Search Results (3,396)

With the rapid development of prefabricated bridge construction, traditional manual bending and welding techniques for stirrups increasingly reveal limitations in efficiency, quality, and safety. To promote intelligent technologies in smart girder yards, this study establishes and reports an automated logistics system covering the entire workflow of bending–delivering–welding–storage for reinforcement processing, alongside key innovations, including an integrated stirrup bending workstation, an intelligent rebar cage welding station, and laser-adaptive seam-tracking technology. The results demonstrate that the system achieves fully automated and standardized construction of rebar cages, achieving 100% compliance in quality parameters (e.g., rebar spacing) while eliminating quality risks. Implementation in the G107 Chinese National Highway retrofit project reduced the site footprint by 27%, labor input by 40%, and construction duration by 60% compared with conventional prefabrication yards, saving CNY 3.38 million per thousand girders and reducing rebar consumption by 50 metric tons. This research provides a replicable technical pathway for intelligent bridge construction and significantly advances the mechanization and digitalization of rebar processing and welding. Full article

Non-muscle invasive bladder cancer (NMIBC) accounts for the majority of bladder cancer diagnoses and remains a clinical challenge due to its high recurrence and progression rates despite intravesical Bacillus Calmette–Guérin (BCG) therapy. In recent years, tumor-infiltrating lymphocytes (TILs) have emerged as promising biomarkers, reflecting the interplay between the tumor and host immune system. However, the evidence regarding their prognostic and predictive role is still conflicting, largely due to methodological heterogeneity, lack of standardized evaluation criteria, and limited prospective validation. This narrative review summarizes the current knowledge on TILs in NMIBC, focusing on their compartmental distribution (stromal, intraepithelial, and tumor–stroma interface), compositional diversity (CD4⁺, CD8⁺, Treg, B cells), and spatial dynamics. Special attention is given to their role in predicting response to BCG immunotherapy, the contribution of tumor-associated macrophages and tertiary lymphoid structures, and the emergence of immune escape pathways, including Programmed Death-Ligand 1 (PD-L1) and the HLA-E/NKG2A axis. Advances in digital pathology, spatial transcriptomics, and integrated immunoscore models provide more accurate metrics compared to simple cell counts, highlighting the importance of functional and spatial signatures. Despite encouraging progress, TILs are not yet ready for routine incorporation into histopathological reporting. Future directions include standardized assessment, integration with molecular biomarkers, and prospective multicenter validation to enable their translation into risk stratification and personalized therapeutic decision-making. Full article

Previous research identified three reasons for valuing nature (i.e., the basis for seeing nature as valuable and important): (1) valuing nature for what it gives to humans (instrumental), (2) valuing nature for its own sake (intrinsic), and (3) valuing nature because of the relationship between people and nature (relational). Of these, relational value-bases have been less studied, especially in non-Western cultures. Using a large sample (n = 2618), with participants from five distinct cultural regions (Japan, Taiwan, Italy, France, USA), the present research tests whether a three-factor framework of environmental value-bases generalizes to other cultures. Our findings demonstrate the configural and metric invariance of the recently validated Environmental Value-Bases Scale, indicating that the latent constructs generalize across sub-samples of the five regions and that the measure can be used to test associations between the value-bases and outcomes across cultures. However, we only found partial scalar invariance, suggesting (a) that caution is needed when comparing scale means between cultures and (b) that such tests are most appropriately performed using latent means. This research further contributes to the growing value-basis literature by comparing the latent means for each value-basis between and within each of the five regions and by demonstrating their associations with place attachment. Full article

Background: Performance status is often cited as an independent predictor of survival after metastatic spine tumor surgery (MSTS), but its standalone predictive value for short-term outcomes remains unclear. We aimed to evaluate how well Karnofsky (KPS) and Eastern Cooperative Oncology Group performance status (ECOG-PS) predict 90-day survival, a common surgical candidacy threshold, in patients managed with MSTS. Methods: We conducted a retrospective study of 175 adult patients who underwent MSTS at a single institution (2012–2025). All patients had documented preoperative KPS and ECOG-PS scores. Univariable logistic regression was used to assess associations with 90-day survival. Predictive performance was assessed by discrimination (AUC), diagnostic accuracy, calibration (Brier score), and clinical utility (decision curve analysis). Results: The crude 90-day survival rate was 73%. Both KPS (OR 1.02 [95% CI 1.01 to 1.05]; p = 0.001) and ECOG-PS (OR 0.51 [95% CI 0.36 to 0.73]; p < 0.001) were statistically associated with survival. However, discrimination was modest (AUC 0.65 for KPS, 0.68 for ECOG-PS), with the most balanced diagnostic accuracy achieved at KPS ≥ 70 (sensitivity 0.66, specificity 0.62) and ECOG-PS ≤ 2 (sensitivity 0.76, specificity 0.5). Calibration was fair (Brier scores 0.185 and 0.182, respectively). Decision curve analysis showed minimal net benefit across most threshold probabilities, with ECOG-PS performing slightly better at intermediate thresholds (30–60%), the zone of greatest clinical uncertainty. Conclusions: Despite being widely cited as an independent predictor of postoperative survival in patients with metastatic spine disease, performance status assessed via the KPS and ECOG-PS demonstrated only modest overall discriminatory ability, diagnostic accuracy, calibration, and clinical utility when used alone to predict 90-day survival after MSTS. While both scores retained meaningful value at the extremes (i.e., patients with very poor or very good performance status had more predictable outcomes), caution is warranted in intermediate cases, where performance status alone may be insufficient to guide treatment decisions. These findings highlight the critical difference between statistical association and the real-world clinical utility of a single metric to predict outcome in this patient population. Full article

Induction motors require effective speed controllers to handle challenging conditions such as indirect vector control, nonlinear dynamics, load-disturbances, and changes in rotor resistance. Although proportional–integral (PI) controllers and type-1 fuzzy logic controllers (T1-FLC) are relatively straightforward to implement, they can produce significant overshoot and slow recovery; type-2 fuzzy logic controllers (T2-FLC), on the other hand, improve uncertainty management at the cost of higher computational complexity. This study proposes a type-3 fuzzy logic controller (T3-FLC) that balances robustness with a single $\alpha$-slice using two inputs and seven membership functions per input (49 rules). In six comparison scenarios, the type-3 FLC (T3-FLC) consistently offers a lower overshoot percentage and shorter recovery/settling times than the PI controller and type-1 FLC (T1-FLC). Overshoot drops to $0.13\%$ with T3-FLC during a high-speed positive step, while this value for the PI controller is $4.43\%$. During a low-amplitude positive step, T3-FLC reaches $1.37\%$, while the PI controller reaches $11.12\%$ and T1-FLC reaches $4.13\%$. After load torque is removed, the recovery time $t_{\mathrm{rec}}$ under T3-FLC is $0.064$ s at high speed and $0.158$ s at low speed, while for PI, these values are $0.400$ s and $1.975$ s, respectively. Under variations in rotor resistance, T3-FLC maintains a significantly smaller overshoot value: with a $-20\%$ change (3–6 s window), the values are $1.45\%$ (T3-FLC) versus $9.59\%$ (PI) and $4.51\%$ (T1-FLC); with a $+20\%$ change (3–6 s), the values are $0.14\%$ (T3-FLC) versus $4.36\%$ (PI) and $0.15\%$ (T1-FLC). Although there are isolated cases in which PI or T1-FLC shows a marginal advantage in a single metric (e.g., slightly smaller overshoot during transition or lower peak error during disturbance), T3-FLC generally provides the best balance, combining low overshoot with short settling/recovery time while keeping steady-state error at zero in all scenarios. Full article

This study investigates the influence of different effective precipitation (P_e) estimation methods on drought index performance in a humid region of Türkiye. The standard precipitation index (SPI) and the reconnaissance drought index (RDI) were compared with their effective precipitation-based counterparts, Agricultural Standardized Precipitation Index (aSPI) and Effective Reconnaissance Drought Index (eRDI), using four P_e estimation methods: USBR (U.S. Bureau of Reclamation), USDA-(Simplified and CROPWAT) (U.S. Department of Agriculture), and FAO (Food and Agriculture Organization). Data from three closely located meteorological stations (Çanakkale, Bozcaada, and Gökçeada) were analyzed across multiple time scales (1-, 3-, 6-, 12-month, and annual). Statistical metrics—coefficient of determination (R²), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE)—were used to assess the indices, and trend analyses were conducted using the Mann–Kendall and Sen’s Slope tests. The USDA-Simplified method consistently showed the highest accuracy across all stations and time scales (R² ≈ 0.99; lowest RMSE ≈ 0.09; NSE > 0.95), while the FAO method performed poorly, particularly at the 1-month scale. Drought frequency and severity were found to increase with time scale, contrary to trends observed in arid regions. Trend analysis revealed no significant changes at short time scales, but statistically significantly increasing drought severity was detected in longer scales, especially in Çanakkale, with slopes reaching up to –0.018 per year. The findings highlight the importance of selecting appropriate P_e estimation methods for accurate drought assessment, even in humid climates, and support the use of aSPI and eRDI with the USDA-Simplified method. Full article

The growing use of electrical and electronic appliances, coupled with shorter product lifespans, has accelerated the rise in waste electrical and electronic equipment (WEEE). Accurate forecasting is essential for addressing environmental challenges, conserving resources, and advancing the circular economy (CE). This research employs a Trigonometry-Based Discrete Grey Model (TBDGM(1,1)) that integrates the Jaya algorithm and Least Squares Estimation (LSE) for parameter estimation. By leveraging Jaya’s parameter-free robustness and LSE’s computational efficiency, the model enhances prediction accuracy for small-sample and nonlinear datasets. WEEE data from Washington State (WA) in the USA and Türkiye are utilized to validate the model, demonstrating cross-context adaptability. To evaluate performance, the model is benchmarked against five state-of-the-art discrete grey models. For the WA dataset, additional benchmarking against methods used in prior e-waste forecasting literature enables a dual-layer comparative analysis, which strengthens the validity and practical relevance of the approach. Across evaluations and multiple performance metrics, TBDGM(1,1) attains satisfactory and competitive prediction performance on the WA and Türkiye datasets relative to comparator models. Using TBDGM(1,1), Türkiye’s e-waste is forecast for 2021–2030, with the 2030 amount projected at approximately 489 kilotones. The findings provide valuable insights for policymakers and researchers, offering a standardized and reliable forecasting tool that supports CE-driven strategies in e-waste management. Full article

Accurate identification of rear light signals in preceding vehicles is pivotal for Advanced Driver Assistance Systems (ADAS), enabling early detection of driver intentions and thereby improving road safety. In this work, we present a novel approach that leverages a meta-learning-enhanced YOLOv8 model to detect left and right turn indicators, as well as brake signals. Traditional radar and LiDAR provide robust geometry, range, and motion cues that can indirectly suggest driver intent (e.g., deceleration or lane drift). However, they do not directly interpret color-coded rear signals, which limits early intent recognition from the taillights. We therefore focus on a camera-based approach that complements ranging sensors by decoding color and spatial patterns in rear lights. This approach to detecting vehicle signals poses additional challenges due to factors such as high reflectivity and the subtle visual differences between directional indicators. We address these by training a YOLOv8 model with a meta-learning strategy, thus enhancing its capability to learn from minimal data and rapidly adapt to new scenarios. Furthermore, we developed a post-processing layer that classifies signals by the geometric properties of detected objects, employing mathematical principles such as distance, area calculation, and Intersection over Union (IoU) metrics. Our approach increases adaptability and performance compared to traditional deep learning techniques, supporting the conclusion that integrating meta-learning into real-time object detection frameworks provides a scalable and robust solution for intelligent vehicle perception, significantly enhancing situational awareness and road safety through reliable prediction of vehicular behavior. Full article

Background/Objectives: Precise breast ultrasound (BUS) segmentation supports reliable measurement, quantitative analysis, and downstream classification yet remains difficult for small or low-contrast lesions with fuzzy margins and speckle noise. Text prompts can add clinical context, but directly applying weakly localized text–image cues (e.g., CAM/CLIP-derived signals) tends to produce coarse, blob-like responses that smear boundaries unless additional mechanisms recover fine edges. Methods: We propose XBusNet, a novel dual-prompt, dual-branch multimodal model that combines image features with clinically grounded text. A global pathway based on a CLIP Vision Transformer encodes whole-image semantics conditioned on lesion size and location, while a local U-Net pathway emphasizes precise boundaries and is modulated by prompts that describe shape, margin, and Breast Imaging Reporting and Data System (BI-RADS) terms. Prompts are assembled automatically from structured metadata, requiring no manual clicks. We evaluate the model on the Breast Lesions USG (BLU) dataset using five-fold cross-validation. The primary metrics are Dice and Intersection over Union (IoU); we also conduct size-stratified analyses and ablations to assess the roles of the global and local paths and the text-driven modulation. Results: XBusNet achieves state-of-the-art performance on BLU, with a mean Dice of 0.8766 and IoU of 0.8150, outperforming six strong baselines. Small lesions show the largest gains, with fewer missed regions and fewer spurious activations. Ablation studies show complementary contributions of global context, local boundary modeling, and prompt-based modulation. Conclusions: A dual-prompt, dual-branch multimodal design that merges global semantics with local precision yields accurate BUS segmentation masks and improves robustness for small, low-contrast lesions. Full article

This article formalizes AI-assisted assessment as a discrete-time policy-level design for iterative feedback and evaluates it in a digitally transformed higher-education setting. We integrate an agentic retrieval-augmented generation (RAG) feedback engine—operationalized through planning (rubric-aligned task decomposition), tool use beyond retrieval (tests, static/dynamic analyzers, rubric checker), and self-critique (checklist-based verification)—into a six-iteration dynamic evaluation cycle. Learning trajectories are modeled with three complementary formulations: (i) an interpretable update rule with explicit parameters $\eta$ and $\lambda$ that links next-step gains to feedback quality and the gap-to-target and yields iteration-complexity and stability conditions; (ii) a logistic-convergence model capturing diminishing returns near ceiling; and (iii) a relative-gain regression quantifying the marginal effect of feedback quality on the fraction of the gap closed per iteration. In a Concurrent Programming course ($n=35$), the cohort mean increased from 58.4 to 91.2 (0–100), while dispersion decreased from 9.7 to 5.8 across six iterations; a Greenhouse–Geisser corrected repeated-measures ANOVA indicated significant within-student change. Parameter estimates show that higher-quality, evidence-grounded feedback is associated with larger next-step gains and faster convergence. Beyond performance, we engage the broader pedagogical question of what to value and how to assess in AI-rich settings: we elevate process and provenance—planning artifacts, tool-usage traces, test outcomes, and evidence citations—to first-class assessment signals, and outline defensible formats (trace-based walkthroughs and oral/code defenses) that our controller can instrument. We position this as a design model for feedback policy, complementary to state-estimation approaches such as knowledge tracing. We discuss implications for instrumentation, equity-aware metrics, reproducibility, and epistemically aligned rubrics. Limitations include the observational, single-course design; future work should test causal variants (e.g., stepped-wedge trials) and cross-domain generalization. Full article

Natural fiber-reinforced unidirectional composites are increasingly adopted in modern industries due to their superior mechanical performance and desirable properties from both material and engineering perspectives. Among various approaches, representative volume element (RVE) generation and analysis is considered one of the most suitable and convenient methods for predicting the elastic moduli of composites. The main aim of this study is to investigate and compare the elastic moduli of natural fiber–reinforced unidirectional composite RVEs using theoretical, numerical, and machine learning models. The numerical predictions in this study were generated using the ANSYS Material Designer tool (version ANSYS 19). A comparison was made between experimental results reported in the literature and different theoretical models, showing high accuracy in validating these numerical outcomes. A dataset comprising 1600 samples was generated from numerical models in combination with the well-known theory of RVE, namely rule of mixture (ROM), to train and test two machine learning algorithms: Random Forest and Linear Regression, with the goal of predicting three major elastic moduli—longitudinal Young’s modulus (E₁₁), in-plane shear modulus (G₁₂), and major Poisson’s ratio (V₁₂). To evaluate model performance, mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R²) were calculated and compared against datasets with and without the theoretical values as input variables. The performance metrics revealed that with the theoretical values, both Linear Regression and Random Forest predict E₁₁, G₁₂, and V₁₂ well, with a maximum MSE of 0.033 for G₁₂ and an R² score of 0.99 for all cases, suggesting they can predict the mechanical properties with excellent accuracy. However, the Linear Regression model performs poorly when theoretical values are not included in the dataset, while Random Forest is consistent in accuracy with and without theoretical values. Full article

This research seeks to identify optimised screen façade patterns and ratios for the effective management of daylight ingress and glare effects. It employs generative adversarial networks (GANs) to generate pattern variations and further evaluates the resultant variations through daylight simulations for application in screen façades. The generated pattern data were classified by hierarchical clustering to distinguish distinct feature groups, and they were subsequently utilised as façade configurations. The pattern data were assessed through daylight performance metrics: spatial daylight autonomy (sDA), annual sunlight exposure (ASE), and daylight glare probability (DGP). The results of the annual-based simulations indicate that façade patterns with frame ratios in the range of 50–65% are useful in reducing the areas exposed to intensive glare on the façade side while maintaining the minimum required lighting conditions. The overall influence of screen façades on interior daylighting in a large space (e.g., 10 m × 10 m) was found to be limited. Their performance is notable in reducing glare discomfort areas within approximately 2.5 m of south-facing façades. This study supports an application strategy in which screen façades are used to manage the extent of areas exposed to daylight ingress within an interior space. Full article

Background/Objectives: Lung cancer (NSCLC), which accounts for approximately 85% of lung cancers, exhibits marked heterogeneity that complicates molecular characterization and treatment selection. Recent advances in deep learning (DL) have enabled the extraction of genomic-related morphological features directly from routine Hematoxylin and Eosin (H&E) histopathology, offering a potential complement to Next-Generation Sequencing (NGS) for precision oncology. This review aimed to evaluate how DL models have been applied to predict molecular alterations in NSCLC using H&E-stained slides. Methods: A systematic search following PRISMA 2020 guidelines was conducted across PubMed, Scopus, and Web of Science to identify studies published up to March 2025 that used DL models for mutation prediction in NSCLC. Eligible studies were screened, and data on model architectures, datasets, and performance metrics were extracted. Results: Sixteen studies met inclusion criteria. Most employed convolutional neural networks trained on publicly available datasets such as The Cancer Genome Atlas (TCGA) to infer key mutations including EGFR, KRAS, and TP53. Reported areas under the curve ranged from 0.65 to 0.95, demonstrating variable but promising predictive capability. Conclusions: DL-based histopathology shows strong potential for linking tissue morphology to molecular signatures in NSCLC. However, methodological heterogeneity, small sample sizes, and limited external validation constrain reproducibility and generalizability. Standardized protocols, larger multicenter cohorts, and transparent validation are needed before these models can be translated into clinical practice. Full article

The soil freeze–thaw process is a dominant disturbance in the seasonally frozen ground and the active layer of permafrost, which plays a crucial role in the surface energy balance, water cycle, and carbon exchange and has a pronounced influence on vegetation phenology. This study proposes a novel density-based Freeze–Thaw Disturbance Index (FTDI) based on the identification of the freeze–thaw disturbance region (FTDR) over the Qinghai–Tibet Plateau (QTP). FTDI is defined as an areal density metric based on geomorphic disturbances, i.e., the proportion of FTDRs within a given region, with higher values indicating greater areal densities of disturbance. As a measure of landform clustering, FTDI complements existing freeze–thaw process indicators and provides a means to assess the geomorphic impacts of climate-driven freeze–thaw changes during permafrost degradation. The main conclusions are as follows: the FTDR results that are identified by the random forest model are reliable and highly consistent with ground observations; the FTDRs cover 8.85% of the total area of the QTP, and mainly in the central and eastern regions, characterized by prolonged freezing durations and the average annual ground temperature (MAGT) is close to 0 °C, making the soil in these regions highly susceptible to warming-induced disturbances. Most of the plateau exhibits low or negligible FTDI values. As a geomorphic indicator, FTDI reflects the impact of potential freeze–thaw dynamic phase changes on the surface. Higher FTDI values indicate a greater likelihood of surface thawing processes triggered by rising temperatures, which impact surface processes. Regions with relatively high FTDI values often contain substantial amounts of organic carbon, and may experience delayed vegetation green-up despite general warming trends. This study introduces the FTDI derived from the FTDR as a novel index, offering fresh insights into the study of freeze–thaw processes in the context of climate change. Full article

Fishery resources of tuna serve as a vital source of global protein. This study investigates the key environmental drivers influencing the spatial distribution of yellowfin tuna (Thunnus albacares) in the western tropical Pacific Ocean. A comprehensive dataset was constructed by linking the catch per unit effort (CPUE) from 43 Chinese longline fishing vessels (2008–2019) with 24 multi-source environmental variables. To accurately model this complex relationship, a total of 16 machine learning regression models, including advanced ensemble methods like Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting Regressor (CatBoost), were evaluated and compared using multiple performance metrics (e.g., Coefficient of Determination [R²], Root Mean Squared Error [RMSE]). The results indicated that the Light Gradient Boosting Machine (LightGBM) model achieved superior performance, demonstrating excellent nonlinear fitting capabilities and generalization ability. For robust feature interpretation, the study employed both the model’s internal feature importance metrics and the SHapley Additive exPlanations (SHAP) method. Both approaches yielded highly consistent results, identifying temporal (month), spatial (longitude, latitude), and key seawater temperature indicators at intermediate depths (T450, T300, T150) as the most critical predictors. This highlights significant spatiotemporal heterogeneity in the distribution of Thunnus albacares. The analysis suggests that mid-layer ocean temperatures directly influence catch rates by governing the species’ vertical and horizontal movements. In contrast, large-scale climate indices such as the Oceanic Niño Index (ONI) exert indirect effects by modulating ocean thermal structures. This research confirms the dominance of spatiotemporal and thermal variables in predicting yellowfin tuna distribution and provides a reliable, data-driven framework for supporting sustainable fishery management, resource assessment, and operational forecasting. Full article