Lessons learned from the COVID-19 pandemic: implications for semiprofessional soccer clubs after the crisis- a team study

In 2019-2020 and 2020-2021, the COVID-19 pandemic led to unexpected behavioral restrictions, allowing only the individual training of the athletes. The purpose of the present study was to record the effects of a home-based training program on physical performance at a semi-professional level, after the Covid-19 confinement, in terms of external load. Twenty soccer players from one semi-professional team participated in this study. The data was collected by GPS devices, with an accelerometer and gyroscope, and a sampling rate 10Hz. The external load is evaluated by the total distance, the high-intensity runs, the sprint distance as well as the number of accelerations and decelerations. Three matches before and twelve games after the lockdown were analyzed and compared. During the confinement period, the players performed 5-6 training sessions per week. This period lasted over 4 four months. The training sessions were monitored by a free smartphone application. Similarly, the players communicated with the technical staff with a free internet application. The results showed significant increases (p < 0.05) in the total distance covered during the matches after the intervention. No significant increase in high-intensity runs and the number of accelerations were found (p > 0.05). The present data suggest that an intervention monitored by a free application could improve athletic performance at the semi-professional level, even after long-term abstinence from team training such as quarantine or off-season periods. These data might provide affordable solutions to the semi-professional soccer teams, which could be used during the off-season period leading to reduce detraining effects and higher performance in the forthcoming championship.

Autonomous Flight Using UWB-Based Positioning System with Optical Flow Sensors in a GPS-Denied Environment

This study presents the positioning method and autonomous flight of a quadrotor drone using ultra-wideband (UWB) communication and an optical flow sensor. UWB communication obtains the distance between multiple ground stations and a mobile station on a robot, and the position is calculated based on a multilateration method similar to global positioning system (GPS). The update rate of positioning using only UWB communication devices is slow;hence, we improved the update rate by combining the UWB and inertial measurement unit (IMU) sensor in the prior study. This study demonstrates the improvement of the positioning method and accuracy by sensor fusion of the UWB device, an IMU, and an optical flow sensor using the extended Kalman filter. The proposed method is validated by hovering and position control experiments and also realizes a sufficient rate and accuracy for autonomous flight.

"To our health!” Perceived benefits offset privacy concerns in using national contact-tracing apps

PurposeCommunity health is placed under the limelight during the COVID-19 crisis, providing a unique context for investigating citizens' health-privacy tradeoff in accepting social surveillance technology. To elucidate this tradeoff dilemma, an extended privacy calculus framework integrated with the Health Belief Model, legislative protection, and individual collectivism was examined using the case of national contact-tracing apps.Design/methodology/approachThe hypotheses were tested through PLS-SEM analysis with data collected from a survey on Bluezone – a national app in Vietnam.FindingsThe results indicated the negative impact of privacy concerns, which was offset by the positive effect of perceived benefits in using contact-tracing apps. The effect size of perceived benefits on usage frequency was twice as large as that of privacy concerns. Individual collectivism was revealed as a mitigator of the tradeoff dilemma, as it was positively associated with perceived benefits, whereas legislative protection had no such role. Citizens may perceive legislation protection as invalid when the technologies are developed, implemented, and monitored by the authorities.Originality/valueThe theoretical contributions lie in the extension of the privacy calculus model as well as its application in the context of mobile health apps and surveillance technology. The study empirically corroborated that the privacy calculus theory holds when technologies move along the pervasiveness spectrum. This study also provided actionable insights for policymakers and developers who advocate the mass acceptance of national contact-tracing apps.

COUNTERSAVIOR: AIoMT and IIoT-Enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics

In the current pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes global positioning system (GPS) spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject's cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behavior, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behavior patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3-D tracker movements of individuals, 3-D contact analysis of COVID-19 and suspected individuals for 24 h, forecasting and risk classification of COVID-19, suspected and safe individuals.

Congruence Mapping of the Activity Flows Allocated in Built Environments: A Pilot Application of Under-Development Software in an Emergency-Care Service

Featured ApplicationThis paper presents the application of a software program that is currently under development that provides feedback for the mapping activities that are carried out in built environments and analyses the congruence in the relationship between the flow of activities and their environments. Exemplary results were obtained in the case study of a healthcare emergency facility, although it is possible to apply this software in other types of complex environments. The obtained data allow facility managers to prioritize and reallocate activities when a change is required. It also shows unmapped relationships. It is important to investigate these data because they can indicate failures in the mapping process and can provide an opportunity to obtain a more complete understanding of the allocation and flow of activities. These data can also help us to identify points of conflict or opportunities for adjustment in the allocation of activities in order to improve the flow of activities.Due to the large number of activities that must be carried out by emergency-care services (ESs), the tasks of facility managers and architects are challenging and complex. Several strategies, guides, and diagnoses have already been developed in order to improve ESs. Part of the solution to this problem depends on obtaining a normative and universal understanding of the problem, and another part depends on conducting a specific and relational analysis between the environment and the flow of activities that are allocated within it. This paper presents the results of a study that was conducted using a software program that is currently under development for mapping the congruence relationship between activities and environments. Here, we present a discussion of the first results that were obtained with the instrument, which was applied to a single case. For this purpose, the fundamentals of the instrument, as well as the environment and the flows of an ES at a university hospital, are described. The forms of analysis, benefits, and limitations of the instrument were investigated, with a view towards its use in supporting the management and the design of large and complex environments, such as emergency departments. In this program, the relationships that are hidden from the managers, the designers, and the researchers due to the aforementioned complexity are revealed through the use of matrices. This mapping can supplement the decision making of the managers and the designers. The application showed advantages in modeling with fewer inputs, mainly in pre-design evaluations.

A decision support model for selecting unmanned aerial vehicle for medical supplies: context of COVID-19 pandemic

PurposeIn recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.Design/methodology/approachThe authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.FindingsThe findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.Research limitations/implicationsThe methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.Practical implicationsThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.Social implicationsThe proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.Originality/valueThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

Early coronavirus disease detection using internet of things smart system

The internet of things (IoT) is quickly evolving, allowing for the connecting of a wide range of smart devices in a variety of applications including industry, military, education, and health. Coronavirus has recently expanded fast across the world, and there are no particular therapies available at this moment. As a result, it is critical to avoid infection and watch signs like fever and shortness of breath. This research work proposes a smart and robust system that assists patients with influenza symptoms in determining whether or not they are infected with the coronavirus disease (COVID-19). In addition to the diagnostic capabilities of the system, the system aids these patients in obtaining medical care quickly by informing medical authorities via Blynk IoT. Moreover, the global positioning system (GPS) module is used to track patient mobility in order to locate contaminated regions and analyze suspected patient behaviors. Finally, this idea might be useful in medical institutions, quarantine units, airports, and other relevant fields.

STRENUOUS: Edge-Line Computing, AI, and IIoT Enabled GPS Spatiotemporal Data-Based Meta-Transmission Healthcare Ecosystem for Virus Outbreaks Discovery

COVID-19 is not the last virus;there would be many others viruses we may face in the future. We already witnessed the loss of economy and daily life through the lockdown. In addition, vaccine, medication, and treatment strategies take clinical trials, so there is a need to tracking and tracing approach. Suitably, exhibiting and computing social evolution is critical for refining the epidemic, but maybe crippled by location data ineptitude of inaccessibility. It is complex and time consuming to identify and detect the chain of virus spread from one person to another through the terabytes of spatiotemporal GPS data. The proposed research aims an HPE edge line computing and big data analytic supported virus outbreak tracing and tracking approach that consumes terabytes of spatiotemporal data. The proposed STRENUOUS system discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus-like COVID-19 epidemic transmission. The method and the mechanical assembly further contained an alert component to demonstrate a suspected case if there was a potential exposure with the confirmed subject. The proposed system tracks location data related to a suspected subject in the confirmed subject route, where the location data expresses one or more geographic locations of each user over a period. It recognizes a subcategory of the suspected subject who is expected to transmit a contagion based on the location data. System measure an exposure level of a carrier to the infection based on contaminated location data and a subset of carriers connected with the second location carrier. They investigated whether the people in the confirmed subject's cross-path can be infected and suggest quarantine followed by testing. The proposed STRENUOUS system produces a report specifying that the people have been exposed to the virus.

Evaluation of Using Genetic Algorithm and ArcGIS for Determining the Optimal-Time Path in the Optimization of Vehicle Routing Applications

Transportation is regarded as one of the most important issues currently being researched;this issue needs the search for approaches or processes that might lessen many contemporary traffic concerns. Congestion, pollution, and accidents have escalated lately, negatively impacting urban environments, economic development, and citizens’ lifestyles. The rise of illnesses and epidemics throughout the world, such as COVID-19, has created an urgent need to find the best way to save people’s lives. The vehicle routing problem (VRP) is a well-known moniker for improving transportation systems and is regarded as one of the ancient and contemporary difficulties in route planning applications. One of the main tasks of VRP is serving many customers by determining the optimal route from an initial point to a destination on a real-time road map. The best route is not necessarily the shortest-distance route, but, in emergency cases, it is the route that takes the least fitness cost (time) and the fastest way to arrive. This paper aims to provide an adaptive genetic algorithm (GA) to determine the optimal time route, taking into account the factors that influence the vehicle arrival time and cause delays. In addition, the Network Analyst tool in ArcGIS is used to determine the optimal route using real-time map based on the user’s preferences and suggest the best one. Experimental results indicate that the performance of GA is mainly determined by an efficient representation, evaluation of fitness function, and other factors such as population size and selection method.

Lessons Learned from COVID19: Enhancing the Discipline of Communications in Pharmacovigilance: An International Journal of Medical Toxicology and Drug Experience

Introduction: Pharmacovigilance (PV) organizations are traditionally structured to address and comply with global regulations. However, PV organizations are well poised to contribute beyond legislation, by sharing insights across communities in a digestible, accessible way. Examining communication needs following the height of the COVID19 pandemic may elucidate opportunities for PV to play a more central role in public health, particularly in generating and disseminating timely, credible and accurate communications. The need for safety to play a larger role in combating vaccine hesitancy is well documented and is fundamental to building trust in patient populations. 1 While previous initiatives have focused on meeting the needs of legislation, research has indicated new approaches that incorporate a listening mechanism for public feedback are needed to provide tailored communications.1-2 PV organizations carry a responsibility to communicate directly to patients, due to their central positioning as a 'source of truth' for emerging safety information. The challenge is the level of uncertainty as a product's benefit/risk profile is characterized over time. Objective: We aim to identify safety-related communication needs across the healthcare ecosystem and propose an enhanced safety communication framework. Methods: A three-part approach was taken to design a safety communications framework, addressing internal and external information needs. We performed a literature search to identify trends in PV innovation (2009-2022). Interviews were conducted with the BeiGene Global Patient Safety (GPS) organization to evaluate needs and communication opportunities within PV. Results were compared with lessons learned from the COVID-19 crisis. Results: Literature results demonstrated that PV innovation publications increased over the past decade, with a peak in 2015 and marked rise in 2021-2022, spanning numerous concepts. Publications relating to safety communications increased yearly since 2010, with over 300 articles published this year to date. Results from BeiGene interviews (N = 5) demonstrated the following opportunities: (1) Building awareness and understanding of PV externally, (2) Building networks of relevant stakeholders, (3) Demonstrating transparency with the public, (4) Generating accessible safety communications for patient populations by drawing on the discipline of communications in other industries. Qualitative research across 19 EU countries demonstrated critical gaps in addressing information needs throughout the pandemic relating to under-resourced public health services and lack of interoperative health IT systems.3 Conclusion: GPS has implemented a safety communications framework focused on internal and external communications, research and publications and ongoing intelligence monitoring. Its focus is to increase transparency, visibility and understanding of PV, with the aim to establish trust and credibility in healthcare communities.

Location Selection of Charging Stations for Electric Taxis: A Bangkok Case

The transition from ICE to BEV taxis is one of the most important methods for reducing fossil fuel consumption and air pollution in cities such as Bangkok. To support this transition, an adequate number of charging stations to cover each area of charging demand must be established. This paper presents a data-driven process for determining suitable charging locations for BEV taxis based on their characteristic driving patterns. The location selection process employs GPS trajectory data collected from taxis and the locations of candidate sites. Suitable locations are determined based on estimated travel times and charging demands. A queueing model is used to simulate charging activities and identify an appropriate number of chargers at each station. The location selection results are validated using data from existing charging services. The validation results show that the proposed process can recommend better locations for charging stations than current practices. By using the traveling time data that take the current traffic condition into account, e.g., via Google Maps API, we can minimize the overall travel time to charging stations of the taxi fleet better than using the distance data. This process can also be applied to other cities.

A Free Simulation Environment Based on ROS for Teaching Autonomous Vehicle Navigation Algorithms

In recent years, engineering degree programs have become fundamental to the teaching of robotics and incorporate many fundamental STEM concepts. Some authors have proposed different platforms for teaching different topics related to robotics, but most of these platforms are not practical for classroom use. In the case of teaching autonomous navigation algorithms, the absence of platforms in classrooms limits learning because students are unable to perform practice activities or cannot evaluate and compare different navigation algorithms. The main contribution of this study is the implementation of a free platform for teaching autonomous-driving algorithms based on the Robot Operating System without the use of a physical robot. The authors present a case study using this platform as a teaching tool for instruction in two undergraduate robotic courses. Students evaluated the platform quantitatively and qualitatively. Our study demonstrates that professors and students can carry out different tests and compare different navigation algorithms to analyze their performance under the same conditions in class. In addition, the proposed platform provides realistic representations of environments and data visualizations. The results claim that the use of simulations helps students better understand the theoretical concepts, motivates them to pay attention, and increases their confidence.

The Bellman-Ford Algorithm Search for the Best Routes to Tourist Attractions amid the COVID-19 Pandemic

Banyumas Regency is one of the districts rich in tourism potential, located in Central Java, Indonesia, with the state of the land-mountain transition zone. This natural potential has finally provided Banyumas with many advantages, especially in tourism. The tourist sites in Banyumas are located downtown, in a mountainous area, and in Baturraden. Banyumas has a very rapid development in economic growth, tourism, culture, and so forth. One of the causes of this growth is the high community travel activities drive it. The Covid-19 pandemic has impacted negatively on Indonesia's tourism industry and creative economy, particularly Banyumas. The number of domestic and foreign tourists experienced a very drastic decline, also experiencing a decline in hotel occupancy. This has an effect on the tourism industry's local revenue. Banyumas tourism management through the relevant agencies also makes efforts to save tourism from attracting tourists and revive the ecosystem. Many tourist locations are attractive, so people need to choose the fastest route to cross between them to get time efficiency when travelling between tourist attractions. In this paper, we are several ways to find the fastest path optimization for connecting one node with another node with the Bellman-Ford Algorithm. The results show that the Bellman-Ford Algorithm produces the shortest and fastest route with node routes 10-8-9-16-13-5-11-17-7-4-3-6-14-15-18-2-12. Determining the shortest path is essential in supporting tourism services to reach tourist sites easily.

IMPROVING THE RELIABILITY OF TRUCKING IN THE CONDITIONS OF A MINING ENTERPRISE

Мета. Шдвищення ефективност вантажних автомобшьних перевезень (ВАШ) в умовах прничодобувного пщприемства за рахунок розробки рекомендацш, спрямованих на пщвищення надшносн транспортного процесу. Методика. Для визначення факторщ впливу на надшнють ВАШ застосовано метод «Functional Resonance Analysis Method» (ада - метод «FRAM»), що базуеться на дослщженш функцш автомобшьного транспортного процесу перевезення вантажу, виходячи з шести рпних аспекта: час, контроль, вихщ, ресурс, передумови та вхщ. Результаты. Транспортний процес ВАШ в умовах прничодобувного пщприемства представлено за допомогою пяти основних функцш: подготовки ВАШ, подачi вантажного автомобщя пщ завантаження, завантаження вантажу, перевезення й розвантаження вантажу в мющ призначення. Для кожно! функцп транспортного процесу ВАШ визначено: ii мшливють, виходячи з показниив точност та своечасност виконання транспортно! операцп;фактори, що впливають на надшнють транспортного процесу - професшний досвщ водш, адмшь стративний контроль, час виконання транспортно! операцп, складнють транспортно! операцп, ергономша робочого мюця, навантаження та стрес, ршень пщтримки кершництва, яи можуть попршити кшцевий результат - доставки вантажу до мюця розвантаження. Встановлено, що найменша надшнють функцп транспортного процесу ВАШ - це подготовка та транспортування вантажу до мюця призначення, що повязано зi значною !х мшливютю й варшбельнютю, великою кшькютю виробничих завдань i високими змшними нормативами транспортно! роботи. Запропоновано для пщвищення надшносн ВАШ в умовах прничодобувного пщприемства та зменшення ймовфносн вщмов при виконанш транспортно! роботи посилити контроль за психофшологiчним станом водiя. Наукова новизна. Шолягае у встановленi взаемозвязку мож функцiями й факторами транспортного процесу ВАШ в умовах прничодобувного пщприемства, що дозволяе провести оцшку ршня надiйностi виконання поставленого завдання у визначений термш. Практична значимють. Шолягае в кщькюнш оцiнцi впливу факторiв транспортного процесу на надшнють ВАШ в умовах прничодобувного пщприемства.Alternate :Purpose. Improving the efficiency of trucking (TR) in the conditions of a mining enterprise by means of developing recommendations aimed at enhancing reliability of the transport process. Methodology. To determine the factors influencing the reliability of TR the Functional Resonance Analysis ethod (hereinafter - FRAM) was used, which is based on the study on the functions of freight automobile transportation process with respect to six different aspects: time, control, output, resource, prerequisites, and entrance. Findings. The transport process of TR in the conditions of a mining enterprise is represented by five main functions: preparation of TR, supply of the truck for loading, loading of cargo, transportation and unloading of cargo at the destination point. For each function of the transport process TR we determined its variability as based on the accuracy and timeliness of the transport operation;identified factors that affect the reliability of the transport process, namely driver experience, administrative control, time of the transport operation, complexity of the transport operation, workplace ergonomics, workload and stress, the level of management support that may worsen the final result of goods delivery to the point of unloading. It is determined that preparation and transportation of cargo to the destination point is the least reliable function of TR transport process. This is due to the significant changeability and variability, a large number of production tasks and high variable standards of transport work. It is proposed to strengthen the control over the psychophysiological condition of the driver in order to improve the reliability of TR in the conditions of a mining enterprise and to reduce the probability of failures during the performance of transportation work. Originality. It consists in establishing the relationship between the functions and factors of the transport process of TR in the conditions of a mining enterprise, which allows assessment of the reliability level of the task in a timely manner. Practical value. It consists in a quantitative assessment of the impact of transportation process factors on the reliability of TR in the conditions of a mining enterprise.

Harnessing social media to understand tourist mobility: the role of information technology and big data

Purpose>This paper aims to examine key parameters of scholarly context and geographic focus and provide an assessment of theoretical underpinnings of studies in the field of social media and visitor mobility. This review also summarised the characteristics of social media data, including how data are collected from different social media platforms and their advantages and limitations. The stocktake of research in this field was completed by examining technologies and applied methods that supported different research questions.Design/methodology/approach>This literature review applied a mix of methods to conduct a literature review. This review analysed 82 journal articles on using social media to track visitors’ movements between 2014 and November 2020. The literature compared the different social media, discussed current applied theories, available technologies, analysed the current trend and provided advice for future directions.Findings>This review provides a state-of-the-art assessment of the research to date on tourist mobility analysed using social media data. The diversity of scales (with a dominant focus on the city-scale), platforms and methods highlight that this field is emerging, but it also reflects the complexity of the tourism phenomenon. This review identified a lack of theory in this field, and it points to ongoing challenges in ensuring appropriate use of data (e.g. differentiating travellers from residents) and the ethics surrounding them.Originality/value>The findings guide researchers, especially those with no computer science background, on the different types of approaches, data sources and methods available for tracking tourist mobility by harnessing social media. Depending on the particular research interest, different tools for processing and visualization are available.

COMPREHENSIVE USE OF 6G CELLULAR TECHNOLOGY ACCOUNTING ACTIVITY COSTS AND CYBER SECURITY

The purpose of this study was in the research of prospects for simultaneous use of 6G generation cellular communications for the purposes of automatization of cost accounting of the activity of enterprises of various branches and cybersecurity of accounting information. The theoretical and methodological aspects of the use of 6G cellular network technologies for accounting and cybersecurity purposes have been studied on the basis of general research methods - institutional and innovative;economic and mathematical methods of analysis using Excel spreadsheets were used to predict the pace of implementation of cellular communication of new generations;to determine perspective areas of use of 6G technology - methods of bibliographic and comparative analysis using the information resource "ResearchGate". The methods of permanent collection and transmission of accounting data about the production process and the procedure for monitoring the stay of employees or outsiders at the workplace using production equipment connected to the 6G cellular network has been developed. The procedure for combining the functional abilities of Global Positioning System (GPS) and cellular positioning (mobile subscribers)for accounting of transport costs and control over the movement and economic use of vehicles has been proposed.The procedure for combining unmanned aerial vehicles in a cluster on the basis of 6G communication with the purpose of aerovisual surveillance of agricultural and construction activities for automated accounting of production costs and prevention of unauthorized getting into an enterprise ofpersons (drones). The methods for determining the cost of rental space from the lessor based on counting the popularity among visitors and identifying offenders (thieves of information and material resources) through automated monitoring of the location of 6G cellular subscribers. The practical implementation of the developments presented in the article on the use of 6G cellular technologies will contribute to reliable costing and accounting of production costs of production, agricultural, construction, trade activities in combination with effective cyber protection of enterprises in preventing and detecting violators of information and territorial security. Further research is needed on the methods of management of business entities on the basis of accounting information obtained with the use of 6G cellular network technology.

Deployment of Wireless Sensor Network and IoT Platform to Implement an Intelligent Animal Monitoring System

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Digitalization for Sustainable Agri-Food Systems: Potential, Status, and Risks for the MENA Region

Digital technologies offer a potential solution to improve sustainability—economic, social, and environmental—of agri-food systems around the globe. While developed countries have led the innovation and adoption of digital agriculture, the potential impact in developing countries—including in the Middle East and North Africa (MENA) region—is massive. This article synthesizes existing evidence to review the potential and current contribution of digital technologies to the agri-food sectors in MENA. Digital agriculture shows promise in addressing the key challenges facing the agri-food sector across MENA countries. Improvements in primary production, supply chain and logistics performance, and optimized use of scarce natural resources (notably agricultural water) could be notable, if digital technologies can be implemented as envisioned. Available evidence shows that adoption of digital agriculture is at early stages, generally led by high-value agricultural production targeting domestic markets in Gulf countries and export markets in Mashreq countries. Economic sustainability appears the strongest force for current adoption, with less focus on social or environmental sustainability. Public policies should not only foster the adoption of digital technologies in MENA but also ensure equity of access, transparency of use, data protections, and labor protections. Policymakers should move beyond traditional, production-centric views to deliver also on social and environmental sustainability.

Twitter’s Messages about Hydrometeorological Events. A Study on the Social Impact of Climate Change

This study is based on an interdisciplinary collaboration between scientists from natural and social sciences to create scientific knowledge about how Twitter is valuable to understand the social impact of hydrometeorological events. The capacity of citizens’ reaction through Twitter to environmental issues is widely analyzed in the current scientific literature. Previous scientific works, for example, investigated the role of social media in preventing natural disasters. This study gives scientific evidence on the existence of diversity in the intentionality of Twitters’ messages related to hydrometeorological events. The methodological design is formed by four experiments implemented in different moments of a temporal axis. The social impact on social media methodology (SISM) is implemented as social media analytics. From the findings obtained, it can be observed that there are different forms of intentionality in Twitter’s messages related to hydrometeorological events depending on the contextual circumstances and on the characteristics of Twitter’s users’ profiles (including the geolocation when this information is available). This content is relevant for future works addressed to define social media communication strategies that can promote specific reactions in vulnerable groups in front the climate change.

Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks

A flying ad hoc network (FANETs), also known as a swarm of unmanned aerial vehicles (UAVs), can be deployed in a wide range of applications including surveillance, monitoring, and emergency communications. UAVs must perform real-time communication among themselves and the base station via an efficient routing protocol. However, designing an efficient multihop routing protocol for FANETs is challenging due to high mobility, dynamic topology, limited energy, and short transmission range. Recently, owing to the advantages of multi-objective optimization, Q-learning (QL)-based position-aware routing protocols have improved the performance of routing in FANETs. In his article, we provide a comprehensive review of existing QL-based position-aware routing protocols for FANETs. We rigorously address dynamic topology, mobility models, and the relationship between QL and routing in FANETs, and extensively review the existing QL-based position-aware routing protocols along with their advantages and limitations. Then, we compare the reviewed protocols qualitatively in terms of operational features, characteristics, and performance metrics. We also discuss important open issues and research challenges with potential research directions.