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.

This research aims to investigate the impact of knowledge-based human resource management (KBHRM) practices on organizational performance through the mediating role of quality and quantity of knowledge worker productivity (QQKWP). The data were collected from 325 employees working in different private universities of Pakistan by using convenience and purposive sampling techniques. The quantitative research technique was used to perform analysis on WarpPLS software. The result revealed that only knowledge-based recruiting practices have a positive and significant direct effect on organizational performance. While knowledge-based performance appraisal practices, training and development practices and compensation practices all were insignificant in this regard. However, through mediator QQKWP, the knowledge-based recruiting practices (KBRP), knowledge-based training and development (KBTD), and knowledge-based compensation practices (KBCP) all were positively and significantly influencing organizational performance but only knowledge-based performance appraisal (KBPA) was insignificant in this mediating relationship. Lastly, the current study provides useful insights into the knowledge management (KM) literature in the context of private higher educational institutes of developing countries like Pakistan. The future studies should consider the impact of KBHRM practices on knowledge workers’ productivity and firms’ performances in the context of public universities.

The study has formulated the objective of synthesizing the extent to which technological barriers intervene in the transparency and effectiveness of public management (PM). Methodologically, the study was of a fundamental or basic nature, with a systematic review design, the databases of Scopus (369), SciELO (2), Web of Science (184) were explored, after the review process a set of 22 articles was available. The registration was made in an Excel table where the main data of the articles were included. 32% of the articles selected for the analysis of the evidence are from the period 2020, 27% were from 2022 and 18% from the year 2023; as far as origin is concerned, 14% of the articles come from Peru and 9% from Australia, Brazil, South Korea, Spain and Indonesia. In summary, the study points out that government institutions are making progress in digitizing and improving the citizen experience through electronic services, but they face challenges in areas such as resource management, the low adoption of advanced technologies such as blockchain and artificial intelligence, as well as the lack of transparency in PM. Despite this, it is highlighted that e-government improves citizen satisfaction, and the need to invest in digital innovation, training and overcoming technological barriers to achieve an effective transformation in state administration and promote a more inclusive and advanced society is emphasized.

The regulation of compressor extraction and energy storage can improve the performance of gas turbine energy system. In order to make the gas turbine system match the external load more flexibly and efficiently, a gas turbine cogeneration system with solar energy coupling compressor outlet extraction and energy storage is proposed. By establishing the variable condition mathematical model of air turbine, waste heat boiler and solar collector, we use Thermoflex software to establish the variable condition model of gas turbine compressor outlet extraction, and analyze the variable condition of the coupling system to study the changes of thermal parameters of the system in the energy storage, energy release and operation cycle. Taking the hourly load of a hotel in South China as an example, this paper analyzes the case of the cogeneration system of solar energy coupling compressor outlet extraction and energy storage, and compares it with the benchmark cogeneration system. The results show that taking a typical day as a cycle, the primary energy utilization rate of the system designed in this paper is 3.2% higher than that of the traditional cogeneration system, and the efficiency is 2.4% higher.

Smallholder paprika farmers in Zimbabwe contribute to local economies and food security but face supply chain challenges like limited market access and poor infrastructure which lead to post harvest losses and unpredictable prices. To survive, these farmers must adopt sustainable value networks to reduce operational costs and improve performance. This study sought to establish the effect of sustainable value networks on the operational performance of smallholder paprika farming in Zimbabwe. This study, using a positivist research philosophy and a quantitative approach, surveyed 288 smallholder paprika farmers in Zimbabwe. Exploratory factor analysis and partial least squares structural equation modelling were used to validate the constructs and test the hypothesised relationships. Results demonstrate a moderate level of implementation of value networks in smallholder paprika farming characterised by successes and challenges. The findings illustrated resource sharing among smallholder farmers, facilitated by initiatives, such as recycled seed exchanges and financial support through village savings and loan associations. However, results show that challenges persist, particularly with market access and financial support. Results indicate that there is a significant awareness and implementation of green supply chain management practices among smallholder paprika farmers even though they do not have access to resources and live in rural areas. The findings demonstrate that value networks significantly influence the adoption of green supply chain management practices, which in turn positively impact operational performance, environmental performance, and social performance. Green supply chain management practices were found to mediate the relationship between value networks and environmental performance, social performance, and operational performance, underlining the critical role of sustainable practices in enhancing performance outcomes. While environmental performance showed a positive effect on operational performance, the direct influence of social performance on operational performance was found to be statistically insignificant, suggesting the need for further exploration of the factors linking social benefits to operational efficiency. The research contributes to both theory and practice by presenting a sustainable value network model for smallholder paprika farmers, integrating value network, green supply chain management practices and environmental performance to enhance operational performance. Practical implications include policy recommendations to strengthen collaboration between smallholder farmers and other stakeholdersand address power imbalances with intermediaries. Future research should extend the study to other agricultural sectors and incorporate more diverse stakeholder perspectives to validate and generalise the proposed sustainable value network model.