ABSTRACT
In recent years, interest in economic, environmental and social sustainability has increased significantly. Companies are gradually adopting behaviors aimed at achieving the Sustainable Development Goals, which represent a crucial aspect of the 2030 Agenda. In practice, they are currently incorporating organizational strategies that jointly consider environmental, social and corporate governance (ESG), with the aim of generating value for all stakeholders. This paper aims to review, through a recognized seven-step procedure, the current literature on the impact that ESG practices have in industry, with a focus on the reduction of carbon emissions. The results are extremely useful for both researchers and entrepreneurs. The bibliometric analysis shows that interest in the ESG paradigm has grown considerably in the last three years. Furthermore, through the analysis of 13 key documents, it emerges that (i) the European community is pushing significantly towards the adoption of ESG practices through new regulations, (ii) the link between industrial operations and carbon emissions can no longer be neglected within the factory of the future, and (iii) significant efforts are still needed to standardize, in terms of variables and KPIs, the adoption of ESG-centric strategies.
ABSTRACT
We propose to introduce a new class of bivariate probability distributions, which we believe is of great interest to statisticians and data scientists. However different from the conventional Weibull it might be, the density function posited herein allows to generalize its properties in two dimensions (2D). This new function, essentially, has structure characteristics and properties different from those of the various bivariate Weibull-type functions found in the literature. The main features, such as the marginal distributions, moments, characteristic functions of this bivariate density are defined. Two related maximum likelihood estimation algorithms are also explicated, tested, and compared. Numerical simulations show the practicality of these algorithms as well as the interest of the new density in several areas of data analysis and extreme values modeling.
ABSTRACT
South Asia is one of the hot-spots of extreme heat events and associated health risks. As heat waves continue to get harsher due to climate change, South Asia's exposure to them is probably going to increase. After a heatwave in 2010, Ahmedabad implemented South Asia’s first heat action plan (HAP). The Ahmedabad HAP can serve as a model for other cities across South Asian nations interested in intervention strategies against excessive heat. In recent years, 2020 and onwards, Ahmedabad’s healthcare system faces an extreme COVID-19 crisis which resulted in severe negligence of heat wave-influenced mortality and morbidity cases. Though the city continued to disseminate the necessary information for public heat preparedness from the existing heat action plan, there was no record made separately for COVID-19 and heat stress-related mortality/morbidity by the health department. Thus, due to a lack of heat-related health records, we were unable to track the HAP intervention effect in 2022.
ABSTRACT
Background: The Coronavirus pandemic has resulted in an extreme challenge for humanity in recent times, like the challenges faced during World War II. Its origin has been pointed out, and the speculation made on its source directly points towards Wuhan in China. Since then, it has spread across the globe. The pandemic has resulted in more than one million deaths, which is a considerable challenge for humanity. Objective: With the pandemic of COVID-19, prevention of patient infection is crucial. This research focused on the orthopedic operating room nursing model effect based on evidence-based nursing and PDCA (Plan-Do-Check-act) cycle during the COVID-19 outbreak. Materials and Methods: From February 2020 to May 2020, 146 patients were admitted and received orthopedic surgery at Xuanwu Chunshu Hospital, Beijing, China, these admissions were grouped into control and intervention groups, which was based on the treatment provided. Satisfaction, time to bed and hospitalization, postoperative incision infection, and the occurrence of deep venous thrombosis of lower extremities, pain degree score, surgical treatment effect, anxiety, and depression scores were compared for all the admissions between the control and intervention groups. Results: In the control group, nursing satisfaction was less than in the intervention group. The time of getting out of bed and hospitalization was less in the intervention group;The total incidence of postoperative incision infection and lower limb deep vein thrombosis in the intervention group decreased. In the first postoperative day, the pain level in the intervention group was less than the control group. The effectiveness rate in the observation group is higher than that of the control group. Anxiety and depression scores of both groups tended to decrease with time and there was an interactive effect between grouping and time, where these differences were found to be statistically significant (P-value<0.05). Conclusion: The clinical application of the orthopedics operating room nursing model based on evidence-based nursing and PDCA cycle is remarkable and worth implementing during the COVID-19 outbreak.
ABSTRACT
Purpose>The purpose of this paper is to develop a system dynamics approach based on Susceptible, Exposed, Infected, Recovered (SEIR) model to investigate the coronavirus pandemic and the impact of therapeutic and preventive interventions on epidemic disaster.Design/methodology/approach>To model the behavior of COVID-19 disease, a system dynamics model is developed in this paper based on SEIR model. In the proposed model, the impact of people's behavior, contact reduction, isolation of the sick people as well as public quarantine on the spread of diseases is analyzed. In this model, data collected by the Iran Ministry of Health have been used for modeling and verification of the results.Findings>The results show that besides the intervening policies, early application of them is also of utmost priority and makes a significant difference in the result of the system. Also, if the number of patients with extreme conditions passes available hospital intensive care capacity, the death rate increases dramatically. Intervening policies play an important role in reducing the rate of infection, death and consequently control of pandemic. Also, results show that if proposed policies do not work before the violation of the hospital capacity, the best policy is to increase the hospital’s capacity by adding appropriate equipment.Research limitations/implications>The authors also had some limitations in the study including the lack of access to precise data regarding the epidemic of coronavirus, as well as accurate statistics of death rate and cases in the onset of the virus due to the lack of diagnostic kits in Iran. These parameters are still part of the problem and can negatively influence the effectiveness of intervening policies introduced in this paper.Originality/value>The contribution of this paper includes the development of SEIR model by adding more policymaking details and considering the constraint of the hospital and public health capacity in the rate of coronavirus infection and death within a system dynamics modeling framework.
ABSTRACT
Indoor air quality (IAQ) has been a growing concern in recent years, only to be expedited by the COVID-19 pandemic. A common provisional measure for IAQ is carbon dioxide (CO2), which is commonly used to inform the ventilation control of buildings. However, few commercially available sensors exist that can reliably measure CO2 while being low cost, exhibiting low power consumption, and being easily deployable for use in applications such as occupancy monitoring. This work presents a polymer composite-based chemiresistive CO2 sensor that leverages branched poly(ethylenimine) (PEI) and poly(ethylene glycol) (PEG) as the CO2 absorbing layer. This polymer blend was incorporated with single wall carbon nanotubes (CNT), which serve as the charge carriers. Prototype sensors were assessed in a bench-top environmental test chamber which varied temperature (22–26 °C), relative humidity level (20–80%), CO2 concentration (400–20,000 ppm), as well as other gas constituents to simulate typical and extreme indoor conditions. The results indicate that the proposed system could ultimately serve as a low-power alternative to current commercially available technologies for indoor CO2 monitoring.
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Overtourism, possibly excessive tourism is a situation where there is an extremely high number of tourists in the destination. During the period of the COVID-19 pandemic, when travel was limited, this phenomenon face to a challenge, and tourism has not yet reached the level it reached before the pandemic period. For the analysis of the perception of sustainability, were selected 10 European destinations of overtourism, on the examples of which individual attitudes to overtourism, perception of its negative as well as positive impacts to destinations or attendance of selected destinations before and during the COVID19 pandemic period were surveyed through questionnaire from respondents who visited selected overtourism destinations. Global Destination Cities Index), ktorý každoročne zostavuje spoločnosť Mastercard s destináciami po celom svete, ktoré sú poznačené overtourizmom vo výraznej miere.
ABSTRACT
The idea of composition relations on Fermatean fuzzy sets based on the maximum-extreme values approach has been investigated and applied in decision making problems. However, from the perspective of the measure of central tendency, this approach is not reliable because of the information loss occasioned by the use of extreme values. Based on this limitation, we introduce an enhanced Fermatean fuzzy composition relation with a better performance rating based on the maximum-average approach. An easy-to-follow algorithm based on this approach is presented with numerical computations. An application of Fermatean fuzzy composition relations is discussed in diagnostic analysis where diseases and patients are mirrored as Fermatean fuzzy pairs characterized with some related symptoms. To ascertain the veracity of the novel Fermatean fuzzy composition relation, a comparative analysis is presented to showcase the edge of this novel Fermatean fuzzy composition relation over the existing Fermatean fuzzy composition relation.
ABSTRACT
Viruses causing flu or milder coronavirus colds are often referred to as 'seasonal viruses' as they tend to subside in warmer months. In other words, meteorological conditions tend to impact the activity of viruses, and this information can be exploited for the operational management of hospitals. In this study, we use 3 years of daily data from one of the biggest hospitals in Switzerland and focus on modelling the extremes of hospital visits from patients showing flu-like symptoms and the number of positive flu cases. We propose employing a discrete generalized Pareto distribution for the number of positive and negative cases. Our modelling framework allows for the parameters of these distributions to be linked to covariate effects, and for outlying observations to be dealt with via a robust estimation approach. Because meteorological conditions may vary over time, we use meteorological and not calendar variations to explain hospital charge extremes, and our empirical findings highlight their significance. We propose a measure of hospital congestion and a related tool to estimate the resulting CaRe (Charge-at-Risk-estimation) under different meteorological conditions. The relevant numerical computations can be easily carried out using the freely available GJRM R package. The empirical effectiveness of the proposed method is assessed through a simulation study.
ABSTRACT
With the advent of the digital age in recent years, the application of artificial intelligence in urban Internet of Things (IoT) systems has become increasingly important. The concept of smart cities has gradually formed, and smart firefighting under the smart city system has also become important. The method of machine learning is now applied in various fields, but seldom to the data prediction of smart firefighting. Various types of applications including data applications of machine learning algorithms in smart firefighting have yet to be explored. In this article, we propose using machine learning algorithms to predict building fire-resistance data, aiming to provide more theoretical and technical support for IoT smart cities. This article adopts the fire-resistance data of building beam components in a real fire environment, using three integrated machine learning algorithms, Extreme random Tree (ET), AdaBoost, and Gradient Boosting Machine (GBM), and the grey wolf optimization algorithm to optimize. We improve the grey wolf algorithm and combine the grey wolf algorithm with the machine learning model. The algorithm constitutes three machine learning hybrid models: GWO-ET, GWO-AdaBoost, and GWO-GBM. Compared with traditional grid tuning, particle swarm optimization (PSO), and genetic algorithm (GA) optimization, the robustness and accuracy of the three optimization algorithms and the machine learning hybrid algorithm on the data set are compared and analyzed. Performance is measured through various performance comparisons and experimental result comparisons. For various building beam component data sets under real fires, the optimization and comparison show that the mean square error (MSE) of the proposed algorithm is extremely small. The results indicate that the GWO machine learning hybrid model is superior to other models and has a smaller prediction error.
ABSTRACT
European seas have a strong economic role both in terms of transport and tourism. Providing more knowledge, regarding the mean and extreme values of the wind and sea state conditions in the areas characterized by high maritime traffic, helps to improve navigational safety. From this perspective, six zones with high maritime traffic are studied. ERA5 database, a state-of-the-art global reanalysis dataset provided by ECMWF (European Centre for Medium-Range Weather Forecasts), is used to assess the average values and the percentiles for the wind speed and the main wave parameters in the target areas considering the period 2001–2020. The main European routes and the extreme conditions along them as well as the areas characterized by high values of wind speed and high waves were also identified. A more comprehensive picture of the expected dynamics of the environmental matrix along the most significant shipping routes is useful because in this way the most dangerous areas could be avoided by ships for the safety of passengers and transported goods.