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Automated Detection and Severity Prediction of Wheat Rust Using Cost‐Effective Xception Architecture
Get PDFWheat crop production is under constant threat from leaf and stripe rust, an airborne fungal disease caused by the pathogen Puccinia triticina. Early detection and efficient crop phenotyping are crucial for managing and controlling the spread of this disease in susceptible wheat varieties. Current detection methods are predominantly manual and labour‐intensive. Traditional strategies such as cultivating resistant varieties, applying fungicides and practicing good agricultural techniques often fall short in effectively identifying and responding to wheat rust outbreaks. To address these challenges, we propose an innovative computer vision‐based disease severity prediction pipeline. Our approach utilizes a deep learning‐based classifier to differentiate between healthy and rust‐infected wheat leaves. Upon identifying an infected leaf, we apply Grabcut‐based segmentation to isolate the foreground mask. This mask is then processed in the CIELAB color space to distinguish leaf rust stripes and spores. The disease severity ratio is calculated to measure the extent of infection on each test leaf. This paper introduces a ground‐breaking disease severity prediction method, offering a low‐cost, accessible and automated solution for wheat rust disease screening in field conditions using digital colour images. Our approach represents a significant advancement in crop disease management, promising timely interventions and better control measures for wheat rust
Optimized machine learning framework for cardiovascular disease diagnosis: a novel ethical perspective
Get PDFAlignment of advanced cutting-edge technologies such as Artificial Intelligence (AI) has emerged as a significant driving force to achieve greater precision and timeliness in identifying cardiovascular diseases (CVDs). However, it is difficult to achieve high accuracy and reliability in CVD diagnostics due to complex clinical data and the selection and modeling process of useful features. Therefore, this paper studies advanced AI-based feature selection techniques and the application of AI technologies in the CVD classification. It uses methodologies such as Chi-square, Info Gain, Forward Selection, and Backward Elimination as an essence of cardiovascular health indicators into a refined eight-feature subset. This study emphasizes ethical considerations, including transparency, interpretability, and bias mitigation. This is achieved by employing unbiased datasets, fair feature selection techniques, and rigorous validation metrics to ensure fairness and trustworthiness in the AI-based diagnostic process. In addition, the integration of various Machine Learning (ML) models, encompassing Random Forest (RF), XGBoost, Decision Trees (DT), and Logistic Regression (LR), facilitates a comprehensive exploration of predictive performance. Among this diverse range of models, XGBoost stands out as the top performer, achieving exceptional scores with a 99% accuracy rate, 100% recall, 99% F1-measure, and 99% precision. Furthermore, we venture into dimensionality reduction, applying Principal Component Analysis (PCA) to the eight-feature subset, effectively refining it to a compact six-attribute feature subset. Once again, XGBoost shines as the model of choice, yielding outstanding results. It achieves accuracy, recall, F1-measure, and precision scores of 98%, 100%, 98%, and 97%, respectively, when applied to the feature subset derived from the combination of Chi-square and Forward Selection methods
Pandemic-related inventory management practices and financial performance: Exploring the roles of pandemic-related production uncertainty and big data analytics capability
No full textYoung people's experiences of setting and monitoring goals in school-based counselling:A thematic analysis
No full textOBJECTIVE: To understand young people's experiences of setting and monitoring goals in the context of school-based counselling.DESIGN: Qualitative interview study of young people aged 13-16 years old who had undertaken school-based counselling and who had explicitly set and monitored goals.METHODS: Nineteen young people who were predominantly female (89.5%) and around half of whom were of white/European and/or British ethnicity (52.6%) were recruited from 4 secondary schools in London, UK. A reflexive thematic analysis was undertaken to identify themes.RESULTS: Fourteen themes were identified, which reflected both helpful and unhelpful aspects of working with goals. For some young people, goals were motivating, provided a tangible representation of progress, and focused the therapeutic work. For others, goals could mirror a sense of "stuckness" and elicit negative emotions when not progressed towards in a linear fashion. Assigning a number to goal progress meant that some young people felt it did not fully capture the context of their experience, although some did find this practice helpful. Similarly, not all young people found it helpful to monitor progress at every session.CONCLUSIONS: Our findings align with the wider adult literature in that experiences of working with goals are mixed. Recommendations for practice include offering choice in the frequency and way goal progress is monitored, and using clinical judgement when working with goals. This might include noticing when goal setting or monitoring is contributing to young people's feelings of low self-worth and adjusting practice accordingly.</p
Exploring facilitator perspectives on four participatory music-based interventions for perinatal mental health: a qualitative study
No full textA comparative longitudinal study of depression among left-behind and non-left-behind adolescents in Romania
No full textEvidence on antidepressant withdrawal: an appraisal and reanalysis of a recent systematic review
No full textThere has been debate about the frequency and severity of antidepressant withdrawal effects. We set out to appraise and reanalyze an influential systematic review by Henssler and colleagues that concluded that withdrawal effects are not particularly common and rarely severe. We repeated the meta-analysis, including only studies where data were derived from systematic measures of withdrawal symptoms. Most data in the Henssler review are derived from pharmaceutical industry-sponsored efficacy studies in which withdrawal was a minor consideration. Shortcomings of the review include the use of spontaneously reported adverse events to estimate withdrawal symptoms, potential misclassification of withdrawal symptoms as relapse, inclusion of data from retrospective case-note studies, short duration of prior antidepressant use, short observation periods, the overlooking of differences between placebo and drug withdrawal effects, and the use of questionable proxies for severe withdrawal. There were also discrepancies and uncertainties in some figures used. In our reanalysis, we included only the five studies that used a systematic and relevant method to assess the incidence of any withdrawal symptom. Prior treatment was short-term (12 weeks or less) in all but one of these. The pooled percentage was 55% (95% confidence interval, CI, 31% to 81%; = 601) without subtracting nocebo effects, with high heterogeneity. Henssler's review is based on unreliable data and does not provide an adequate basis for the evaluation of antidepressant withdrawal effects. Further good-quality research on antidepressant withdrawal is required
Automated NLP-Based Classification of Nonfunctional Requirements in Blockchain and Cross-Domain Software Systems Using BERT and Machine Learning
No full textAutomated nonfunctional requirements (NFRs) classification enhances consistency and traceability by systematically labeling requirements, saving effort, supporting early architectural and testing decisions, improving stakeholder communication, and enabling quality across diverse software domains. While prior work has applied natural language processing (NLP) and machine learning (ML) to NFR classification, existing datasets are often limited in size, domain diversity, and contextual richness. This study presents a novel dataset comprising over 2400 NFRs spanning 269 software projects across 26 software application domains, including nine blockchain projects. The raw requirements are standardized using Rupp’s boilerplate to reduce vagueness and ambiguity, and the classification of NFRs types follows ISO/IEC 25,010 definitions. We employ a range of traditional ML, deep learning (DL), and a transformer-based model (i.e., BERT-base) for automated classification of NFRs, evaluating performance across cross-domain and blockchain-specific NFRs. Results highlight that domain-aware adaptation significantly enhances classification accuracy, with traditional ML and DL models showing strong performance on blockchain requirements. This work contributes a publicly available, context-rich dataset and provides empirical insights into the effectiveness of NLP-based NFR classification in both general and blockchain-specific settings
Quality, determinants and financial consequences of climate change disclosures: a structured review
No full textRecent years have seen rapid growth in research on climate-related disclosures, yet existing reviews mainly focus on carbon and GHG reporting through the Carbon Disclosure Project (CDP), overlooking the broader framework introduced by the Task Force on Climate-related Financial Disclosures (TCFD). This study conducts a structured review of 134 peer-reviewed articles (2010–2025), all published in journals ranked 2* or higher according to the ABS 2024 Journal Guide, to integrate insights from both CDP and TCFD perspectives. The review makes three key contributions over past studies. First, it provides a structured and integrative synthesis to date by bridging CDP and TCFD based disclosure research, while prior reviews largely examined carbon disclosure in isolation. Second, it develops a thematic framework encompassing disclosure characteristics, quality and compliance, determinants, financial consequences and greenwashing, dimensions that earlier reviews treated separately. Finally, it advances the methodological rigor of prior reviews on climate related disclosures by applying a structured review and by identifying underexplored empirical gaps to guide future research. The findings offer actionable insights for regulators, investors and firms aiming to strengthen the quality, comparability and credibility of climate-related disclosures
