Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions.

The Sustainable Development Report 2023 showed that 2.2 billion people lacked access to safely managed drinking water in 2022, with 703 million unable to access even basic services. In addition to this, the Afrobarometer’s 2024 survey indicated that Sub-Saharan Africa water supply was ranked among the top governance challenges in 39 surveyed countries. This study explores regional and urban–rural disparities in access to drinking water, while also assessing the scope and geography of Hungary’s water-related development cooperation on the continent. The methodology combines quantitative indicators from the UNICEF–WHO Joint Monitoring Programme with geospatial visualization techniques. The analysis reveals substantial inequalities in rural Eastern Africa, over 97 million people rely on surface water or unimproved sources, while Middle Africa reports more than 55 million in the same categories. In contrast, urban areas in Northern Africa show significantly better outcomes, with over 111 million having access to safely managed drinking water. These figures highlight persistent spatial divides and the critical need for targeted investment in rural service provision. Hungarian development engagement was examined through project records from the Ministry of Foreign Affairs and Trade, alongside publicly available data from Hungarian NGOs and private sector actors. The study finds that Hungary has contributed to water-related initiatives in countries such as the Democratic Republic of the Congo, Ghana, and Uganda, but has had limited involvement in other severely affected countries, including Niger (31% unsafe access), Madagascar (42%), and the Central African Republic (37%). This study addresses a significant research gap since the intersection of Hungarian development cooperation and African water security has received minimal scholarly attention to date. By offering a comprehensive, data-driven analysis of both African water access and Hungary’s related foreign engagement, the research contributes to the understanding of potential synergies and future avenues for international collaboration in this field.

The hybrid ionic liquid (IL) − amine solvents have demonstrated high efficiency in CO2 capture. However, rigorous simulations of carbon capture processes employing IL-amine blended solvents have been scarce. This study presents detailed thermodynamic modeling and process simulations for carbon capture in the steel process and natural gas combined cycle (NGCC) power plant. We investigated two hybrid solvent systems, i.e., [BMIM][BF4]/PZ/MDEA and [BMIM][TF2N]/PZ/MDEA with the blended amine PZ/MDEA used as a benchmark. The phase equilibria of the CO2-PZ-MDEA-H2O-IL system was regressed with the NRTL model. The CO2 molar loading in the lean solvent (αlean) and mass fraction of ILs (xIL) in the mixture solvent were optimized to minimize the regeneration energy (Qreg) of the capture processes. The results indicate that the [BMIM][TF2N]/PZ/MDEA-based process is most energy-efficient (Qreg = 2.845 GJ/tCO2 at αlean = 0.14 and xIL = 1.0 wt%) for the steel plant and (Qreg = 2.749 GJ/tCO2 at αlean = 0.08 and xIL = 1.5 wt%) for the NGCC power plant. Compared to the PZ/MDEA-based benchmark process, IL's inclusion led to a 2.90 % and 0.11 % reduction in the regeneration energy for the steel process and NGCC power plant, respectively, demonstrating the benefit of introducing IL into the amine solvents for CO2 capture.

Work addiction negatively impacts health and well-being, yet little research has focused on managers, whose excessive work involvement can affect entire organizations. This study examined psychological predictors of work addiction and differences between work-addicted and non-addicted managers. Two hundred managers were assessed via the Qualtrics research platform, with work addiction classified using the Bergen Work Addiction Scale. We analyzed 11 psychological measures: exhaustion, disengagement, stress, obsessive and harmonious passion, well-being, work-family and family-work conflict, perceived physical and mental health, and sleep quality. Logistic regression significantly predicted group membership (p < 0.001), explaining 39.7%–57.0% of the variance and correctly classifying 84.5% of cases. Multivariate analysis of variance showed significant differences across all measures except one between the two groups. Work-addicted managers also showed poorer physical and mental health, and lower sleep quality. The prevalence of work addiction was high (29%) in this sample, highlighting the need for targeted interventions. As the first study to identify multiple psychological predictors of work addiction in managers, the findings may be valuable for organizations concerned with occupational mental health. However, cross-national replication is necessary before generalizing results. Recognizing the psychological toll of work addiction can help policymakers develop effective, sustainable interventions.

The rise in biodiesel production results in an excess of crude glycerol, which further leads to environmental concerns. Consequently, transforming crude glycerol into valuable products is deemed an effective way to address this issue. Process Integration techniques are introduced to enhance the overall economic viability by maximizing the energy recovery in the biodiesel plant. However, most of the existing studies merely focused on a single optimal heat exchanger network (HEN) generated. In this study, P-HENS software is utilized to generate viable HENs for a glycerol-derived 1,3-propanediol plant. Subsequently, piping costs of each HEN are estimated to determine the optimal HEN by assuming the respective heat exchanger is placed at the centroid. Finally, the optimal HEN is identified based on the total annualized costs (TAC) (which include the capital cost of the heat exchanger, utility cost, and piping costs) and energy-related carbon emissions of the network. The results show that, among the 4,188 feasible networks generated, network #623 possesses the best overall performance when both cost and environmental aspects are considered. The carbon emissions of network #623 is 16.7% lower than that of the case without heat recovery. This work demonstrates the usefulness of the generated near-best HENs in enabling a more comprehensive HEN optimization. By application of the proposed methodology, the most economical and environmentally friendly HEN can be determined. This contributes to both cost savings and sustainability in HEN design.

Boucardicus is a Madagascan endemic genus of the family Hainesiidae (subfamily Boucardicinae). The over 200 known species are variable in terms of shell shape, but their common trait is the presence of pre-constriction ribs, under which microtunnels run. Here we report three Boucardicus species without microtunnels, and as a consequence, transfer them to the genus Acroptychia as follows: Acroptychia boulangeri (Fischer-Piette, C.P. Blanc, F. Blanc & Salvat, 1993), Acroptychia (?) culminans (Fischer-Piette, C. P. Blanc, F. Blanc & F. Salvat, 1993) and Acroptychia (?) optio (Fischer-Piette, C. P. Blanc, F. Blanc & F. Salvat, 1993). The variability of the genus Acroptychia is briefly discussed.

Background: Since its inception in 1974, the Essential Programme on Immunization (EPI) has achieved remarkable success, averting the deaths of an estimated 154 million children worldwide through routine childhood vaccination. However, more recent decades have seen persistent coverage inequities and stagnating progress, which have been further amplified by the COVID-19 pandemic. In 2019, WHO set ambitious goals for improving vaccine coverage globally through the Immunization Agenda 2030 (IA2030). Now halfway through the decade, understanding past and recent coverage trends can help inform and reorient strategies for approaching these aims in the next 5 years. Methods: Based on the Global Burden of Diseases, Injuries, and Risk Factors Study 2023, this study provides updated global, regional, and national estimates of routine childhood vaccine coverage from 1980 to 2023 for 204 countries and territories for 11 vaccine-dose combinations recommended by WHO for all children globally. Employing advanced modelling techniques, this analysis accounts for data biases and heterogeneity and integrates new methodologies to model vaccine scale-up and COVID-19 pandemic-related disruptions. To contextualise historic coverage trends and gains still needed to achieve the IA2030 coverage targets, we supplement these results with several secondary analyses: (1) we assess the effect of the COVID-19 pandemic on vaccine coverage; (2) we forecast coverage of select life-course vaccines up to 2030; and (3) we analyse progress needed to reduce the number of zero-dose children by half between 2023 and 2030. Findings: Overall, global coverage for the original EPI vaccines against diphtheria, tetanus, and pertussis (first dose [DTP1] and third dose [DTP3]), measles (MCV1), polio (Pol3), and tuberculosis (BCG) nearly doubled from 1980 to 2023. However, this long-term trend masks recent challenges. Coverage gains slowed between 2010 and 2019 in many countries and territories, including declines in 21 of 36 high-income countries and territories for at least one of these vaccine doses (excluding BCG, which has been removed from routine immunisation schedules in some countries and territories). The COVID-19 pandemic exacerbated these challenges, with global rates for these vaccines declining sharply since 2020, and still not returning to pre-COVID-19 pandemic levels as of 2023. Coverage for newer vaccines developed and introduced in more recent years, such as immunisations against pneumococcal disease (PCV3) and rotavirus (complete series; RotaC) and a second dose of the measles vaccine (MCV2), saw continued increases globally during the COVID-19 pandemic due to ongoing introductions and scale-ups, but at slower rates than expected in the absence of the pandemic. Forecasts to 2030 for DTP3, PCV3, and MCV2 suggest that only DTP3 would reach the IA2030 target of 90% global coverage, and only under an optimistic scenario. The number of zero-dose children, proxied as children younger than 1 year who do not receive DTP1, decreased by 74·9% (95% uncertainty interval 72·1–77·3) globally between 1980 and 2019, with most of those declines reached during the 1980s and the 2000s. After 2019, counts of zero-dose children rose to a COVID 19-era peak of 18·6 million (17·6–20·0) in 2021. Most zero-dose children remain concentrated in conflict-affected regions and those with various constraints on resources available to put towards vaccination services, particularly sub-Saharan Africa. As of 2023, more than 50% of the 15·7 million (14·6–17·0) global zero-dose children resided in just eight countries (Nigeria, India, Democratic Republic of the Congo, Ethiopia, Somalia, Sudan, Indonesia, and Brazil), emphasising persistent inequities. Interpretation: Our estimates of current vaccine coverage and forecasts to 2030 suggest that achieving IA2030 targets, such as halving zero-dose children compared with 2019 levels and reaching 90% global coverage for life-course vaccines DTP3, PCV3, and MCV2, will require accelerated progress. Substantial increases in coverage are necessary in many countries and territories, with those in sub-Saharan Africa and south Asia facing the greatest challenges. Recent declines will need to be reversed to restore previous coverage levels in Latin America and the Caribbean, especially for DTP1, DTP3, and Pol3. These findings underscore the crucial need for targeted, equitable immunisation strategies. Strengthening primary health-care systems, addressing vaccine misinformation and hesitancy, and adapting to local contexts are essential to advancing coverage. COVID-19 pandemic recovery efforts, such as WHO's Big Catch-Up, as well as efforts to bolster routine services must prioritise reaching marginalised populations and target subnational geographies to regain lost ground and achieve global immunisation goals. Funding: The Bill & Melinda Gates Foundation and Gavi, the Vaccine Alliance.

Globally, economies are highly concerned about the balance between climatic issues and attaining agricultural sustainability. However, empirical evidence regarding the nexus of agricultural sustainability, emissions, land use, and agricultural trade is scarce and requires appropriate policy-level attention. The current study examines the influence of land-use resources, agricultural exports, and foreign direct investment on agriculture-related greenhouse gas emissions in Brazil. Using various time series diagnostic measures on quarterly data from 1990Q1 to 2020Q4 reveals non-normality and a mixed order of stationarity in variables. The autoregressive distributed lag (ARDL) model and quantile ARDL approach are employed for comprehensive empirical analysis. The results assert that land resources and foreign investments are harmful to environmental sustainability, as they significantly enhance agricultural greenhouse gas emissions. Additionally, agricultural exports and green energy significantly contribute to emissions mitigation by tackling land-use and agricultural emissions in the short and long run. The results are robust across the ARDL and quantile regressions and pairwise granger causality. The study concludes that agricultural exports and land use are key factors inducing agricultural sustainability by inducing emissions. The study recommends increased spending on research and development, solar-based irrigation, and promotion of green energy projects. The study discusses novel findings and implications apropos land resources, foreign investments, agricultural exports, and emissions in the lens of COP 28.

Transitioning heat exchanger network (HEN) synthesis designs to industrial application involves operational, environmental, and cost considerations, posing computational challenges. This study proposes a systematic optimization approach integrating multi-objective, multi-period optimization HEN synthesis with waste heat recovery and multiple utilities. The proposed methodology incorporates a novel two-step unit reduction strategy to overcome the increase of model combinational complexities arisen from the multi-period features, thereby facilitating the solving of large-scale problems. Meanwhile, environmental impacts are concerned by using the technique for order preference by similarity to ideal solution approach. A new optimization route, Enhanced Pinch-assisted Multi-Objective Optimization is proposed to obtain the final decision in this multi-objective problem time-efficiently. The case study includes a 15 streams problem, and a real industrial-scale crude oil distillation preheat system. The results showed that assigning carbon compensation to the waste heat recovery option can significantly reduce carbon emissions and change energy distribution.