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Cost is a key performance indicator for evaluating the success of architectural construction projects. While previous studies have relied on quantitative data and statistical models to evaluate cost performance, recent advancements in methods have enabled analysis using unstructured data. Unstructured data, particularly in construction supervision reports, can be considered the significant variables for performance evaluation, as they represent independent third-party monitoring of the construction project’s execution. This study aims to present a framework that supports cost performance evaluation using unstructured data and random forests (RFs), a representative method of machine learning. Specifically, association rule analysis and social network analysis were used to identify the main keywords, and an RF model was applied to these data to evaluate cost performance. The tuning of hyper-parameters in the RF was implemented by the Bayesian optimization technique with the augmentation of the original dataset. The accuracy of cost performance evaluation was 59% for the traditional logistic regression (LR), 74% for the regularization-based logistic regression (BLR) designed to prevent overfitting, and 76% for the RF model utilizing augmented data. The complementary utility of the models consisting of the proposed framework can be useful for deriving various evaluation explanations about cost performance. The applicability is expected to increase as more data become available in the future. Full article

The construction industry is a major contributor to global resource consumption and waste. This sector extracts over two billion tons of raw materials each year and contributes over 30% of all solid waste generated annually through construction and demolition debris. The movement toward circularity in the built environment aims to replace linear processes of extraction and disposal by promoting policies favoring building preservation and adaptive reuse, as well as the salvage and reuse of building materials. Few North American cities have implemented explicit policies that incentivize circularity to decouple urban growth from resource consumption, and there remain substantial hurdles to adoption. Nonetheless, existing regulatory and planning tools, such as zoning codes and historic preservation policies, may already influence redevelopment in ways that could align with circularity. This article examines spatial patterns in these indirect pathways through a case study of a college town in New York State, assessing how commonly used local planning tools shape urban redevelopment trajectories. Using a three-stage spatial analysis protocol, including exploratory analysis, Geographically Weighted Regressions (GWRs), and Geographic Random Forest (GRF) modeling, the study evaluates the impact of zoning regulations and historic preservation designations on patterns of demolition, reinvestment, and incremental change in the building stock. National historic districts were strongly associated with more building adaptation permits indicating reinvestment in existing buildings. Mixed-use zoning was positively correlated with new construction, while special overlay districts and low-density zoning were mostly negatively correlated with concentrations of building adaptation permits. A key contribution of this paper is a replicable protocol for urban building stock analysis and insights into how land use policies can support or hinder incremental urban change in moves toward the circular city. Further, we provide recommendations for data management strategies in small cities that could help strengthen analysis-driven policies. Full article

Background: Achieving a complete response after therapy is an important predictor of long-term survival in lymphoma patients. However, previous predictive models have primarily focused on overall survival (OS) and progression-free survival (PFS), often overlooking treatment response. Predicting the likelihood of complete response before initiating therapy can provide more immediate and actionable insights. Thus, this study aims to develop and validate predictive models for treatment response to first-line therapy in patients with B-cell lymphomas. Methods: The study used 2763 patients from the Lymphoma and Related Diseases Registry (LaRDR). The data were randomly divided into training (n = 2221, 80%) and validation (n = 553, 20%) cohorts. Seven algorithms: logistic regression, K-nearest neighbor, support vector machine, random forest, Naïve Bayes, gradient boosting machine, and extreme gradient boosting were evaluated. Model performance was assessed using discrimination and classification metrics. Additionally, model calibration and clinical utility were evaluated using the Brier score and decision curve analysis, respectively. Results: All models demonstrated comparable performance in the validation cohort, with area under the curve (AUC) values ranging from 0.69 to 0.70. A nomogram incorporating the six variables, including stage, lactate dehydrogenase, performance status, BCL2 expression, anemia, and systemic immune-inflammation index, achieved an AUC of 0.70 (95% CI: 0.65–0.75), outperforming the international prognostic index (IPI: AUC = 0.65), revised IPI (AUC = 0.61), and NCCN-IPI (AUC = 0.63). Decision curve analysis confirmed the nomogram’s superior net benefit over IPI-based systems. Conclusions: While our nomogram demonstrated improved discriminative performance and clinical utility compared to IPI-based systems, further external validation is needed before clinical integration. Full article

Unmanned aerial vehicle (UAV)-captured RGB imagery, with high spatial resolution and ease of acquisition, is increasingly applied to individual tree crown detection (ITCD). However, ITCD in dense subtropical forests remains challenging due to overlapping crowns, variable crown size, and similar spectral responses between neighbouring crowns. This paper investigates to what extent the ITCD accuracy can be improved by using dual-seasonal UAV-captured RGB imagery in different subtropical forest types: urban broadleaved, planted coniferous, and mixed coniferous–broadleaved forests. A modified YOLOv8 model was employed to fuse the features extracted from dual-seasonal images and perform the ITCD task. Results show that dual-seasonal imagery consistently outperformed single-seasonal datasets, with the greatest improvement in mixed forests, where the F1 score range increased from 56.3%–60.7% (single-seasonal datasets) to 69.1%–74.5% (dual-seasonal datasets) and the AP value range increased from 57.2%–61.5% to 70.1%–72.8%. Furthermore, performance fluctuations were smaller for dual-seasonal datasets than for single-seasonal datasets. Finally, our experiments demonstrate that the modified YOLOv8 model, which fuses features extracted from dual-seasonal images within a dual-branch module, outperformed both the original YOLOv8 model with channel-wise stacked dual-seasonal inputs and the Faster R-CNN model with a dual-branch module. The experimental results confirm the advantages of using dual-seasonal imagery for ITCD, as well as the critical role of model feature extraction and fusion strategies in enhancing ITCD accuracy. Full article

The timely and accurate prediction of flue-cured tobacco yield is crucial for its stable yield and income growth. Based on yield and meteorological data from 2003 to 2023 (from the NASA POWER database) of Yunnan Province, this study constructed a coupled framework of polynomial regression and a Stacking ensemble model. Four trend yield separation methods were compared, with polynomial regression selected as being optimal for capturing long-term trends. A total of 135 meteorological features were built using flue-cured tobacco’s growth period data, and 17 core features were screened via Pearson’s correlation analysis and Recursive Feature Elimination (RFE). With Random Forest (RF), Multi-Layer Perceptron (MLP), and Support Vector Regression (SVR) as base models, a ridge regression meta-model was developed to predict meteorological yield. The final results were obtained by integrating trend and meteorological yields, and core influencing factors were analyzed via SHapley Additive exPlanations (SHAP). The results showed that the Stacking model had the best predictive performance, significantly outperforming single models; August was the optimal prediction lead time; and the day–night temperature difference in the August maturity stage and the solar radiation in the April transplantation stage were core yield-influencing factors. This framework provides a practical yield prediction tool for Yunnan’s flue-cured tobacco areas and offers important empirical support for exploring meteorology–yield interactions in subtropical plateau crops. Full article

The influence of microplastics (MPs) on the availability of soil heavy metals (HMs) is a current research hotspot, but how MPs regulate HM availability via soil properties and the bacterial community remains unclear. This study investigated the effects of polyethylene (PE) MP concentrations on soil properties, bacterial communities, surface chemistry, and the speciation of cadmium (Cd) and lead (Pb) through soil incubation. Results indicated that as PE MP concentration increased, soil pH and cation exchange capacity declined, while organic carbon concentration increased. Available phosphorus and alkali–hydrolyzable nitrogen concentrations increased at 0.1% and 1% PE MPs, but decreased at 10% PE MPs. Bacterial community indices, including Simpson, ACE, and Chao1, increased at 0.1% and 1% PE MPs but decreased at 10% PE MPs. PE MPs (0.1% and 1%) reduced DTPA–Cd/Pb, promoting their transformation into stable forms and surface complexation with oxygen–containing groups. In contrast, 10% PE MPs disrupted the formation of PbO, PbCO₃, and Cd(OH)₂, producing the opposite effect. The random forest model revealed that soil organic carbon and available phosphorus were the primary factors influencing DTPA–Pb and DTPA–Cd, respectively. Partial least squares path modeling demonstrated that PE MPs altered the physicochemical characteristics of soil and structure of bacterial communities, ultimately impacting transformation of Cd and Pb speciation, with these changes being highly dependent on PE MP concentration. Full article

In recent years, drone-based wildfire detection technology has advanced rapidly, yet existing methods still encounter numerous challenges. For instance, high background complexity leads to frequent false positives and false negatives in models, which struggle to accurately identify both small-scale fire points and large-scale wildfires simultaneously. Furthermore, the complex model architecture and substantial parameter count hinder lightweight deployment requirements for drone platforms. To this end, this paper presents a lightweight drone-based wildfire detection model, DFE-YOLO. This model utilizes dynamic frequency domain enhancement technology to resolve the aforementioned challenges. Specifically, this study enhances small object detection capabilities through a four-tier detection mechanism; improves feature representation and robustness against interference by incorporating a Dynamic Frequency Domain Enhancement Module (DFDEM) and a Target Feature Enhancement Module (C2f_CBAM); and significantly reduces parameter count via a multi-scale sparse sampling module (MS3) to address resource constraints on drones. Experimental results demonstrate that DFE-YOLO achieves mAP50 scores of 88.4% and 88.0% on the Multiple lighting levels and Multiple wildfire objects Synthetic Forest Wildfire Dataset (M4SFWD) and Fire-detection datasets, respectively, whilst reducing parameters by 23.1%. Concurrently, mAP50-95 reaches 50.6% and 63.7%. Comprehensive results demonstrate that DFE-YOLO surpasses existing mainstream detection models in both accuracy and efficiency, providing a reliable solution for wildfire monitoring via unmanned aerial vehicles. Full article

Microplastic pollution in marine environments represents a significant ecological threat due to its persistence and harmful effects on biodiversity and human health. In Colombia, coastal ecosystems (particularly in La Guajira) have exhibited increasing microplastic concentrations, but systematic monitoring remains limited. This study explored the application of remote sensing, including multispectral satellite imagery (Sentinel-2) and machine learning algorithms, to detect and monitor microplastics in the coastal zone of Riohacha, La Guajira. To inform the model selection and ensure methodological relevance, a focused systematic literature review was conducted, serving as a foundational step in identifying effective remote sensing strategies and machine learning algorithms previously applied to microplastic detection in aquatic environments. Moreover, microplastic samples were collected from four coastal sites on Riohacha’s coast and analyzed via Fourier transform infrared spectroscopy (FTIR), while environmental parameters were recorded in situ. The remote sensing data were processed and integrated with field observations to train linear regression, random forest, and artificial neural network (ANN) models. The ANN model achieved the highest accuracy (MAE = 0.040; RMSE = 0.071), outperforming the other models in estimating the microplastic concentrations. Based on these results, environmental risk maps were generated, identifying critical zones of pollution. The findings support the integration of remote sensing tools and field data for scalable, cost-efficient microplastic monitoring, offering a methodological framework for marine pollution assessment in Colombia and other developing coastal regions. Full article

Early warning systems on edge devices such as satellites and unmanned aerial vehicles (UAVs) are essential for effective forest fire prevention. Edge Intelligence (EI) enables deploying deep learning models on edge devices; however, traditional convolutional neural networks (CNNs)/Transformer-based models struggle to balance local-global context integration and computational efficiency in such constrained environments. To address these challenges, this paper proposes HybriDet, a novel hybrid-architecture neural network for wildfire detection. This architecture integrates the strengths of both CNNs and Transformers to effectively capture both local and global contextual information. Furthermore, we introduce efficient attention mechanisms—Windowed Attention and Coordinate-Spatial (CS) Attention—to simultaneously enhance channel-wise and spatial-wise features in high-resolution imagery, enabling long-range dependency modeling and discriminative feature extraction. Additionally, to optimize deployment efficiency, we also apply model pruning techniques to improve generalization performance and inference speed. Extensive experimental evaluations demonstrate that HybriDet achieves superior feature extraction capabilities while maintaining high computational efficiency. The optimized lightweight variant of HybriDet has a compact model size of merely 6.45 M parameters, facilitating seamless deployment on resource-constrained edge devices. Comparative evaluations on the FASDD-UAV, FASDD-RS, and VOC datasets demonstrate that HybriDet achieves superior performance over state-of-the-art models, particularly in processing highly heterogeneous remote sensing (RS) imagery. When benchmarked against YOLOv8, HybriDet demonstrates a 6.4% enhancement in mAP50 on the FASDD-RS dataset while maintaining comparable computational complexity. Meanwhile, on the VOC dataset and the FASDD-UAV dataset, our model improved by 3.6% and 0.2%, respectively, compared to the baseline model YOLOv8. These advancements highlight HybriDet’s theoretical significance as a novel hybrid EI framework for wildfire detection, with practical implications for disaster emergency response, socioeconomic security, and ecological conservation. Full article

Ecosystem services play a crucial role in sustaining human life, providing numerous benefits that are indispensable for our well-being. However, these vital functions are increasingly compromised by land use changes that have been instigated by human activities. This study aims to evaluate the spatiotemporal variability of ecosystem service value (ESV) within the urban agglomeration located on the northern slope of the Tianshan Mountains over a historical period stretching from 1990 to 2020, utilizing land use data to conduct a thorough analysis. Subsequently, the Future Land Use Simulation (FLUS) model was employed to forecast ESV in 2030 under three developmental pathways: Ecological Protection Scenario (EPS), Cultivated Land Protection Scenario (CLPS), and Natural Development Scenario (NDS). The evaluation incorporated six primary land classes: cultivated land, forest land, grassland, water bodies, construction land, and unused land. The FLUS model was validated with strong accuracy (overall accuracy = 0.97, Kappa = 0.94). ESV was estimated using the value coefficient method based on equivalent factors, adjusted with a local economic coefficient for crop production. All values are expressed in constant 2020 CNY without further price normalization. Our results show that between 1990 and 2020, cultivated land expanded by 27.18% (17,721 to 22,538 km²) and construction land increased by 75.91% (1926 to 3388 km²), while grassland decreased from 63,502 to 59,027 km² and unused land declined from 106,292 to 104,690 km². Minor changes occurred in forest land and water bodies. Total ESV decreased from 679.06 × 10⁸ CNY in 1990 to 657.67 × 10⁸ CNY in 2020, a decline of 3.15%. Regulating, supporting, and cultural services all decreased, while provisioning services increased. Spatially, vegetated areas functioned as ESV hot spots, whereas construction-degraded areas were identified as cold spots. Scenario projections for 2030 show that under the CLPS and NDS, ESV would further decline by 11.49 × 10⁸ CNY (−1.75%) and 10.18 × 10⁸ CNY (−1.55%), respectively. In contrast, the EPS is projected to increase ESV by 4.53 × 10⁸ CNY (+0.69%), reaching 662.20 × 10⁸ CNY. Full article

Compaction quality is a critical factor in ensuring the long-term performance of subgrade structures; however, traditional testing methods are limited by their destructive nature and delayed feedback. To address these shortcomings, this study proposes a dynamic identification method for subgrade compaction based on embedded attitude sensors. A customized sensor unit integrated with an inertial measurement module was embedded in soil samples to record triaxial acceleration and attitude angles during the compaction process. Signal processing techniques, including an improved wavelet-based denoising strategy, were employed to separate long-term compaction trends from transient impact disturbances. Attitude features such as cumulative angular change, angular velocity, root mean square values, and a comprehensive inclination index were extracted as predictive variables. Ridge regression, random forest, and XGBoost models were constructed to establish the mapping relationship between attitude features and compaction degree. Experimental results on clay, loam, and sand samples indicate that the yaw angle is most sensitive to vertical settlement, while pitch and roll angles provide complementary information on lateral and rotational behaviors. Comparative analysis of filtering methods shows that the transient masking interpolation (TMI) approach outperforms the traditional asymmetric wavelet thresholding (AWT) method in effectively preserving baseline trends. Among the regression models, XGBoost demonstrated the best predictive performance, achieving an R² exceeding 0.995 at high compaction levels. The proposed method has been experimentally demonstrated as a laboratory-scale proof of concept, showing strong potential for future real-time field application, offering a novel technological pathway for intelligent quality control in road construction. Full article

Background/Objectives: Non-variceal upper gastrointestinal bleeding (NVUGIB) is associated with considerable morbidity and mortality, particularly in emergency department (ED) settings. While traditional clinical scores such as the Glasgow-Blatchford Score (GBS), AIMS65, and Pre-Endoscopic Rockall are widely used for risk stratification, their accuracy in mortality prediction is limited. This study aimed to evaluate the performance of multiple supervised machine learning (ML) models in predicting 30-day all-cause mortality in NVUGIB and to compare these models with established risk scores. Methods: A retrospective cohort study was conducted on 1233 adult patients with NVUGIB who presented to the ED of a tertiary center between January 2022 and January 2025. Clinical and laboratory data were extracted from electronic records. Seven supervised ML algorithms—logistic regression, ridge regression, support vector machine, random forest, extreme gradient boosting (XGBoost), naïve Bayes, and artificial neural networks—were trained using six feature selection techniques generating 42 distinct models. Performance was assessed using AUROC, F1-score, sensitivity, specificity, and calibration metrics. Traditional scores (GBS, AIMS65, Rockall) were evaluated in parallel. Results: Among the cohort, 96 patients (7.8%) died within 30 days. The best-performing ML model (XGBoost with univariate feature selection) achieved an AUROC > 0.80 and F1-score of 0.909, significantly outperforming all traditional scores (highest AUROC: Rockall, 0.743; p < 0.001). ML models demonstrated higher sensitivity and specificity, with improved calibration. Key predictors consistently included age, comorbidities, hemodynamic parameters, and laboratory markers. The best-performing ML models demonstrated very high apparent AUROC values (up to 0.999 in internal analysis), substantially exceeding conventional scores. These results should be interpreted as apparent performance estimates, likely optimistic in the absence of external validation. Conclusions: While machine-learning models showed markedly higher apparent discrimination than conventional scores, these findings are based on a single-center retrospective dataset and require external multicenter validation before clinical implementation. Full article

The development of remote methods for identifying plant light stress (LS) is an urgent task in agriculture and forestry. Evergreen conifers, which experience winter light stress (WLS) annually, are ideal subjects for studying the mechanisms of light stress and developing identification methods. Proximal hyperspectral imaging (HSI) was used to identify WLS in Platycladus orientalis. Using the random forest (RF), the spectral characteristics of P. orientalis shoots were analysed and the conditions ‘Winter Light Stress’ and ‘Optimal Condition’ were classified with high accuracy. The out-of-bag (OOB) estimate of the error rate was only 0.35%. Classification of the conditions ‘Cold Stress’ and ‘Optimal Condition’—with an OOB estimate of error rate of 3.19%—can also be considered successful. The conditions ‘Winter Light Stress’ and ‘Cold Stress’ were more poorly separated (OOB error rate 15.94%). Verifying the RF classification model for the three states ‘Optimal condition’, ‘Cold stress’ and ‘Winter Light Stress’ simultaneously using data from the crown field survey showed that the ‘Winter Light Stress’ state was well identified. In this case, ‘Optimal condition’ was mistakenly defined as ‘Cold stress’. The following vegetation indices were significant for identifying WLS: CARI, CCI, CCRI, CRI₅₅₀, CTRI, LSI, PRI, PRI_m1, modPRI and TVI. Therefore, spectral phenotyping using HSI is a promising method for identifying WLS in conifers. Full article

Forest fires in Yanyuan County, a typical dry-hot valley region, pose serious threats to ecological security and public safety. Conventional fire warning methods rely heavily on manual surveys, making them time-consuming, labor-intensive, and prone to missing the critical window for effective intervention. This paper presents a 3D visualization decision support system for fire prevention, developed on a Web-GIS platform using the Cesium engine. The system integrates multi-source data, including a 12.5 m DEM, remote sensing imagery, and real-time video streams. It employs a YOLO11 model for automated fire and smoke detection, achieving a precision of 82.4%. Compared to conventional 2D systems, the platform enhances emergency response speed by 37% while significantly improving spatial awareness and operational coordination. This cross-platform tool facilitates sustainable forest management through efficient resource allocation and real-time monitoring, offering a scalable and practical solution for fire prevention in complex terrains. Full article

Understanding spatiotemporal dynamics and drivers of ecosystem service value (ESV) is critical for informing ecological restoration and sustainable land management, particularly in arid inland river basins. Analyzing the spatiotemporal dynamics of ESV in arid river basins and identifying key ecological and environmental drivers enable more precise diagnosis of ecological problems and provide a scientific basis for effective governance. This study evaluated the changes in ESV in the Aksu River Basin from 1990 to 2020 using the InVEST model, based on land use data, meteorological records, and biophysical parameters. A comprehensive assessment of seven key ecosystem services—including food production, water conservation, and biodiversity protection—was conducted. SHAP (SHapley Additive exPlanations) values were applied to interpret the contribution of ecological and environmental variables to ESV changes. The results showed that total ESV increased from CNY 189.05 billion in 1990 to a peak of CNY 22.326 billion in 2010, followed by a slight decline to CNY 20.805 billion in 2020. Spatially, Wensu, Xinyuan, and Bachu counties exhibited substantial ESV gains, while Atushi, Akto, and Awat counties experienced significant losses. SHAP analysis identified forest quality, soil erosion, and grassland condition as the dominant drivers of ESV variation, surpassing the influence of land area alone. By combining biophysical modeling with interpretable machine learning, this study highlights the critical role of ecosystem quality rather than land area alone, offering a transferable approach for diagnosing ecological risk assessment in arid regions. Full article