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This paper aims to examine the impact of social capital and self-efficacy in the performance of producer organizations. It also tests the mediating influence of self-efficacy in the relationship of social capital and performance of producer organizations.

The study used data from a survey of 226 members of farmer producer organizations (FPO) in India. The model was tested through structural equation modeling wherein all hypotheses were tested using “R” studio.

The findings reveal that social capital and self-efficacy play a significant role in predicting the performance of FPO. It was found that in the process of social capital influencing the performance of FPO, self-efficacy plays a significant role as a partial mediator with a mediating effect of approximately 69.28%.

The study considered only one antecedent while identifying the reasons for perceived performance of FPOs. Hence, further studies of the various other constructs such as attitude, subjective norms, etc., may be considered.

No previous work has examined the mediating role of self-efficacy in the relationship between social capital and perceived performance of FPO. This study is possibly the only one that joins two streams of thought – social capital and self-efficacy – to examine the performance of FPO.

Performance management system (PMS) is a crucial element of strategic human resource practices in any organization. This research aims to provide a concise overview of how bibliometric analysis is employed to assess the influence and significance of cutting-edge technologies in shaping of PMS. This study seeks to identify key trends, emerging technologies and their impact on the evolution of performance management practices, contributing valuable insights for researchers, practitioners and policymakers in this field.

This investigation is carried out utilizing total of eight research questions, which are examined through VOS Viewer and Biblioshiny software. The research offers visual diagrams and tables depicting the data extracted from the Scopus Database.

The study’s results underscore a noticeable increase in research literature pertaining to PMS, indicating a shift from conventional methods to a strategic, technology-driven approach. These findings cover the way for further investigation across various disciplines, offering opportunities to enhance the efficacy and productivity of PMS.

The implementation of new technologies such as Artificial intelligence (AI), machine learning and robotics etc. in PMS have also been analysed to give a sneak peak of the bigger future picture of AI and strategic human resource integration.

To the best of the authors' understanding, this analysis represents the inaugural application of bibliometric techniques to evaluate the advancement of research on Performance Management System (PMS) dating back to 1978, utilizing academic literature sourced from the Scopus database.

This study aims to understand the current production scenario emphasizing the significance of green manufacturing in achieving economic and environmental sustainability goals to fulfil future needs; to determine the viability of particular strategies and actions performed to increase the process efficiency of electrical discharge machining; and to uphold the values of sustainability in the nonconventional manufacturing sector and to identify future works in this regard.

A thorough analysis of numerous experimental studies and findings is conducted. This prominent nontraditional machining process’s potential machinability and sustainability challenges are discussed, along with the current research to alleviate them. The focus is placed on modifications to the dielectric fluid, choosing affordable substitutes and treating consumable tool electrodes.

Trans-esterified vegetable oils, which are biodegradable and can be used as a substitute for conventional dielectric fluids, provide pollution-free machining with enhanced surface finish and material removal rates. Modifying the dielectric fluid with specific nanomaterials could increase the machining rate and demonstrate a decrease in machining flaws such as micropores, globules and microcracks. Tool electrodes subjected to cryogenic treatment have shown reduced tool metal consumption and downtime for the setup.

The findings suggested eco-friendly machining techniques and optimized control settings that reduce energy consumption, lowering operating expenses and carbon footprints. Using eco-friendly dielectrics, including vegetable oils or biodegradable dielectric fluids, might lessen the adverse effects of the electrical discharge machine operations on the environment. Adopting sustainable practices might enhance a business’s reputation with the public, shareholders and clients because sustainability is becoming increasingly significant across various industries.

A detailed general review of green nontraditional electrical discharge machining process is provided, from high-quality indexed journals. The findings and results contemplated in this review paper can lead the research community to collectively apply it in sustainable techniques to enhance machinability and reduce environmental effects.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.