The Importance of Low-Cost Live Feed Culture Technology to the Marine Shrimp Industry during COVID-19

The importance of live feed as a beginning diet in marine shrimp (Penaeus sp.) is largely dependent on careful management during the early stages of larval growth. The COVID-19 pandemic has a significant impact on Malaysian aquaculture, which is critical for seafood supply and security. Cladocerans are an alternative live feed species that outperform Artemia nauplii in terms of nutritional value, economic value, availability, and reproduction rate. In terms of supplying live feed for commercial aquaculture, cladoceran culture and cultivation can therefore be an economically viable, sustainable, and desirable live feed species. The purpose of this study is to collect information on low-cost live feed for marine shrimp used in aquaculture and how COVID-19 affects the sector. Thus, a survey was conducted at a private hatchery and farm in Pekan and Badong, Pahang, Malaysia to investigate the importance of low-cost live feed culture technology to the marine shrimp industry during COVID-19. A total of 20 respondents took part in the survey. During the COVID-19 period in Malaysia, data were gathered using a questionnaire that was planned to be completed via an online form from August 2020 through December 2020. The software utilised was a Google application (Google Form). The data gathered revealed the importance of utilising live feed for maintaining shrimp larvae production in the sector. The current data are critical for developing policy actions to support seafood industries affected by the pandemic at both the national and international levels. Copyright©2023 by authors, all rights reserved.

Investigating the relationship between government support and SMEs' sustainability through financial and green lenses

PurposeTo be successful on a global scale, small- and medium-sized enterprises (SMEs) need government support (GS) for innovation, sustainability and creativity. GS has always been a constructive influence on enterprises. This paper aims to examine the role of GS in assessing financial literacy (FL), access to finance (AF) and green value co-creation (GVC) for the sustainability of SMEs. Design/methodology/approachThis study's sample comprises SMEs in Lahore, Pakistan. Data collection started in December 2021 and ended in February 2022. Using convenient sampling, 320 responses were collected from SMEs and included in data analysis. Hypotheses were tested, and model fit was checked through the software AMOS 22. FindingsIt has been examined that GS plays a pivotal role in acquiring FL, AF and GVC for the sustainability of SMEs. Research limitations/implicationsIncreasing the sample size will give a more demonstrative picture as the population size is quite large. Future researchers should design causal relationships, linking these variables through longitudinal research. Originality/valueNo study has been conducted on SMEs of developing economies using these variables. This study contributes to the literature by providing a comprehensive model and identifying GSs importance in achieving SMEs' sustainability through financial and green lenses. This research significantly impacts government policymakers and SMEs by giving them insight into the importance of green practices, financial capabilities and SMEs' sustainability.

Sentiment Analysis of Covid-19 Tweets by Supervised Machine Learning Models

The COVID-19 virus's transmissibility has sparked intense debate on social media sites, particularly Twitter. As a result, to employ resources efficiently and effectively, a comprehensive assessment of the situation is crucial. Therefore, COVID-19 tweet sentiment analysis is implemented in this research by employing a supervised machine learning (ML) approach. Data is retrieved from Twitter using the Tweepy API, pre-processed using pre-processing techniques, and sentiment extracted and labelled as positive or negative sentiments using the TextBlob library. Three separate feature extraction techniques are used: Bag-of-words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF) combination with 1-gram, and TF-IDF combination with 2-gram. The sentiment is then analyzed using ML classifiers such as Random Forest (RF) and Support Vector Machine (SVM). For clarity, the dataset is studied further using the deep learning method which is Long Short-Term Memory (LSTM) architecture. The four standard evaluation metrics, Receiver Operating Characteristic (ROC), and Area Under the Curve (AUC) were used to evaluate the performance of the models. The findings show that the RF classifier surpasses all other models with a 0.98 accuracy score when combining 2-gram TF-IDF features. In summary, the model may be used to categorize perspectives and will assist policymakers in making more educated decisions about how to respond to the current pandemic. © 2022, Success Culture Press. All rights reserved.

Optimization of Coronavirus Pandemic Model Through Artificial Intelligence

Artificial intelligence is demonstrated by machines, unlike the natural intelligence displayed by animals, including humans. Artificial intelligence research has been defined as the field of study of intelligent agents,which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals. The techniques of intelligent computing solve many applications of mathematical modeling. The researchworkwas designed via a particularmethod of artificial neural networks to solve the mathematical model of coronavirus. The representation of the mathematical model is made via systems of nonlinear ordinary differential equations. These differential equations are established by collecting the susceptible, the exposed, the symptomatic, super spreaders, infection with asymptomatic, hospitalized, recovery, and fatality classes. The generation of the coronavirus model's dataset is exploited by the strength of the explicit Runge Kutta method for different countries like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, validation, and testing processes for each cyclic update in Bayesian Regularization Backpropagation for the numerical treatment of the dynamics of the desired model. The performance and effectiveness of the designed methodology are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis. © 2023 Tech Science Press. All rights reserved.

Human Rights in Compliance of Health Services for Accidents Relating to Covid-19 Women's Prison

Health care is a type of human right provided by the government to its citizens, including convicts. It was quite likely that the virus will spread more quickly in 2020 due to the overcrowding in jails and the early appearance of Covid-19. Since the government-provided assimilation program is insufficient to stop the virus from spreading, it is also necessary to strengthen the delivery of health care.This study uses qualitative empirical legal research techniques and is presented as a descriptive analysis. According to the study's findings, the Class II A Sungguminasa women's prison's medical care for Covid-19 patients is quite good. Fulfilling these rights, however, is complicated by a number of issues, such as a lack of staff and medical resources at the Sungguminasa Women's Prison. Copyright © 2022, Anka Publishers. All rights reserved.

Depression, anxiety, coping strategies, quality of life of the elderly during the COVID-19 pandemic

The occurrence of COVID-19 has a psychological impact on the elderly which will affect mental health and quality of life. This study aimed to identify the relationship between depression, anxiety, coping strategies with the quality of life of the elderly. This cross sectional study was conducted during the COVID-19 pandemic. Cluster sampling technique was used to select 232 sample. This study employed geriatric depression scale (GDS 15) to measure depression, the geriatric anxiety inventory (GAI) to measure anxiety, brief resilient coping skala (BRCS) to measure coping stratecgies, and the WHOQOOL-BRIEF questionnaire to measure quality of life among the elderly. Data analysis used Multiple Linear Regression statistical test. This study showed that there is a correlation between depression and quality of life (p=0.000), anxiety and quality of life (p=0.000) with coping strategies and quality of life (p=0.027). This study recommended the provision of appropriate psychological interventions to improve and maintain the quality of life among the elderly. © 2022, Intelektual Pustaka Media Utama. All rights reserved.

FLOSSC: A Computer-Aided Tool for Detecting Corona Virus Infections through Chest X-Ray Images

Artificial intelligence has radically altered the world, and it continues to progress at an alarming rate as time passes. AI applications include healthcare and medical solutions, illness diagnostics, agriculture, constructing security infrastructures, autonomous cars, intelligent systems, industrial production, robotics, and much more. COVID19 is a deadly virus that first appeared in China in 2019 and soon spread over the world. By 2020, the globe had witnessed a tremendous epidemic, with countless lives lost as a result of this dreadful virus, which has now become a severe health danger. Furthermore, in 2021, several nations will be infected with new Covid19 forms that are more deadly and spread quicker. The research describes the proposed methodology for diagnosing covid-19 and pneumonia from human chest X-ray images using transfer learning with Resnet-18 and VGG-16 neural networks. The focal loss function was also used to homogenize the imbalanced dataset, which included X-ray images of normal, pneumonia, and Covid-19 patients. The purpose is to assess the performance and accuracy of fine-tuned neural networks after including Binary Cross-Entropy (BCE) and Focal Loss (FL) functions. However, when we used our Resnet-18 and VGG-16 neural networks with BCE and FL functions, the VGG-16 with FL function outperformed all other models, with training and validation accuracy of 98.37 percent and 97.37 percent, correspondingly.

Green Practices: A Solution for Environmental Deregulation and the Future of Energy Efficiency in the Post-COVID-19 Era

The world has paid increasing attention to energy efficiency projects since the Paris agreement and UN climate summit. Recently, the COVID-19 pandemic accelerated the process of the green energy transition, which has attracted considerable attention from economists, environmentalists, and international organizations and has led to significant research in energy. This study addresses the importance of green energy practices in the post-COVID-19 era to deal with environmental deregulation using bibliometric analysis. Data were extracted from the Scopus database from 2020 to 2022. Results indicate that China gained a prominent place in publishing topic-related articles. However, Italy stands at the top position in total and average article citations. Sustainability is the most productive journal, followed by Energies and the Journal of Cleaner Production. Nazarbayev University and the University of Cambridge are the most contributing research institutes. In general, the cooperation of authors, institutes, and countries strengthens research;however, collaboration at the author level across the nation was lower than in others. The study highlights three research streams and four themes by systematically conducting a bibliometric coupling and co-occurrence network that anticipates and significantly segregates literature. Bibliometric coupling identifies three research streams of sustainable green business strategies, green infrastructure requirements, and green solutions and opportunities from COVID-19. Furthermore, the co-occurrence network proposes four main themes related to green innovation in the epidemic era, security and sustainable development goals with green practices, public health protection and green finance, and investment and risk management. The results provide insights into current research in the field of energy and will assist future work promoting environmentally friendly projects.

A Review: Deep Learning Classification Performance of Normal and COVID-19 Chest X-ray Images

COVID19 chest X-ray has been used as supplementary tools to support COVID19 severity level diagnosis. However, there are challenges that required to face by researchers around the world in order to implement these chest X-ray samples to be very helpful to detect the disease. Here, this paper presents a review of COVID19 chest X-ray classification using deep learning approach. This study is conducted to discuss the source of images and deep learning models as well as its performances. At the end of this paper, the challenges and future work on COVID19 chest X-ray are discussed and proposed. © 2021 Institute of Physics Publishing. All rights reserved.

A Review of Cyber Security Awareness (CSA) Among Young Generation: Issue and Countermeasure

Cybersecurity has emerged as an essential concept in everyday life, requiring the involvement of individuals. Although cybersecurity is empowered by government to all level of user, as a critical demanding situation confronted, however, the visibility and public focus stays limited especially to young user. The engagement of Internet is regularly taken into necessity for sharing information, learning, transactions and controlling the physical world, mainly during pandemic Covid-19. Hence, Cybersecurity Awareness (CSA) is a key defense in the protection of user and cyberspace. This review paper elaborates the CSA issue and methodology that had been done by other researcher, consequently the encounter effort taken to enhance the CSA among user, focus on young generation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Triggering of Herding Instincts due to COVID-19 Pandemic in Pakistan Stock Exchange

The present research intends to examine the herding aspect during the COVID-19 outbreak. The study is conducted to achieve specific objectives, so the underlying sampling technique is purposive sampling. The considered data source is the Pakistan Stock Exchange (PSX). Daily stock prices of 528 listed companies in PSX have been taken from the official website of PSX from 1998 to 2021. The current study envisions investigating the herding aspects for pre-pandemic and the time covering the pandemic period. The study has also targeted ten sectors of PSX. The present study's motive is to investigate investors' herding prospects before and during the pandemic in the Pakistan Stock Exchange (PSX) and its selected sectors. Daily closing stock prices of listed companies have been collected from the official website of PSX to calculate the stock returns. The Cross-Sectional Absolute Deviation (CSAD) has been used as a herding measure. Findings revealed that herding has not been observed in PSX during both time spans and even not during the bullish and bearish trends. However, robust sectoral evidence has been observed during the pandemic. It implies that investors in PSX tend to follow the crowd irrespective of making their own decisions to avoid further losses.

Maintaining a continuous programme of support and education for hospital transfusion laboratory professionals during the SARS-CoV-2 pandemic

Implementation of stringent infection prevention and control measures, including social distancing, created a significant challenge for laboratories to maintain an on-going programme of training, education and continuing professional development for professionals working in blood transfusion during the SARS-CoV-2 pandemic. The NHS Blood and Transplant (NHSBT) Patient Blood Management (PBM) team developed a remote, free at the point of access education group, open to newly qualified biomedical scientists and those new to transfusion science. Meeting monthly, an industry expert speaker is invited to deliver a lecture on a specialist area of blood transfusion before opening the session for discussion between delegates and speakers. The curriculum is flexible and reactive to feedback from delegates and considers key industry recommendations, such as those published in the 2019 SHOT annual report. We have received 650 registrations to join the Biomedical Scientist Empowerment and Discussion Group. The membership spans the whole United Kingdom, as well as Ireland and overseas. We invited the delegates that attended the sixth meeting of the group to respond to a short survey. 72 delegates responded. 62.5% (n = 45) respondents felt that blood transfusion training time been reduced or difficult to facilitate during the last 12 months, due to the SARS-CoV-2 pandemic. 98.61% (n = 71) respondents felt that the education provided during these sessions enabled them to provide a better service to patients and service-users. By operating remotely, we were able to maintain a continuous programme of support and education for hospital transfusion laboratory professionals during the SARS-CoV-2 pandemic. As a result, delegates felt empowered to provide a better service to patients and serviceusers. This accessible, cost-effective, and successful model should be considered by other organisations working within other pathology specialisms to enhance individual and service performance.

The effect of COVID-19 on the hospitality industry: The implication for open innovation

The current coronavirus pandemic (COVID-19) has led the world toward severe socioeconomic crisis and psychological distress. It has severely hit the economy;but the service sector, particularly the hospitality industry, is hard hit by it. It increases the sense of insecurity among the employees and their perception of being unemployed, adversely affecting their mental health. This research aims to contribute to the emerging debate by investigating the effect of economic crisis and non-employability on employees’ mental health through perceived job insecurity under the pandemic situation. It empirically examines the underlying framework by surveying 372 employees of the hospitality industry during COVID-19. Results indicate that perceived job insecurity mediates the relationship of fear of economic crisis, non-employability, and mental health. Furthermore, the contingency of fear of COVID-19 strengthens the indirect relationship of fear of economic crisis on mental health through perceived job insecurity. The findings will provide a new dimension to the managers to deal with the psychological factors associated with the employees’ mental health and add to the emerging literature of behavioral sciences. The study also highlights the increasing need for investment in the digital infrastructure and smart technologies for the hospitality industry. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Impact of Movement Control Order (MCO) due to Coronavirus Disease (COVID-19) on Food Waste Generation: A Case Study in Klang Valley, Malaysia

The outbreak of a neurological disorder was first discovered as a new coronavirus (2019-nCoV) in Wuhan, China. The infection spread rapidly in China and throughout the world, including Malaysia. Malaysia recorded its initial case on 25 January 2020 with intensifying numbers since March 2020. Due to this uncertain circumstance, Malaysia has introduced the Movement Control Order (MCO) with the main aim of isolating the source of the COVID-19 outbreak, which was effective from 18 March 2020. The restriction has observed fewer vehicles on the road with industrial and commercial activities being suspended. The objective of the study is to quantify the effects of MCO to food waste generation in town and district areas of Klang Valley, Malaysia. Food waste generation data was derived from the Project Delivery Department, KDEB Waste Management on a daily basis before (19 February-17 March 2020) and during the MCO (18 March-14 April 2020) at 12 local authorities in Selangor, Malaysia. The data was obtained with the limitation of assumption that there is no waste compositional analysis to be conducted in 2020. Despite the stay-at-home order, food waste data showed a descriptive reduction of up to 15.1% during the MCO. Statistical analysis of food waste generation from one-way variance has shown that municipal and district local authorities recorded a significant reduction (p < 0.50) during the MCO. The food waste reduction during the MCO will deliver as the evidence-based results to push the need for policies in Malaysia as per the goals outlined in Sustainable Development Goals of global food loss and waste.