Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

On the morning of the 7th of October, the Hamas organization launched an attack on Israel, causing over 1200 casualties and kidnapping over 250 Israeli citizens. Israeli workers, their families, and the country’s economy were all directly impacted by the war. The effects of the war were vast and created many challenges for the HR departments in various organizations. The HR departments had to respond to these issues with promptness, determination, and thoroughness. I will discuss the functions of HR departments during times of war in this opinion piece. They include eliminating ambiguity and uncertainty; providing managers with the tools they need to handle such circumstances; maintaining resilience and high morale; overseeing volunteer programs; maintaining contact with the reserve forces; providing support to war-affected families; carrying out an effective workforce reduction without jeopardizing the company’s ability to continue operations; and managing workplace tensions.

In the rapidly evolving landscape of contemporary business, the strategic alignment of employees with their designated roles is a pivotal determinant of organizational success. Employee misfit, characterized by a misalignment between employees’ skills, interests, and assigned roles, poses formidable challenges to individual and collective performance. This comprehensive research report delves into the intricate implications of employee misfit, explores evolving trends in career consciousness among job seekers, outlines the multifaceted challenges HR managers face, and fervently advocates for implementing a comprehensive selection process to address this prevalent issue effectively. The report underscores the proactive role of management in cultivating a supportive work environment, fostering diverse career pathways, and embedding an inclusive selection framework to confront and mitigate the persistent issue of employee misfit.

Soil erosion is characterized by the wearing away or loss of the uppermost layer of soil, driven by water, wind, and human activities. This process constitutes a significant environmental issue, with adverse effects on water quality, soil health, and the overall stability of ecosystems across the globe. This study focuses on the Anuppur district of Madhya Pradesh, India, employing the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information System (GIS) tools to estimate and spatially analyze soil erosion and fertility risk. The various factors of the model, like rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), conservation practices (P), and cover management factor (C), have been computed to measure annual soil loss in the district. Each factor was derived using geospatial datasets, including rainfall records, soil characteristics, a Digital Elevation Model (DEM), land use/land cover (LULC) data, and information on conservation practices. GIS methods are used to map the geographical variation of soil erosion, providing important information on the area's most susceptible to erosion. The outcome of the study reveals that 3371.23 km2, which constitutes 91% of the district's total area, is identified as having mild soil erosion; in contrast, 154 km2, or 4%, is classified as moderate soil erosion, while 92 km2, representing 2.5%, falls under the high soil erosion category. Additionally, 50 km2, or 1.35%, is categorized as very high soil erosion and around 30 km2 of the study area is classified as experiencing severe soil erosion. The analysis further discovers that the annual soil loss in the district varies between 0 and 151 tons per hectare per year. This study indicates that most of the district is classified under low soil erosion; only a tiny fraction of the area is categorized as experiencing high and very high soil erosion. The study provides significant insights into soil erosion for policymakers and human society to bring their attention to the need for sustainable soil conservation practices in the undulating terrain/topography and agriculturally dominated district of Anuppur.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.