Agriculture in Haryana is central to regional food security and rural livelihoods, yet it faces increasing pressure from climate variability, groundwater depletion, declining soil quality, and resource-intensive farming practices. Increasing temperatures, erratic precipitation, and a propensity for severe weather are intensifying production risks while accelerating environmental degradation. Under these changing conditions, Climate-Aware Farming provides a practical pathway to improve productivity, enhance adaptability and minimize agricultural greenhouse gas emissions. This review evaluates climate-smart farming strategies suitable for rural Haryana by synthesizing scientific literature and policy initiatives. It examines the potential of crop diversification, conservation agriculture, residue management, agroforestry, combined cattle and crops systems, and efficient water and nutrient control to strengthen climate resilience and resource sustainability. The importance of soil organic carbon restoration, micro-irrigation technologies, weather-based agro-advisories, and climate-resilient crop varieties in improving adaptive capacity and input efficiency is also highlighted. State initiatives such as climate action planning, climate-smart village programs, crop residue management interventions, and promotion of water-efficient irrigation systems and agroforestry reflect growing institutional commitment toward climate-resilient agriculture. Adoption of Climate-Aware Farming practices offers multiple benefits, including increased resilience to climate shocks, better income stability, lower farming costs, and healthier soil.
The growing demand for ultra-low-latency, high-throughput services (with 5G networks) will succeed the (proliferation of 5G networks) multi-access edge computing (MEC). Thus enabling the next phase of communication systems. In a 5G MEC architecture, the choice of when to offload a computation in an Edge Cloud server or Cloud server has serious implications for latency, resource use and quality of service. In this paper, we compare six approaches to this server-selection decision: a Random Forest, XGBoost, Support Vector Machine, Gradient Boosting, an Artificial Neural Network, and a decision engine based on a Large Language Model (Claude, Anthropic). All six methodologies were put to assess on a common, reproducible pipeline from exploratory data analysis to feature engineering to stratified data partitioning and feature standardisation, utilising the same dataset of 35,000 task instances of network and server telemetry. All of the five trained machine learning and deep learning models achieved near-optimal classification performance. The use of a three-pronged leakage diagnostic called feature-importance analysis, cross-tabulation, and ablation experiments revealed that the models relied on informative distance-linked features as opposed to inherent model capability. In terms of a no-leakage feature subset, the XGBoost model, which is the strongest classical model, achieved an accuracy of 99.70% while the Claude-based LLM engine achieved 96.0% accuracy on a noleakage comparable subset. In line with the requirement that using an existing LLM does not, by itself, demonstrate original contribution, we further propose and evaluate a Hybrid Confidence-Gated Decision Engine based on XGBoost and Claude, which escalates only cases deemed genuinely uncertain to the LLM. This hybrid engine obtained an overall accuracy of 99.90% but failed to beat the XGBoost rejected predictions on the very few escalated cases a negative result reported honestly. Meanwhile, a deeper diagnostic demonstrated that every field in our dataset, beyond the original four that we flagged, was strongly correlated with the target label. The data leakage and hybrid decision systems are examined in this multi-access edge computing assisted server selection using machine learning in 5G networks research article by Claude and more.
Amandeep, Ankit, Dharmender Kumar et al.· International Journal of Sci...· 0 citations
Neurodevelopmental and neurodegenerative disorders are related disorders lying on a spectrum of neural dysfunction with overlapping molecular and cellular mechanisms. Early-life diseases like autism spectrum disorder and attention-deficit/hyperactivity disorder are the result of disturbed neurodevelopment, while late-onset diseases such as Alzheimer’s disease and Parkinson’s disease are defined by progressive neuronal loss and loss of function. Emerging evidence shows that environmental exposures are important, modifiable factors that affect brain health throughout the lifespan. Factors such as air pollution, heavy metals, pesticides, and endocrine-disrupting chemicals act in combination with genetic susceptibility to disrupt neurogenesis, synaptic plasticity, and neuro-immune signaling. These exposures cause persistent epigenetic modifications, oxidative stress, mitochondrial dysfunction, and chronic neuro-inflammation, linking early developmental insults to neurodegenerative processes later in life. The concepts of the exposome and the developmental origins of health and disease provide additional support for the cumulative, lifelong impact of environmental interactions. Mechanistically, the recurrent process of neuronal damage is caused by impaired proteostasis, microglial stimulation, and blood–brain barrier dysfunction. Furthermore, the gut-brain axis is a hyperactive system in which immune responses and microbial metabolites trigger neurobiological responses to external stimuli. This review integrates multidisciplinary evidence to elucidate the mechanism and emphasizes environmental risk mitigation and translational strategies to reduce disease burden and promote lifelong brain resilience.
Snehashis Mandal, Priti Dipa, Neha et al.· Frontiers in Neurology· 0 citations
BACKGROUND First-year nursing students may encounter difficulties while adapting to academic requirements, hostel living, separation from family, clinical responsibilities, and changes in their social environment. These challenges can influence their physical, psychological, social, spiritual, and academic well-being. AIM To assess adjustment problems and coping strategies among students in selected nursing colleges of District Mandi, Himachal Pradesh, and to determine the relationship between adjustment problems and coping strategies. METHODOLOGY A quantitative approach with a descriptive research design was adopted. The study involved 300 first-year B.Sc. Nursing and GNM students selected through non-probability purposive sampling. Data were collected using a self-structured checklist for adjustment problems and a five-point Likert scale for coping strategies. Frequency, percentage, mean, standard deviation, Chi-square test, and Pearson’s correlation coefficient were used for data analysis. RESULTS The findings showed that 206 (68.7%) students had moderate adjustment problems, 94 (31.3%) had mild adjustment problems, and none had severe adjustment problems. Regarding coping strategies, 220 (73.33%) students demonstrated average coping, 43 (14.33%) demonstrated poor coping, and 37 (12.33%) demonstrated good coping. The mean adjustment problem score was 11.06 ± 2.88, while the mean coping strategy score was 72.58 ± 13.64. A strong positive correlation was observed between adjustment problems and coping strategies (r = 0.724, p < 0.001). Significant associations were found between adjustment problems and age, father’s occupation, and mother’s occupation (p < 0.05). CONCLUSION The study concluded that first-year nursing students commonly experienced moderate adjustment problems and generally used average coping strategies. Early identification of adjustment difficulties and supportive measures such as orientation, counseling, and guidance may help promote students’ well-being and academic functioning.
Meenakshi, S. Pathania, Priyanka Sharma et al.· International Journal of Sci...· 0 citations
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