Harrismurray5332

In contrast, the unavoidable substance related CO2-point sources in Germany could probably fulfill the carbon requirement for material use of the chemical industry in 2050. The authors conclude that the utilization of CO2 as a carbon source is necessary to close the carbon cycle where material or chemical recycling is technically not feasible or reasonable. The very high demand for renewable electricity indicates that the required production facilities for CO2-based chemicals will probably not be completely based in Germany.Occupant behavior in residential buildings has a direct impact on the effectiveness of energy-saving measures. In order to realize a buildings' carbon mitigation targets, the impact of individual occupancy profiles needs to be integrated with building simulation models. This paper introduces a decision support framework as a potential solution to make energy performance upgrade choices based on different occupancy profiles. This framework has been demonstrated through a case study of two single-family detached homes in Canada, which were highly instrumented with sensors for monitoring energy input and output. The case studies represented two common occupancy profiles-(1) a family of four (consisting of 2 working adults and 2 teenagers); and (2) a retired couple. Firstly, calibrated energy models were developed by using one-year energy use data collected through an intrusive load monitoring technique. Secondly, energy upgrade combinations were considered for each profile and tested for additional investment, payback period and greenhouse gas (GHG) emissions. Lastly, the most suitable combination of energy upgrade for each profile was ranked using a multi-criteria decision-making method (e.g., TOPSIS). Results indicated that the retired couple used more energy than the family of four and required energy upgrades with usually higher payback periods to achieve the same level of GHG emission reduction. The results of this research are timely for energy policymaking and developing best management practices, which need to be implemented along with the deployment of more stringent building standards and codes.The increase of the industrialization process brought the growth of pollutant emissions into the atmosphere. At the same time, the demand for advances in aerosol filtration is evolving towards more sustainable technologies. Electrospinning is gaining notoriety, once it enables to produce polymeric nanofibers with different additives and also the obtaining of small pore sizes and fiber diameters; desirable features for air filtration materials. Therefore, this work aims to evaluate the filtration performance of cellulose acetate (CA) nanofibers and cationic surfactant cetylpyridinium bromide (CPB) produced by electrospinning technique for retention of aerosol nanoparticles. The pressure drop and collection efficiency measurements of sodium chloride (NaCl) aerosol particles (diameters from 7 to 300 nm) were performed using Scanning Mobility Particle Sizer (SMPS). The average diameter of the electrospun nanofibers used was 239 nm, ranging from 113 to 398 nm. Experimental results indicated that the nanofibers showed good permeability (10-11 m2) and high-efficiency filtration for aerosol nanoparticles (about 100 %), which can include black carbon (BC) and the new coronavirus. The pressure drop was 1.8 kPa at 1.6 cm s-1, which is similar to reported for some high-efficiency nanofiber filters. In addition, it also retains BC particles present in air, which was about 90 % for 375 nm and about 60 % for the 880 nm wavelength. Finally, this research provided information for future designs of indoor air filters and filter media for facial masks with renewable, non-toxic biodegradable, and potential antibacterial characteristics.Biological control of odors and bioaerosols in wastewater treatment plants (WWTPs) have gained more attention in recent years. The simultaneous removal of odors, volatile organic compounds (VOCs) and bioaerosols in each unit of a full-scale integrated-reactor (FIR) in a sludge dewatering room was investigated. The average removal efficiencies (REs) of odors, VOCs and bioaerosols were recorded as 98.5 %, 94.7 % and 86.4 %, respectively, at an inlet flow rate of 5760 m3/h. The RE of each unit decreased, and the activated carbon adsorption zone (AZ) played a more important role as the inlet flow rate increased. The REs of hydrophilic compounds were higher than those of hydrophobic compounds. For bioaerosols, roughly 35 % of airborne heterotrophic bacteria (HB) was removed in the low-pH zone (LPZ) while over 30 % of total fungi (TF) was removed in the neutral-pH zone (NPZ). Most bioaerosols removed by the biofilter (BF) had a particle size larger than 4.7 μm while bioaerosols with small particle size were apt to be adsorbed by AZ. The microbial community in the BF changed significantly at different units. Health risks were found to be associated with H2S rather than with bioaerosols at the FIR outlet.As for the SARS coronavirus in the 2003 epidemic, the presence of SARS-CoV-2 has been demonstrated in faeces and, in some cases, urine of infected people, as well as in wastewater. This paper proposes a critical review of the state of the art regarding studies on the presence of SARS-CoV-2 in wastewater and sewage sludge, the factors affecting its inactivation and the main proposed treatments. In-vitro tests demonstrated low resistance of SARS-CoV-2 to high temperature, while even significant changes in pH would not seem to determine the disappearance of the virus. In real wastewater and in sewage sludge, to date studies on the influence of the different parameters on the inactivation of SARS-CoV-2 are not available. Therefore, studies involving other HCoVs such as SARS-CoV and HCoV-229E have been also considered, in order to formulate a hypothesis regarding its behaviour in sewage and throughout the steps of biological treatments in WWTPs. Finally, SARS-CoV-2 in wastewater might track the epidemic trends although being extremely promising, an effective and wide application of this approach requires a deeper knowledge of the amounts of viruses excreted through the faeces and the actual detectability of viral RNA in sewage.In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that possess unique methodical features to model dependencies in complex networks, such as forward and backward propagation (inference) of disruptions. BNs have transitioned from an emerging topic to a growing research area in supply chain (SC) resilience and risk analysis. As a result, there is an acute need to review existing literature to ascertain recent developments and uncover future areas of research. Despite the increasing number of publications on BNs in the domain of SC uncertainty, an extensive review on their application to SC risk and resilience is lacking. To address this gap, we analyzed research articles published in peer-reviewed academic journals from 2007 to 2019 using network analysis, visualization-based scientometric analysis, and clustering analysis. Through this study, we contribute to literature by discussing the challenges of current research, and, more importantly, identifying and proposing future research directions. The results of our survey show that further debate on the theory and application of BNs to SC resilience and risk management is a significant area of interest for both academics and practitioners. The applications of BNs, and their conjunction with machine learning algorithms to solve big data SC problems relating to uncertainty and risk, are also discussed.Given the importance that two-stage Data Envelopment Analysis (DEA) models have attained in recent years, this paper presents a systematic review of the literature on the topic focusing on the banking industry. We discuss the two-stage terminology itself, which is not yet not consolidated. We also discuss the current state-of-the-art and present opportunities, as well as challenges, for future studies. We analyse 59 papers, divided them into ten classes that cover various perspectives of two stage DEA studies, such as the economic context, geographic region of the banking units, methodological characteristics, and type of the models, either internal or external. Additionally, we investigate several controversial points regarding two-stage DEA models, such as the variable selection approach, the technique used in the second stage, and the possible impact of non-discretionary variables on efficiency. Results of the literature review indicate the lack of a uniform or universal terminology for two-stage DEA models in the baking industry. Moreover, the main objective of most papers involves extending or improving DEA models. Radial models, with variable returns of scale, and the intermediation approach are the most frequent configurations. Finally, we identify seven gaps in the literature for both internal and external two-stage DEA models and two specific gaps to external ones. Each gap is discussed in depth in the text and can be considered opportunities for future studies.The recent outbreak of the respiratory ailment COVID-19 caused by novel coronavirus SARS-Cov2 is a severe and urgent global concern. selleck compound In the absence of effective treatments, the main containment strategy is to reduce the contagion by the isolation of infected individuals; however, isolation of unaffected individuals is highly undesirable. To help make rapid decisions on treatment and isolation needs, it would be useful to determine which features presented by suspected infection cases are the best predictors of a positive diagnosis. This can be done by analyzing patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes. We developed a model that employed supervised machine learning algorithms to identify the presentation features predicting COVID-19 disease diagnoses with high accuracy. Features examined included details of the individuals concerned, e.g., age, gender, observation of fever, history of travel, and clinical details such as the severity of cough and incidence of lung infection. We implemented and applied several machine learning algorithms to our collected data and found that the XGBoost algorithm performed with the highest accuracy (>85%) to predict and select features that correctly indicate COVID-19 status for all age groups. Statistical analyses revealed that the most frequent and significant predictive symptoms are fever (41.1%), cough (30.3%), lung infection (13.1%) and runny nose (8.43%). While 54.4% of people examined did not develop any symptoms that could be used for diagnosis, our work indicates that for the remainder, our predictive model could significantly improve the prediction of COVID-19 status, including at early stages of infection.Spherical fuzzy sets (SFSs) have gained great attention from researchers in various fields. The spherical fuzzy set is characterized by three membership functions expressing the degrees of membership, non-membership and the indeterminacy to provide a larger preference domain. It was proposed as a generalization of picture fuzzy sets and Pythagorean fuzzy sets in order to deal with uncertainty and vagueness information. The similarity measure is one of the essential and advantageous tools to determine the degree of similarity between items. Several studies on similarity measures have been developed due to the importance of similarity measure and application in decision making, data mining, medical diagnosis, and pattern recognition in the literature. The contribution of this study is to present some novel spherical fuzzy similarity measures. We develop the Jaccard, exponential, and square root cosine similarity measures under spherical fuzzy environment. Each of these similarity measures is analyzed with respect to decision-makers' optimistic or pessimistic point of views.