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Ecosystem condition can be understood as the quality of an ecosystem in terms of its abiotic, biotic, and landscape characteristics. It is a measure of structural integrity, functional capacity, and resilience of any given ecological system. Its assessment is essential to support environmental objectives (e.g., nature restoration or sustainable use). Spatially explicit assessment of ecosystem condition requires integrating diverse geospatial data. Here, we present the EcoCondition Toolset, a QGIS plugin implementing a user-friendly GIS weighted-sum methodology for ecosystem condition assessments. It simplifies data preparation and analysis through five sequential toolsets: i) layer alignment and resampling; ii) no-data handling; iii) multicollinearity testing; iv) indicator normalisation and inversion; and v) condition assessment. The plugin calculates six specific ecosystem attribute - or state - composites (Physical, Chemical, Compositional, Structural, Functional, Landscape) from user-selected variables (in raster format), according to the System of Environmental-Economic Accounting. After data preparation and verification, the tool displays default equal weights for each composite and related variables, which users can adjust (e.g., to reflect stakeholder preferences). The toolset automates best-practice multicollinearity screening, normalisation, and flexible weighting for ecosystem condition assessment and monitoring. The resulting index preserves true severity and variation among ecosystem states. The results can support robust policy instruments and land-use decision-making, prioritising conservation and restoration actions.

Hand injuries are among the most common musculoskeletal injuries and can significantly impair an individual's ability to perform activities of daily living (ADL), thereby impacting quality of life. Self-efficacy plays a vital role in influencing daily performance and recovery following injury. This cross-sectional study aims to explore the relationship between ADL performance and self-efficacy among clients with hand injuries within the Indian context. Secondary objectives of this study include assessing self-efficacy levels and evaluating ADL performance in this population. • A self-administered, closed-ended, structured questionnaire comprising performance-based and self-efficacy measures will be used for data collection. Participants will include adults aged 18 years and above who have sustained fractures of the hand or wrist, including digits, and have undergone surgical treatment. • Clients will be recruited from the Occupational Therapy department. • The findings aim to highlight the importance of considering both objective and subjective measures in occupational therapy assessment and to emphasize the role of self-efficacy in ADL performance following hand injuries, potentially informing culturally sensitive rehabilitation interventions.

This study proposes an advanced spatio-temporal framework to forecast strategic food commodity prices in Indonesia using Geographically and Temporally Weighted Spline Regression (GTWSR), a nonparametric extension of GTWR designed to capture nonlinear spatio temporal effects. Monthly data from the Strategic Food Price Information Center (SFPIC) and Statistics Indonesia (BPS), covering eight key commodities and the Farmer Price Index across 34 provinces (January 2022-August 2024), were analyzed through spatial distance measurement, bandwidth optimization, local parameter estimation, and statistical validation. The GTWSR model demonstrated strong predictive performance (overall accuracy: R² = 91.61 %, RMSE = 1.22, MAE = 0.94, MAPE = 3.7 %), with rice and garlic achieving the highest accuracy, while red and cayenne chili showed greater errors due to price volatility. Spatial disparities were evident, as eastern provinces such as Papua, Maluku, and East Nusa Tenggara consistently faced higher prices compared to western regions. These findings underscore the need for region-specific interventions to strengthen logistics and stabilize horticultural supply chains. Limitations include reliance on monthly aggregated data, the temporal scope ending in 2024, and dependence on secondary datasets, which may affect replication and long-term applicability.

Behavioural testing in larval zebrafish often involves pipetting the larvae into well plates for individual testing. Transferring larvae into plates the day prior to experimentation can increase efficiency of testing. Furthermore, pharmacological and toxicological studies can require a prolonged dosing period requiring the larvae to be pre-plated into the well plate the day prior to experimentation. Here, we compared the behavioural impact of pre-plating larval zebrafish at 4 days post-fertilization (dpf) to fish transferred at 5 dpf on the day of testing. Motion-tracking software was used to examine locomotion and zone preference, and responses to light, dark, and mechanical startle stimuli. We found no significant differences in distance moved, time spent in the thigmotaxis zone (outside edge of the arena), high mobility, immobility, light startle, dark startle, and mechanical startle responses. This data suggests that pre-plating larval zebrafish one day prior to testing does not have a significant impact on behaviour in a spontaneous swim task, dark startle test, light startle test, or mechanical startle test. Pre-plating larval zebrafish can increase the efficiency of behavioural testing.•Compare plating larvae one day prior to testing to plating day of testing.•Test the behaviour in a spontaneous swimming test, and measure light-, dark-, and mechanical-startle responses.•There were no significant differences in locomotion or startle responses.

This study presents a novel computer-aided diagnostic (CAD) system for detecting and grading the severity of knee osteoarthritis(KOA) from X-ray images, utilizing a hybrid deep learning and machine learning framework. The system combines YOLOv5 for precise knee joint localization and segmentation with a Random Forest classifier for ordinal Kellgren-Lawrence (KL) grading. Trained on a curated and augmented dataset of 1535 X-ray images, the model achieves an overall KL grading accuracy of 87 %. Evaluation includes ROC-AUC curves, Cohen's kappa scores, and grade-wise sensitivity and specificity metrics. This hybrid approach offers a scalable, interpretable, and clinically relevant tool for supporting radiologists in early KOA diagnosis, especially in resource-constrained settings.•Combines the powerful feature extraction capabilities of the YOLOv5 deep learning architecture with the classification strength of the Random Forest model.•YOLOv5 is used for knee joint segmentation to reduce background noise and improve classifier accuracy by focusing on the region of interest.•Achieves 87 % overall accuracy in KL grading, with enhanced sensitivity to subtle changes in early-stage KOA (Grades 1-2).

This study introduces a methodological framework for predicting road accident severity using a SHAP-enhanced Machine Learning model. Road traffic accidents remain a major global concern, with India reporting over 150,000 fatalities annually. Traditional models fail to capture the complex relationships among various risk factors. This research applies machine learning, specifically Random Forest and Gradient Boosting, to identify and analyse key factors influencing accident severity. SHAP values are used to enhance model interpretability, providing insights into the contribution of each feature.•Develop a Random Forest model and a Gradient Boosting model to predict road accident severity based on a comprehensive set of features.•Utilise SHAP to identify and rank the importance of features, such as vehicle type, weather, and road conditions.•Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrices. Polynomial curve fits are used only as post-hoc visualizations of the Actual-Predicted relationship (on ordinal codes), not as classifier evaluation metrics.The findings highlight that factors like vehicle type, accident location, and road conditions significantly influence accident severity. This approach provides a scalable and interpretable framework for improving road safety on Indian highways, offering data-driven insights for proactive safety measures and infrastructure enhancements.

Pneumonia is a dangerous respiratory illness that has to be precisely and promptly diagnosed in order to be treated effectively and prevent consequences. In order to distinguish between pneumonic and normal chest X-ray pictures, a hybrid deep learning technique is proposed in this study. For efficient and complementary feature extraction, the proposed system leverages the strengths of two popular convolutional neural networks, VGG16 and ResNet. Before training the model, the image is enhanced and the lungs are segmented by performing histogram equalisation, normalising contrast, and converting to grayscale. A richer feature representation of input photos is produced by fusing the features of VGG16 and ResNet. A model for identifying pneumonia is classified using the fused feature set. The system processes X-rays of new patients in order to extract features and categorise them using Random Forest (RF) and Support Vector Machine (SVM) classifiers. To increase accuracy and efficiency, feature dimensions are optimised using Principal Component Analysis (PCA). Key Contributions: 1. Dual-CNN feature fusion (VGG16 + ResNet) instead of single-model learning 2. PCA-based dimensionality optimization retaining 95% variance 3. Use of SVM and Random Forest for more interpretable diagnosis instead of CNN softmax.

Early detection of brain tumors is essential for successful treatment and better patient outcomes. Traditional imaging methods like X-rays, MRIs, CT scans, and PET scans have been important in detecting brain tumors, but they are expensive with many drawbacks and areas where access is limited. Antenna-based method has recently emerged as a practical alternative for real-time, non-invasive detection of brain tumors. This paper explores different antennas and various types of substrates that are adaptable to human sensitive tissues for detecting brain tumors. This review highlights the antenna working principles, and the advantages and challenges associated with each type. The effectiveness of several antenna-based methods in medical diagnostics, including microwave imaging and ultra-wideband (UWB) systems, is discussed. To assess their impact on detection accuracy, essential factors such as penetration depth, resolution, operating frequency, and antenna design are considered. The integration of antennas with machine learning and signal processing techniques is investigated.

Leukemia is the cancerous disease of the blood and the bone marrow that causes excessive proliferation of abnormal white blood cells, if detected too late, it can lead to potentially fatal consequences. Peripheral blood smear examinations and bone marrow biopsy are example of conventional diagnostic techniques that are invasive, time-consuming and subject to human variability. Recent advances in artificial intelligence (AI) particularly in the areas of Machine Learning (ML) and Deep Learning (DL)offer encouraging answers by making it possible to detect and classify leukemia using automated, effective and precise techniques. With an emphasis on image-based techniques based on publicly available datasets such as ALL-IDB, C-NMC, AML_Cytomorphology_LMU, SN-AM and CPTAC-AML, this paper reviews the most recent research on Machine Learning and Deep Learning approaches includes Convolutional Neural Networks (CNNS), ResNet, DenseNet, MobileNet and ensemble models for leukemia diagnosis. The survey highlights some of the most significant issues, such as dataset imbalance, stain variability, lack of standard annotations and limited clinical validation. The paper also discusses research gap and future initiatives including Explainable AI, lightweight deployment models, clinically reliable diagnostic system and hierarchical classification framework aligned with WHO 2022 classification standards.