Search Results (512)

With deepening global higher education reforms, outcome-based education has emerged as the core paradigm for teaching model innovation. This study investigates the structural dependencies and teaching effectiveness of the Environmental Design curriculum at Hubei Engineering University in medium-sized cities, China, addressing challenges of enrollment decline and market contraction critical for urban sustainability. Using network analysis, we construct curriculum support and contribution networks and course temporal networks to assess structural dependencies and teaching effectiveness, revealing structural patterns and optimizing the OBE-based Environmental Design curriculum to enhance educational quality and student competencies. Analysis reveals computer basic courses as knowledge transmission hubs, creating a course network with a distinct core–periphery structure. Technical course reforms significantly outperform theoretical course reforms in improving student performance metrics, such as higher average scores, better grade distributions, and reduced performance gaps, while innovative practice courses show peripheral isolation patterns, indicating limited connectivity with core curriculum modules, which reduces their educational impact. These findings provide empirical insights for curriculum optimization, supporting urban sustainable development through enhanced professional talent cultivation equipped to address environmental challenges like sustainable design practices and resource-efficient urban planning. Network analysis applications introduce innovative frameworks for curriculum reform strategies. Future research expansion through larger sample validation will support urban sustainable development goals and enhance professional talent cultivation outcomes. Full article

As large language models (LLMs) increasingly enter specialized domains, inference without external resources often leads to knowledge gaps, opaque reasoning, and hallucinations. To address these challenges in power equipment defect grading, we propose a zero-shot question-answering framework that requires no task-specific examples. Our system performs two-stage retrieval—first using a Sentence-BERT model fine-tuned on power equipment maintenance texts for coarse filtering, then combining TF-IDF and semantic re-ranking for fine-grained selection of the most relevant knowledge snippets. We embed both the user query and the retrieved evidence into a Chain-of-Thought (CoT) prompt, guiding the pre-trained LLM through multi-step reasoning with self-validation and without any model fine-tuning. Experimental results show that on a held-out test set of 218 inspection records, our method achieves a grading accuracy of 54.2%, which is 6.0 percentage points higher than the fine-tuned BERT baseline at 48.2%; an Explanation Coherence Score (ECS) of 4.2 compared to 3.1 for the baseline; a mean retrieval latency of 28.3 ms; and an average LLM inference time of 5.46 s. Ablation and sensitivity analyses demonstrate that a fine-stage retrieval pool size of k = 30 offers the optimal trade-off between accuracy and latency; human expert evaluation by six senior engineers yields average Usefulness and Trustworthiness scores of 4.1 and 4.3, respectively. Case studies across representative defect scenarios further highlight the system’s robust zero-shot performance. Full article

The rapid evolution of deep learning (DL) has fundamentally transformed the paradigm for detecting special components in agricultural products, addressing critical challenges in food safety, quality control, and precision agriculture. This comprehensive review systematically analyzes many seminal studies to evaluate cutting-edge DL applications across three core domains: contaminant surveillance (heavy metals, pesticides, and mycotoxins), nutritional component quantification (soluble solids, polyphenols, and pigments), and structural/biomarker assessment (disease symptoms, gel properties, and physiological traits). Emerging hybrid architectures—including attention-enhanced convolutional neural networks (CNNs) for lesion localization, wavelet-coupled autoencoders for spectral denoising, and multi-task learning frameworks for joint parameter prediction—demonstrate unprecedented accuracy in decoding complex agricultural matrices. Particularly noteworthy are sensor fusion strategies integrating hyperspectral imaging (HSI), Raman spectroscopy, and microwave detection with deep feature extraction, achieving industrial-grade performance ($RPD$ > 3.0) while reducing detection time by 30–100× versus conventional methods. Nevertheless, persistent barriers in the “black-box” nature of complex models, severe lack of standardized data and protocols, computational inefficiency, and poor field robustness hinder the reliable deployment and adoption of DL for detecting special components in agricultural products. This review provides an essential foundation and roadmap for future research to bridge the gap between laboratory DL models and their effective, trusted application in real-world agricultural settings. Full article

This review presents a comprehensive synthesis of recent advances in the tensile performance of adhesively bonded joints, focusing on applied aspects and modeling developments rather than providing a full theoretical analysis. Although many studies have addressed individual joint types or modeling techniques, an integrated review that compares joint configurations, modeling strategies, and performance optimization methods under tensile loading remains lacking. This work addresses that gap by examining the mechanical behavior of key joint types, namely, single-lap, single-strap, and double-strap joints, and highlighting their differences in stress distribution, failure mechanisms, and structural efficiency. Modeling and simulation approaches, including cohesive zone modeling, extended finite element methods, and virtual crack closure techniques, are assessed for their predictive accuracy and applicability to various joint geometries. This review also covers material and geometric enhancements, such as adherend tapering, fillets, notching, bi-adhesives, functionally graded bondlines, and nano-enhanced adhesives. These strategies are evaluated in terms of their ability to reduce stress concentrations and improve damage tolerance. Failure modes, adhesive and adherend defects, and delamination risks are also discussed. Finally, comparative insights into different joint configurations illustrate how geometry and adhesive selection influence strength, energy absorption, and weight efficiency. This review provides design-oriented guidance for optimizing bonded joints in aerospace, automotive, and structural engineering applications. Full article

Improving curriculum structure is critical for enhancing student success and on-time graduation, yet few methods exist to evaluate how prerequisite paths shape student progression and graduation outcomes. This study proposes a data-driven, graph-based framework that integrates course prerequisite networks with student performance data to systematically analyze curricular structure and student outcomes. We identify high-risk courses by jointly modeling their structural importance and pass rates, and quantify the time and survivability of different prerequisite paths using probabilistic models. Additionally, we introduced grade transition patterns to capture more nuanced transitions in student performance and pinpoint bottlenecks along prerequisite paths. Applying the model on four science and engineering majors from a public institution, the results not only identify high-risk courses often missed in conventional analyses, but also reveal path-level disparities and structural bottlenecks that affect student progression and time to graduation. For example, in the Computer Science major, we identified that the architecture and operating systems pathway is more challenging than the software engineering pathway. A closer examination of the course pairs along this trajectory revealed that the difficulty stems from a significant drop in student performance between a prerequisite–successor course pairs.This type of analysis fills a gap in conventional curriculum studies, which often overlook path-level dynamics, and offers actionable insights for educators a to identify high risk curricular components. Full article

Objective: To evaluate the outcomes of a modified infratemporal fossa approach (ITFA) that preserves the posterior external auditory canal (EAC) in patients with tumors in the infratemporal fossa and skull base, focusing on postoperative hearing and facial nerve function. Methods: This retrospective study included nine patients who underwent ITFA with posterior EAC preservation for tumor removal while minimizing facial nerve rerouting. All surgeries were performed by a single surgeon. Preoperative and postoperative hearing levels, facial nerve function, tumor characteristics, and surgical outcomes were analyzed. Air-bone gaps (ABG) were assessed using pure tone audiometry, and facial nerve function was assessed using the House–Brackmann grading system. Results: The cohort consisted of eight female patients and one male patient, with a mean tumor size of 3.0 cm. Surgical outcomes were promising, with no statistically significant increase in postoperative ABG and well-preserved facial nerve function. Only one patient developed postoperative grade II facial palsy. A residual tumor was identified in one case with extensive meningioma, which has remained stable, and no recurrence or regrowth was noted during the follow-up period (mean: 3.7 years). The modified approach minimized complications related to conductive hearing loss and facial nerve dysfunction. Conclusions: The modified ITFA with posterior EAC preservation provides a promising alternative to conventional ITFA for managing deep-seated tumors. It preserves both hearing and facial nerve function while ensuring adequate tumor resection. Full article

A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational web-based AI ensemble for coastal flood risk assessment integrating real-time multi-agency data, (2) an automated regional calibration system that corrects systematic model biases through machine learning, and (3) browser-accessible implementation of research-grade modeling previously requiring specialized computational resources. The system combines Bayesian neural networks with optional LSTM and attention-based models, implementing automatic regional calibration and multi-source elevation consensus through a modular Python architecture. Real-time API integration achieves >99% system uptime with sub-3-second response times via intelligent caching. Validation against Hurricane Isabel (2003) demonstrates correction from 197% overprediction (6.92 m predicted vs. 2.33 m observed) to accurate prediction through automated identification of a Chesapeake Bay-specific reduction factor of 0.337. Comprehensive validation against 15 major storms (1992–2024) shows substantial improvement over standard methods (RMSE = 0.436 m vs. 2.267 m; R² = 0.934 vs. −0.786). Economic assessment using NACCS fragility curves demonstrates 12.7-year payback periods for flood protection investments. The open-source Streamlit implementation democratizes access to research-grade risk assessment, transforming months-long specialist analyses into immediate browser-based tools without compromising scientific rigor. Full article

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article

The Qiubudong silver deposit on the western margin of the Fuping ore cluster in the central North China Craton is a representative breccia-type deposit characterized by relatively high-grade ores, thick mineralized zones, and extensive alteration, indicating considerable potential for economic resource development and further exploration. Previous studies on this deposit have not addressed its genetic mineralogical characteristics. This study focuses on pyrite and quartz to investigate their typomorphic features, such as crystal morphology, trace element composition, thermoelectric properties, and luminescence characteristics, and their implications for ore-forming processes. Pyrite crystals are predominantly cubic in early stages, while pentagonal dodecahedral and cubic–dodecahedral combinations peak during the main mineralization stage. The pyrite is sulfur-deficient and iron-rich, enriched in Au, and relatively high in Ag, Cu, Pb, and Bi contents during the main ore-forming stage. Rare earth element (REE) concentrations are low, with weak LREE-HREE fractionation and a strong negative Eu anomaly. The thermoelectric coefficient of pyrite ranges from −328.9 to +335.6 μV/°C, with a mean of +197.63 μV/°C; P-type conduction dominates, with an occurrence rate of 58%–100% and an average of 88.78%. A weak–low temperature and a strong–high temperature peak characterize quartz thermoluminescence during the main mineralization stage. Fluid inclusions in quartz include liquid-rich, vapor-rich, and two-phase types, with salinities ranging from 10.11% to 12.62% NaCl equiv. (average 11.16%) and densities from 0.91 to 0.95 g/cm³ (average 0.90 g/cm³). The ore-forming fluids are interpreted as F-rich, low-salinity, low-density hydrothermal fluids of volcanic origin at medium–low temperatures. The abundance of pentagonal dodecahedral pyrite, low Co/Ni ratios, high Cu contents, and complex quartz thermoluminescence signatures are key mineralogical indicators for deep prospecting. Combined with thermoelectric data and morphological analysis, the depth interval around 800 m between drill holes ZK3204 and ZK3201 has high mineralization potential. This study fills a research gap on the genetic mineralogy of the Qiubudong deposit and provides a scientific basis for deep exploration. Full article

Predicting bending fractures in advanced high-strength steel (AHSS) is challenging due to complex microstructural behaviors and strain rate dependencies, particularly in industrial forming processes. Current models and standards primarily focus on quasi-static tension or slow bending speeds, leaving a gap in understanding the accelerated failure of AHSS without necking. In this study, direct bending experiments were conducted on dual-phase, complex-phase, and martensitic AHSS grades under varying bending speeds and radii. Since the bending crack is irrelevant to the load drop, surface crack evolution was measured using three-dimensional surface profile analysis. The results showed that accelerated bending significantly delayed crack initiation across all tested materials, with small-radius bending showing reduced strain localization due to strain rate hardening. Larger-radius bending benefited primarily from increased fracture strain. Full article

The organic Rankine cycle (ORC) is a key technology for the recovery of low-grade waste heat, but its efficient and stable operation is challenged by complex kinetic coupling. This paper proposes a model partitioning strategy based on gap measurement to construct a high-fidelity ORC system model and combines the setting of observer decoupling and multi-model switching strategies to reduce the coupling impact and enhance adaptability. For control optimization, the reinforcement learning method of deep deterministic Policy Gradient (DDPG) is adopted to break through the limitations of the traditional discrete action space and achieve precise optimization in the continuous space. The proposed DDPG-MGPC (Hybrid Model Predictive Control) framework significantly enhances robustness and adaptability through the synergy of reinforcement learning and model prediction. Simulation shows that, compared with the existing hybrid reinforcement learning and MPC methods, DDPG-MGPC has better tracking performance and anti-interference ability under dynamic working conditions, providing a more efficient solution for the practical application of ORC. Full article

Background: Endometrial cancer (EC) is the most common gynecological malignancy, increasingly affecting premenopausal women. While hysterectomy is the standard treatment, it eliminates reproductive potential, highlighting the need for effective fertility-sparing alternatives. Current ESHRE/ESGO/ESGE guidelines recommend progestin-based therapies, often with hysteroscopic resection. However, these are based on limited pharmacological options and moderate to low-quality evidence. Novel and combination therapies have shown promise but remain absent from current clinical guidelines. This review aims to evaluate the efficacy and safety of fertility-preserving treatments for early-stage EC, emphasizing the need to update current strategies based on emerging data. Methods: A systematic review and meta-analysis will follow PRISMA guidelines and the Cochrane Handbook. Eligible studies, including randomized and non-randomized designs, will assess fertility-preserving treatments for early-stage EC. Data will be extracted on complete response, recurrence, and long-term fertility outcomes. The GRADE system will assess evidence certainty. Risk of bias will be evaluated using RoB 2 for RCTs and ROBINS-I for non-randomized studies. Meta-analysis will be performed if sufficient data are available. Conclusions: This review will provide a comprehensive analysis of fertility-sparing treatments for early-stage EC, support personalized strategies, identify evidence gaps, and guide future research. Trial registration—Prospero: CRD420251032161. Full article

Currently, high-tech assistive technologies (ATs), particularly Socially Assistive Robots (SARs), virtual reality (VR) and conversational AI (ConvAI), are considered very useful in supporting professionals in Speech and Language Therapy (SLT) for children with communication disorders. However, despite a positive public perception, therapists face difficulties when integrating these technologies into practice due to technical challenges and a lack of user-friendly interfaces. To address this gap, a design-based research approach has been employed to streamline the integration of SARs, VR and ConvAI in SLT, and a new software platform called “ATLog” has been developed for designing interactive and playful learning scenarios with ATs. ATLog’s main features include visual-based programming with graphical interface, enabling therapists to intuitively create personalized interactive scenarios without advanced programming skills. The platform follows a subprocess-oriented design, breaking down SAR skills and VR scenarios into microskills represented by pre-programmed graphical blocks, tailored to specific treatment domains, therapy goals, and language skill levels. The ATLog platform was evaluated by 27 SLT experts using the Technology Acceptance Model (TAM) and System Usability Scale (SUS) questionnaires, extended with additional questions specifically focused on ATLog structure and functionalities. According to the SUS results, most of the experts (74%) evaluated ATLog with grades over 70, indicating high acceptance of its usability. Over half (52%) of the experts rated the additional questions focused on ATLog’s structure and functionalities in the A range (90–100), while 26% rated them in the B range (80–89), showing strong acceptance of the platform for creating and running personalized interactive scenarios with ATs. According to the TAM results, experts gave high grades for both perceived usefulness (44% in the A range) and perceived ease of use (63% in the A range). Full article

Duplex ball bearings are common components in space satellite mechanisms, and their behaviour impacts the overall performance and reliability of these systems. During rocket launches, these bearings suffer high vibrational loads, making their dynamic response essential for their survival. To predict the dynamic behaviour under vibration, simulations and experimental tests are performed. However, published models for space applications fail to capture the variations observed in test responses. This study presents a multi-degree-of-freedom nonlinear multibody model of a hard-preloaded duplex space ball bearing, particularized for this work to the case in which the outer ring is attached to a shaker and the inner ring to a test dummy mass. The model incorporates the Hunt and Crossley contact damping formulation and employs quaternions to accurately represent rotational dynamics. The simulated model response is validated against previously published axial test data, and its response under step, sine, and random excitations is analysed both in the case of radial and axial excitation. The results reveal key insights into frequency evolution, stress distribution, gapping phenomena, and response amplification, providing a deeper understanding of the dynamic performance of space-grade ball bearings. Full article

The global increase in early-onset cancers among adolescents and young adults has happened at the same time as the rise in the consumption of ultra-processed foods (UPFs). Far beyond their poor nutritional quality, UPFs are increasingly seen as Trojan horses, complex biological agents that interfere with many functions of the human organism. In this review, we utilise the Trojan horse model to explain the quiet and building health risks from UPFs as foods that seem harmless, convenient, and affordable while secretly delivering endocrine-disrupting chemicals (EDCs), causing chronic low-grade inflammation, altering the microbiome, and producing epigenetic alterations. We bring together new proof showing that UPFs mess up hormonal signals, harm the body’s ability to fight off harmful germs, lead to an imbalance of microbes, and cause detrimental changes linked to cancer. Important components, such as bisphenols and phthalates, can migrate from containers into food, while additional ingredients and effects from cooking disrupt the normal balance of cells. These exposures are especially harmful during vulnerable developmental periods and may lay the groundwork for disease many years later. The Trojan horse model illustrates the hidden nature of UPF-related damage, not through a sudden toxin but via chronic dysregulation of metabolic, hormonal, and genetic control. This model changes focus from usual diet worries to a bigger-picture view of UPFs as causes of life-disrupting damage. Ultimately, this review aims to identify gaps in current knowledge and epidemiological approaches and highlight the need for multi-omics, long-term studies and personalised nutrition plans to assess and reduce the cancer risk associated with UPFs. Recognising UPFs as a silent disruptor is crucial in shaping public health policies and cancer prevention programs targeting younger people. Full article