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The influence of academic staff job performance on job burnout: the moderating effect of psychological counselling
Get PDFCurrent research on addressing burnout in higher education predominantly focuses on post-measurements, after job burnout has occurred, rather than emphasizing the long-recognized tradition of preventive philosophy and applying pre-measurements of burnout. This study focuses on the influence of academic staff job performance on job burnout, as well as the moderating effect of psychological counselling. Using a quantitative approach with panel data over a four-year period, information was collected from 1091 academic staff across 12 universities. It utilized archived data on their job performance (KPI) and mental health reports. The findings revealed that job performance exerts a negative influence on burnout (β = −0.037, P < 0.001). Furthermore, psychological counselling moderates the relationship between job performance and job burnout (β = −0.005, P < 0.001), although it does not directly enhance job performance. Overall, this research contributes to understanding and addressing burnout among academic staff, by suggesting that job performance and psychological counselling can serve as preventive measures against burnout. Therefore, universities are encouraged to implement proactive recruitment strategies that assess academic staff holistically so that the onset of burnout can be mitigated
A review of the pathways, limitations, and perspectives of plastic waste recycling
Get PDFThe valorisation of plastic waste through diverse recycling technologies offers a strategic response to the escalating global plastic crisis, combining waste reduction with resource and energy recovery. This review critically examines both conventional and emerging methods—including mechanical recycling, incineration for energy recovery, pyrolysis, gasification, hydrogenation, hydrocracking, and solvent-based treatments—focusing on their technical efficacy, environmental footprint, and economic feasibility. Mechanical recycling remains the most widely adopted method, involving collection, sorting, grinding, washing, drying, and granulation processes. However, challenges such as polymer degradation, contamination, and incompatibility among mixed plastics limit the quality and applicability of recycled products. Advanced sorting technologies, including Near-Infrared (NIR) spectroscopy, Artificial Intelligence (AI), and electrostatic separation, are increasingly employed to enhance recycling outcomes. Incineration provides energy in the form of electricity, heat, or steam while significantly reducing waste volume, yet it raises environmental concerns due to the release of toxic gases and particulates. Chemical recycling emerges as a critical pillar of the circular plastic economy, enabling the breakdown of polymers into valuable chemical feedstocks. Techniques such as pyrolysis, gasification, and hydrocracking produce valuable by-products, including char, syngas, and bio-oil. The review underscores the potential of integrating incineration with carbon capture technologies to mitigate emissions and improve sustainability. It advocates for region-specific strategies supported by comprehensive techno-economic and environmental assessments. This work provides a comparative framework to inform the selection of recycling technologies, guide policy development, and identify research priorities in advancing plastic waste valorisation
A weighted difference loss approach for enhancing multi-label classification
Get PDFConventional multi-label classification methods often fail to capture the dynamic relationships and relative intensity shifts between labels, treating them as independent entities. This limitation is particularly detrimental in tasks like sentiment analysis where emotions co-occur in nuanced proportions. To address this, we introduce a novel Weighted Difference Loss (WDL) framework. WDL operates on three core principles: (1) transforming labels into a normalized distribution to model their relative proportions; (2) computing learnable, weighted differences across this distribution to explicitly capture inter-label dynamics and trends; and (3) employing a label-shuffling augmentation to ensure the model learns intrinsic, order-invariant relationships. Our framework not only achieves state-of-the-art performance on four public benchmarks, but more importantly, it substantially improves the recognition of minority classes. This demonstrates the framework’s ability to learn from sparse data by effectively leveraging the underlying label structure, offering a robust, loss-driven alternative to complex architectural modifications