Carbon footprint assessment of face masks in the context of the COVID-19 pandemic: Based on different protective performance and applicable scenarios.

Widespread concerns have been raised about the huge environmental burden caused by massive consumption of face masks in the context of the COVID-19 pandemic. However, most of the existing studies only focus on the environmental impact associated with the product itself regardless of the actual usage scenarios and protective performance of products, resulting in unrealistic conclusions and poor applicability. In this context, this study integrated the product performance into the existing carbon footprint assessment methodology, with focus on the current global concerns regarding climate change. Computational case studies were conducted for different mask products applicable to the scenarios of low-, medium- and high-risk levels. The results showed that reusable cotton masks and disposable medical masks suitable for low-risk settings have a total carbon footprint of 285.484 kgCO2-eq/FU and 128.926 kgCO2-eq/FU respectively, with a break-even point of environmental performance between them of 16.886, which implies that cotton masks will reverse the trend and become more environmentally friendly after 17 washes, emphasizing the importance of improving the washability of cotton masks. Additionally, the total carbon footprints of disposable surgical masks and KN95 respirators were 154.328 kg CO2-eq/FU and 641.249 kg CO2-eq/FU respectively, while disposable medical masks and disposable surgical masks were identified as alternatives with better environmental performance in terms of medium- and high-risk environments respectively. The whole-life-cycle oriented carbon footprint evaluation further indicated that the four masks have greater potential for carbon emission reduction in the raw material processing and production processes. The results obtained in this study can provide scientific guidance for manufacturers and consumers on the production and use of protective masks. Moreover, the proposed model can be applied to other personal protective equipment with similar properties, such as protective clothing, in the future.

Preparation of low-cost functionalized diatomite and its effective removal of ammonia nitrogen from wastewater.

A low-cost functionalization method was used to treat diatomite, and an efficient adsorbent for ammonia nitrogen was prepared by optimizing the functionalization conditions. The functionalized diatomite (DTCA-Na) was characterized by SEM, EDS, BET, XRD, FT-IR, and TG. The results demonstrate that DTCA-Na has excellent adsorption performance after being modified with H2SO4 (60.00 wt.%), NaCl (5.00 wt.%), and calcination at 400 °C for 2 h. While studying the effect of adsorption factors on the removal of ammonia nitrogen, the kinetic and thermodynamic behaviors in the adsorption process were discussed. The removal efficiency of the simulated wastewater with the initial ammonia nitrogen concentration of 10.00 mg L-1 by the DTCA-Na was more than 80% when the contact time was 60 min, pH was 6-10, the dosage of adsorbent was 1.00 g, and the temperature was 25 °C. The adsorption process of ammonia nitrogen was conformed to the pseudo-first-order and Langmuir isothermal model. The removal efficiency of ammonia nitrogen was still above 80% after 5 times adsorption-desorption experiments. The DTCA-Na has a brighter prospect of application in the field of ammonia nitrogen wastewater treatment due to its excellent adsorption performance and low-cost advantage.

Multiple Cost Optimisation for Alzheimer's Disease Diagnosis.

Current machine learning techniques for dementia diagnosis often do not take into account real-world practical constraints, which may include, for example, the cost of diagnostic assessment time and financial budgets. In this work, we built on previous cost-sensitive feature selection approaches by generalising to multiple cost types, while taking into consideration that stakeholders attempting to optimise the dementia care pathway might face multiple non-fungible budget constraints. Our new optimisation algorithm involved the searching of cost-weighting hyperparameters while constrained by total budgets. We then provided a proof of concept using both assessment time cost and financial budget cost. We showed that budget constraints could control the feature selection process in an intuitive and practical manner, while adjusting the hyperparameter increased the range of solutions selected by feature selection. We further showed that our budget-constrained cost optimisation framework could be implemented in a user-friendly graphical user interface sandbox tool to encourage non-technical users and stakeholders to adopt and to further explore and audit the model - a humans-in-the-loop approach. Overall, we suggest that setting budget constraints initially and then fine tuning the cost-weighting hyperparameters can be an effective way to perform feature selection where multiple cost constraints exist, which will in turn lead to more realistic optimising and redesigning of dementia diagnostic assessments. Clinical Relevance-By optimising diagnostic accuracy against various costs (e.g. assessment administration time and financial budget) predictive yet practical dementia diagnostic assessments can be redesigned to suit clinical use.

The Impact of COVID-19 on Postdischarge Outcomes for Dialysis Patients in the United States: Evidence from Medicare Claims Data.

Background: Recent investigations have shown that, on average, patients hospitalized with coronavirus disease 2019 (COVID-19) have a poorer postdischarge prognosis than those hospitalized without COVID-19, but this effect remains unclear among patients with end-stage kidney disease (ESKD) who are on dialysis. Methods: Leveraging a national ESKD patient claims database administered by the US Centers for Medicare and Medicaid Services, we conducted a retrospective cohort study that characterized the effects of in-hospital COVID-19 on all-cause unplanned readmission and death within 30 days of discharge for patients on dialysis. Included in this study were 436,745 live acute-care hospital discharges of 222,154 Medicare beneficiaries on dialysis from 7871 Medicare-certified dialysis facilities between January 1 and October 31, 2020. Adjusting for patient demographics, clinical characteristics, and prevalent comorbidities, we fit facility-stratified Cox cause-specific hazard models with two interval-specific (1-7 and 8-30 days after hospital discharge) effects of in-hospital COVID-19 and effects of prehospitalization COVID-19. Results: The hazard ratios due to in-hospital COVID-19 over the first 7 days after discharge were 95% CI, 1.53 to 1.65 for readmission and 95% CI, 1.38 to 1.70 for death, both with P<0.001. For the remaining 23 days, the hazard ratios were 95% CI, 0.89 to 0.96 and 95% CI, 0.86 to 1.07, with P<0.001 and P=0.50, respectively. Effects of prehospitalization COVID-19 were mostly nonsignificant. Conclusions: In-hospital COVID-19 had an adverse effect on both postdischarge readmission and death over the first week. With the surviving patients having COVID-19 substantially selected from those hospitalized, in-hospital COVID-19 was associated with lower rates of readmission and death starting from the second week.

Low Cost Interconnected Architecture for the Hardware Spiking Neural Networks.

A novel low cost interconnected architecture (LCIA) is proposed in this paper, which is an efficient solution for the neuron interconnections for the hardware spiking neural networks (SNNs). It is based on an all-to-all connection that takes each paired input and output nodes of multi-layer SNNs as the source and destination of connections. The aim is to maintain an efficient routing performance under low hardware overhead. A Networks-on-Chip (NoC) router is proposed as the fundamental component of the LCIA, where an effective scheduler is designed to address the traffic challenge due to irregular spikes. The router can find requests rapidly, make the arbitration decision promptly, and provide equal services to different network traffic requests. Experimental results show that the LCIA can manage the intercommunication of the multi-layer neural networks efficiently and have a low hardware overhead which can maintain the scalability of hardware SNNs.