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Greening the Path to Carbon Neutrality: The Role of Technical Factors in Reducing Carbon Emissions in South Asia Post‐COP 28
No full textThe rapid industrialization and economic growth of South Asia have improved living standards but also exacerbated CO2 emissions, intensifying the region's climate vulnerabilities. While existing literature has extensively examined green growth strategies in developed economies, few studies explore how green technological innovation, finance, and trade policies interact to shape emissions in South Asia, a region with distinct developmental challenges and high climate risks. This study investigates whether green energy adoption, technological innovation, and sustainable investments can decouple economic growth from emissions in South Asian economies from 1995 to 2022. Using second‐generation panel econometrics—accounting for cross‐sectional dependence and slope heterogeneity—along with AMG and CCEMG estimators, we assess long‐term relationships, supplemented by causal analysis, CuP‐FM and CuP‐BC for robustness. The results demonstrate that green technological innovation, green energy, and green finance significantly reduce CO2 emissions, while trade liberalization increases them, likely due to carbon‐intensive export structures and weak environmental regulations, a critical finding for regional policymaking. Furthermore, green investment mitigates emissions but requires stronger institutional support to align with COP28 mandates and SDGs (7, 9, 11–13). This study contributes to the literature by addressing the gap in South Asia–specific green growth analyses, integrating COP28 resolutions into empirical policy recommendations, and demonstrating the underutilized potential of green finance and innovation in achieving carbon neutrality. The findings urge policymakers to prioritize sustainable infrastructure, reform trade policies to reduce emissions leakage, and scale targeted green investments to reconcile economic and environmental goals
Enhancing Parkinson’s disease prediction using meta-heuristic optimized machine learning models
No full textParkinson’s disease is a progressive neurological disorder affecting movement and cognition. Early detection is crucial but challenging with traditional methods. This study applies meta-heuristic optimization to enhance machine learning prediction models. A Parkinson’s dataset with demographic, lifestyle, medical, clinical, and cognitive features was analyzed using three feature selection techniques: Whale Optimization Algorithm, Artificial Bee Colony Optimization, and Backward Elimination (BE). Random Forest (RF) models were optimized using Artificial Ant Colony Optimization for hyperparameter tuning. The optimized RF model with BE achieved 93% accuracy and 97% AUC, outperforming K-Nearest Neighbors, Support Vector Machines, Logistic Regression, XGBoost, and Stacked Ensemble models. Optimization reduced tuning time from 133 to 18 minutes. A comparison with traditional approaches and negative controls validated the results, though clinical validation remains essential before deployment. Meta-heuristic optimization significantly improves Parkinson’s prediction performance and efficiency
A Duoethnographic Account of Anti‐Racism Training in Counselling Pedagogy
Get PDFIntroduction: As authors, we co‐facilitate a teaching session on anti‐racism as part of a module on ethical and cultural issues on an MA in counselling and psychotherapy in the North of England to a cohort of between 14 and 24 students. We have been ongoingly evaluating and conducting research with the student groups involved to try and improve the teaching session. Methodology: This article reports on our duoethnographic approach to consider our learning from our pedagogical experience. Findings: Themes emerged from our conversations using Colaizzi's phenomenological method which were: defences, pedagogies of discomfort, the work, and transformation. We also present an exhaustive description of the anti‐racist work involved. Discussion and Recommendations: We draw out implications and recommendations for an anti‐racist pedagogy in counselling education, focusing on each of our responsibility to undertake the deep self‐examination and listening to others that this requires
Intelligent Face Recognition: Comprehensive Feature Extraction Methods for Holistic Face Analysis and Modalities
Get PDFFace recognition technology utilizes unique facial features to analyze and compare individuals for identification and verification purposes. This technology is crucial for several reasons, such as improving security and authentication, effectively verifying identities, providing personalized user experiences, and automating various operations, including attendance monitoring, access management, and law enforcement activities. In this paper, comprehensive evaluations are conducted using different face detection and modality segmentation methods, feature extraction methods, and classifiers to improve system performance. As for face detection, four methods are proposed: OpenCV’s Haar Cascade classifier, Dlib’s HOG + SVM frontal face detector, Dlib’s CNN face detector, and Mediapipe’s face detector. Additionally, two types of feature extraction techniques are proposed: hand-crafted features (traditional methods: global local features) and deep learning features. Three global features were extracted, Scale-Invariant Feature Transform (SIFT), Speeded Robust Features (SURF), and Global Image Structure (GIST). Likewise, the following local feature methods are utilized: Local Binary Pattern (LBP), Weber local descriptor (WLD), and Histogram of Oriented Gradients (HOG). On the other hand, the deep learning-based features fall into two categories: convolutional neural networks (CNNs), including VGG16, VGG19, and VGG-Face, and Siamese neural networks (SNNs), which generate face embeddings. For classification, three methods are employed: Support Vector Machine (SVM), a one-class SVM variant, and Multilayer Perceptron (MLP). The system is evaluated on three datasets: in-house, Labelled Faces in the Wild (LFW), and the Pins dataset (sourced from Pinterest) providing comprehensive benchmark comparisons for facial recognition research. The best performance accuracy for the proposed ten-feature extraction methods applied to the in-house database in the context of the facial recognition task achieved 99.8% accuracy by using the VGG16 model combined with the SVM classifier
Innovation for Sustainable Development: Assessing Sustainability‐Oriented Innovations in the UK Palm Oil Supply Chains
Get PDFThis research evaluates the adoption of Sustainability‐Oriented Innovations (SOIs) by UK food manufacturers, particularly in the palm oil sector. Despite the global palm oil industry's environmental and social impacts, such as deforestation and labour exploitation, empirical studies on SOIs are limited. This paper addressed this gap by mapping current SOI adoption patterns and assessing the effectiveness of the framework proposed by Adams et al. (2016). Findings indicate that larger organisations demonstrate engagement with advanced SOIs, driven by legislative pressures and corporate sustainability mandates. The study highlights the need for a nuanced approach within the assessed framework to better capture diverse sustainability practices and recommends enhancing transparency and incentivising SOI adoption across all business sizes. This research contributes to theoretical discourse and practical applications, offering insights for policy‐makers and industry leaders
Reply to letter to the editor: "From prediction to translation: Key limitations of current machine learning models for CAD".
No full textSexual health is an extra struggle for women with disabilities: findings from 10 African countries
No full textDeveloping a pedagogy of critical reflection and reflexivity on a professional doctorate towards equity, ethics and social justice
No full textThe Professional Doctorate in Education (EdD) is designed for researching practitioners to address problems of practice and to develop theoretically informed practice-based knowledge, based on equity, ethics and social justice, according to the Carnegie Project on the Education Doctorate. Within the EdD programmes, practitioner reflection is a key characteristic. While extensive literature on reflection in educational programmes exists, there is little literature on how critically reflective approaches might be developed in practice at the beginning of a EdD programme. The article takes the example of the first module on a EdD programme and shows how such approaches can develop and deepen researching practitioners’ (EdD students) understandings of problems of practice. This article contributes to understandings of EdD pedagogy. Co-written with EdD students who have completed their first module of the programme, it includes their first-person responses to the approaches taken to foster critical reflection and reflexivity and offers a model for this form of collaborative writing. The article highlights the importance of considering students’ standpoint and positionality as researching practitioners and the value of a critically reflective and reflexive approach which is guided by the challenge of theory