An Intelligent IoT-based COVID-19 Contact Tracing System for Ashesi University

The outbreak of contagious diseases demand isolation and quarantining of infected persons and people they have been in close proximity with. This can be easily achieved if technology-based systems are designed to facilitate contact tracing. The aim of this research project is to develop a privacy focused IoT-based COVID-19 contact tracing system that leverages mobile devices and artificial intelligence for the Ashesi University community. To achieve this, we divided the project into two main parts: The software sub-system and the hardware subsystem. The software sub-system comprises of a cross-platform mobile application that tracks users, and an admin portal to monitor user activities. The hardware sub-system is an IoT-based system that uses a Raspberry Pi to capture indoor images with the aid of a Raspberry Pi camera module. It processes the images to determine whether the occupants of the room have been in close proximity with one another or not while relaying feedback to them via its actuators and at the same time updates the admin portal. Through system testing, it was identified that 32% our system users considered privacy during the pandemic as critical even though 95% confirmed that the system assures very high level of privacy. © 2022 IEEE.

Packaging plastic waste management in the cosmetic industry

PurposeThis paper aims to explore the relationship between the various variables present in the packaging plastic waste management system in the cosmetics industry.Design/methodology/approachIn this paper, the authors deal with plastic packaging waste in the cosmetic industry with the help of system dynamics. The model broadly divides the system into six sections – Cosmetic Packaging, Waste Generation, Waste Collected, Waste Sorted, Waste Treated and Waste Dumped. Businesses have been investing in each section depending on their progress and targets. The authors are looking at case studies of two leading cosmetic brands, L'Oréal and L'Occitane en Provence, to validate the industry practices against our model.FindingsFrom a business perspective, using the case study methodology for L'Oréal and L'Occitane, the authors inferred that out of the various investment vehicles available, companies are targeting technological advancement and third-party collaborations as they have the potential to offer the greatest visible change. However, most of these investments are going toward the treatment subsection. Still, there is a scope for improvement in the collection and sorting subsystems, increasing the efficiency of the whole chain.Originality/valueThere has been a lot of research on packaging plastic waste management in the past, but only a few of them focused on the cosmetic industry. This study aims to connect all the possible variables involved in the cosmetic industry's packaging plastic waste management system and provide a clear output variable for various businesses looking to manage their packaging waste because of their products efficiently.

Urban Logistics Services Supply Chain Process Modelling Based on the Underground Logistics System via the Hierarchical Colored Petri Net

The implementation of the urban underground logistics system (ULS) can effectively mitigate the contradiction between the surging logistics demand and the increased negativity of urban logistics. The widespread implementation of ULS still suffers from a lack of research into its operation in the marketplace, although the research on ULS system technology and network design appears to be sufficient. A new supply chain for logistics service based on ULS (ULS-SSC) was proposed, as ULS embedded in the urban logistics system could lead to the evolution of the role of supply chain participants. This article analyzed the organizational structure and operation characteristics of ULS-SSC and designed a top-down ULS-SSC operation process model based on the designed functional structure and subsystems relationship using the hierarchical colored Petri net (HCPN). The simulation results show that the integrated information management platform based on ULS can integrate urban logistics service supply chain resources and operate effectively under the two main service modes designed. The high-time delay intermediate links can be upgraded by system optimization, and the links with initial pickup and terminal distribution can be improved through outsourcing and supply chain collaboration. The findings provide new insights into the feasibility of the operation of ULS in the market and help stimulate the implementation of ULS.

Theoretical Model for the Analysis of Community-Based Tourism: Contribution to Sustainable Development

This work aims to show a theoretical model of community-based tourism, to explain its component subsystems, to provide its theoretical–methodological foundation and to discuss the indications of its practical instrumentation in facing the changes that tourism of the future imposes and will impose. The research was carried out in the tourist context of Ecuador, for which the deductive method was applied, which allowed for examining the problem, and the more general theories related to tourist activity, which allowed for identifying the premises and objectives of the work to reach accurate conclusions on the subject studied. This was a mixed investigation that allowed for integrating the contributions of qualitative and quantitative analyses in the treatment and processing of information. The results included achieving systematization of the theoretical models linked to community-based tourism and, from a practical point of view, obtaining a new model of community-based tourism, a graphic representation of the subsystems that form this model, and its arguments. The findings show the need to update the community-based tourism model as a contribution to the scientific development of tourism as well as the systemic nature of its components from a new perspective of analysis that considers the need for changes as a developmental factor.

Study on Spatial–Temporal Evolution Characteristics and Restrictive Factors of Urban–Rural Integration in Northeast China from 2000 to 2019

With the rapid development of urbanization and the widening gap between urban and rural areas, how to effectively enhance the balanced development of urban and rural areas as well as promote the integrated development of urban and rural areas have become heated topics. Based on the basic theory of urban–rural integration and spatial balance, this paper establishes a comprehensive evaluation system. Using a coupling coordination model and an obstacle degree model, this paper selects 32 indicators from aspects of economy, society and ecology to measure the development level of urban–rural integration in 34 prefecture-level cities in Northeast China from 2000 to 2019. We also analyze the spatial pattern, evolution type characteristics and obstacle factors of urban–rural relations. The results show that: (1) The growth rate of urban development in Northeast China is higher than that of rural development. Except for rural ecology, all dimensions of urban and rural areas have increased year by year, but there are large spatial differences within the region. (2) The urban–rural integration development level of Northeast China is growing constantly and the types of urban and rural coordination are all rising to a higher level. Areas with high urban–rural coordination levels are concentrated in the Harbin–Changchun urban agglomeration and the central and southern Liaoning urban agglomeration, with obvious agglomeration effects. (3) In the coordinated development of urban and rural areas, the restrictive degree of the rural social subsystem was higher than that of the urban subsystem. The restrictive factors in the coordinated development of urban and rural areas are relatively stable, and include per capita consumption expenditure, per capita public budget expenditure, books in public libraries per thousand people, etc. (4) The functions of rural social elements should be enhanced, and the social urban–rural integrated development mechanism should be established to promote the integrated development of urban and rural society.

Automatic COVID-19 Diagnostic and Classification Intelligent System (ACDCIS)

Coronaviruses are a huge gathering of infections that can contaminate both creatures and people. An assortment of coronaviruses is known to cause respiratory infections in people, extending from the common cold to more extraordinary sicknesses such as the Center East Respiratory Disorder (MERS) and extraordinary intense respiratory disorder (SARS). COVID-19 is caused by a recently distinguished coronavirus. Recognizing and diagnosing these cases of respiratory infections and the COVID-19 infection has been troublesome for health associations. Therefore, in this research, we will develop an Automatic COVID-19 Diagnostic and Classification Intelligent System (ACDCIS) based on Machine Learning (ML). The proposed ACDCIS consists of two sub-systems the three-classifier system and two-classifier system. The two-classifier system is used to determine if the patient has a Covid-19 or not, while the three-classifier system is used to specify the type of infection COVID-19, bacterial infection, or virus pneumonia infection. The high accuracy of the proposed ACDCIS system will increase the speed of diagnosis. © 2022 IEEE.

Autonomous Robots for Deep Mask-Wearing Detection in Educational Settings during Pandemics

The COVID-19 pandemic has severely impacted various aspects of life, where countries closed their borders, and workplaces and educational institutions shut down their premises in response to lockdowns. This has adversely affected the lives of everyone, including millions of students worldwide, socially, mentally, and physically. Governments and educational authorities worldwide have taken preventive measures, such as social distancing and mask wearing, to control the spread of the virus. This paper proposes an AI-powered autonomous robot for deep mask-wearing detection to enforce proper mask wearing in educational settings. The system includes (1) Simultaneous Localization and Mapping framework to map and navigate the environment (i.e., laboratories and classrooms), (2) a multiclass face mask detection software, and (3) an auditory system to identify and alert improper or no mask wearing. We train our face mask detector using MobileNetV2 architecture and YOLOv2 object detector classification. The results demonstrate that our robot can navigate an educational environment while avoiding obstacles to detect violations. The proposed face mask detection and classification subsystem achieved a 91.4% average precision when tested on students in an engineering laboratory environment.

Research on Supply and Demand of Aged Services Resource Allocation in China: A System Dynamics Model

With the rapid growth of the elderly population of China in recent years, the service demands of older Chinese people continue to increase. The increasingly severe situation with respect to the elderly population is an important social problem that China will face for a long time into the future. It is urgent to solve the problem of how to scientifically carry out allocation planning of service resources for the aged and guide the effective supply of service resources. This paper analyzes the factors affecting service resources for the aged, divides China’s service resource supply and demand system into a supply subsystem, a demand subsystem, and a population and economy subsystem. Using system dynamics methods to analyze the causal relationship between variables and the state space method to build a mathematical model and perform simulation analysis, we research the the current situation of China’s service resources supply and demand balance for the aged. In addition, we put forward resource configuration optimization measures for the future allocation of service resources for the aged, providing a practical basis for future decision-making.

Exploration on the Construction of Cross-Border E-Commerce Logistics System Using Deep Learning

The intention of this article is to solve the disadvantages of the current logistics model and promote the healthy development of modern cross-border e-commerce (CBEC) Logistics. First, this paper expounds on and compares the traditional and CBEC logistics models. Then, the CBEC logistics system is constructed and adjusted according to system construction requirements. Further, two key subsystems are designed: the logistics object distribution subsystem and risk detection subsystem, based on the deep learning backpropagation neural network (BPNN) algorithm. The relevant parameters of the object distribution subsystem are calculated and sent to the risk detection subsystem model and tested. It is concluded that the sorting completion rate before 18:00 can reach 95.2%, indicating that the proposed CBEC logistic system can meet the needs of CBEC logistics enterprises. Logistics risk detection’s expected and actual outputs can fit 99%, indicating a tiny deviation. The research has certain reference significance for clarifying the logistics system and service mode of CBEC.

Pharmaceutical plant machine availability prediction using Artificial Neural Network

During the 2nd phase of COVID-19 pandemic, pharmaceutical plant industry is facing lot of production pressure and machine availability plays vital role in maximizing the manufacturing pharmacy product output. In this paper, Artificial Neural Networks (ANNs) based information processing algorithm has been used to provide a solution to this problem and it has been found suitable to predict machines availability as a prediction function. The considered pharmaceutical plants are dealing with production of medicines related common symptoms in case of COVID-19 (fever, coughing, and breathing problems). The pharmaceutical plant data corresponding to different values of repair and failure rates of different subsystems is collected from plant and analyzed with the help of validated neural network value of availability. This configuration of ANNs approach developed in this research allowed simplifying computational complexities of conventional approaches to solve a large plant machines availability problem. The ANNs methodology in the paper permitted making no assumption, no explicit coding of the problem, no complete knowledge of system configuration, only raw input and clean data found to be sufficient to determine the value of machine availability function for different value of failure and repair rates considered in the paper. The results obtained in the paper are useful for the plant leadership, as the value of failure and repair rates of various subsystems can be fine-tuned at a require clear-cut level to achieve higher availability, and avoid considerably loss of production, loss of man power, and by-pass complete breakdown of concerned system.

The Balanced Allocation of Medical and Health Resources in Urban Areas of China from the Perspective of Sustainable Development: A Case Study of Nanjing

The balanced allocation of medical and health resources is an important basis for the sustainable development of health undertakings. In recent years, China has made remarkable achievements in the medical and health services, but there is still a phenomenon of unbalanced allocation of medical and health resources among different regions, which has become an urgent problem to be solved in deepening the reform of the medical and health system during the 14th Five-Year Plan period. From the perspective of people’s needs for health, this study analyzed the equity and efficiency of urban medical and health resources allocation in China by using the Theil index method and DEA method. Meanwhile, the authors used the coupling coordination degree model to construct a balanced development model with equity and efficiency as subsystems, taking the city of Nanjing as an example to analyze its balanced allocation of medical and health resources from 2008 to 2019. In general, taking Nanjing as an example, it shows that the balanced allocation of medical and health resources in Chinese cities is good, but in geographical dimension, the level of balanced allocation is low, and there are still significant differences in the equity and efficiency of allocation among regions. In the future, the government can strengthen the rationality of regional planning, appropriately increasing health investment and medical supply, considering both equity and efficiency to further realize the balanced allocation of medical and health resources and improve the sustainability of urban medical service system. The main contribution of this paper lies in that, from the perspective of sustainable development, the evaluation system is integrated to measure the equity and efficiency respectively, and the balanced development model is used to investigate the allocation of urban medical and health resources. The research results can provide reference for optimizing resources allocation and promoting the sustainable development of medical and health undertakings.

Systems Theory and Algorithmic Futures: Interview with Elena Esposito

By introducing us into core concepts of Niklas Luhmann's theory of social systems, Elena Esposito shows their relevance for contemporary social sciences and the study of unsettled times. Contending that society is made not by people but by what connects them - as Luhmann does with his concept of communication - creates a fertile ground for addressing societal challenges as diverse as the Corona pandemic or the algorithmic revolution. Esposito more broadly sees in systems theory a relevant contribution to critical theory and a genuine alternative to its Frankfurt School version, while extending its reach to further conceptual refinement and new empirical issues. Fueling such refinement is her analysis of time and the complex intertwinement between past, present and future - a core issue that runs throughout her work. Her current study on the future as a prediction caught between science and divination offers a fascinating empirical case for it, drawing a thought-provoking parallel between the way algorithmic predictions are constructed today and how divinatory predictions were constructed in ancient times.

Modeling in the COVID-19 Pandemic: Overcoming the Water Sector’s Data Struggles to Realize the Potential of Hydraulic Models

Hydraulic models can provide efficient and cost-effective ways for water utilities to evaluate changes in operating conditions (e.g., population dynamics, disasters), thereby increasing system resiliency during crises. Unfortunately, model development remains out of reach for many utilities because of high software costs, data needs, or personnel requirements. This study seeks to classify hydraulic modeling data needs, identify success factors and challenges associated with model development, and determine whether modeling a subzone of a larger water distribution network can provide useful insights during a crisis, specifically the COVID-19 pandemic. At the pandemic onset, we began developing a hydraulic model of the water distribution system of the University of Texas at Austin campus—a subsystem of the water distribution network of Austin, Texas—to understand how spatiotemporal changes in water demands impacted system performance. We found that the completed model can offer useful insight into the impacts of demand changes within the modeled subsystem (e.g., potential locations of water stagnation). However, the data collection and processing challenges encountered (e.g., siloed collection efforts, lack of standardization, lengthy processing) reflect barriers to model development and use. The amount of time required to gather and process the necessary data shows that model development cannot occur during a time-sensitive crisis, likely rendering any insight too late for use. Here, we make recommendations to address data-related challenges and support utilities in incorporating hydraulic modeling into emergency planning.

Thermodynamic Analysis of a Regenerative Brayton Cycle Using H2, CH4 and H2/CH4 Blends as Fuel

Considering a simple regenerative Brayton cycle, the impact of using different fuel blends containing a variable volumetric percentage of hydrogen in methane was analysed. Due to the potential of hydrogen combustion in gas turbines to reduce the overall CO2 emissions and the dependency on natural gas, further research is needed to understand the impact on the overall thermodynamic cycle. For that purpose, a qualitative thermodynamic analysis was carried out to assess the exergetic and energetic efficiencies of the cycle as well as the irreversibilities associated to a subsystem. A single step reaction was considered in the hypothesis of complete combustion of a generic H2/CH4 mixture, where the volumetric H2 percentage was represented by fH2, which was varied from 0 to 1, defining the amount of hydrogen in the fuel mixture. Energy and entropy balances were solved through the Engineering Equation Solver (EES) code. Results showed that global exergetic and energetic efficiencies increased by 5% and 2%, respectively, varying fH2 from 0 to 1. Higher hydrogen percentages resulted in lower exergy destruction in the chamber despite the higher air-excess levels. It was also observed that higher values of fH2 led to lower fuel mass flow rates in the chamber, showing that hydrogen can still be competitive even though its cost per unit mass is twice that of natural gas.

Quantitative Analysis of COVID-19 Pandemic Responses Based on an Improved SEIR-SD Model

In late 2019, the COVID-19 pandemic began to spread over the world, causing millions of deaths. In the first few months of the pandemic, several countries (such as China) prevented the spread of the pandemic successfully. By contrast, the pandemic in many other countries was not controlled well. For example, India encountered a second serious outbreak of COVID-19 from April 2021 due to the poor resistance measures implemented by the government. To figure out the effective countermeasures to the pandemic, this research proposes a COVID-19 pandemic and its response system, which consists of the infection subsystem, the quarantine subsystem, and the medical subsystem. On this basis, an improved SEIR-SD model is established which is utilized to analyze the response measures to the pandemic quantitatively. This model successfully simulates the actual epidemic scenarios in Wuhan, which verifies its effectiveness. Afterward, the impact of hospital administration rate, quarantine rate, average contact number, and contact infection rate on the cumulative number of infections and deaths are analyzed by simulation. The results show that both the medical and administrative efforts, especially in the early stage of the epidemic, are significant in reducing the number of infections and shortening the epidemic period. In the medical aspect, the more stringent quarantine brings the earlier inflection point of the epidemic;more importantly, improving the treatment rate significantly reduces the scale of the epidemic. In the administrative aspect, enforcing individual protection and strict community closure can effectively cut off the transmission of the virus and curb the spread of the epidemic. Finally, this research proposes several practical suggestions in response to the COVID-19 pandemic. The main contribution of this research is that the effects of different response measures on the number of new infections daily and the cumulative number of deaths of a country or region in the COVID-19 pandemic are estimated quantitatively based on modeling and simulation.

Applying Infinite Petri Nets to the Cybersecurity of Intelligent Networks, Grids and Clouds

Correctness of networking protocols represents the principal requirement of cybersecurity. Correctness of protocols is established via the procedures of their verification. A classical communication system includes a pair of interacting systems. Recent developments of computing and communication grids for radio broadcasting, cellular networks, communication subsystems of supercomputers, specialized grids for numerical methods and networks on chips require verification of protocols for any number of devices. For analysis of computing and communication grid structures, a new class of infinite Petri nets has been introduced and studied for more than 10 years. Infinite Petri nets were also applied for simulating cellular automata. Rectangular, triangular and hexagonal grids on plane, hyper cube and hyper torus in multidimensional space have been considered. Composing and solving in parametric form infinite Diophantine systems of linear equations allowed us to prove the protocol properties for any grid size and any number of dimensions. Software generators of infinite Petri net models have been developed. Special classes of graphs, such as a graph of packet transmission directions and a graph of blockings, have been introduced and studied. Complex deadlocks have been revealed and classified. In the present paper, infinite Petri nets are divided into two following kinds: a single infinite construct and an infinite set of constructs of specified size (and number of dimensions). Finally, the paper discusses possible future work directions.

Toward an Organizational Theory of Resilience: An Interim Struggle

While organizational resilience is widely considered as critical to sustainability, gaps in both the scholarly and professional literature exist. First, stronger conceptualization of the term is needed. Second, little is known about how organizational resilience can be continuously accomplished via daily practices and processes. Finally, the ongoing organization theory development does not sufficiently address these gaps. Contributing to the literature by filling in these fundamental gaps, the present study integrates the disconnectedly growing literature into an organizational theory of resilience. Based on the General Systems Theory, the resulting theory comprises inputs of human resources, socio-cultural values, institutional settings, and social and environmental issues, enabling organizational structure, value and belief subsystem, resilience mindset, sustainability practices, adaptive and buffering capacities, and sustainability performance as the output. Their dynamic relationships are discussed and expressed via a model and propositions, followed by implications for researchers and practitioners.

A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems.

Goal: We propose a speech modeling and signal-processing framework to detect and track COVID-19 through asymptomatic and symptomatic stages. Methods: The approach is based on complexity of neuromotor coordination across speech subsystems involved in respiration, phonation and articulation, motivated by the distinct nature of COVID-19 involving lower (i.e., bronchial, diaphragm, lower tracheal) versus upper (i.e., laryngeal, pharyngeal, oral and nasal) respiratory tract inflammation, as well as by the growing evidence of the virus' neurological manifestations. Preliminary results: An exploratory study with audio interviews of five subjects provides Cohen's d effect sizes between pre-COVID-19 (pre-exposure) and post-COVID-19 (after positive diagnosis but presumed asymptomatic) using: coordination of respiration (as measured through acoustic waveform amplitude) and laryngeal motion (fundamental frequency and cepstral peak prominence), and coordination of laryngeal and articulatory (formant center frequencies) motion. Conclusions: While there is a strong subject-dependence, the group-level morphology of effect sizes indicates a reduced complexity of subsystem coordination. Validation is needed with larger more controlled datasets and to address confounding influences such as different recording conditions, unbalanced data quantities, and changes in underlying vocal status from pre-to-post time recordings.