Integrated Water Resources Plan Using An Optimization Model

As part of the City of Atlanta's Department of Watershed Management (DWM) transition into a 5-year rolling Capital Improvement Program (CIP), the Atlanta Program Management Services Team (PMST) was tasked with developing the Atlanta Integrated Water Resources Plan (IWRP) to incorporate project recommendations from the City's three recently completed master plans for water, wastewater, and stormwater into an integrated CIP. This effort was especially difficult as the City's available budget for CIP projects was being significantly reduced from normal years because of the adverse revenue impacts associated with the coronavirus pandemic and the uncertain economic recovery forecasts for the 5-year rolling CIP time frame. This paper details the successful development of an optimization model designed to maximize triple bottom line (TBL) and risk reduction benefits from the universe of potential water, wastewater, and stormwater projects while meeting tight financial budget limitations. The optimization model was based on OptimizerTM software by Optimatics that uses a heuristic learning algorithm, which is an approach designed to solve multi-criteria problems in a faster and more efficient manner that favors speed of process over absolute accuracy or completeness. The model used in Atlanta was the 3-dimensional (3-D) version to accumulate as much triple bottom line per dollar (TBL/$) and risk reduction per dollar (RRB/$) as early as possible in the planning horizon while minimizing budget expenditures. Copyright © 2022 Water Environment Federation.

A model for green-resilient supplier selection: fuzzy best-worst multi-criteria decision-making method and its applications.

Supplier selection is regarded as the primary goal of supply chain management (SCM) because it affects its performance, productivity, pleasure, flexibility, and system speed in lockdown. A new method is proposed based on a multi-stage fuzzy sustainable supplier index (FSSI). Experts can use the triple bottom line (TBL) criteria to select the best supplier. In addition, the worst method is proposed based on trapezoidal membership and fuzzy membership functions, which can cover uncertainties and ambiguous environments. Because it collects the related criteria and sub-criteria and uses a direct fuzzy methodology, this research has impacted the SCM literature because it helps solve previous expert methods' computational difficulties. In addition, an ordered mean integration representation method has been implemented to prioritize the selection of the best supplier (SS) based on the sustainability performance of the best supplier, which improves the selection accuracy compared to the previous ranking method. This study can be used as a benchmark to determine which supplier is the best in sustainability. To provide the superiority and broad applicability of the proposed model, a practical case study was completed. On the other hand, the COVID-19 pandemic harms productivity, company performance, and selecting the best suppliers based on sustainability performance. The lockdown situation caused by the COVID-19 pandemic hurts company performance and management.

How Internet of Things can influence the sustainability performance of logistics industries – A Chinese case study

The introduction of new information technologies has started reshaping global industrial sectors and supply chains. Due to the introduction of the Internet of Things (IoT) real-time management strategies have been adopted by the global logistics industry, turning the branch into an intelligent service supplier. This paper assesses the influence of IoT on the Chinese logistics sector and related environmental performance between 2011 and 2018. This paper establishes an evaluation framework for the logistics performance under social, economic and environmental dimensions by using time entropy weighting. Using a grey correlation approach, we identify a strong positive correlation between the logistics performance and the IoT market scale. We further find a significant and increasing correlation trend for an expansion of the IoT market and the reduction of carbon and PM 2.5 intensity. The environmental regulation though positively correlated with logistics sustainability, shows less potential to directly improve economic and social performance. It also indirectly promotes sustainability performance of the logistics industry through support for technological innovation. High investment in IoT is estimated to limit the potential of small and medium-sized enterprises to increase their labor compensation and expand the scale of employment. Finally, we project China's IoT market developments for 2021–2025 using a grey forecasting model considering the influence of investment confidence and COVID-19. The results indicate that China's share of the global IoT market will likely rise from 18% to 30% by 2025.

Sustainable Participatory Governance: Data-Driven Discovery of Parameters for Planning Online and In-Class Education in Saudi Arabia During COVID-19

Everything about our life is complex. It should not be so. New approaches to governance are needed to tackle these complexities and the rising global challenges. Smartization of cities and societies has the potential to unite us, humans, on a sustainable future for us through its focus on the triple bottom line (TBL) – social, environmental, and economic sustainability. Data-driven analytics are at the heart of this smartization. This study provides a case study on sustainable participatory governance using a data-driven parameter discovery for planning online, in-class, and blended learning in Saudi Arabia evidenced during the COVID-19 pandemic. For this purpose, we developed a software tool comprising a complete machine learning pipeline and used a dataset comprising around 2 million tweets in the Arabic language collected during a period of over 14 months (October 2020 to December 2021). We discovered fourteen governance parameters grouped into four governance macro parameters. These discovered parameters by the tool demonstrate the possibility and benefits of our sustainable participatory planning and governance approach, allowing the discovery and grasp of important dimensions of the education sector in Saudi Arabia, the complexity of the policy, the procedural and practical issues in continuing learning during the pandemic, the factors that have contributed to the success of teaching and learning during the pandemic times, both its transition to online learning and its return to in-class learning, the challenges public and government have faced related to learning during the pandemic times, and the new opportunities for social, economical, and environmental benefits that can be drawn out of the situation created by the pandemic. The parameters and information learned through the tool can allow governments to have a participatory approach to governance and improve their policies, procedures, and practices, perpetually through public and stakeholder feedback. The data-driven parameter discovery approach we propose is generic and can be applied to the governance of any sector. The specific case study is used to elaborate on the proposed approach. Copyright © 2022 Alswedani, Mehmood and Katib.

A Synchronous Hybrid Team-Based Learning Class: Why and How to Do It?

COVID-19 pandemic has transformed much of the medical curriculum delivery from in person to online. Given that interpersonal interaction facilitates team cohesion and professional identity formation, prolonged online learning with minimal social interaction might impact these competencies in medical education. To mitigate the impact of prolonged social isolation, we conducted synchronous team-based learning (TBL) classes, where half the class is physically present and the other is connected via an online platform, termed hybrid TBL. We present practical tips in implementing hybrid TBL for educators teaching in large-sized classes, should conditions exist where not all students can attend in person.

The impact of asynchronous online anatomy teaching and smaller learning groups in the anatomy laboratory on medical students' performance during the Covid-19 pandemic.

Due to the Covid-19 pandemic, National Taiwan University anatomy teachers adopted asynchronous online video teaching and reduced the size of anatomy laboratory groups in April 2020. The aim of this study was to investigate the impact of these changes on medical students' learning. Before Covid-19, the performance of the 2019-2020 cohort was significantly better than that of the 2018-2019 cohort. However, the implementation of modified teaching strategies significantly lowered the laboratory midterm score of the 2019-2020 cohort in the second semester. Conversely, the final laboratory examination score of the 2019-2020 cohort was significantly higher than that of the 2018-2019 cohort. Through correlation analysis, lecture and laboratory examination scores were highly correlated. Additionally, the difference in lecture and laboratory z-scores between two cohorts, the Likert scale survey and free-text feedback of the 2019-2020 cohort, were conducted to show the impact of modified teaching strategies. There were several important findings in this study. First, the change in teaching strategies may temporarily negatively influence medical students to learn anatomy. Besides, analyzing the performance of laboratory assessments could be a complementary strategy to evaluate online assessments. Applying lecture examination scores to predict laboratory performance was a feasible way to identify students who may have difficulty in learning practical dissection. Finally, reducing group size together with reduced peer discussion may have a negative effect on learning cadaver dissection for students with low academic performance. These findings should be taken into consideration when anatomy teachers apply new teaching strategies in anatomy courses.

Going green supply chain management during covid-19, assessing the best supplier selection criteria: A triple bottom line (tbl) approach

In the past ten years, sustainable supply chain management (SSCM) attach great importance due to consumers, for-profit and profitless organizations, laws and regulations to the social and corporate responsibilities of consumers, so it has been recognized by practitioners and scholars. Supplier selection, environmental effect like a lockdown, and social cooperation and other SSCM programs can play an important part in realizing the triple bottom line (TBL) of economic, environmental, social assistances. In supply chain management (SCM), the sustainable supplier selection (SSS) and firm performance plays an important role. Traditionally, when evaluating SSS performance, organizations will consider a new framework to obtain the overall criteria/sub-criteria of the sustainability index by encapsulating sustainability. In this paper 12 sub-criteria for 3 pillars of sustainability as economic, environment and social performance is collected. Although there are many articles on SSS and evaluation, so far, research on sustainability issues is very limited. This study endeavours to propose a fuzzy multi-criteria approach to discuss SSCM planning, and studies the issue of determining a current model for SSS in the supply chain during COVID-19 based on the TBL method. For express the linguistic value of the subjective preference of experts we use triangular fuzzy numbers. By using fuzzy numbers to find standard weights for qualitative performance evaluation, then fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is proposed to find the ranking of SSS. However, COVID-19 has a negative role in SSS and in firm performance. The situation of lockdown due to COVID-19 has a negative effect on the performance of the organizations. An example is given of the proposed method. © 2022, Politechnika Lubelska. All rights reserved.

Online anatomy team-based learning using blackboard collaborate platform during COVID-19 pandemic.

During the COVID-19 pandemic, many educational institutions followed the blended learning system. Using the participants' opinions, we evaluated the Blackboard (Bb) collaborate platform for online team-based learning (TBL) sessions for undergraduate students from different medical programs in the KSA. The participants were students on the MBBS Program (157 year two and 149 year three), together with 53 students in year one of the Nursing Program, 25 in year two of the Doctor of Pharmacy Program, and 11 in year two of the Medical Laboratory Sciences Program in Fakeeh College for Medical Sciences, (FCMS) KSA. To assess students' recall, engagement, and satisfaction with the sessions, an online TBL plan was designed and reviewed by the Medical Education Department. The students completed an online survey at the end of each session. All responses in this study showed a statistically significant positive difference from the neutral mid-point response (p < 0.05), reflecting high satisfaction. In the MBBS Program, the survey was completed by 40 students in year two and 76 in year three. The mean responses were 4.1 ± 0.3 and 3.9 ± 0.2 respectively (mean ± SD). In the BSN Program, 19 students completed the survey. The mean response was 4.6 ± 0.2. In the Pharm D Program, 10 students completed the survey. The mean response was 4.9 ± 0.12. In the MLS Program, eight students completed the survey. The mean response was 4.8 ± 0.12. It was concluded that online TBL using Bb collaborate is a successful anatomy-learning tool among FCMS students on different programs.

Investigating the COVID-19 pandemic's impact on sustainable supplier selection in the Nigerian manufacturing sector.

Over the years, sustainable supplier selection (SSS) has become increasingly popular among scholars and practitioners as a viable means to actualize supply chain sustainability. Little, however, is known about the impact of the COVID-19 pandemic on sustainable supplier selection particularly in the manufacturing sector. In this paper, we present pandemic response strategies as a significant aspect of the COVID-19 pandemic's impact and investigate the relative importance of such strategies in SSS implementation. Drawing upon a rich data pool from the Nigerian manufacturing sector, we proposed an integrated multi-criteria decision making (MCDM) methodology to analyse the interrelationships between the COVID-19 pandemic response strategies and Triple-Bottom-Line (TBL) criteria for SSS. Our analysis shows that economic criteria and pandemic response strategies are the highest ranked in terms of relative importance and thereby pinpoints the need for manufacturing firms to emphasize such during SSS implementation in the COVID-19 pandemic. Specifically, quality, cost, use of personal protective equipment and use of information technologies for customer demand prediction are inferred as highly significant in SSS implementation in the COVID-19 pandemic era. Furthermore, the efficiency of the proposed methodology was validated by a comparative analysis with other MCDM methods. Therefore, this study presents implications on the significance of pandemic response strategies in SSS and thereby enriches literature on the COVID-19 pandemic's impact on supply chains.

Metabolomics reveals sex-specific metabolic shifts and predicts the duration from positive to negative in non-severe COVID-19 patients during recovery process.

Metabolic profiling in COVID-19 patients has been associated with disease severity, but there is no report on sex-specific metabolic changes in discharged survivors. Herein we used an integrated approach of LC-MS-and GC-MS-based untargeted metabolomics to analyze plasma metabolic characteristics in men and women with non-severe COVID-19 at both acute period and 30 days after discharge. The results demonstrate that metabolic alterations in plasma of COVID-19 patients during the recovery and rehabilitation process were presented in a sex specific manner. Overall, the levels of most metabolites were increased in COVID-19 patients after the cure relative to acute period. The major plasma metabolic changes were identified including fatty acids in men and glycerophosphocholines and carbohydrates in women. In addition, we found that women had shorter length of hospitalization than men and metabolic characteristics may contribute to predict the duration from positive to negative in non-severe COVID-19 patients. Collectively, this study shed light on sex-specific metabolic shifts in non-severe COVID-19 patients during the recovery process, suggesting a sex bias in prognostic and therapeutic evaluations based on metabolic profiling.

COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning.

Today's societies are connected to a level that has never been seen before. The COVID-19 pandemic has exposed the vulnerabilities of such an unprecedently connected world. As of 19 November 2020, over 56 million people have been infected with nearly 1.35 million deaths, and the numbers are growing. The state-of-the-art social media analytics for COVID-19-related studies to understand the various phenomena happening in our environment are limited and require many more studies. This paper proposes a software tool comprising a collection of unsupervised Latent Dirichlet Allocation (LDA) machine learning and other methods for the analysis of Twitter data in Arabic with the aim to detect government pandemic measures and public concerns during the COVID-19 pandemic. The tool is described in detail, including its architecture, five software components, and algorithms. Using the tool, we collect a dataset comprising 14 million tweets from the Kingdom of Saudi Arabia (KSA) for the period 1 February 2020 to 1 June 2020. We detect 15 government pandemic measures and public concerns and six macro-concerns (economic sustainability, social sustainability, etc.), and formulate their information-structural, temporal, and spatio-temporal relationships. For example, we are able to detect the timewise progression of events from the public discussions on COVID-19 cases in mid-March to the first curfew on 22 March, financial loan incentives on 22 March, the increased quarantine discussions during March-April, the discussions on the reduced mobility levels from 24 March onwards, the blood donation shortfall late March onwards, the government's 9 billion SAR (Saudi Riyal) salary incentives on 3 April, lifting the ban on five daily prayers in mosques on 26 May, and finally the return to normal government measures on 29 May 2020. These findings show the effectiveness of the Twitter media in detecting important events, government measures, public concerns, and other information in both time and space with no earlier knowledge about them.