification, and outside fact-checking. Our findings emphasize pragmatic protocols for the secure implementation of RAG on premises, incorporating encryption, provenance tagging, and audit trails. Future directions encompass the incorporation of clinician feedback and the expansion of multimodal inputs to genomics and proteomics for precision medicine. Full article

Learning Analytics Dashboards (LADs) are increasingly deployed to support self-regulated learning on online courses. Yet many existing dashboards lack strong theoretical grounding, contextual alignment, or actionable feedback, and some designs have been shown to inadvertently discourage learners through excessive social comparison or high inference costs. In this study, we designed and evaluated a LAD grounded in the COPES model of self-regulated learning and tailored to a credit-bearing Massive Open Online Course (MOOC) using a data-driven approach. We conducted a randomized controlled trial with 8745 learners, comparing a control group, a dashboard without feedback, and a dashboard with ARCS-framed actionable feedback. The results showed that the dashboard with feedback significantly increased learners’ likelihood of verification (i.e., paying for the certification track), with mixed effects on engagement and no measurable impact on final grades. These findings suggest that dashboards are not uniformly beneficial: while feedback-supported LADs can enhance motivation and persistence, dashboards that lack interpretive support may impose cognitive burdens without improving outcomes. This study contributes to the literature on learning analytics by (1) articulating the design principles for theoretically and contextually grounded LADs and (2) providing experimental evidence on their impact in authentic MOOC settings. Full article

Reinforced concrete durability depends on a passive oxide film protecting embedded steel, sustained by high-alkalinity pore solutions. Cracking fundamentally alters transport, allowing rapid chloride and carbon dioxide ingress, which undermines passivity and accelerates corrosion. Self-healing concrete technologies aim to autonomously restore transport barriers and reestablish electrochemical stability. This review critically synthesizes evidence on healing effectiveness for corrosion mitigation through a dual framework of barrier restoration and interface stabilization, integrating depth-resolved chloride profiles with electrochemical performance indices. Critically, visual crack closure proves an unreliable indicator of corrosion protection. Healing mechanisms exhibit characteristic spatial signatures: autogenous and microbial approaches preferentially seal surface zones with diminishing effectiveness at reinforcement depth, while encapsulated low-viscosity polymers achieve greater depth continuity. However, electrochemical recovery consistently lags transport recovery, with healed specimens achieving only partial restoration of intact corrosion resistance. Recovery effectiveness depends on crack geometry, moisture conditions, and healing mechanism characteristics, with systems performing effectively only within narrow, condition-specific windows. Effective corrosion protection requires coordinated barrier and interface strategies targeting both bulk transport and steel surface chemistry. The path forward demands rigorous field validation emphasizing electrochemical outcomes over appearance metrics, long-term durability assessment, and performance-based verification frameworks to enable predictable service life extension. Full article

Autonomous driving relies on multimodal sensors to acquire environmental information for supporting decision making and control. While significant progress has been made in 3D object detection regarding point cloud processing and multi-sensor fusion, existing methods still suffer from shortcomings—such as sparse point clouds of foreground targets, fusion instability caused by fluctuating sensor data quality, and inadequate modeling of cross-frame temporal consistency in video streams—which severely restrict the practical performance of perception systems. To address these issues, this paper proposes a multimodal video stream 3D object detection framework based on reliability evaluation. Specifically, it dynamically perceives the reliability of each modal feature by evaluating the Region of Interest (RoI) features of cameras and LiDARs, and adaptively adjusts their contribution ratios in the fusion process accordingly. Additionally, a target-level semantic soft matching graph is constructed within the RoI region. Combined with spatial self-attention and temporal cross-attention mechanisms, the spatio-temporal correlations between consecutive frames are fully explored to achieve feature completion and enhancement. Verification on the nuScenes dataset shows that the proposed algorithm achieves an optimal performance of 67.3% and 70.6% in terms of the two core metrics, mAP and NDS, respectively—outperforming existing mainstream 3D object detection algorithms. Ablation experiments confirm that each module plays a crucial role in improving overall performance, and the algorithm exhibits better robustness and generalization in dynamically complex scenarios. Full article

Water scarcity is a relevant issue whose impact can be mitigated through sustainable solutions. Humidification–dehumidification (HDH) cycles powered by photovoltaic thermal (PVT) modules enable pure water production in remote areas. In this study, models have been developed and validated for the main components of the system, the humidifier and the dehumidifier. A unique HDH-PVT prototype was built and experimentally tested at the SolarTech Lab of Politecnico di Milano in Milan, Italy. The experimental system is a Closed Air Closed Water—Water Heated (CACW-WH) that mimics a Closed Air Open Water—Water Heated (CAOW-WH) cycle through brine cooling, pure water mixing, and recirculation, avoiding a continuous waste of water. Tests were performed varying the mass flow ratio (MR) between 0.346 and 2.03 during summer and autumn in 2023 and 2024. The experimental results enabled the verification of the developed models. The optimal system performance was obtained for an MR close to 1 and a maximum cycle temperature of $44 °\mathrm{C}$, enabling a 0.51 gain output ratio (GOR) and 0.72% recovery ratio (RR). The electrical and thermal energy generation of the PVT modules satisfied the whole consumption of the system enabling pure water production exploiting only the solar resource available. The PVT-HDH system proved the viability of the proposed solution for a sustainable self-sufficient desalination system in remote areas, thus successfully addressing water scarcity issues exploiting a renewable energy source. Full article

Compared with other fossil energy sources, natural gas is characterized by compressibility, low energy density, high storage costs, and imbalanced usage. Natural gas pipeline supply systems possess unique attributes such as closed transportation and a highly integrated upstream, midstream, and downstream structure. Moreover, pipelines are almost the only economical means of onshore natural gas transportation. Given that the upstream of the pipeline features multi-entity and multi-channel supply including natural gas, coal-to-gas, and LNG vaporized gas, while the downstream presents a competitive landscape with multi-market and multi-user segments (e.g., urban residents, factories, power plants, and vehicles), there is an urgent social demand for non-discriminatory and fair opening of natural gas pipeline network infrastructure to third-party entities. However, after the fair opening of natural gas pipeline networks, the original “point-to-point” transaction model will be replaced by market-driven behaviors, making the verification and allocation of gas transmission capacity a key operational issue. Currently, neither pipeline operators nor government regulatory authorities have issued corresponding rules, regulations, or evaluation plans. To address this, this paper proposes a multi-dimensional quantitative evaluation model based on the Analytic Hierarchy Process (AHP), integrating both commercial and technical indicators. The model comprehensively considers six indicators: pipeline transportation fees, pipeline gas line pack, maximum gas storage capacity, pipeline pressure drop, energy consumption, and user satisfaction and constructs a quantitative evaluation system. Through the consistency check of the judgment matrix (CR = 0.06213 < 0.1), the weights of the respective indicators are determined as follows: 0.2584, 0.2054, 0.1419, 0.1166, 0.1419, and 0.1357. The specific score of each indicator is determined based on the deviation between each evaluation indicator and the theoretical optimal value under different gas volume allocation schemes. Combined with the weight proportion, the total score of each gas volume allocation scheme is finally calculated, thereby obtaining the recommended gas volume allocation scheme. The evaluation model was applied to a practical pipeline project. The evaluation results show that the AHP-based evaluation model can effectively quantify the advantages and disadvantages of different gas volume allocation schemes. Notably, the gas volume allocation scheme under normal operating conditions is not the optimal one; instead, it ranks last according to the scores, with a score 0.7 points lower than that of the optimal scheme. In addition, to facilitate rapid decision-making for gas volume allocation schemes, this paper designs a program using HTML and develops a gas volume allocation evaluation program with JavaScript based on the established model. This self-developed program has the function of automatically generating scheme scores once the proposed gas volume allocation for each station is input, providing a decision support tool for pipeline operators, shippers, and regulatory authorities. The evaluation model provides a theoretical and methodological basis for the dynamic optimization of natural gas pipeline gas volume allocation schemes under the fair opening model. It is expected to, on the one hand, provide a reference for transactions between pipeline network companies and shippers, and on the other hand, offer insights for regulatory authorities to further formulate detailed and fair gas transmission capacity transaction methods. Full article

The robust stabilization of the attitude of quadrotors with respect to disturbance torques is a fundamental and crucial control problem in many unmanned aerial vehicle (UAV) applications. For this problem, a control Lyapunov function (CLF)-based robust adaptive control was previously proposed by the authors, and its effectiveness was confirmed through numerical simulations. In this article, we tackle the experimental verification of this controller. We first construct a quadrotor testbed equipped with the self-developed flight controller. Then, we implement the proposed robust adaptive controller and perform flight experiments. According to the results of comparative experiments using a PID-type controller and a non-robust controller, we demonstrate the effectiveness of the proposed controller. Full article

A significant number of digital marketers use unethical marketing methods that violate Search Engine Optimization guidelines, with the objective of deceiving engines into displaying a specific website as the top result. The practice of fake comments constitutes a violation of Search Engine Optimization policies and is directly impeding market transparency. In addition, the absence of established standards between search engines, evaluation platforms and other trusted agencies makes exploitation easy. Therefore, in order to ensure fair competition among digital businesses, we propose a decentralized system for detecting fake comments, leveraging Blockchain technology for verification. The implementation of smart contracts as self-executing agreements will be achieved by utilizing the Ethereum network and the Truffle Suite. The Ethereum smart contracts will immutably record every comment as a transaction, eliminating any central authority. When a comment is flagged as suspicious, a digital business can trigger a verification request. Stakeholders or reviewers then vote on authenticity. Smart contracts collect these votes and issue a definitive verdict on whether the comment is fake. Full article

The chlorophyll-a (Chl-a) concentration, a critical parameter for characterizing marine primary productivity and ecological health, plays a vital role in providing ecological environment monitoring and climate change assessment while serving as a core retrieval product in ocean color remote sensing. Currently, more than ten ocean color satellites operate globally, including China’s HY-1C, HY-1D and HY-1E satellites. However, significant spatial data gaps exist in Chl-a concentration retrieval from satellites because of cloud cover, sun-glint, and limitation of sensor swath. This study aimed to systematically enhance the spatiotemporal integrity of ocean monitoring data through multisource data merging and reconstruction techniques. We integrated Chl-a concentration datasets from four major sensor types—Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Ocean and Land Color Instrument (OLCI), and Chinese Ocean Color and Temperature Scanner (COCTS)—and quantitatively evaluated their global coverage performance under different payload combinations. The key findings revealed that single-sensor 4-day continuous observation achieved effective coverage levels ranging from only 10.45–26.1%, while multi-sensor merging substantially increased coverage, namely, homogeneous payload merging provided 25.7% coverage for two MODIS satellites, 41.1% coverage for three VIIRS satellites, 24.8% coverage for two OLCI satellites, and 37.1% coverage for three COCTS satellites, with 10-payload merging increasing the coverage rate to 55.4%. Employing the Data Interpolating Empirical Orthogonal Functions (DINEOFS) algorithm, we successfully reconstructed data for China’s ocean color satellites. Validation against VIIRS reconstructions indicated high consistency (a mean relative error of 26% and a linear correlation coefficient of 0.93), whereas self-verification yielded a mean relative error of 27% and a linear correlation coefficient of 0.90. Case studies in Chinese offshore and adjacent waters, waters east of Mindanao Island and north of New Guinea, demonstrated the successful reconstruction of spatiotemporal Chl-a dynamics. The results demonstrated that China’s HY-1C, HY-1D, and HY-1E satellites enable daily global-scale Chl-a reconstruction. Full article

To explore the potential of Large Language Models (LLMs) as AI Annotators in the domain of sustainability reporting, this study establishes a systematic evaluation methodology. We use the specific case of European football clubs, quantifying their sustainability reports based on the sport Positive matrix as a benchmark to compare the performance of three state-of-the-art models (i.e., GPT-4o, Qwen-2-72b-instruct, and Llama-3-70b-instruct) against human expert scores. The evaluation is benchmarked on dimensions including accuracy, mean absolute error (MAE), and hallucination rates. The results indicate that GPT-4o is the top performer, yet its average accuracy of approximately 56% shows it cannot fully replace human experts at present. The study also reveals significant issues with overconfidence and factual hallucinations in models like Qwen-2-72b-instructon. Critically, we find that by implementing further data processing, specifically a Chain-of-Verification (CoVe) self-correction method, GPT-4o’s initial hallucination rate is successfully reduced from 16% to 10%, while accuracy improved to 58%. In conclusion, while LLMs demonstrate immense potential to streamline and democratize sustainability ratings, inherent risks like hallucinations remain a primary obstacle. Adopting verification strategies such as CoVe is a crucial pathway to enhancing model reliability and advancing their effective application in this field. Full article

Studies have shown that the iron concentration in the peritoneal fluid of women is associated with the severity of endometriosis. Therefore, investigation of iron metabolism-related genes (IM-RGs) in endometriosis holds significant implications for both prevention and therapeutic strategies in affected patients. Differentially expressed IM-RGs (DEIM-RGs) were identified by intersecting IM-RGs with differentially expressed genes derived from GSE86534. Mendelian randomization analysis was employed to determine DEIM-RGs causally associated with endometriosis, with subsequent verification through sensitivity analyses and the Steiger test. Biomarkers associated with IM-RGs in endometriosis were validated using expression data from GSE86534 and GSE105764. Functional annotation, regulatory network construction, and immunological profiling were conducted for these biomarkers. Single-cell RNA sequencing (scRNA-seq) (GSE213216) was utilized to identify distinctively expressed cellular subsets between endometriosis and controls. Experimental validation of biomarker expression was performed via reverse transcription–quantitative polymerase chain reaction (RT-qPCR). BMP6 and SLC48A1, biomarkers indicative of cellular BMP response, were influenced by a medicus variant mutation that inactivated PINK1 in complex I, concurrently enriched by both biomarkers. The lncRNA NEAT1 regulated BMP6 through hsa-mir-22-3p and hsa-mir-124-3p, while SLC48A1 was modulated by hsa-mir-423-5p, hsa-mir-19a-3p, and hsa-mir-19b-3p. Immune profiling revealed a negative correlation between BMP6 and monocytes, whereas SLC48A1 displayed a positive correlation with activated natural killer cells. scRNA-seq analysis identified macrophages and stromal stem cells as pivotal cellular components in endometriosis, exhibiting altered self-communication networks. RT-qPCR confirmed elevated expression of BMP6 and SLC48A1 in endometriosis samples relative to controls. Both BMP6 and SLC48A1 were consistently overexpressed in endometriosis, reinforcing their potential as biomarkers. Moreover, macrophages and stromal stem cells were delineated as key contributors. These findings provide novel insights into therapeutic and preventive approaches for patients with endometriosis. Full article

The influence of lighting color on food is a multidimensional process, linking visual interventions with people’s perception of food appearance, physiological responses, and psychological associations. This study, as a preliminary exploratory research, aims to initially investigate the effects of different lighting colors on food-induced consumer appetite and emotional perception. By measuring consumers’ physiological facial expression data, we verify whether the results are consistent with self-reported subjective evaluations. Questionnaires, Shapiro–Wilk tests, and one-sample t-tests were employed for data mining and cross-validation and combined with generalized facial expression recognition (GFER) technology to analyze participants’ emotional perceptions under various lighting colors. The results show that consumers displayed the most positive emotions and the highest appetite under 2700 K warm white light. Under this condition, the average intensity of participants’ “happy” emotion was 0.25 (SD = 0.12), indicating a clear positive emotional state. Eating willingness also reached its peak at 2700 K. In contrast, blue light-induced negative emotions and lower appetite. Among all lighting types, blue light evoked the strongest “sad” emotion (M = 0.39). This study provides a preliminary exploration of the theoretical framework regarding the relationship between food and consumer behavior, offering new perspectives for product marketing in the food industry and consumer food preference cognition. However, the generalizability of its conclusions still requires further verification in subsequent studies. Full article

With the rapid growth of Artificial Intelligence of Things (AIoT), identity management and trusted communication have become critical for system security and reliability. Continuous AI learning and large-scale device connectivity introduce challenges such as permission drift, cross-domain access, and fine-grained API calls. Traditional identity management often fails to balance privacy protection with efficiency, leading to risks of data leakage and misuse. To address these issues, this paper proposes a blockchain-based self-sovereign identity (SSI) management mechanism for AIoT. By integrating SSI with a zero-trust framework, it achieves decentralized identity storage and continuous verification, effectively preventing unauthorized access and misuse of identity data. The mechanism employs selective disclosure (SD) technology, allowing users to submit only necessary attributes, thereby ensuring user control over self-sovereign identity information and guaranteeing the privacy and integrity of undisclosed attributes. This significantly reduces verification overhead. Additionally, this paper designs a context-aware dynamic permission management that generates minimal permission sets in real time based on device requirements and environmental changes. Combined with the zero-trust principles of continuous verification and least privilege, it enhances secure interactions while maintaining flexibility. Performance experiments demonstrate that, compared with conventional approaches, the proposed zero-trust architecture-based SSI management mechanism better mitigates the risk of sensitive attribute leakage, improves identity verification efficiency under SD, and enhances the responsiveness of dynamic permission management, providing robust support for secure and efficient AIoT operations. Full article