Search Results (97)

Rapid urbanization has intensified inequalities in the built environment (BE), yet their relationship with well-being in areas where vulnerable populations are concentrated remains unclear. Using Shanghai as a case, this study examines the association between BE equity and area-level happiness across five types of vulnerable areas: those with larger numbers of minors, older adults, or migrant residents; economically disadvantaged areas; and areas with limited educational resources. We integrated geotagged Weibo check-in texts collected from January to December 2023, multi-source spatial data, street-view imagery, and interpretable machine learning. ERNIE 3.0 was used to derive sentiment scores from individual posts, which were then aggregated within each spatial unit to construct an area-level happiness score. Thirty-four BE indicators were organized within the 5D framework and analyzed using Mask2Former, Moran’s I, Gini coefficients, LightGBM, and SHAP. Results reveal marked central–peripheral disparities in BE resources and happiness, together with nonlinear and group-specific spatial associations. High-Gini patterns are evident in peri-urban areas, accounting for approximately 67% of vulnerability-focused areas, while strict equity–happiness discordance accounts for 42%. These findings suggest that BE distributional imbalance, adverse high-inequality–low-happiness co-occurrence, and equity–happiness discordance should be distinguished for targeted and inclusive planning. Full article

When large language models (LLMs) are implemented in the field of health information quality evaluation, ensuring that their outputs align with human judgment, logic, and strategic preferences has become a focal point of current research on AI alignment. This study proposes the Health Information Quality Evaluation Framework based on AI Post-Alignment (HIQE-PA) to examine whether large language models remain consistent with expert judgment standards in health information quality evaluation tasks. Using expert evaluation results as the benchmark, the framework assesses the consistency, stability, and cross-source adaptability of model outputs through structured task implementation and multidimensional statistical indicators. In the experimental study, ERNIE-3.5 and ChatGLM2-6B-32K were selected as general-purpose models, and the corresponding ERNIE-3.5 + HIQE-PA model and ChatGLM2-6B-32K + HIQE-PA model were constructed to compare post-alignment performance under different model conditions. Under three post-alignment performance evaluation indicators, including deviation degree, predictability, and fitness, the LLMs’ post-alignment performance was examined through their outputs in the health information quality evaluation task. The results show that the general LLMs achieved a high level of post-alignment performance only in the dimensions of readability and completeness, while the LLMs + HIQE-PA improved post-alignment performance across all dimensions. In particular, the ERNIE-3.5 + HIQE-PA model performed prominently, producing evaluation outputs that were closer to expert consensus and maintaining consistency with the expert benchmark across different text sources. This study demonstrates that post-alignment examinations can provide empirical support for model selection and governance in the health information domain. Full article

Frequent seasonal phase transitions in cold and arid lakes require different remote sensing indices for frozen and open-water periods, complicating the use of traditional empirical indices for automated monitoring. To address this challenge, this study proposes an intelligent indexing framework integrating the heuristic reasoning of Large Language Models (LLMs) with Random Forest (RF) feature selection. Leveraging the Google Earth Engine (GEE) and Landsat 8 data from Ulansuhai Lake, five LLMs such as Gemini and ERNIE were employed to generate candidate spectral indices based on typical sample spectra. Optimal band combinations were identified via RF importance, and Land Surface Temperature (LST) was incorporated as a physical constraint for unified cross-seasonal classification and determine the optimal threshold. Results show that the LLM-derived ERNIE-WISI and Gemini-WISI exhibit high robustness. During the freezing period, ERNIE-WISI significantly outperformed other indices, achieving an Overall Accuracy (OA) of 89% and a Kappa of 0.86. Spatially, it yielded snow and ice mapping with clear textures and low commission errors. During the non-freezing period, ERNIE-WISI achieved an OA of 95% with a Kappa of 0.84. While Gemini-WISI achieved an OA of 94% with a Kappa of 0.80, performing comparably to MNDWI. Notably, ERNIE-WISI effectively suppressed background interference in complex landscapes like narrow channels and aquaculture areas, maintaining high geometric fidelity and spatial continuity. A key advantage of ERNIE-WISI is its consistent performance without seasonal threshold adjustments. Aligned with the AI for Science paradigm, this methodology bridges AI-driven heuristic discovery and physical remote sensing, offering a robust, transferable solution for long-term dynamic lake monitoring in extreme environments, thereby facilitating sustainable water management. Full article

Pre-made dishes have drawn growing attention because of their convenience and rapid market expansion. Their food safety risks, however, are shaped not only by products themselves, but also by the gap between public perception, reported incidents, and inspection records. This study develops a three-stage analytical approach by combining Weibo public opinion data, news media reports, and food inspection records from Gansu Province. First, ERNIE and BERTopic are used to identify public sentiment and discussion topics. The results show that negative sentiment slightly exceeds positive sentiment, with school meals, additives, and food safety as the main concerns. Second, 11,110 pre-made dish-related food safety reports from Food Partner Network are clustered and assessed for incident severity. The results point to drug residues in aquatic products, microbial contamination in egg products, authenticity disputes over meat ingredients, and quality issues in frozen composite foods. Third, based on the 2024 official definition, 12,121 inspection records are screened, and 2783 definition-constrained pre-made dish-associated products are retained. Six imbalanced classification models are then constructed. The Weight + RF model performs relatively well for starch and starch products, with a Precision of 0.7857, an AUC-ROC of 0.7778, and an MCC of 0.4429. The study provides a reference for risk identification and inspection resource optimization under limited pre-made dish inspection data. Full article

In people-centered urban planning, enhancing the well-being of residents and tourists is one of the core objectives. Tourist emotion serves not only as a key indicator of the tourism experience but also indirectly reflects the quality of a city’s public spaces and built environment. In recent years, user-generated content has provided abundant data for understanding human emotional responses in urban environments, while deep learning models offer new technological pathways for extracting spatial–emotional associations from such data. However, existing research lacks a systematic evaluation of emotion analysis models from an urban spatial perspective and their application to uncover the relationship between emotional distribution and spatial characteristics in specific urban contexts. Based on a dataset of 9419 manually annotated travel reviews from Harbin, this study developed a multi-level evaluation framework and conducted a systematic comparison of seven emotion analysis models. This study then screened for the optimal model combinations based on two dimensions—spatial location and emotion polarity—to create a model matching matrix for mapping Harbin’s emotion map. Subsequently, a regression analysis was performed to examine the relationship between emotions and built environment elements. The results show that the ERNIE model demonstrated the best overall performance. Road density, green space density, and accommodation facility density were positively correlated with emotion, while POI diversity showed a negative correlation. This study demonstrates that emotion analysis technology can serve as a valuable analytical tool for identifying spatial patterns of sentiment, thereby offering empirical support for optimizing spatial design parameters and advancing a more people-centered approach to urban development. Full article

The electrocardiogram (ECG) is a central tool in cardiovascular diagnostics, yet interpretation requires expertise and remains subject to variability. Multimodal large language models (MLLMs) have shown emerging capabilities in medical image analysis, but their performance in ECG interpretation remains insufficiently characterized. This study evaluated the diagnostic accuracy and inter-run reliability of five MLLMs across ECG interpretation tasks. Thirteen standard 12-lead ECGs were presented to five models (ChatGPT-5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent runs per case, yielding 2275 task-level assessments. Six categorical interpretation tasks (rhythm, electrical axis, PR/P-wave morphology, QRS duration, ST/T-wave morphology, and QTc interval) were compared with expert-consensus ground truth, while heart rate estimation was evaluated using mean absolute error (MAE). Overall categorical accuracy ranged from 52.3% to 64.9%. QRS duration classification achieved the highest accuracy (66.2–90.8%), whereas ST/T-wave assessment showed the lowest performance (20.0–41.5%). Heart rate MAE ranged from 14.8 to 46.7 bpm. A dissociation between diagnostic accuracy and inter-run reliability was observed across models. These findings indicate that current MLLMs do not achieve clinically reliable ECG interpretation performance and highlight the importance of assessing diagnostic accuracy and inter-run reliability when evaluating artificial intelligence systems in biomedical diagnostics. Full article

Background/Objectives: Multimodal large language models (MLLMs) are increasingly evaluated for diagnostic tasks in medical imaging, including radiographic interpretation. However, most studies focus primarily on binary fracture detection and rarely assess clinically relevant fracture characteristics such as displacement or intra-articular extension, which influence treatment decisions. In addition, most evaluations rely on single-run inference designs that do not assess response reproducibility. This study evaluated the diagnostic performance and inter-run reliability of five MLLMs for radiographic assessment of distal radius fractures. Methods: Fifty fracture-positive distal radius radiographs were evaluated by five MLLMs (ChatGPT 5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent zero-shot inference runs (n = 1250 observations). Diagnostic tasks included fracture detection, intra-articular extension, and displacement. Sex and age were exploratory endpoints. Performance was summarized using sensitivity (fracture detection) and accuracy (other tasks), with inter-run reliability assessed via Fleiss’ κ. Results: Performance varied across tasks and models. Fracture detection sensitivity ranged from 39.6% to 99.6%, with two models exceeding 90%. Intra-articular extension accuracy ranged from 51.6% to 55.6%, consistent with chance-level performance. Displacement classification ranged from 34.8% to 70.4%. One model achieved substantial inter-run agreement across binary tasks (κ > 0.60), whereas two models showed slight agreement (κ < 0.20). Conclusions: Only two models exceeded 90% sensitivity for fracture detection, while intra-articular extension remained at chance level (≤55.6%). Substantial inter-run reliability (κ > 0.60) was observed in only one model. These findings indicate that current MLLMs do not reliably support multidimensional fracture assessment and that single-run evaluations overestimate robustness. Full article

The microstructural evolution and tensile behavior of Inconel 617 welded joints produced by gas tungsten arc welding (GTAW) with ERNiCrCoMo-1 filler were systematically investigated. Detailed microstructural characterization revealed that Cr-rich M₂₃C₆ and Ti-rich MC carbides are the dominant precipitates, while Mo-rich M₆C forms locally along grain boundaries after thermal exposure. The fusion and weld zones exhibit fine dendritic morphologies with uniformly distributed precipitates, resulting in significant strengthening through precipitation and dislocation–pinning mechanisms. Owing to the low heat input and compositional compatibility between the weld and base metals, the heat-affected zone remains extremely narrow and free of compositional transitions. The welded joint attains tensile strengths of 920 MPa at room temperature and 605.5 MPa at 750 °C, corresponding to joint efficiencies of 117% and 121%, respectively, with fracture consistently occurring in the base metal. Deformation analysis shows that plasticity at room temperature is governed by planar slip and dislocation entanglement, whereas deformation twinning predominates at elevated temperatures owing to the reduced stacking-fault energy and the pinning effect of M₂₃C₆ carbides. These results provide key insights into the deformation and strengthening mechanisms controlling the high-temperature performance of GTAW-welded Inconel 617 joints and offer guidance for their application in advanced nuclear and high-temperature energy systems. Full article

Background/Objectives: Isolated methylmalonic acidemia (iMMA) is a rare autosomal recessive metabolic disorder caused by defects in methylmalonyl-CoA mutase (MCM) activity or in the biosynthesis of its cofactor, adenosylcobalamin. Mutations in five genes—MMUT, MMAA, MMAB, MMADHC, and MCEE—are known to underlie this condition. This study aimed to characterize the clinical features and molecular spectrum of iMMA in Malaysian patients of diverse ethnic backgrounds. Material and Methods: Patients with biochemical evidence suggestive of iMMA, including elevated propionylcarnitine (C3), increased C3/C2 ratio, and raised urine methylmalonic acid levels in the absence of hyperhomocysteinemia, were selected for genetic testing. Sanger sequencing was performed to identify pathogenic variants in the MMUT, MMAA, MMAB, MMADHC, or MCEE genes. Results: The cohort consisted predominantly of Iban patients (n = 5), with the remaining cases comprising one Malay and one Thai–Malay individual. Age at diagnosis ranged from Day 1 of life to 6 years. All 7 patients were confirmed to have iMMA through molecular analysis. A total of seven pathogenic or likely pathogenic variants were identified, including two novel MMUT variants (c.246_250delinsGA and c.1358G>C), four known MMUT variants (c.560C>G, c.693C>G, c.982C>T, c.1106G>A), and one known MMAB variant (c.644+1G>A). Clinical presentation and disease severity varied across cases, reflecting underlying genotypic heterogeneity. Conclusions: This study highlights the molecular diversity and clinical variability of iMMA in Malaysia. Our findings reinforce the importance of integrating metabolic screening with molecular diagnostics to identify disease-causing variants and guide patient management strategies effectively. Full article

In Philippine state universities and colleges (SUCs), faculty are mandated to balance instruction, research, and extension as “trifocal” functions. Yet, research often competes with heavy teaching loads, administrative work, and community engagement, especially in science disciplines that demand laboratory-based and fieldwork. This qualitative multiple-case study examined how twelve science faculty members across academic ranks in a Philippine SUC system enact the research function within their trifocal roles. Drawing on semi-structured interviews, institutional and policy documents, and cross-case analysis, this study employed a case study design through the lens of systems thinking to identify how research function is embedded in institutional structures and professional life-worlds. Findings show that faculty construct research as (1) a catalyst that propels instruction and anchors extension programs; (2) a strategic requirement intertwined with promotion and career progression; and (3) a relational and infrastructural practice dependent on collegial networks, mentoring, and institutional support systems. Feedback loops link these themes wherein research output fuels promotion and time protection, which, in turn, shape opportunities for further research and mentoring. Additionally, verbatim accounts reveal how faculty members navigate structural pressures, such as bureaucratic processes and workload policies, while framing research as a moral and professional responsibility. This article argues that designing research support in SUCs requires moving beyond compliance-driven metrics to system-level arrangements that honor research as a form of scholarly work deeply connected with teaching quality and community impact. Implications are suggested for workload policy, mentoring, and research-capable learning environments in the Philippines and comparable higher education contexts. Full article

This study systematically evaluated the performance of three advanced large language models (LLMs)—GPT-4.0, ERNIE Bot 4.0, and GPT-4o—in the 2023 Chinese Medical Licensing Examination. Employing a dataset of 600 standardized questions, we analyzed the accuracy of each model in answering questions from three comprehensive sections: Basic Medical Comprehensive, Clinical Medical Comprehensive, and Humanities and Preventive Medicine Comprehensive. Our results demonstrate that both ERNIE Bot 4.0 and GPT-4o significantly outperformed GPT-4.0, achieving accuracies above the national pass mark. The study further examined the strengths and limitations of each model, providing insights into their applicability in medical education and potential areas for future improvement. These findings underscore the promise and challenges of deploying LLMs in multilingual medical education, suggesting a pathway towards integrating AI into medical training and assessment practices. Full article

Pipeline steels under cyclic loading in corrosive environments are prone to wear and corrosion–wear synergy. Low-dilution, high-reliability Ni-based Cold Metal Transfer (CMT) overlays are therefore required to ensure structural integrity. In this work, three overlay architectures were deposited on L415QS pipeline steel: a single-layer ERNiFeCr-1 coating, a double-layer ERNiFeCr-1/ERNiFeCr-1 coating, and an ERNiCrMo-3 interlayer plus ERNiFeCr-1 working layer. The microstructure, interfacial composition gradients, and dry sliding wear behavior were systematically characterized to clarify the role of interlayer design. The single-layer ERNiFeCr-1 coating shows a graded transition from epitaxial columnar grains to cellular/dendritic and fine equiaxed grains, with smooth Fe dilution, Ni–Cr enrichment, and a high fraction of high-angle grain boundaries, resulting in sound metallurgical bonding and good crack resistance. The double-layer ERNiFeCr-1 coating contains coarse, strongly textured columnar grains and pronounced interdendritic segregation in the upper layer, which promotes adhesive fatigue and brittle spalling and degrades wear resistance and friction stability. The ERNiCrMo-3 interlayer introduces continuous Fe-decreasing and Ni-Cr/Mo-increasing gradients, refines grains, suppresses continuous brittle phases, and generates dispersed second phases that assist crack deflection and load redistribution. Under dry sliding, the tribological performance ranks as follows: interlayer + overlay > single-layer > double-layer. The ERNiCrMo-3 interlayer system maintains the lowest and most stable friction coefficient due to the formation of a dense tribo-oxidative glaze layer. These results demonstrate an effective hierarchical alloy-process design strategy for optimizing Ni-based CMT overlays on pipeline steels. Full article

This study examines initial and repair welds between creep-resistant steels, P22 and P91, using ER90S-B3 and ERNiCrMo-3 steel-based and nickel-based filler materials, respectively. TIG welding with and without PWHT was applied. Microstructural evaluation revealed martensitic transformation in HAZ, decarburization in repairs, and the presence of Laves phase. Ni-based filler welds showed greater inhomogeneity. Hardness profiles confirmed softening in P91 HAZ and improved uniformity with PWHT. Steel-based filler provided better compatibility, especially in repair scenarios. The results support the use of ER90S-B3 with PWHT for enhanced reliability. Our findings align with EPRI guidelines and standards for weld integrity in high-temperature piping applications. Full article

Using Shanghai as a case study, this paper develops a multi-source fusion and interpretable machine learning framework. Sentiment indices were extracted from Weibo check-ins with ERNIE 3.0, street-view elements were identified using Mask2Former, and urban indicators like the Normalized Difference Vegetation Index, floor area ratio, and road network density were integrated. The coupling between residents’ sentiments and streetscape features during heatwaves was analyzed with Extreme Gradient Boosting, SHapley Additive exPlanations, and GeoSHAPLEY. Results show that (1) the average sentiment index is 0.583, indicating a generally positive tendency, with sentiments clustered spatially, and negative patches in central areas, while positive sentiments are concentrated in waterfronts and green zones. (2) SHapley Additive exPlanations analysis identifies NDVI (0.024), visual entropy (0.022), FAR (0.021), road network density (0.020), and aquatic rate (0.020) as key factors. Partial dependence results show that NDVI enhances sentiment at low-to-medium ranges but declines at higher levels; aquatic rate improves sentiment at 0.08–0.10; openness above 0.32 improves sentiment; and both visual entropy and color complexity show a U-shaped relationship. (3) GeoSHAPLEY shows pronounced spatial heterogeneity: waterfronts and the southwestern corridor have positive effects from water–green resources; high FAR and paved surfaces in the urban area exert negative influences; and orderly interfaces in the vitality corridor generate positive impacts. Overall, moderate greenery, visible water, openness, medium-density road networks, and orderly visual patterns mitigate negative sentiments during heatwaves, while excessive density and hard surfaces intensify stress. Based on these findings, this study proposes strategies: reducing density and impervious surfaces in the urban area, enhancing greenery and quality in waterfront and peripheral areas, and optimizing urban–rural interfaces. These insights support heat-adaptive and sustainable street design and spatial governance. Full article

In dealing with emergency events, it is very important when making scientific and correct decisions. As an important premise, the creation of information needs is quite essential. Taking earthquakes as a type of unexpected event, this paper constructs a large and model-driven system for automating the generating process of information requirements for earthquake response. This research explores how the different departments interact during an earthquake emergency response, how the information interacts with each other, and how the information requirement process operates. The system is designed from three points of view, building a knowledge base, designing and developing prompts, and designing the system structure. It talks about how computers automatically make info needs for sudden emergencies. During the experimental process, the backbone architectures used were four Large Language Models (LLMs): chatGLM (GLM-4.6), Spark (SparkX1.5), ERNIE Bot (4.5 Turbo), and DeepSeek (V3.2). According to the desired system process, information needs is generated by real-word cases and then they are compared to the gathered information needs by experts. In the comparison process, the “keyword weighted matching + text structure feature fusion” method was used to calculate the semantic similarity. Like true positives, false positives, and false negatives can be used to find differences and calculate metrics like precision and recal. And the F1-score is also computed. The experimental results show that all four LLMs achieved a precision and recall of over 90% in earthquake information extraction, with their F1-scores all exceeding 85%. This verifies the feasibility of the analytical method a chatGLM dopted in this research. Through comparative analysis, it was found that chatGLM exhibited the best performance, with an F1-score of 93.2%. Eventually, Python is used to script these aforementioned processes, which then create complete comparison charts for visual and test result checking. In the course of researching we also use Protege to create the knowledge requirements ontology, so it is easy for us to show and look at it. This research is particularly useful for emergency management departments, earthquake emergency response teams, and those working on intelligent emergency information systems or those focusing on the automated information requirement generation using technologies such as LLMs. It provides practical support for optimizing rapid decision-making in earthquake emergency response. Full article