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Pandemic Politics in Central Asia: Authoritarian Contagion
No full textThis book examines how the authoritarian regimes of Kazakhstan, Turkmenistan, and Uzbekistan exploited the Covid-19 pandemic to consolidate their political control over Central Asia. Through restrictive policies and strategic manipulation, these governments reshaped the region’s politics and societies during 2020–2022.
This volume offers readers an insight into three key areas where Central Asia’s pandemic power grab was most visible: mobility control, whereby restrictive legislation limited freedom of movement and suppressed dissent; authoritarian information flows, whereby Covid-related measures aligned media and digital information to government messaging, further curtailing freedom of expression; and the international politics of the pandemic, whereby the regimes capitalized on global instability to strengthen their kleptocratic hold over Central Asia’s politics and society. This book provides a detailed analysis of these strategies, offering a compelling exploration of how crises can be used to entrench authoritarianism.
It is ideal for scholars, students, and professionals in political science, international relations, and Central Asian Studies. The book is also valuable to readers interested in understanding the intersection between public health crises and authoritarian governance
Trans men, pregnancy, and ‘living in the acquired gender’: W v Gender Recognition Panel [2025] EWHC 2685 (Fam)
Get PDFNo abstract available
Sustainable resource management towards circular supply chain in dairy industry in Vietnam: a hierarchical structure and interdependence relationships
No full textThis study contributes to understand the sustainable resource management (SRM) attributes towards circular supply chain (CSC) in the dairy sector in Vietnam. The industry arises from serious pressings such as high levels of waste, water use and greenhouse gas emissions, resource scarcity, finite resources dependence, rising costs of raw materials and supply chain vulnerability. SRM is the foundation for CSC practices; yet the attributes are always with interdependence and hierarchical structure. In this scenario, the circularity is the scarcity of resources; therefore, prioritising SRM towards CSC must be analysed. This study focuses on determining pivotal SRM attributes and CSC under interdependence and hierarchical structure. A hybrid method is proposed to deal with the interdependence and hierarchical structure in nature. As a result, the cause group encompasses technology advancement, circular human resource strategy, and circular collaboration, whereas effect group covers sustainable product stewardship and zero-waste practice. Managerial implications are discussed
Antipsychotic-induced weight gain in psychosis: causal mediation analysis and feasibility study of causal actionable prediction model development using counterfactuals to target obesity
No full textBackground:
People with psychosis have a life expectancy that is reduced by 15 years, mainly owing to preventable physical illnesses of which obesity is a precursor. Obesity is three times more common in individuals with psychosis, and antipsychotics are an important cause. Prediction could individualise obesity treatment, but current models are not fully actionable for individuals.
Aims:
To test whether antipsychotic-induced weight increase at 1 year is causally mediated by weight change in the first 12 weeks of treatment, and then develop and internally validate a causal actionable prediction pathway to prevent antipsychotic-induced obesity.
Method:
This was a post hoc analysis of a clinical trial of olanzapine versus haloperidol which recruited 263 participants with first-episode psychosis. We conducted two distinct analyses: causal mediation and prediction modelling, within which there were two sequential models (a baseline model to predict 12-week outcome and a 12-week model to predict 1-year outcome), followed by counterfactual prediction. In the first analysis, we used parallel causal mediation analysis to determine the natural direct and indirect and total effects of antipsychotic choice on weight in 97 participants, considering two mediators: weight change from 0 to 12 weeks, and weight change from 12 to 52 weeks. In the second analysis, we first developed a baseline causal actionable prediction model to predict weight gain at 12 weeks in 172 participants and then a 12-week model to predict obesity at 1 year in 97 of the participants. Finally, we demonstrated counterfactual prediction.
Results:
Antipsychotic-induced weight gain at 1 year appeared to be causally mediated by weight change during the first 12 weeks of treatment (indirect effect 5.70; 95% CI 2.83 to 8.66). At internal validation, the discrimination c-statistic for the baseline causal actionable prediction model was 0.728 (95% CI 0.661 to 0.801), and the calibration slope was 0.768 (95% CI 0.436 to 1.21). For the 12-week model, the c-statistic was 0.904 (95% CI 0.820 to 0.961), and the calibration slope was 0.601 (95% CI −0.0633 to 1.21). We used the models to predict the counterfactual outcomes of antipsychotic choice and 12-week weight change.
Conclusions:
Our results show that it may be early rather than later weight change that causally mediates antipsychotic-induced weight gain at 1 year. They also demonstrate the potential for causal actionable prediction of counterfactuals for true precision medicine, although this is tempered by the feasibility scope of this study and small sample size. Our results are hypothesis-generating and not yet clinically deployable
Observation of a cross-section enhancement near the t̅t production threshold in √s=13 TeV pp collisions with the ATLAS detector
Get PDFA measurement of t̅t production is presented in the invariant-mass region near the pair production threshold, 𝑚ₜ-ₜ ∼ 345 GeV, in final states with two charged leptons and multiple jets. The measurement is based on 140 fb⁻¹ of proton–proton collision data collected at √𝑠 = 13 TeV with the ATLAS detector at the Large Hadron Collider. The data are compared to two models of t̅t production: a baseline model including only perturbative QCD predictions for the hard process, and an extended model that, in addition, incorporates non-relativistic QCD simulations of colour-singlet quasi-bound-state formation near the t̅t threshold. The agreement between the data and the models is quantified via a profile-likelihood fit to the reconstructed 𝑚ₜ-ₜ distributions, in bins of two angular observables sensitive to spin-correlations in the t̅t system. An excess of events is observed over the baseline perturbative QCD prediction, with an observed significance over 8 standard deviations. This excess is consistent with the formation of colour-singlet and spin-singlet 𝑆-wave quasi-bound t̅t states, as predicted by non-relativistic QCD, and corresponds to an observed cross-section of 9.3+1.4 −1.3 pb
Physically interpretable and AI-powered applied-field thrust modelling for magnetoplasmadynamic space thrusters using symbolic regression: towards more explainable predictions
Get PDFMagnetoplasmadynamic thrusters (MPDTs) are becoming increasingly viable as electric propulsion (EP) technology for space missions, yet their complex plasma behaviour, intricate thrust-generation process, and nonlinear multi-physics thrust–field interactions prove difficult for conventional modelling approaches, including empirical techniques. Traditional empirical modelling shortcomings include failure to predict accurately across wide operational regimes. This paper introduces a physically interpretable, artificial intelligence (AI)-powered thrust model for Applied-Field Magnetoplasmadynamic Thrusters (AF-MPDTs), developed using symbolic regression (SR) to address the gap between data-driven prediction and physics-based understanding. The proposed method, an alternative to traditional black box AI methods, incorporates physics-aware composite-term operators, ensuring that the resulting analytical expressions are bounded by known physical behaviours while retaining the flexibility to discover previously overlooked nonlinear couplings. A comprehensive dataset of AF-MPDTs undergoes rigorous preprocessing to ensure dimensional consistency and noise robustness. The SR model then evolves candidate equations, balancing predictive accuracy with interpretability through Tree-Structured Parzen Estimator (TPE) optimisation. The results, closed-form surrogate correlations with 95.98% of accuracy as goodness of fit, root mean square error of 0.0199, mean absolute error of 0.0143, and mean absolute percentage error reduction of 28.91% against the benchmark model in the literature. A post-discovery protocol for numerical robustness and physical consistency is implemented, with Shapley Additive Explanations (SHAP) providing insight into the influence of each composite-term in the developed correlation, followed by a numerical robustness and physical consistency validation using a Monte Carlo (MC) envelope. A StabilityScore is calculated for all developed correlations, enabling explicit accuracy–complexity–stability comparisons. In doing so, we demonstrated that SR can systematically recover known physical relationships—such as the scaling of thrust with discharge current and applied magnetic field—while proposing interpretable higher-order corrections that improve fit quality. The resulting SR-based thrust models not only achieve competitive accuracy relative to state-of-the-art numerical and empirical methods but also offer more explainable and interpretable results capable of revealing compact formulations that capture essential acceleration mechanisms with transparency. Overall, this paper, using SR, advances explainable AI (XAI) methodologies capable of generating trustworthy, analytically transparent models for next-generation electric propulsion systems
Peer evaluation enhances group presentations through a structured, evidence-informed pedagogical framework
No full textNo abstract available
Evaluating risk of burnout and psychological challenges among forensic odontologists: A pilot study
No full textForensic odontologists (FOs) are routinely exposed to emotionally intense, high-stakes environments such as disaster victim identification (DVI) missions and criminal investigations. Despite the psychological demands of their role, there is limited empirical research on the mental health and burnout risk among FOs. This pilot study aimed to explore burnout risk and psychological challenges among FOs through an international online survey, with particular attention to trauma exposure, coping strategies, and access to mental health resources. A cross-sectional survey was disseminated via professional FO networks. The questionnaire included selected items from the Maslach Burnout Inventory (MBI) and traumarelated symptoms items adapted from PCL-5 DSM-5-TR PTSD criteria, alongside an open-ended question. Quantitative data were analyzed using R, and qualitative responses were examined through thematic analysis. A total of 105 responses were analyzed. Most participants (69.5%) reported DVI experience, and 40% had over 20 years of practice. Moderate risk of burnout levels were observed, with lower scores in more experienced FOs. Notably, respondents with repeated DVI exposure reported lower risk of burnout but elevated trauma-related symptoms. Only 9.5% had sought psychological support. Psychological coping training correlated with reduced risk of burnout. Thematic analysis highlighted recurrent needs for peer support, access to psychological care, resilience training, institutional recognition, and personal coping methods. FOs face persistent psychological risks that may be inadequately addressed by current systems. This study underscores the need for trauma-informed resources, institutional support structures, and targeted interventions to safeguard FO mental health in high-risk occupational forensic investigations
Policy and public health implications for mental health after the COVID-19 pandemic
No full textThe COVID-19 pandemic revealed essential weaknesses in mental health systems and intensified existing inequities, highlighting the need for a comprehensive assessment of policy responses and strategies for future resilience. Guided by four questions relating to system adaptations, approaches to inequities, financing strategies, and evidence gaps, we synthesised evidence from a structured literature search (2020–24), expert consultation, and lived experience. We found that public health systems embedded infodemic management, expanded digital services, and mobilised community workforces, but responses varied in equity and effectiveness. Although gender, age, socioeconomic, and racial disparities worsened during the COVID-19 pandemic, social protection, gender-sensitive policies, school-based services, and culturally adapted interventions showed promise. High-income countries buffered shocks with welfare measures while low-income and middle-income countries faced sharp fiscal constraints. Few studies evaluated cost-effectiveness or equity impacts of psychosocial interventions. Building resilient, equitable mental health systems requires integrated policies spanning communication, digital and community care, gender-responsive and youth-responsive strategies, and sustainable financing, alongside investment in longitudinal and cross-national research
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