Commonly used mixed-refrigerants are azeotropic mixtures of hydrofluorocarbons (HFCs) with high global warming potential. There is a need for reclamation and recovery of these HFCs. Solvent-based extractive distillation is a promising separation technique for recycling of these refrigerant components. Ionic liquids are suitable solvents for this application due to their negligible vapor pressures, tunable properties, and near-zero waste in closed-loop operations. However, the numerous potential combinations of cation-anion pairs make the selection of the optimal ionic liquid challenging. Moreover, the choice of ionic liquid critically affects energy efficiency and separation performance. To address this challenge, we present a hierarchical, multiscale computational workflow for computer-aided molecular and process design (CAMPD) that combines aspects of molecular simulation, machine learning, process performance measures, and equation-oriented process optimization for the solvent-based separation of azeotropic refrigerant mixtures. We employ a decomposition-based solution approach for CAMPD, where we first perform computer-aided molecular design (CAMD) to identify promising ionic liquid candidates through high-throughput screening, considering 16,352 known and generated ionic liquids. Next, we perform a focused CAMPD to identify the solvents that give the best process performance. We highlight the application of our method for the separation of refrigerants R-32 from R-125, which belong to the binary azeotropic refrigerant mixture commonly known and used as R-410A. Our method identified 43 ionic liquids (24 known and 19 generated) that matched all solvent and separation process specifications. Among these, five ionic liquids are found to be more sustainable and superior to others.

Transmission line faults can cause both power loss and failure. To mitigate the effects of such faults, the energy supply must quickly identify and remove faults, as well as ensure grid restoration after a failure occurs. As a result, it is critical to design a system capable of quickly and reliably identifying and eliminating errors. The level of fault identification accuracy is an important indicator for ensuring the reliability of main equipment in power systems, such as generators and transformers. This paper proposes a two-phase approach for identifying faults in transmission systems. In the first phase, unsupervised learning techniques like K-Mean clustering are used to assign labels to datasets for transmission line fault classification. During the second phase, four machine learning techniques called logistic regression (LR), decision tree classifier (DTC), random forest classifier (RFC), and XGBoost Classifier (XGB) are employed to identify faults. Applications are validated on fault detection datasets. The tested approach provides an efficient model for fault detection and classification in transmission lines, as well as a productive framework for fault detection prediction based on machine learning and ensemble learning methods. The experimental simulation results from this study show an accuracy of 83.6% for LR, 99% for DTC, 99% for RFC, and 99.9% for XGB, LightGBM, and CatBoost at 0.99995%. The paper's findings demonstrate the effectiveness of machine learning and ensemble learning techniques in accurately identifying and classifying transmission line faults at competitive performance indices.

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.

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.

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.

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.

Low-cost air temperature sensors are an emerging theme in environmental monitoring. These sensors offer the advantage of making microclimate monitoring feasible due to their affordability. However, they are limited by the quality of the data they provide; in many cases, they have been reported to have presented errors in the sensor readings. These errors have been shown to improve after calibration was applied. The lack of a comprehensive understanding of the available calibration techniques, models, and sensor types has led to studies presenting heterogeneity in models and techniques alongside different performance metrics. To address this gap, this study conducted a systematic review following the PRISMA guidelines, reviewing studies from 2015 to 2024 across the databases Web of Science and Scopus, alongside the search engine Google Scholar. The aim was to identify the calibration techniques and models, the commercially available low-cost air temperature sensors used, the performance metrics utilised, and the calibration settings. The findings presented three main categories of calibration models utilised in the collected studies: linear, polynomial, and machine learning. Twenty-two commercially available low-cost sensors were identified, with the DHT22 sensor being the most utilised. Indoor settings were identified as the most preferred for conducting calibrations. Key challenges included limitations in reported results for calibration by the studies, the use of different performance metrics across studies, insufficient studies conducting calibration, and the diversity in sensor types utilised.