17 research outputs found
Removal as a Method: A Fourth Wave HCI Approach to Understanding the Experience of Self-Tracking
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Rethinking reintegration in Nigeria: community perceptions of former Boko Haram combatants
Since the emergence of Boko Haram and its terrorist activities in Nigeria, policy initiatives have included deradicalisation and reintegration of former combatants to curtail extremism and bolster stability. Central to deradicalisation is the efficacy of reintegration programmes. While much emphasis is placed on recidivism as a basis for determining the efficacy of reintegration programmes, studies on how communities perceive the reintegration of deradicalised former combatants, and those labelled terrorists, are scarce. To address this issue of the quality of reintegration programmes, a qualitative method using semi-structured interviews was employed for this study. Twenty-four Christian and Muslim participants were recruited from Lagos and Plateau states in Nigeria. Thematic data analysis was deployed from a social identity theoretical framework. The study found perceived indifference and fear regarding the ability of former Boko Haram combatants to genuinely reform or repent from terrorist acts. The study therefore recommends the provision of context-specific counter-narratives that shift the perceived public fear of unrepentant former combatants to a more positive outlook. Such optimism can embrace reconciliation to aid the successful reintegration of former terrorist combatants into Nigerian communities
Mikrochirurgische Ausbildungs- und Trainingsmöglichkeiten ohne Versuche am lebenden Tier
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Community perspectives of former terrorist combatants, militants and reintegration programmes in Nigeria: a systematic review
Community perspectives of repentant terrorist combatants and militants alongside the trust accorded reintegration programmes are important for successful reintegration. This review attempt to fill a significant gap through the synthesis of evidence on community perspectives of interventions adopted to foster reintegration of former terrorist combatants and militants in Nigeria. Six databases including the International Bibliography of the Social Sciences, Applied Social Science Index and Abstracts, Web of Science, Scopus, Proquest and EBsco were searched. Eighteen studies met the inclusion criteria. Informed by the principles of thematic analysis and conceptual framework of synthesis, five themes emerged: (1) Design of reintegration programmes devoid of community consultation; (2) Scepticism towards the sincerity behind monetising rehabilitation and reintegration programmes; (3) Resistance towards reintegration due to perceived favourable incentives provided to repentant combatants; (4) Lack of confidence in the genuine repentance of former repentant combatants; and (5) Lack of confidence in government’s reintegration programme. The review recommends randomised controlled trials which incorporate context-specific community-centred interventions to encourage successful reintegration
Fathers, Young Children and Technology:Changes in Device Use and Family Dynamics During the COVID-19 UK Lockdown
Leaf spot and fruit rot of strawberry caused by Neopestalotiopsis clavispora in Indo-Gangetic plains of India
Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms
Purpose: Since the prompt recognition of acute pulmonary embolism (PE) and the immediate initiation of treatment can significantly reduce the risk of death, we developed a deep learning (DL)-based application aimed to automatically detect PEs on chest computed tomography angiograms (CTAs) and alert radiologists for an urgent interpretation. Convolutional neural networks (CNNs) were used to design the application. The associated algorithm used a hybrid 3D/2D UNet topology. The training phase was performed on datasets adequately distributed in terms of vendors, patient age, slice thickness, and kVp. The objective of this study was to validate the performance of the algorithm in detecting suspected PEs on CTAs. Methods: The validation dataset included 387 anonymized real-world chest CTAs from multiple clinical sites (228 U.S. cities). The data were acquired on 41 different scanner models from five different scanner makers. The ground truth (presence or absence of PE on CTA images) was established by three independent U.S. board-certified radiologists. Results: The algorithm correctly identified 170 of 186 exams positive for PE (sensitivity 91.4% [95% CI: 86.4–95.0%]) and 184 of 201 exams negative for PE (specificity 91.5% [95% CI: 86.8–95.0%]), leading to an accuracy of 91.5%. False negative cases were either chronic PEs or PEs at the limit of subsegmental arteries and close to partial volume effect artifacts. Most of the false positive findings were due to contrast agent-related fluid artifacts, pulmonary veins, and lymph nodes. Conclusions: The DL-based algorithm has a high degree of diagnostic accuracy with balanced sensitivity and specificity for the detection of PE on CTAs