Milk quality has a growing importance for farmers as component-based pricing becomes more widespread. Food quality and precision manufacturing techniques demand consistent milk composition. Udder health, general cow condition, environmental factors, and especially feed composition all influence milk quality. The large volume of routinely collected milk data can be used to build prediction models that estimate valuable constituents from other measured parameters. In this study, casein was chosen as the target variable because of its high economic value. We developed a multiple linear-regression model and a feed-forward neural network model to estimate casein content from twelve commonly recorded milk traits. Evaluated on an independent test set, the regression model achieved R² = 0.86 and RMSE = 0.018%, with mean bias = +0.003% and slope bias = −0.10, whereas the neural network improved performance to R² = 0.924 and RMSE = 0.084%. In silico microgreen inclusion from 0% to 100% of dietary dry matter raised the predicted casein concentration from 2.662% to 3.398%, a relative increase of 27.6%. To extend practical applicability, a simulation module was created to explore how microgreen supplementation might modify milk casein levels, enabling virtual testing of dietary strategies before in vivo trials. Together, the predictive models and the microgreen simulation form a cost-effective, non-invasive decision-support tool that can accelerate diet optimization and improve casein management in precision dairy production.

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.

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.

This study investigates the factors that lead to smartphone addiction in young adults as well as the negative health effects of this addiction. This study used confirmatory factor analysis (CFA) and structural equation modeling (SEM) to analyze the data from 430 online questionnaires of young adults aged between 15 and 55. The findings show that the association between smartphone addiction and health hazards is significantly mediated by bedtime procrastination, which links excessive usage of the device to negative mental and physical effects. The findings show that impulsivity, depression, gaming addiction, cyber-loafing, and other behavioral and psychological characteristics all play a major role in smartphone addiction, which has an adverse impact on both mental and physical health. Among the participants, 80% were single and 61.1% were female; these groups had greater rates of addiction and related health issues. The results have significant implications for psychiatrists, psychologists, parents, and anyone concerned about smartphone addiction and its associated health risks. The study highlights the crucial role of bedtime procrastination as a mediator in this context. This material will be particularly beneficial for therapists specializing in treating smartphone addiction in both children and adults.

Due to their high energy density and power potential, 21700 lithium-ion battery cells are a widely used technology in hybrid and electric vehicles. Efficient thermal management is essential for maximizing the performance and capacity of Li-ion cells in both low- and high-temperature operating conditions. Optimizing thermal management systems remains critical, particularly for long-range and weight-sensitive applications. In these contexts, passive heat dissipation emerges as an ideal solution, offering effective thermal regulation with minimal additional system weight. This study aims to deepen the understanding of passive heat dissipation in 21700 battery cells and optimize their performance. Special emphasis is placed on analyzing heat transfer and the relative contributions of convective and radiative mechanisms under varying temperature and discharge conditions. Laboratory experiments were conducted under controlled environmental conditions at various discharge rates, ranging from 0.5×C to 5×C. A 3D-printed polymer casing was applied to the cell to enhance thermal dissipation, designed specifically to increase radiative heat transfer while minimizing system weight and reliance on active cooling solutions. Additionally, a numerical model was developed and optimized using experimental data. This model simulates convective and radiative heat transfer mechanisms with minimal computational demand. The optimized numerical model is intended to facilitate further investigation of the cell envelope strategy at the module and battery pack levels in future studies.

This study examines the interfacial bond characteristics of twenty mix proportions, comprising ten self-compacting concrete (SCC) and ten fiber-reinforced self-compacting concrete (FRSCC) formulations, the latter enhanced with 0.1 % polypropylene fibers for repair applications. Initially, experiments such as slump flow, 28-day compressive strength, and tensile strength tests were conducted to evaluate the mechanical properties of the repair layers intended for use in slant shear tests. The primary focus of the research then shifted to determining shear bond strength (SBS) and calculating interfacial cohesion and friction angles using slant shear tests across various inclination angles on these mix proportions applied over a normal vibrated concrete (NVC) substrate. Notably, FRSCC mixtures with 10 % microsilica exhibited notable enhancements, showing increased cohesion of 8.28 MPa and a tensile strength increase of 24.50 % compared to their SCC counterparts. Additionally, a general trend was observed where FRSCC mixtures demonstrated higher cohesion values compared to SCC, underscoring the effectiveness of fiber reinforcement. Furthermore, the research introduces a novel predictive model employing a fuzzy system with a generalized Mamdani's interference engine and Hamacher family of t-norms to accurately predict the SBS, achieving a predictive accuracy with an R² value up to 0.94. Employing the fuzzy model, characterized by its high predictive accuracy, can significantly reduce the frequency of experimental tests required in the field, thereby lowering construction testing costs and enhancing repair efficiency. These findings not only advance our understanding of SCC and FRSCC behaviors in repair scenarios but also contribute significantly to the development of more reliable and sustainable construction practices by improving the precision of SBS predictions in theoretical modeling and empirical testing.