Criteria Clustering and Supplier Segmentation Based on Sustainable Shared Value Using BWM and PROMETHEE

With the advent of healthy visions, two of the trends that have become extremely important in the supply chain in recent decades are corporate social responsibility (CSR) and sustainability, which have affected the activities of buyers and suppliers. The next trend that is emerging is the vision of creating shared value (CSV), which wants to move the supply chain toward solving social problems in a completely strategic way. This research intends to develop a step-by-step framework for evaluating and segmenting suppliers based on CSV criteria in the supply chain. In the first stage, the criteria for creating sustainable shared value (CSSV) are obtained through existing activities in the field of CSR. The obtained criteria are then divided into two categories, strategic and critical, and then the weight of each criterion is obtained using the best–worst method (BWM). In the next step, based on the Kraljic model, the suppliers are divided into four clusters using the preference ranking organization method for enrichment evaluation (PROMETHEE) technique. This framework helps the buyer to conclude and select purchasing decisions and relationships with suppliers through the lenses of CSV and sustainability.

Pyrolysis kinetic behavior of composite polypropylene-biomass solid fuels derived via co-hydrothermal carbonization process

A treatment method producing solid fuel from surgical face masks was developed to address the high cost of medical waste management and the environmental concerns of large-scale, hazardous waste dumping during the coronavirus 19 (COVID-19) pandemic. The fuel properties were investigated of solid fuel produced from surgical face masks (polypropylene material (PP)) and from PP mixed with biomass (composite PP-biomass) using a hydrothermal carbonization process (HTC), followed by pyrolysis using thermogravimetric analysis (TGA). The Coats-Redfern (CR) method was used to analyze the pyrolysis kinetics and thermodynamic parameters. The results demonstrated that the thermal decomposition of raw PP material was less stable than for its hydrochar and the fuel properties of hydrochar obtained from the PP material were substantially improved using an increased HTC temperature. The activation energy value of the raw composite PP-biomass material was higher than for its hydrochar. This research identified an option for a potentially environmental-friendly treatment method for PP-material and its utilization as solid fuel.

Probabilistic Forecasting Model for the COVID-19 Pandemic Based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System

There are several techniques to support simulation of time series behavior. In this chapter, the approach will be based on the Composite Monte Carlo (CMC) simulation method. This method is able to model future outcomes of time series under analysis from the available data. The establishment of multiple correlations and causality between the data allows modeling the variables and probabilistic distributions and subsequently obtaining also probabilistic results for time series forecasting. To improve the predictor efficiency, computational intelligence techniques are proposed, including a fuzzy inference system and an Artificial Neural Network architecture. This type of model is suitable to be considered not only for the disease monitoring and compartmental classes, but also for managerial data such as clinical resources, medical and health team allocation, and bed management, which are data related to complex decision-making challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Monitoring the Construction Sector in Valle del Cauca

The objective of this research is to develop a monthly indicator that synthesizes the economic acti-vity of the construction sector in Valle del Cauca, as a tool for analyzing the production cycle and as a single, public measure that contributes to decision-making. Dynamic factorial models, the Kalman filter and the Litterman method are used, also employed in the Monthly Index of Economic Activity (IMAE), in order to capture signals, changes in the productive cycle of construction and compile the sectoral economic facts that affect production. In the results, the dynamics of the sectoral and economic variables are observed that explain the behavior of the sector in Valle del Cauca during 2009-2020. A change of slope is observed in 2020, as a direct consequence of the crisis generated by COVID-19 and the restrictive measures taken by the government to contain its advance.

COVID-19: District level vulnerability assessment in India.

OBJECTIVES: COVID-19 Pandemic has brought a threatening challenge to the world and as well as for Indian society and economy. In India, it has become a public health disaster and its' intensity increasing continuously. For the disaster risk reduction, and capacity building against the COVID-19 pandemic understanding of the relationship between socio-environmental conditions with the pandemic is very necessary. The objective of the present work is to construct a socio-environmental vulnerability index of the potential risk of community spread of COVID-19 using socio-economic and environmental variables. METHODOLOGY: In this, cross-sectional study principal component analyses have been used to drive SoEVI. 4 uncorrelated sub-index has been extracted from 16 sub-indicators which reflects 59% of the variance. Aggregation of 4 Sub-Index has been done to obtain the final vulnerability Index. RESULTS: Results show that there is spatial variability in vulnerability based on environmental and socio-economic conditions. Districts of north and central India found more vulnerable then south India. Statistical significance has been tested using regression analysis, positive relation has been found between vulnerability index and confirmed and active cases. CONCLUSION: The vulnerability index has highlighted environmentaly and socioeconomicallybackward districts. These areas will suffer more critical problems against COVID-19 pandemic for their socio-environmental problem.

Sonocrystallization of a novel ZIF/zeolite composite adsorbent with high chemical stability for removal of the pharmaceutical pollutant azithromycin from contaminated water.

Water pollution management, reduction, and elimination are critical challenges of the current era that threaten millions of lives. By spreading the coronavirus in December 2019, the use of antibiotics, such as azithromycin increased. This drug was not metabolized, and entered the surface waters. ZIF-8/Zeolit composite was made by the sonochemical method. Furthermore, the effect of pH, the regeneration of adsorbents, kinetics, isotherms, and thermodynamics were attended. The adsorption capacity of zeolite, ZIF-8, and the composite ZIF-8/Zeolite were 22.37, 235.3, and 131 mg/g, respectively. The adsorbent reaches the equilibrium in 60 min, and at pH = 8. The adsorption process was spontaneous, endothermic associated with increased entropy. The results of the experiment were analyzed using Langmuir isotherms and pseudo-second order kinetic models with a R2 of 0.99, and successfully removing the composite by 85% in 10 cycles. It indicated that the maximum amount of drug could be removed with a small amount of composite.

Novel polymer composite coated with ethylcellulose nanoparticle from waste paper as an alternative material to extracorporeal oxygenation membrane

Extracorporeal membrane oxygenator (ECMO) is a valuable technology to support people with acute respiratory distress syndrome (ARDS) and is recommended for COVID-19 patients. This study aims to fabricate polymer-based composite membranes coated with ethylcellulose nanoparticles from waste paper and identify the performance of the composite as ECMO candidates. Composite membranes were made from four types of polymers, namely, nylon, PTFE (polytetrafluoroethylene), Pebax® MH-1657, and SBS (poly-(styrene-b-butadiene-b-styrene)). PDMS (polydimethylsiloxane) 1 wt.% and ethylcellulose nanoparticles (3% and 10 wt.%) were used as membrane coatings to increase their hydrophobic properties. The success of cellulose isolation and ethylcellulose synthesis from waste paper was confirmed by the FTIR and XRD analysis. The size of the synthesized ethylcellulose nanoparticles was 32.68 nm. The coating effect on composite membranes was studied by measuring the contact angle, membrane porosity, protein quantification tests, and single gas permeation of O2 and CO2. Based on the protein quantification test, the protein could not pass through the Pebax/PDMS and SBS/PDMS composites coated with 10 wt.% ethylcellulose;this indicated less risk of plasma leakage. The gas permeation test on nylon/PDMS, PTFE/PDMS, and SBS/PDMS composites coated with 10% ethylcellulose resulted high CO2/O2 selectivity, respectively, 2.17, 3.48, and 3.22 as good indication for extracorporeal oxygenation membrane.

ABS/AgZrP Nanocomposite Additive Manufacturing Filament for Antibacterial Applications

The emergence of COVID-19 raised awareness in hygiene practices and reminded us of the harm that microbes bring to our health. Incorporating antibacterial agents in polymeric materials would allow us to combat lingering bacteria on surfaces that we often use. The utilization of composite filaments with antibacterial activity would allow us to employ better precautions in reducing contact with harmful bacteria. Antibacterial acrylonitrile‐butadiene‐styrene (ABS) nanocomposites were prepared by incorporating silver zirconium phosphate (AgZrP) nanoparticles via twin screw extruder. The ABS/AgZrP nanocomposite filament with 5 wt % and 20 wt% of AgZrP were synthesized and characterized with Differential scanning calorimetry (DSC), Thermogravimetric Analysis (TGA), X-ray diffraction analysis (XRD), and Fourier transform infrared spectroscopy (FTIR). DSC and XRD data denote an increase in the presence of crystalline regions as the AgZrP content is increased. TGA data indicate that the addition of AgZrP has no effect on the thermal stability of the material. FTIR data indicate a decrease in transmission at higher AgZrP loading. The decreasing trend in tensile properties of the 3D-printed neat and AgZrP-filled ABS may have been due to particle agglomeration acting as stress concentrators. Antibacterial activity assessment via disk diffusion test showed a zone of inhibition within the sample indicating that there is no bacterial growth both for Escherichia coli and Staphylococcus aureus. © 2023 Trans Tech Publications Ltd, Switzerland.

The separation membranes in artificial organs

Separation membranes play a crucial role in the functioning of artificial organs, such as hemodialysis machines, membrane oxygenators, and artificial liver models. The current COVID-19 pandemic has highlighted the importance of these technologies in the medical community. However, membrane technology in artificial organs faces significant challenges, such as the clearance of low-middle-molecule and protein-bound toxins and limited blood compatibility. In this review, we will discuss the separation mechanisms, separation performance, and biocompatibility of different types of separation membranes used in artificial organs. We will also highlight the opportunities and challenges for next-generation membrane technology in this field, including the need for improved clearance of toxins and increased blood compatibility, as well as the potential for microfluidic devices.

Oral Azvudine for hospitalised patients with COVID-19 and pre-existing conditions: a retrospective cohort study.

Background: As the COVID-19 pandemic continues to spread, the number of associated deaths continues to increase, especially among those with pre-existing conditions. Azvudine is recommended as a priority treatment for patients with COVID-19, but its efficacy in patients with pre-existing conditions is unknown. Methods: This is a single-centre, retrospective cohort study between December 5, 2022 and January 31, 2023 in Xiangya Hospital of Central South University in China to evaluate the clinical efficacy of Azvudine in hospitalised patients with COVID-19 and pre-existing conditions. Patients with Azvudine and controls were propensity score-matched (1:1) for age, gender, vaccination status, time from symptom onset to treatment exposure, severity at admission, concomitant treatments initiated at admission. The primary outcome was a composite outcome of disease progression, and the secondary outcome was each of these individual disease progression outcomes. The univariate Cox regression model was used to estimate a hazard ratio (HR) with 95% confidence interval (CI) for each result between the groups. Findings: We identified 2118 hospitalised patients with COVID-19 during the study period, with a follow-up of up to 38 days. After exclusions and propensity score matching, we included 245 Azvudine recipients and 245 matched controls. Azvudine recipients had lower crude incidence rate of composite disease progression outcome compared with matched controls (7.125/1000 person-days vs. 16.004/1000 person-days, P = 0.018). There was no significant difference in all-cause death between these two groups (1.934/1000 person-days vs. 4.128/1000 person-days, P = 0.159). Azvudine treatment was associated with significantly lower risks of composite disease progression outcome compared with matched controls (HR: 0.49; 95% CI: 0.27-0.89, P = 0.016). A significant difference in all-cause death was not found (HR: 0.45; 95% CI: 0.15-1.36, P = 0.148). Interpretation: These findings indicate that Azvudine therapy showed substantial clinical benefits in hospitalised patients with COVID-19 and pre-existing conditions, and should be considered for this population of patients. Funding: This work was supported by the National Natural Science Foundation of China (Grant Nos. 82103183 to F. Z., 82102803, 82272849 to G. D.), National Natural Science Foundation of Hunan Province (Grant Nos. 2022JJ40767 to F. Z., 2021JJ40976 to G. D.), Huxiang Youth Talent Program (Grant Nos. 2022RC1014 to M.S.) and Ministry of Industry and Information Technology of China (Grant Nos. TC210804V to M.S.).

Mixed frequency composite indicators for measuring public sentiment in the EU.

Monitoring the state of the economy in a short time is a crucial aspect for designing appropriate and timely policy responses in the presence of shocks and crises. Short-term confidence indicators can help policymakers in evaluating both the effect of policies and the economic activity condition. The indicator commonly used in the EU to evaluate the public opinion orientation is the Economic Sentiment Indicator (ESI). Nevertheless, the ESI shows some drawbacks, particularly in the adopted weighting scheme that is static and not country-specific. This paper proposes an approach to construct novel composite confidence indicators, focusing on both the weights and the information set to use. We evaluate these indicators by studying their response to the policies introduced to contain the COVID-19 pandemic in some selected EU countries. Furthermore, we carry out an experimental study where the proposed indicators are used to forecast economic activity.

Preparation of PLA-based SMS nonwoven composites and its applications in protective apparel

The global COVID-19 pandemic has triggered a huge demand for the protective nonwovens. However, the main raw material of nonwovens comes from petroleum, and the massive consumption of petroleum-based polymers brings great pressure to ecosystem. Therefore, it is significant to develop biodegradable protective barrier products. In this work, a polylactic-based composite (a tri-layer nonwovens composed of spunbond, meltblown and spunbond, SMS) was prepared and applied for protective apparel. The surface morphology and chemical changes of the fibers were characterized and analyzed by scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR) and energy dispersive spectroscopy (EDS). The liquid contact angle and permeability, breathability and moisture permeability, frictional charge and mechanical strength of the samples were evaluated and compared. The samples degradability was also recorded. The results demonstrate that the optimum formula for anti-fouling treatment on SMS is F-30. The treated fabric possesses superior liquid repellency and anti-permeability, with contact angles of water and alcohol at 128° and 115° respectively, while the alcohol repellent grade reaches level 7. The treated sample has less strength loss but exhibits favorable breathability, moisture permeability and anti-static properties, which can meet the requirements of protective apparels. After fluorine resin coating, the composite still provide excellent degradation performance, and the weight loss rate reaches more than 80% after 10 days water degradation. These results provide new insights for the application of PLA-based SMS in biodegradable protective apparel. © 2023 The Textile Institute.

Speech Recognition Using Intelligent Piezoresistive Sensor Based on Polystyrene Sphere Microstructures

Rapid advances in wearable sensing technology have demonstrated unprecedented opportunities for artificial intelligence. In comparison with the traditional hand-held electrolarynx, a wearable and intelligent artificial throat with sound-sensing ability is a more comfortable and versatile method to assist disabled people with communication. Herein, a piezoresistive sensor with a novel configuration is demonstrated, which consists of polystyrene (PS) spheres as microstructures sandwiched between silver nanowires and reduced graphene oxide layers. In fact, changes in the device's conducting patterns are obtained by spay-coating the various weight ratios and sizes of the PS microspheres, which is a fast and convenient way to establish microstructures for improving sensitivity. The wearable artificial throat device also exhibits high sensitivity, fast response time, and ultralow intensity level detection. Moreover, the device's excellent mechanical-electrical performance allows it to detect subtle throat vibrations that can be converted into controllable sounds. In this case, an intelligent artificial throat is achieved by combining a deep learning algorithm with a highly flexible piezoresistive sensor to successfully recognize five different words (help, sick, patient, doctor, and COVID) with an accuracy exceeding 96%. Herein, new opportunities in voice control as well as other human-machine interface applications are opened.

Electrospray-on-Electrospun Breathable, Biodegradable, and Robust Nanofibrous Membranes with Photocatalytic Bactericidal Activity

The increasing emergence of infectious diseases like COVID-19 has created an urgent need for filtration/purification materials coupled with multifunctional features such as mechanical integrity, excellent airflow/filtration, and antibacterial/antimicrobial properties. Polymer membranes and metal-organic frameworks (MOFs) have demonstrated high effectiveness in air filtration and purification. MOF nanoparticles have been introduced into electrospun polymer nanofibrous membranes through embedding or postsolution growth. However, the derived hybrids are still facing the issue of (1) limited MOF exposure, which leads to low efficacy;and (2) uncontrollable growth, which leads to pore blocking and low breathability. In this work, we customized an electrospray-on-electrospinning in situ process to dynamically integrate MOF nanoparticles into a robust and elastic continuous nanofibrous membrane for advanced properties including high mechanical strength and flexibility, excellent breathability, particle filtration, and good antimicrobial performance. Biodegradable polylactic acid was reinforced by the poly(hydroxybutyrate)-di-poly(DLA-CL)x copolymer (PHBR) and used as an electrospinning matrix, while MOF nanoparticles were simultaneously electrically sprayed onto the nanofibers with easily controllable MOF loading. The MOF nanoparticles were homogeneously deposited onto nanofibers without clogging the pores in the membrane. The collision of PLA and MOF under the wet status during electrospinning and the hydrogen bonding through C=O and N-H bonds strengthen the affinity between PLA nanofibers and MOF nanoparticles. Because of these factors, the MOF-incorporated PLA/PHBR nanofibrous membrane achieved over 95% particle filtration efficiency with enhanced mechanical properties while maintaining high breathability. Meanwhile, it exhibits excellent photocatalytic antibacterial performance, which is necessary to kill microbes. The electrospray-on-electrospinning in situ process provides an efficient and straightforward way to hybridize one-dimensional (1D) or two-dimensional (2D) nanomaterials into a continuous nanofibrous membrane with strong interaction and controllable loading. Upon integrating proper functionalities from the materials, the obtained hybrids are able to achieve multifunctionalities for various applications.

Monitoring the Construction Sector in Valle del Cauca

The objective of this research is to develop a monthly indicator that synthesizes the economic acti-vity of the construction sector in Valle del Cauca, as a tool for analyzing the production cycle and as a single, public measure that contributes to decision-making. Dynamic factorial models, the Kalman filter and the Litterman method are used, also employed in the Monthly Index of Economic Activity (IMAE), in order to capture signals, changes in the productive cycle of construction and compile the sectoral economic facts that affect production. In the results, the dynamics of the sectoral and economic variables are observed that explain the behavior of the sector in Valle del Cauca during 2009-2020. A change of slope is observed in 2020, as a direct consequence of the crisis generated by COVID-19 and the restrictive measures taken by the government to contain its advance.

Data-Driven Evaluation of the Synergistic Development of Economic-Social-Environmental Benefits for the Logistics Industry

The receding globalization has reshaped the logistics industry, while the additional pressure of the COVID-19 pandemic has posed new difficulties and challenges as has the pressure towards sustainable development. Achieving the synergistic development of economic, social, and environmental benefits in the logistics industry is essential to achieving its high-quality development. Therefore, we propose a data-driven calculation, evaluation, and enhancement method for the synergistic development of the composite system of economic, social, and environmental benefits (ESE-B) of the logistics industry. Based on relevant data, the logistics industry ESE-B composite system sequential parametric index system is then constructed. The Z-score is applied to standardize the original index data without dimension, and a collaborative degree model of logistics industry ESE-B composite system is constructed to estimate the coordinated development among the subsystems of the logistics industry's ESE-B system. The method is then applied to the development of the logistics industry in Anhui Province, China from 2011 to 2020. The results provide policy recommendations for the coordinated development of the logistics industry. This study provides theoretical and methodological support for the sustainable development aspects of the logistics industry. © 2023 by the authors.

Regional Differentiation of the Human Potential in Russia

: The study assessed the readiness of federal subjects for modernization processes in the Russian economy based on the qualitative characteristics of human potential. The relevance of these processes had escalated even before the epidemiological crisis of COVID-19 and modern geopolitical challenges. At present, it continues to increase due to a serious change in the structure of social needs for modernization transformations, and, accordingly, requests for quality human potential. Since this varies significantly across federal subjects, it should also be assessed in the regional context. This article focuses on methods for assessing the quality of human potential by region, including dynamics, which make it possible to perform assessments in a changing socioeconomic environment. At the interregional level, these were done by comparison, using characteristics common to regions, including demography, health, education, and sociocultural behavior. The demographic component is included as a qualitative characteristic of the population's ability to reproduce. For the comparison, the index method was chosen, which involves calculating the composite index for indicators for each characteristic and an integral one for federal subjects. The results of the calculations are applied to rank regions based on the average Russian integral index (at, above, and below the average level);thus, the adequacy of the human potential of federal subjects for modernization was assessed. With a general increase in readiness in the period under review, a high level was detected in only 19 regions, an average level in 23 regions, and a low level in 43 regions. According to the composite indices, the demographic problem remains the most urgent, which is currently expressed in the depopulation that began in 2018. © 2023, Pleiades Publishing, Ltd.

The Modeling of Jakarta Composite Index Data Before and During COVID-19 Pandemic and its Alignment into Government Policy in Energy Sector

The COVID-19 pandemic brings significant effects to the global stock market, including Indonesia. This study investigates the behavior and fluctuation of Jakarta Composite Index (JKSE) before the COVID-19 pandemic arises (2018–2019) and 2 years during the COVID-19 pandemic (2020–2021) and its alignment with the government policy in the energy sector. This study will use the JKSE data before and during the Covid-19 pandemic. The study showed that before COVID-19 pandemic, the JKSE was in normal conditions and showed an increasing trend. However, the study found anomalies in the JKSE volatility when COVID-19 pandemic was officially announced in Indonesia during 1st quarter 2020. This study is able to find the forecasted next 30 days best models that can describe the pattern of JKSE data are AR (2)–GARCH (1,1) models for the closing price of JKSE data before the COVID-19 pandemic and AR (5)–GARCH (1,1) models for the closing price of JKSE data during the COVID-19 pandemic. With the government economic recovery program related to the energy sector, this study was able to forecast the next 30 days for the closing price of JKSE during COVID-19, which showed the improvement of JKSE into the small increasing trend. These findings are expected to increase public investor trust, especially foreign investors investing their money in the JKSE. The positive trend in JKSE will ensure the government continues its economic policy recovery plan. © 2023, World Scientific and Engineering Academy and Society. All rights reserved.

Recycling of Plastic Polymer: Reinforcement of Building Material Using Polymer Plastics of Used COVID-19 Syringes

Plastic waste causes severe environmental impacts worldwide and threatens the lives of all creatures. In the medical field, most of the equipment, especially personal protective equipment (PPE), is made from single-use plastic. During COVID-19, the usage of PPE has increased, and is disposed of in landfills after being used once. Worldwide, millions of tons of waste syringes are generated from COVID-19 vaccination. A practical alternative to utilizing this waste is recycling it to reinforce building materials. This research introduces an approach to using COVID-19 syringe plastic waste to reinforce building material as composite concrete. Reinforced fiber polymer (FRP) concrete materials were used to mold cylindrical specimens, which underwent mechanical tests for mechanical properties. This study used four compositions with 0%, 5%, 10%, and 15% of FRP to create cylindrical samples for optimum results. Sequential mechanical tests were carried out on the created samples. These specimens were cured for a long period to obtain water absorption capability. After several investigations, the highest tensile and compressive strengths, approximately 2.0 MPa and 10.5 MPa, were found for the 5% FRP composition samples. From the curing test, the lowest water absorbability of around 5% was found for the 5% FRP composition samples.