The Impact of COVID-19 on Supply Chain in UAE Food Sector

The COVID-19 outbreak has significantly impacted supply chains and has caused several supply chain disruptions in almost all industries worldwide. Moreover, increased transportation costs, labor shortages, and insufficient storage facilities have all led to food loss during the pandemic, and this disruption has affected the logistics in the food value chain. As a result, we examine the food supply chain, which is one of the key industries COVID-19 has detrimentally affected, impacting, indeed, on the entire business process from the supplier all the way to the customer. Retail businesses are thus facing supply issues, which affect consumer behavior by creating stress regarding the availability of food. This has a negative impact on the amount of food that is available as well as its quality, freshness, safety, access to markets, and affordability. This study examines the impact of COVID-19 on the United Arab Emirates food distribution systems and how consumer behavior changed in reaction to interruptions in the food supply chain and the food security problem. Hypothesis testing was used in the study's quantitative methodology to assess consumer behavior, and participants who were consumers were given a descriptive questionnaire to ascertain whether the availability and security of food had been impacted. The study used JASP 0.17.2 software to develop a model of food consumption behavior and to reveal pertinent connections between each construct. Results show that consumer food stress and consumption behavior are directly impacted by food access, food quality and safety, and food pricing. Furthermore, food stress has an impact on how consumers behave when it comes to consumption. Food stress, however, is not significantly influenced by food supply.

The smart city and COVID-19

Smart cities are concepts much loved by politicians and technologists but are very difficult to bring about in practice. There are many isolated applications in cities such as operating streetlamps, but very few, if any, examples of integrated applications sharing data and managing the city as a holistic entity rather than a set of disparate and unconnected applications. This is despite hundreds of trials and indicates how difficult bringing about a smart city will be. The key challenge is the wide range of interested parties in a city including the elected city authority, subcontractors and suppliers to the authority, emergency services, transport providers, businesses, residents, workers, tourists, and other visitors. Some of these entities will be primarily driven by finance, such as businesses and transport providers. Some will be driven by political considerations. Some will be concerned with the quality of life as well as financial costs. In some cases, there will be conflicting interests-the city may want as much information as possible on people in the city, whereas individuals may want privacy and the minimum data stored concerning their movements and attributes. COVID-19 does not change any of these issues, but it does increase the importance of some applications such as smart health, logistics, people surveillance, data security, and crisis management, while reducing the importance of others such as traffic management. It may result in more willingness for monitoring and data sharing if this can be shown to result in better control of the virus. © 2023 the authors.

Demand Forecasting in SCM for Automobile Industry based on Time-series Modeling

In supply chain management (SCM), product classification and demand forecasting are crucial pillars to ensure companies to have production in the right category and quantity for long-term profitability. Due to COVID-19 from 2019, the automobile industry has been seriously negatively affected as the demand dropped dramatically. Therefore, it is necessary to make reasonable product classification and accurate demand forecasting to facilitate automobile companies in SCM to reduce unpopular product manufacture and unnecessary storage costs. In this paper, the Canada automobile market has been chosen with the period from 1946 to 2022. To classify a number of different types of motor vehicles into several categories with general characteristics, K-means Clustering method is applied. With the seasonal patterns and random generated features for auto sales, the time series models ARIMA and SARIMA are adopted for demand forecasting. According to the analysis, the automobiles fitting in the category with high demand and low price are valuable for further production. In addition, SARIMA Model is more accurate and fits better than ARIMA Model for both the training and test datasets for long-term prediction. The classification and forecasting results shed light on guiding manufacturers to adjust production schemes and ensuring auto dealers to predict more accurate sales in order to optimize the strategic planning. © 2023 SPIE.

Cold Comfort for Bio/Pharma

The COVID-19 pandemic highlighted how vital cold chain is for the pharmaceutical industry, particularly as some vaccines needed to be produced, transported, and stored at -70 °C. Market projections for cold chain logistics of pharmaceuticals are projected to grow at a compound annual growth rate of 9.03% by 2025, which is reported to be driven by greater global demand for pharmaceuticals, increasing initiatives to promote cold chain, and more demand for reefer containers from the pharma industry (1). Gilmore (Tower Cold Chain): Putting the European success of the COVID-19 vaccine rollout to one side, the demand for effective temperature-controlled packaging solutions in the pharmaceutical supply chain has increased significantly in recent years. Today, the cold chain is grappling with additional challenges: serving a global market, driving out costs and waste, addressing capacity and resource constraints, and dealing with continually mounting regulations-all whilst handling valuable pharmaceutical cargo. Cold chain logistics providers must invest in the latest on-board equipment built into containers to track temperature and location, and to make data available to partners and customers in real time, to prevent or mitigate loss.

Self-Sovereign Identity for Consented and Content-Based Access to Medical Records Using Blockchain

Electronic health records (EHRs) and medical data are classified as personal data in every privacy law, meaning that any related service that includes processing such data must come with full security, confidentiality, privacy, and accountability. Solutions for health data management, as in storing it, sharing and processing it, are emerging quickly and were significantly boosted by the COVID-19 pandemic that created a need to move things online. EHRs make a crucial part of digital identity data, and the same digital identity trends - as in self-sovereign identity powered by decentralized ledger technologies like blockchain, are being researched or implemented in contexts managing digital interactions between health facilities, patients, and health professionals. In this paper, we propose a blockchain-based solution enabling secure exchange of EHRs between different parties powered by a self-sovereign identity (SSI) wallet and decentralized identifiers. We also make use of a consortium IPFS network for off-chain storage and attribute-based encryption (ABE) to ensure data confidentiality and integrity. Through our solution, we grant users full control over their medical data and enable them to securely share it in total confidentiality over secure communication channels between user wallets using encryption. We also use DIDs for better user privacy and limit any possible correlations or identification by using pairwise DIDs. Overall, combining this set of technologies guarantees secure exchange of EHRs, secure storage, and management along with by-design features inherited from the technological stack. © 2023 Marie Tcholakian et al.

Molecular Diagnostics on Smartphone

During the COVID-19 pandemic, point-of-care genetic testing (POCT) devices were used for on-time and on-site detection of the virus, which helped to prevent and control the spread of the pandemic. Smartphones, which are widely used electronic devices with many functions, have the potential to be used as a molecular diagnostic platform for universal healthcare monitoring. Several integrated diagnostics platforms for the real-time and end-point detection of COVID-19 were developed using the functions of smartphones, such as the operating system, power, sound, camera, data storage, and display. These platforms use the 5V output power of smartphones, which can be amplified to power a micro-capillary electrophoresis system or a thin-film heater, and the CMOS camera of smartphones can capture the color change during a colorimetric loop-mediated isothermal amplification test and detect fluorescence signals. Smartphones can also be used with self-written web-based apps to enable automatic and remote pathogen analysis on POCT platforms. Our lab developed a handheld micro-capillary electrophoresis device for end-point detection of SARS-CoV-2, as well as an integrated smartphone-based genetic analyzer for the qualitative and quantitative colorimetric detection of foodborne pathogens with the help of a custom mobile app. © 2023 SPIE.

Comparison of Comorbidities between Adults and Children with Glycogen Storage Disease Type Ia (GSDIA)

Objectives: Glycogen Storage Disease Type Ia (GSDIa) is a rare inherited disorder resulting in acute hypoglycemia due to impaired release of glucose from glycogen. Despite dietary management practices to prevent hypoglycemia in patients with GSDIa, complications still occur in children and throughout adulthood. This retrospective cohort study compared the prevalence of complications in adults and children with GSDIa. Method(s): Using ICD-10 diagnosis codes, the IQVIA Pharmetrics Plus database was searched for patients with >=2 GSDI claims (E74.01) from January 2016 through February 2020, with >=12 months continuous enrollment beginning prior to March 2019 (for one year of follow-up before COVID-19), and no inflammatory bowel disease diagnoses (indicative of GSDIb). Complication prevalence in adults and children with GSDIa was summarized descriptively. Result(s): In total, 557 patients with GSDIa were identified (adults, 67%;male, 63%), including 372 adults (median age, 41 years) and 185 children (median age, 7 years). Complications occurring only in adults were atherosclerotic heart disease (8.6%), pulmonary hypertension (3.0%), primary liver cancer (1.9%), dialysis (0.8%), and focal segmental glomerulosclerosis (0.3%). Other complications with the greatest prevalence in adults/children included gout (11.8%/0.5%), insomnia (10.0%/1.1%), osteoarthritis (22.0%/2.7%), severe chronic kidney disease (4.3%/0.5%), malignant neoplasm (10.8%/1.6%), hypertension (49.7%/8.7%), acute kidney failure (15.3%/2.7%), pancreatitis (3.0%/0.5%), gallstones (7.8%/1.6%), benign neoplasm (37.4%/8.1%), hepatocellular adenoma (7.0%/1.6%), neoplasm (41.1%/9.7%), and hyperlipidemia (45.2%/10.8%). Complications with the greatest prevalence in children/adults included poor growth (22.2%/1.9%), gastrostomy (29.7%/3.2%), kidney hypertrophy (2.7%/0.8%), seizure (1.6%/0.5%), hypoglycemia (27.0%/11.3%), hepatomegaly (28.7%/15.9%), kidney transplant (1.6%/1.1%), diarrhea (26.5%/18.6%), nausea and/or vomiting (43.8%/35.8%), acidosis (20.0%/17.2%), and anemia due to enzyme disorders (43.8%/40.6%). Conclusion(s): GSDIa is associated with numerous, potentially serious complications. Compared with children, adults with GSDIa had a greater prevalence of chronic complications, potentially indicating the progressive nature of disease. Children with GSDIa had more acute complications related to suboptimal metabolic control.Copyright © 2023

Marginal Cost of Energy Volatility in the Brazilian Interconnected Power System

From 2018 to 2022, on average, 70% of the Brazilian effective electric generation was produced by hydropower, 10% by wind power, and 20% by thermal power plants. Over the last five years, Brazil suffered from a series of severe droughts. As a result, hydropower generation was reduced, but demand growth was also declined as results of the COVID-19 pandemic and economic recession. From 2012 to 2022, the Brazilian reservoir system operated with, on average, only 40% of the active storage, but storage recovered to normal levels in the first three months of 2022. Despite large capacity of storage reservoirs, high volatility of the marginal cost of energy was observed in recent years. In this paper, we used two optimization models, NEWAVE and HIDROTERM for our study. These two models were previously developed for mid-range planning of the operation of the Brazilian interconnected power system. We used these two models to optimize the operation and compared the results with observed operational records for the period of 2018-2022. NEWAVE is a stochastic dual dynamic programming model which aggregates the system into four subsystems and 12 equivalent reservoirs. HIDROTERM is a nonlinear programming model that considers each of the 167 individual hydropower plants of the system. The main purposes of the comparison are to assess cooperation opportunities with the use of both models and better understand the impacts of increasing uncertainties, seasonality of inflows and winds, demand forecasts, decisions about storage in reservoirs, and thermal production on energy prices. © World Environmental and Water Resources Congress 2023.All rights reserved

A survey of automatic contact tracing approaches using bluetooth low energy

To combat the ongoing Covid-19 pandemic, many new ways have been proposed on how to automate the process of finding infected people, also called contact tracing. A special focus was put on preserving the privacy of users. Bluetooth Low Energy as base technology has the most promising properties, so this survey focuses on automated contact tracing techniques using Bluetooth Low Energy. We define multiple classes of methods and identify two major groups: systems that rely on a server for finding new infections and systems that distribute this process. Existing approaches are systematically classified regarding security and privacy criteria.Copyright © 2021 ACM.

Development of a Decentralized Web App (D-App) for Covid-19 using AI

A horrifying number of people died because of the COVID-19 pandemic. There was an unexpected threat to food systems, public health, and the workplace. The pandemic has severely disturbed society and there was a serious impediment to the economy. The world went through an unprecedented state of chaos during this period. To avoid anything similar, we can only be cautious. The project aims to develop a web application for the preliminary detection of COVID-19 using Artificial Intelligence(AI). This project would enable faster coordination, secured data storage, and normalized statistics. First, the available chest X-ray datasets were collected and classified as Covid, Non-Covid, and Normal. Then they were trained using various state-of-the-art pre-trained Convolutional Neural Network (CNN) models with the help of Tensor-flow. Further, they were ranked based on their accuracy. The best-performing models were ensembled into a single model to improve the performance. The model with the highest accuracy was transformed into an application programming interface (API) and integrated with the Decentralized application (D-App). The user needs to upload an image of their chest X-ray, and the D-App then suggests if they should take a reverse transcription-polymerase chain reaction (RT-PCR) test for confirmation. © 2022 IEEE.

Classifying Chest X-Ray Images using Residual Neural Network

This unprecedented time of the COVID-19 outbreak challenged the status-quo whether it is on business operation, political leadership, scientific capability, engineering implementation, data analysis, and strategic thinking, in terms of resiliency, agility, and innovativeness. Due to some identified constraints, while addressing the issue of global health, human ingenuity has proven again that in times of crisis, it is our best asset. Constraints like limited testing capacity and lack of real-time information regarding the spread of the virus, are the highest priority in the mitigation process, aside from the development of vaccines and the pushing through of vaccination programs. Using the available Chest X-Ray Images dataset and an AI-Computer Vision Technique called Convolutional Neural Network, features of the images were extracted and classified as COVID-19 positive or not. This paper proposes the usage of the 18-layer Residual Neural Network (ResNet-18) as an architecture instead of other ResNet with a higher number of layers. The researcher achieves the highest validation accuracy of 99.26%. Moving forward, using this lower number of layers in training a model classifier, resolves the issue of device constraints such as storage capacity and computing resources while still assuring highly accurate outputs. © 2022 IEEE.

When do data visualizations persuade? The impact of prior attitudes on learning about correlations from scatterplot visualizations

Data visualizations are vital to scientific communication on critical issues such as public health, climate change, and socioeconomic policy. They are often designed not just to inform, but to persuade people to make consequential decisions (e.g., to get vaccinated). Are such visualizations persuasive, especially when audiences have beliefs and attitudes that the data contradict? In this paper we examine the impact of existing attitudes (e.g., positive or negative attitudes toward COVID-19 vaccination) on changes in beliefs about statistical correlations when viewing scatterplot visualizations with different representations of statistical uncertainty. We find that strong prior attitudes are associated with smaller belief changes when presented with data that contradicts existing views, and that visual uncertainty representations may amplify this effect. Finally, even when participants' beliefs about correlations shifted their attitudes remained unchanged, highlighting the need for further research on whether data visualizations can drive longer-term changes in views and behavior. © 2023 ACM.

Development of Data Logger for COVID Vaccine Temperature Monitoring System

The Data Logger (DL) is a unique tool created to carry out the typical duty of gathering data in a specific area. This common task can include measuring humidity, temperature, pressure or any other physical quantities. Due to the current pandemic situation, its use in temperature monitoring of Covid vaccine will be crucial. According to World Health Organization (WHO) guidelines, COVID vaccine can be stored and transported at -80 °C, -20°C and +2-8°C and shelf life is reduced as vaccine is transferred from one storage temperature to another. So cost effective, efficient and standalone Data Logger (DL) is the need of the hour. The Data logger is proposed to be developed with the use of ESP8266 Node MCU microcontroller. It takes power from a 5V Battery. DS18B20 sensor will be used for temperature sensing. Here we will use Wi-Fi module of ESP8266 Node MCU to send the temperature data from sensor to the Google Sheet over the internet. This real time data will be stored in the format of time and month/date/year. Data logged in Google Sheet will be displayed to the user with the help of graphical user interface (GUI) which is developed using PYTHON scripting language. GUI will allow user to interact with Data Logger through visual graphs. The Data Logger components are mounted on a double layered PCB. © 2022 IEEE.

Detecting Ransomware Attacks in Cloud Environment Using Machine Learning-Based Intelligence System in COVID-19 Chaos

2020-2022 provided nearly ideal circumstances for cybercriminals, with confusion and uncertainty dominating the planet due to COVID-19. Our way of life was altered by the COVID-19 pandemic, which also sparked a widespread shift to digital media. However, this change also increased people's susceptibility to cybercrime. As a result, taking advantage of the COVID-19 events' exceedingly unusual circumstances, cybercriminals launched widespread Phishing, Identity theft, Spyware, Trojan-horse, and Ransomware attacks. Attackers choose their victims with the intention of stealing their information, money, or both. Therefore, if we wish to safeguard people from these frauds at a time when millions have already fallen into poverty and the remaining are trying to survive, it is imperative that we put an end to these attacks and assailants. This manuscript proposes an intelligence system for identifying ransomware attacks using nature-inspired and machine-learning algorithms. To classify the network traffic in less time and with enhanced accuracy, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two widely used algorithms are coupled in the proposed approach for Feature Selection (FS). Random Forest (RF) approach is used for classification. The system's effectiveness is assessed using the latest ransomware-oriented dataset of CIC-MalMem-2022. The performance is evaluated in terms of accuracy, model building, and testing time and it is found that the proposed method is a suitable solution to detect ransomware attacks. © 2022 IEEE.

An LLMS Remotely Controlled Experiment for Smart Hydro Energy Storage and Irrigation System Powered by Photo-Voltaic Array and IoT

Many students all over the world have faced some educational issues due to the Covid-19 epidemic. As a consequence, many educational institutes focused on shifting to an E-learning system. This paper introduces a design and implementation steps of a remotely controlled experiment representing a smart hydro energy storage and irrigation system with monitoring capability using photovoltaic power and the Internet of Things (IoT). The experiment is running within the newly proposed Laboratory Learning Management System (LLMS). The remotely controlled experiment is a smart hydro energy storage and irrigation system, where the stored water during the daytime is used at night for smart irrigation of three different types of plants based on the moisture and temperature, in addition to the amount of water that the user sets for every area. In this experiment, during the daytime, the utilities are feeding from the solar panel and battery, but at night, the utilities are feeding from the battery or the hydro turbine that converts the water potential energy to electric energy. The overall Experiment is controlled using IoT sensors and relays which are connected and driven by the parameters that the user sets and can be communicated with the system using the Internet which allows the system to be proactive and take the needed decision in the right time. The main contribution of this system's experiment is the pumping of underground water in irrigation using a renewable and clean energy source, in addition to controlling the systems using IoT through the proposed LLMS. © 2022 IEEE.

Characterization and Optimization of Trajectory Errors on Remote-Controlled Differential Drive Robot through Speed Calibration

An increase in interest in research projects which involves the design of robotic systems that minimizes interactions between humans has been caused by the COVID-19 outbreak, as such technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. The utilization of remote-controlled robots in many different fields, especially in the medical field is becoming more and more necessary. However, mobile robots are susceptible to both systematic and nonsystematic errors that cause deviations in its trajectory. In view thereof, the researchers explored the possibility of minimizing the trajectory errors through speed calibration. The differential drive robot was navigated to finish a five-meter linear path of forward and backward motion. The test was conducted with a default linear speed of 0.5 m/s in which a high trajectory error was observed. Upon changing the speed of the robot, the same trajectory test was conducted at four additional different speeds, namely;0.25 m/s, 0.35 m/s, 0.65m/s and 0.75 m/s. With the gathered data, the researchers conducted a linear least-squares regression model using MATLAB wherein there is only one predictor variable (speed of the robot) and one response variable (deviation). Based on the results, the researchers concluded that the speed of 0.35 m/s is the optimal speed in which the trajectory error of the robot is minimal. The researchers recommend improving the design of the caster wheels to minimize the effects of nonsystematic errors. © 2022 IEEE.

Need for Vision: A data-centric approach towards analysing impact of COVID-19

From the beginning of 2020, we saw a rise of a new virus called the Coronavirus and ultimately a pandemic that anyone reading this paper must have been through. With the rise of COVID,many vaccines were found, the global vaccination drive as a result of this naturally fueled a possibility of Pro-Vaxxers and Anti-Vaxxers strongly expressing their support and concerns regarding the vaccines on social media platforms and along with this came up the need of quick identification of people who are experiencing COVID-19 symptoms. So in this paper, an effort has been made to facilitate the understanding of all these complications and help the concerned authorities. With the help of data in the form of Covid-19 tweets, a (machine-learning) classifier has been built which can classify users as per their vaccine related stance and also classify users who have reported their symptoms through tweets. © FIRE 2022: Forum for Information Retrieval Evaluation.

Creating added-value filet product from rainbow trout (Oncorhynchus mykiss) by salting and smoking method: physicochemical and textural attributes

Rainbow trout (Oncorhynchus mykiss) are currently consumed as live fish, primarily for catering or consumers, as an alternative to salmon in sashimi or dishes. However, Covid-19 has hampered store and restaurant operations. Therefore, developing suitable processing conditions to extend its shelf life, such as online distribution specifications while enhancing the filets' commercial value, would raise its production value. In this study, we investigated the fish filets salted in a 5% salt solution for 2 days and then smoked at 65 degrees C for 4 h under different storage conditions. As result, the higher rate of salt penetration and water loss in the resolved rigor mortis group was associated with tenderization of the meat compared to the rigor mortis group. Thermal-shrinkage and thermal-induced tissue destruction of the smoked fish filets during processing which affects the appearance, flavor, chewiness and overall acceptability. Nevertheless, according to the results of a consumer-type evaluation, the product characteristics of the fish filets from the resolution of rigor mortis group were consistent with those of the rigor mortis group, except for a weaker aroma. Thus, these results explain the relationship between frozen stored fish and the quality of processed products. The economic concept of regulating and distributing scheduling production between raw materials and finished products in the food industry conveys promising findings that will contribute to developing sustainable food processing systems.

Development of Nanopackaging for Storage and Transport of Loaded Lipid Nanoparticles.

Easily deploying new vaccines globally to combat disease outbreaks has been highlighted as a major necessity by the World Health Organization. RNA-based vaccines using lipid nanoparticles (LNPs) as a drug delivery system were employed to great effect during the recent COVID-19 pandemic. However, LNPs are still unstable at room temperature and agglomerate over time during storage, rendering them ineffective for intracellular delivery. We demonstrate the suitability of nanohole arrays (nanopackaging) as patterned surfaces to separate and store functionalized LNPs (fLNPs) in individual recesses, which can be expanded to other therapeutics. Encapsulating calcein as a model drug, we show through confocal microscopy the effective loading of fLNPs into our nanopackaging for both wet and dry systems. We prove quantifiably pH-mediated capture and subsequent unloading of over 30% of the fLNPs using QCM-D on alumina surfaces altering the pH from 5.5 to 7, displaying controllable storage at the nanoscale.