A BWM-TOPSIS Hazardous Waste Inventory Safety Risk Evaluation

A review on e-waste contamination, toxicity, and sustainable clean-up approaches for its management

Ecosystems and human health are being negatively impacted by the growing problem of electrical waste, especially in developing countries. E-waste poses a significant risk to ecological systems because it can release a variety of hazardous substances into the environment, containing polybrominated diphenyl ethers and heavy metals, brominated flame retardants, polychlorinated dibenzofurans and polycyclic aromatic hydrocarbons, and dioxins. This review article provides a critical assessment of the toxicological consequences of e-waste on ecosystems and human health and data analyses from scientific journals and grey literature on metals, BFRs, PBDEs, PCDFs, and PAHs in several environmental compartments of commercial significance in informal electronic trash recycling. The currently available techniques and tools employed for treating e-waste are sustainable techniques such as bioremediation, chemical leaching, biological leaching, and pyrometallurgy have been also discussed along with the necessity of implementing strong legislation to address the issue of unregulated exports of electronic trash in recycling practices. Despite the ongoing hurdles, implementing environmentally sustainable recycling methods have the potential to address the detrimental impacts of e-waste and foster positive economic development.

A healthcare evaluation system based on automated weighted indicators with cross-indicators based learning approach in terms of energy management and cybersecurity

OBJECTIVE

Hospital performance evaluation is vital in terms of managing hospitals and informing patients about hospital possibilities. Also, it plays a key role in planning essential issues such as electrical energy management and cybersecurity in hospitals. In addition to being able to make this measurement objectively with the help of various indicators, it can become very complicated with the participation of subjective expert thoughts in the process.

METHOD

As a result of budget cuts in health expenditures worldwide, the necessity of using hospital resources most efficiently emerges. The most effective way to do this is to determine the evaluation criteria effectively. Machine learning (ML) is the current method to determine these criteria, determined by consulting with experts in the past. ML methods, which can remain utterly objective concerning all indicators, offer fair and reliable results quickly and automatically. Based on this idea, this study provides an automated healthcare system evaluation framework by automatically assigning weights to specific indicators. First, the ability of hands to be used as input and output is measured.

RESULTS

As a result of this measurement, indicators are divided into only input group (group A) and both input and output group (group B). In the second step, the total effect of each input on the output is calculated by using the indicators in group B as output sequentially using the random forest of the regression tree model.

CONCLUSION

Finally, the total effect of each indicator on the healthcare system is determined. Thus, the whole system is evaluated objectively instead of a subjective evaluation based on a single output.