Patient–Robot Co-Navigation of Crowded Hospital Environments

Intelligent multi-purpose robotic assistants have the potential to assist nurses with a variety of non-critical tasks, such as object fetching, disinfecting areas, or supporting patient care. This paper focuses on enabling a multi-purpose robot to guide patients while walking. The proposed robotic framework aims at enabling a robot to learn how to navigate a crowded hospital environment while maintaining contact with the patient. Two deep reinforcement learning models are developed;the first model considers only dynamic obstacles (e.g., humans), while the second model considers static and dynamic obstacles in the environment. The models output the robot's velocity based on the following inputs;the patient's gait velocity, which is computed based on a leg detection method, spatial and temporal information from the environment, the humans in the scene, and the robot. The proposed models demonstrate promising results. Finally, the model that considers both static and dynamic obstacles is successfully deployed in the Gazebo simulation environment.

Challenges in the Adoption of Artificial Intelligence and Machine Learning in Zimbabwe's Insurance industry

This study sought to investigate the challenges in the adoption of AI and ML in the Zimbabwean insurance industry. The TechnologyOrganisation-Environment (TOE) model was selected as the base theory underpinning the study. The study adopted a pragmatic research philosophy and a census was carried out on twenty insurance companies. Questionnaires were administered on operations managers representing their insurance companies. Interviews were used to collect data from 12 operation managers. NVivo version 16 was used to analyse the data thematically. The study results show that adoption of AI by the insurance sector in Zimbabwe is hindered by shortage of resources, lack of expertise and high cost of AI compliant products. These researchers recommend resource allocation, training of employees, culture change, and updated technological environment to ensure effective adoption of AI. This study will contribute to the body of knowledge, be significant to insurance practitioners and policy makers whilst giving direction for future studies. © 2022 IEEE.

Mathematical Modeling of Technical and Economic Systems in Agriculture IV (MMTES-IV)

Dear Authors and (Online) Participants of MMTES-IV, International Scientific Conference «Mathematical Modeling of Technical and Economic Systems in Agriculture (MMTES-IV)» was successfully held online on November 17-19, 2021 at the Department of Mathematical Modeling and Computer Science (location of organizers - Volgograd, Agricultural University, Russian Federation). Due to COVID-2019 and the pandemic-related nationwide lockdowns and other coordinated restrictive measures, the organizers decided to hold a conference in a virtual format with the organization of access for all participants to the presented reports with comprehensive discussion for ensuring the event at a high scientific level.The safety and well-being of all conference participants is our priority. The COVID-19 is unpredictable, so conference postponement met uncertainty, while many scholars and researchers want to attend this long-waited conference and have academic exchanges with their peers. But we want to note that there are no barriers to science, and we continue to work on our research areas remotely, using modern technical means. The conference was held virtually for 2 days (November 17-19, 2021) including keynote and invited speeches, authors’ presentation session. The event was held online using Zoom communication platform for synchronous conversation. The conference was organized as a large-scale online meeting of participants in topical sessions with the opportunity for speakers to provide regulated demonstrations of slides and video files according to the approved technical program. Each speaker had 15 min presentation and 5 min Q&A session. There were always 20-25 participants in the network. During the online presentation, participants discussed more than 50 questions on the problems of modeling technical and economic systems in the agro-industrial complex. Participants of the conference were from the leading institutions of Russian Federation, China, Turkey, France, Uzbekistan, Germany, Republic of Belarus (Instituto Politécnico Nacional, St. Petersburg Federal Research Centre of the Russian Academy of Sciences, Matrosov Institute for Systems Dynamics and Control Theory of Siberian Branch of the Russian Academy of Sciences, Far Eastern Federal University, Southwest State University, Belgorod National Research University, Federal Research Center «Computer Science and Control», Russian Academy of Sciences, Namangan Engineering and Technology Institute, Technical University Of Munich, All-Russian Research Institute of Irrigated Agriculture, Belarusian State Agrarian Technical University, Volgograd State Technical University and others), dealing with problems of using artificial intelligence and digital technologies in technical systems. The outstanding performance of all the presenters made this conference truly spectacular. Main topics of the conference were:1. Mathematical modeling of socio-technical and organizational-economic systems.2. Modeling of natural-technical and agro-ecological processes.3. Artificial intelligence and neural network technologies in agricultural production.4. Modeling of innovative and technological processes in agricultural engineering.5. Modeling of reclamation and environmental technologies in agricultural production.6. Resource and technological support for the production of agricultural products.7. Seed production, plant breeding and biotechnology.8. Innovative technologies in solving economic problems in the agro-industrial complex.9. Modern electrical technologies, electrical equipment and power supply in rural areas.List of Committee is available in this Pdf.

The Impact of Digital Technology on the Optimization of Higher Education Teaching Models in an Epidemic Environment

The Corona Virus Disease 2019 epidemic broke out in 2020, and digital technologies pervaded all aspects of people’s lives, resulting in a significant shift in how education is delivered. The importance and role of digital technologies and online learning are highlighted in this paper, which examines the challenges posed by the sudden epidemic crisis to higher education institutions, analyses the factors that universities must consider in order to effectively create flexible learning pathways, and examines the challenges posed by the sudden epidemic crisis to higher education institutions. In the postepidemic era, the use of the Internet and online teaching platforms by university faculty to integrate online and offline teaching has not only facilitated the construction of “golden courses” but also added impetus to teaching reform, and digital technology-based teaching models have provided higher education practitioners with the opportunity to rethink scholarship and innovative teaching. In this paper, we propose a personalized learning resource recommendation system that includes user profiles to fully explore and analyze users’ learning behaviors and cognitive characteristics and enhance the depth and breadth of personalized education with the help of the Internet and artificial intelligence technologies in order to provide meaningful information and thoughts for higher education institutions to discuss and adapt to the education model in the postepidemic era. The goal is to provide useful information and ideas for higher education institutions to discuss and adapt to the postepidemic education paradigm.

Optimizing the Sensitivity of Biological Particle Detectors through Atmospheric Particle Analysis According to Climatic Characteristics in South Korea

Biological agents used in biological warfare or bioterrorism are also present in bioaerosols. Prompt identification of a biological weapon and its characteristics is necessary. Herein, we optimized an environmentally adaptive detection algorithm that can better reflect changes in the complex South Korean environment than the current models. The algorithm distinguished between normal and biological particles using a laser-induced fluorescence-based biological particle detector capable of real-time measurements and size classification. We ensured that the algorithm operated with minimal false alarms in any environment by training based on experimental data acquired from an area where rainfall, snow, fog and mist, Asian dust, and water waves on the beach occur. To prevent time and money wastage due to false alarms, the detection performance for each level of sensitivity was examined to enable the selection of multiple sensitivities according to the background, and the appropriate level of sensitivity for the climate was determined. The basic sensitivity was set more conservatively than before, with a 3% alarm rate at 20 agent-containing particles per liter of air (ACPLA) and a 100% alarm rate at 63 ACPLA. The reliability was increased by optimizing five variables. False alarms did not occur in situations where no alarm was unnecessary.

A Systematic Literature Review of Sustainable Packaging in Supply Chain Management

This exploratory study utilises quantitative analysis to deliver a systematic literature review of published journal papers from 1993 to 2020 with the aim to identify research trends and present a comprehensive overview of research focus conducted in the sustainable packaging domain within the scope of supply chain management. This research is conducted with the data mining software, NetMiner 4, utilising the three analytical tools of statistical analysis, keyword network analysis, and topic analysis. The research also utilises the qualitative method of in-depth interviews in order to investigate current trends and perspectives on the future of sustainable packaging and to validate the analysis results. The research findings reveal that research in the field of ‘sustainable packaging in supply chain management’ field has been extremely limited, and this study acts to address this research gap. The results confirm that the vast majority of research focus has been in the fields of engineering and science. Research on the topic has gained momentum and has significantly increased since 2013 with research trends becoming increasingly diversified and gradually aligned with the concept of circular economy, while the topic of operational management has been highlighted as an area requiring additional attention. The keyword frequency analysis reveals the following highest occurring keywords in TF: life cycle;environmental impact;consumer;transportation;and production. The highest occurring keywords in TF-IDF: production;transportation;consumer;food;and environmental impact. Topic modelling revealed the following six topics: consumer behaviour;environmental pollution;circular economy;waste management;resource conservation;and operational management. This study contributes to understanding past, present, and future research agendas, and can be utilised as foundation for research development, as it provides insight to current research status and trends provided by the keyword network analysis highlighting research focus and trends in ‘sustainable packaging in supply chain management’.

Integration and Deployment of Cloud-Based Assistance System in Pharaon Large Scale Pilots—Experiences and Lessons Learned

The EU project Pharaon aims to support older European adults by integrating digital services, tools, interoperable open platforms, and devices. One of the objectives is to validate the integrated solutions in large-scale pilots. The integration of mature solutions and existing systems is one of the preconditions for the successful realization of the different aims of the pilots. One such solution is an intelligent, privacy-aware home-care assistance system, SmartHabits. After briefly introducing the Pharaon and SmartHabits, the authors propose different Pharaon models in the Ambient/Active Assisted Living (AAL) domain, namely the Pharaon conceptual model, Pharaon reference logical architecture view, AAL ecosystem model, meta AAL ecosystem model, and Pharaon ecosystem and governance models. Building on the proposed models, the authors provide details of the holistic integration and deployment process of the SmartHabits system into the Pharaon ecosystem. Both technical and supporting integration challenges and activities are discussed. Technical activities, including syntactic and semantic integration and securing the transfer of the Pharaon sensitive data, are among the priorities. Supporting activities include achieving legal and regulatory compliance, device procurement, and use-case co-designing in COVID-19 conditions.

New goal-oriented requirements extraction framework for e-health services: a case study of diagnostic testing during the COVID-19 outbreak

Purpose>This paper aims to introduce the goal-oriented requirements extraction approach (GOREA). It is an elicitation approach that uses, specifically, healthcare business goals to derive the requirements of e-health system to be developed.Design/methodology/approach>GOREA consists of two major phases: (1) modelling e-health business requirements phase and (2) modelling e-health information technology (IT) and systems requirements phase. The modelling e-health business requirements phase is divided into two main stages: (1) model e-health business strategy stage and (2) model e-health business environment stage. The modelling e-health IT and systems requirements phase illustrates the process of obtaining requirements of e-health system from the organizational goals that are determined in the previous phase. It consists of four main steps that deal with business goals of e-health system: (1) modelling e-health business process (BP) step;(2) modelling e-health business goals step;(3) analysing e-health business goals step;and (4) eliciting e-health system requirements step. A case study based on the basic operations and services in hospital emergency unit for checking patient against COVID-19 virus and taking its diagnostic testing has been set and used to examine the validity of the proposed approach by achieving the conformance of the developed system to the business goals.Findings>The results indicate that (1) the proposed GOREA has a positive influence on the system implementation according to e-health business expectations;and (2) it can successfully fulfil the need of e-health business in order to save the citizens life by checking them against COVID-19 virus.Research limitations/implications>The proposed approach has some limitations. For example, it is only validated using one e-health business goal and thus it has to be authenticated with different e-health business goals in order to address different e-health problems.Originality/value>Many e-health projects and innovations are not established based on robust system requirements engineering phase. In order to ensure the success delivery of e-health services, all characteristics of e-health systems and applications must be understood in terms of technological perspectives as well as the all system requirements.

Evaluation of Comfort Models Considering the Peculiarities of Hospitalization: Bedding, Clothing and Reduced Activity of Patients

The study of thermo-hygrometric comfort in hospitals involves several factors: the presence of different subjects: patients, operators, visitors;different conditions of hospitalization: patients bedridden or out of bed;psychological aspects and therapeutic treatments. In this paper, the analysis focuses on patients in ordinary hospitalization rooms of a hospital located in southern Italy. Different room orientations, several characteristics, and specific factors concerning hospitalized patients’ conditions that significantly influence the comfort indices have been considered. In total, 41 scenarios have been defined and analyzed by means of two comfort models: static and adaptive. The study aims to investigate the application of these models to the complex environment of hospitals, finding strengths and weaknesses, which also results in a re-definition of the HVAC system operation. Results show that patient position (in bed or out), clothing type, and level of coverage in the bed can make the same microclimatic condition more suitable for one scenario over another. Furthermore, room exposure has an effect on the comfort of the indoor temperature. The seasonal analyses highlight that during summer, for all scenarios considering bedridden patients, more than 50% of the PMV calculated values are out of the comfort zone. In winter, the indoor conditions are good for bedridden patients with a cover level of 67% during the nighttime (almost 100% PMV values in comfort zone), while during the daytime, they are more suitable for a 48% coverage level if the patient is in bed or if they are walking (lower than 10% dissatisfied).

Collaborative Commerce Model for SMEs A Knowledge and Resources Sharing Mechanism for Co Creation

As a response to the severe impact of the COVID-19 pandemic, Small and Medium-sized Enterprises (SMEs) must collaborate and work together;namely, using collaborative commerce (c-commerce). To some extent, SMEs have taken advantage of various web, e-marketplace, and social media resources to collaborate in their business activities. However, this only occurs to a partial extent. Consequently, a study was conducted to build a conceptual model of c-commerce for SMEs, for use as a standard in these collaborative activities. Data collection was performed through in-depth interviews, observation, and literature reviews. The sample selection was done via purposive sampling;specifically, SMEs’ fashion products with high personalisation and good production quality. The conceptual model of c-commerce is based on the Soft Systems Methodology (SSM), a suitable method for building models for new and complex ecosystems. The conceptual model is compared with a rich picture and then analysed. The final result of the conceptual model is employed to construct a c-commerce system for SMEs in creating joint products and sharing resources with their stakeholders.

Exploratory Study on Modelling Agricultural Carbon Emissions in Ireland

In 2020 Ireland missed its EU climate emissions target and without additional measures will not be on the right trajectory towards decarbonisation in the longer 2030 and 2050 challenges. Agriculture remains the single most significant contributor to overall emissions in Ireland. In the absence of effective mitigating strategies, agricultural emissions have continued to rise. The purpose of the review is to explore current research conducted in Ireland regarding environmental modelling within agriculture to identify research gap areas for further research. 10 models were selected and reviewed regarding modelling carbon emissions from agriculture in Ireland, the GAINS (Air pollution Interactions and Synergies) model used for air pollutants, the JRC-EU-TIMES, (Joint Research Council-European Union-The Integrated MARKAL-EFOM System) and the Irish TIMES model used for energy, the integrated modelling project Ireland (GAINS & TIMES), the environmental, economic model ENV-Linkages and ENV-Growth along with the IE3 and AGRI-I models. The review found that data on greenhouse gas emissions for 2019 reveals that emissions can be efficiently lowered if the right initiatives are taken. More precise emission factors and adaptable inventories are urgently needed to improve national CO2 reporting and minimise the agricultural sector’s emissions profile in Ireland. The Climate Action Delivery Act is a centrally driven monitoring and reporting system for climate action delivery that will help in determining optimal decarbonisation from agriculture in Ireland. Multi-modelling approaches will give a better understanding of the technology pathways that will be required to meet decarbonisation ambitions.