Low-carbon agricultural (LCA) practices, including nutrient, water, and soil management, present viable strategies for mitigating greenhouse gas (GHG) emissions while enhancing agricultural productivity. However, their long-term impacts on food security and emission reduction at the national scale require further investigation. This study employs scenario-based analysis to assess the role of LCA in reducing carbon dioxide, nitrous oxide, and methane emissions while evaluating its effects on food production, accessibility, and availability in India. By conceptualizing LCA as a baseline scenario, the study examines the influence of technology adoption, government policies, and sustainable agricultural practices in enhancing food security and mitigating climate change. A systematic literature review, following the PRISMA protocol, was conducted using keyword co-occurrence analysis from major global databases, including Scopus, ScienceDirect, Web of Science, and government and organizational sources. The findings indicate that efficient resource and nutrient management significantly strengthen food security while reducing annual GHG emissions, supporting India’s progress toward food self-sufficiency and climate resilience. These insights provide a foundation for strengthening national and global food policies and climate mitigation strategies, aligning with multiple Sustainable Development Goals (SDGs), including SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), SDG 15 (Life on Land), and SDG 17 (Partnerships for the Goals). This study underscores the critical role of LCA in integrating food security with environmental sustainability, offering a policy-driven approach to climate adaptation and sustainable agricultural development in India.

This manuscript delineates a thorough study on the heat and mass transfer phenomenon of the Williamson fluid flow embedded with a tetra-hybrid nanofluid evaluating a wide range of considered and physical effects such as magnetohydrodynamics (MHD), porous medium, radiative heat flux, Joule heating, Soret and Dufour effects, and a Stefan blowing parameter at the boundary, and the rest. A tetra-hybrid nanofluid containing nanofluid gold (Au), silver (Ag), titanium dioxide (TiO₂), and aluminium oxide (Al₂O₃) is used for the improvement of significant thermal and mass transport characteristics. In the back, the demand for efficient thermal systems relates to sets with multiple, integrated transport mechanisms; however, the synergistic transport mechanisms have been largely unexplored, and the coupled hybrid advanced dimensions nanofluids have been unexplored in terms of their combined influences on these parameters. The core target was to examine the active relationships within the physical dynamics parameters while also evaluating the relative increases in the velocity, temperature, and concentration. This paper employs a robust computational approach to the study by solving the governing systems of non-linear ordinary differential equations using an appropriate method of similarity transformation and subsequent numerical techniques. The integration of artificial neural network (ANN) models within this spectrum for the first time, with predictions and optimization set for the outputs, adds a new dimension to this work. The data show that incorporating Soret and Dufour effects, along with the tetra-hybrid nanoparticles, markedly increases the Nusselt and Sherwood numbers, indicating improved heat and mass transfer rates. Furthermore, streamline plots are created to illustrate alterations in the flow structure induced by the Soret and Dufour parameters. This research makes valuable contributions to the development of refined cooling technologies, particularly in energy, chemical, and other process-oriented industries, highlighting the practical utility and innovation potential of the synergistic application of artificial neural networks alongside sophisticated nanofluid models.

Algorithmic management (am) has become a key research focus in the sociology of work, especially concerning platform work. However, am tools are also impacting traditional workplaces. This study investigates three main questions: the impact of ai on high vs. low-skilled jobs, its effect on employee's role, and the formation of collective voices around am, including non-traditional labour relations actors. The context is the Hungarian industrial relations system, known for low union membership and company-level bargaining. The study compares two cases: a medium-sized company in high-value-added business services and a Hungarian subsidiary of a multinational employing warehouse workers. Contrary to literature suggesting am reduces employee autonomy, the study finds its impact complex, decreasing employee's roles some areas while increasing it in others. Notably, transparency and wage predictability improved. The study also highlights the importance of considering new actors, such as clients and external consultants, in am analysis. Keywords

The safe speeds and also the general speed limits are quite different outside and within built-up areas. If it does not follow from the road design, whether the given scene is within or outside built-up area, drivers are uncertain about their appropriate speed.This paper shows two approaches to assess the degree of uncertainty of the drivers. The first was a questionnaire survey of requested speeds at various road scenes. In the second method, the recognition process of drivers was simulated by image classification software.Output indicators of these methods (standard deviation of speeds and certainty score) can serve as tools to identify road scenes and road elements leading to uncertain and therefore risky situations.

We propose a novel method to identify the transfer functions of single-input-single-output linear time invariant (SISO-LTI) dynamic systems. Our approach makes use of the operator based generalization of Prony's method. In particular, the operator based Prony algorithm is used to reconstruct the transfer function of the system as a linear combination of rational basis functions. A considerable benefit of the proposed method is its robustness against the estimated system order. That is, if system order is over estimated, the correct system order can be found naturally. Another important benefit is that the proposed method is shown to be asymptotically robust towards zero expectation noise with the correct choice of certain evaluation functionals. The effectiveness of the proposed method is demonstrated through numerical experiments.

Artificial intelligence is expanding rapidly, intensifying policy concerns that more vibrant AI ecosystems may displace workers and increase unemployment. This study aims to test whether national AI vibrancy is associated with higher unemployment across education groups (advanced, intermediate and basic). Using an unbalanced panel of 34–35 countries from 2017 to 2023, the analysis combines Stanford’s AI Vibrancy Score with World Bank indicators and estimates two-way fixed-and random-effects models, employing Box–Cox/log transformations and dependence-robust inference (including country/time clustering and Driscoll–Kraay standard errors). The results provide little support for the displacement hypothesis. For advanced-education unemployment, AI vibrancy is statistically insignificant in the two-way FE model. It remains insignificant under all robust corrections (ln(AI vibrancy): β=−0.099, country-clustered p=0.494, time-clustered p=0.544, Driscoll–Kraay p=0.468). For basic-education unemployment, AI vibrancy is likewise insignificant in the two-way FE model (p=0.782). It remains insignificant under country clustering (p=0.830), time clustering (p=0.813) and Driscoll–Kraay inference (p=0.819). For intermediate-education unemployment, the AI coefficient remains insignificant under country clustering (p=0.273), time clustering (p=0.310), and Driscoll–Kraay corrections (p=0.226), indicating no robust unemployment-increasing effect across education groups during the observed period.

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Introduction. The aim of research is to examine the knowledge about plant-based diets, what beliefs and misconceptions exist about plant-based diets (PBDs), and how these differ between lifestyle groups among Hungarian females. Materials and methods. Data were collected through an online survey on social media. These data were processed using univariate statistics (general description of the sample), exploratory factor analysis (identification of healthy lifestyles), cluster analysis (segmentation purposes), chi-square statistics (cluster profiling), F-statistics (comparing attitudes toward PBDs), and cross tabulation (knowledge and perceptions of PBDs). Results and discussion. Four health-related lifestyle dimensions (health-conscious eating, mindfulness, carbohydrate avoidance, red meat avoidance) were identified, and four segments emerged (healthy food choosers, red meat avoiders, stress-free women, rejecters). Healthy food choosers (40.9%) prioritize healthy eating, avoid sugary snacks, and monitor carbohydrate intake. Red meat avoiders (27.9%) are neutral about healthy eating, but avoid red meat and processed foods; don't focus on carbohydrates. Stress-free women (20.8%) value mindfulness, relaxation, and outdoor physical activity for a stress-free life. Rejecters (10.4%) have a negative attitude toward healthy eating, mindfulness, carbohydrates, and red meat. Red meat avoiders live in the capital city, eat fruits and vegetables more often or at least once a day. Rejecters live in villages and eat fruits and vegetables every 4–5 days in a week or do not eat fruits and vegetables in a week. Healthy eaters eat fruits and vegetables more times a day. Stress-free people eat fruits and vegetables every 2–3 days in a week. They differed in their knowledge, attitudes and perceptions of PBDs. 72.1% of healthy food choosers, 84.8% of red meat avoiders, 75.8% of stress-free people and 71.9% of rejecters thought that plant-based diet was similar to vegan and vegetarian diet. The attitudes range from “may have health benefits for certain diseases” as the attitude with the highest mean level of agreement (4.26), especially among red meat avoiders, to “encourages diary consumption” as the attitude with the lowest mean level of agreement (1.69), especially among red meat avoiders. Red meat avoiders, healthy food choosers, and stress-free women had more positive attitudes toward PBDs than did rejecters. The majority of females were thinking about trying out PBDs. Red meat avoiders, healthy eaters, and stress-free women had more positive attitudes toward PBD than did rejecters. Healthy eaters perceived PBD as healthy. Red meat avoiders perceived the plant-based diet as healthy, safe, varied, exciting, environmentally friendly, and a complete diet. Stress-free women thought the plant-based diet was unhealthy and environmentally unfriendly. Rejecters attached more negative attributes to the PBD. They perceived the meatless diet as unhealthy, dangerous, monotonous, boring, environmentally unfriendly, difficult to digest, and malnutrition. Conclusions The results contribute to the literature by adding empirical evidence to the emerging trends (PBD, vegan, vegetarian diets), as well as generating suggestions for nutrition and dietetics professionals and the government, as targeted marketing programs can be planned to change dietary behavior.

With the growing environmental concerns, the existing literature mostly highlights the industrial pollution while neglecting the factors associated with the agriculture-related greenhouse gas emissions. Regarding this, the study explores the impacts of green energy, tech development, and urbanization on agriculture's greenhouse gas emissions. The prime objective of the current research is to unveil the green energy, tech innovations, and environmental sustainability nexus to draw novel implications in the context of Sustainable Development Goals (SDGs). In doing so, the authors employ the quarterly data of China from 1990Q1 to 2020Q4. For the long-run empirical analysis, the authors utilize various time-series estimating approaches, such as Quantile regression, which performs better in testing the nexus at different quantiles. However, the Fully Modified OLS, Dynamic OLS, and Canonical Cointegration Regression methods are used as robustness tools to authenticate the estimate of the primary approach. The results suggest that greener energy and technological innovations significantly reduce agriculture sector emissions. Furthermore, the presence of green energy transforms its negative influence into a positive one. Contrastingly, the use of traditional fossil fuel energy, urbanization, and financial development are significant drivers of emissions. This study's findings support SDGs, particularly SDG-2, which supports the stance of sustainable agriculture and encourages green energy use. Overall, the study discourses policy-related suggestions in the sustainability's context.