Displaying publications 1 - 20 of 32 in total

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  1. Khan A, Khan A, Ullah M, Alam MM, Bangash JI, Suud MM
    Front Comput Neurosci, 2022;16:1001803.
    PMID: 36405784 DOI: 10.3389/fncom.2022.1001803
    Cancer is one of the most prevalent diseases worldwide. The most prevalent condition in women when aberrant cells develop out of control is breast cancer. Breast cancer detection and classification are exceedingly difficult tasks. As a result, several computational techniques, including k-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), and genetic algorithms, have been applied in the current computing world for the diagnosis and classification of breast cancer. However, each method has its own limitations to how accurately it can be utilized. A novel convolutional neural network (CNN) model based on the Visual Geometry Group network (VGGNet) was also suggested in this study. The 16 layers in the current VGGNet-16 model lead to overfitting on the training and test data. We, thus, propose the VGGNet-12 model for breast cancer classification. The VGGNet-16 model has the problem of overfitting the breast cancer classification dataset. Based on the overfitting issues in the existing model, this research reduced the number of different layers in the VGGNet-16 model to solve the overfitting problem in this model. Because various models of the VGGNet, such as VGGNet-13 and VGGNet-19, were developed, this study proposed a new version of the VGGNet model, that is, the VGGNet-12 model. The performance of this model is checked using the breast cancer dataset, as compared to the CNN and LeNet models. From the simulation result, it can be seen that the proposed VGGNet-12 model enhances the simulation result as compared to the model used in this study. Overall, the experimental findings indicate that the suggested VGGNet-12 model did well in classifying breast cancer in terms of several characteristics.
  2. Shahid MA, Alam MM, Su'ud MM
    Sensors (Basel), 2023 Feb 09;23(4).
    PMID: 36850563 DOI: 10.3390/s23041965
    Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing's challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system's ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.
  3. Leong WH, Rawindran H, Ameen F, Alam MM, Chai YH, Ho YC, et al.
    Chemosphere, 2023 Oct;339:139699.
    PMID: 37532206 DOI: 10.1016/j.chemosphere.2023.139699
    Sustainable energy transition has brought the attention towards microalgae utilization as potential feedstock due to its tremendous capabilities over its predecessors for generating more energy with reduced carbon footprint. However, the commercialization of microalgae feedstock remains debatable due to the various factors and considerations taken into scaling-up the conventional microalgal upstream processes. This review provides a state-of-the-art assessment over the recent developments of available and existing microalgal upstream cultivation systems catered for maximum biomass production. The key growth parameters and main cultivation modes necessary for optimized microalgal growth conditions along with the fundamental aspects were also reviewed and evaluated comprehensively. In addition, the advancements and strategies towards potential scale-up of the microalgal cultivation technologies were highlighted to provide insights for further development into the upstream processes aimed at sustainable circular bioeconomy.
  4. Ishaq M, Khan A, Su'ud MM, Alam MM, Bangash JI, Khan A
    Comput Math Methods Med, 2022;2022:8691646.
    PMID: 35126641 DOI: 10.1155/2022/8691646
    Task scheduling in parallel multiple sequence alignment (MSA) through improved dynamic programming optimization speeds up alignment processing. The increased importance of multiple matching sequences also needs the utilization of parallel processor systems. This dynamic algorithm proposes improved task scheduling in case of parallel MSA. Specifically, the alignment of several tertiary structured proteins is computationally complex than simple word-based MSA. Parallel task processing is computationally more efficient for protein-structured based superposition. The basic condition for the application of dynamic programming is also fulfilled, because the task scheduling problem has multiple possible solutions or options. Search space reduction for speedy processing of this algorithm is carried out through greedy strategy. Performance in terms of better results is ensured through computationally expensive recursive and iterative greedy approaches. Any optimal scheduling schemes show better performance in heterogeneous resources using CPU or GPU.
  5. Zango ZU, Ethiraj B, Al-Mubaddel FS, Alam MM, Lawal MA, Kadir HA, et al.
    Environ Res, 2023 Aug 15;231(Pt 2):116102.
    PMID: 37196688 DOI: 10.1016/j.envres.2023.116102
    Perfluoroalkyl carboxylic acids (PFCAs) are sub-class of perfluoroalkyl substances commonly detected in water matrices. They are persistent in the environment, hence highly toxic to living organisms. Their occurrence at trace amount, complex nature and prone to matrix interference make their extraction and detection a challenge. This study consolidates current advancements in solid-phase extraction (SPE) techniques for the trace-level analysis of PFCAs from water matrices. The advantages of the methods in terms of ease of applications, low-cost, robustness, low solvents consumption, high pre-concentration factors, better extraction efficiency, good selectivity and recovery of the analytes have been emphasized. The article also demonstrated effectiveness of some porous materials for the adsorptive removal of the PFCAs from the water matrices. Mechanisms of the SPE/adsorption techniques have been discussed. The success and limitations of the processes have been elucidated.
  6. Bostan Ali W, Olayinka JA, Alam MM, Immelman A
    PLoS One, 2024;19(2):e0294890.
    PMID: 38349933 DOI: 10.1371/journal.pone.0294890
    Micro, Small, and Medium-sized Enterprises (MSMEs) in Thailand were assessed in this study to determine the short-term and long-term economic effects of post-COVID- 19 -, with the goal of developing policy guidelines that focus on the methods and strategies that will further develop and help recover these sectors. MSMEs are the most vulnerable and require assistants to combat the pandemic. This study assesses the perspectives of stakeholders on the development of mechanisms and the strategies applied to support vulnerable groups in Thailand, which mostly consist of women and children. The main data collection was gathered through online questionnaires that were distributed to various stakeholder groups. The tools used for analysis were advanced quantitative analysis tools that aid in achieving this research study's objectives, and data was examined primarily through the usage of path modeling, structural equation modeling (SEM), and descriptive analysis was among the methods used. The findings reveal that in the short term, MSMEs' ability to respond to COVID-19 implications has a significant impact on both financial and non-financial performance. Non-financial performance, on the other hand, is more affected by adaptability than financial performance. Demand shock from lockdowns and other COVID-19 cautionary interventions has a negative and significant impact on MSMEs' adaptability, financial performance, and non-financial performance. The demand shocks increased the vulnerability of MSMEs significantly but it was found that proper management of demand shock has helped stabilized and improve MSMEs' financial and non-financial performances, as well as helped decrease their vulnerability. When it comes to government policy, the focus is usually on enhancing the flexibility and financial performance of MSMEs. The government's legislative actions have little impact on MSMEs' non-financial performance and vulnerability. This could be because the majority of the programs are more focused on providing financial assistance to businesses or their consumers. COVID-19's supply and demand shock only hindered MSMEs' ability to respond to the changes and challenges caused by the pandemic, according to vendors. The vulnerability of MSMEs caused by COVID-19 creates grave effects on their financial performance. The findings of this research paper will assist policymakers in identifying the most vulnerable aspects of MSMEs, as well as their expectations- and determine the forms of support that will be required to combat the current and future pandemic situations that may occur in Thailand. In addition, it will aid policymakers in the establishment of procedures and supporting strategies for MSMEs to reduce the unemployment rate and stimulate the Thai economy, among other factors of improvement.
  7. Sarkar T, Alam MM, Parvin N, Fardous Z, Chowdhury AZ, Hossain S, et al.
    Toxicol Rep, 2016;3:346-350.
    PMID: 28959555 DOI: 10.1016/j.toxrep.2016.03.003
    This study is aimed to assess the heavy metals contamination and health risk in Shrimp (Macrobrachium rosenbergii and Penaeus monodon) collected from Khulna-Satkhira region in Bangladesh. The results showed that the Pb concentrations (0.52-1.16 mg/kg) in all shrimp samples of farms were higher than the recommended limit. The Cd levels (0.05-0.13 mg/kg) in all samples and Cr levels in all farms except tissue content at Satkhira farm were higher than the permissible limits. The individual concentration of Pb, Cd, and Cr between shrimp tissue and shell in all rivers and farms were not statistically significant (P > 0.05). Target hazard quotient (THQ) and hazard index (HI) were estimated to assess the non-carcinogenic health risks. Shrimp samples from all locations under the current study were found to be safe for consumption, the possibility of health risk associated with non-carcinogenic effect is very low for continuous consumption for 30 years.
  8. Arafath MA, Kwong HC, Adam F, Mohiuddin M, Sarker MS, Salim M, et al.
    Acta Crystallogr E Crystallogr Commun, 2020 Jan 01;76(Pt 1):91-94.
    PMID: 31921459 DOI: 10.1107/S2056989019016852
    The mol-ecule of the title compound, C28H22N4O9, exhibits crystallographically imposed twofold rotational symmetry, with a dihedral angle of 66.0 (2)° between the planes of the two central benzene rings bounded to the central oxygen atom. The dihedral angle between the planes of the central benzene ring and the terminal phenol ring is 4.9 (2)°. Each half of the mol-ecule exhibits an imine E configuration. An intra-molecular O-H⋯N hydrogen bond is present. In the crystal, the mol-ecules are linked into layers parallel to the ab plane via C-H⋯O hydrogen bonds. The crystal studied was refined as a two-component pseudomerohedral twin.
  9. Alam MM, Wei H, Wahid ANM
    Aust Econ Pap, 2020 Nov 27.
    PMID: 33349733 DOI: 10.1111/1467-8454.12215
    The outbreak of COVID-19 has weakened the economy of Australia and its capital market since early 2020. The overall stock market has declined. However, some sectors become highly vulnerable while others continue to perform well even in the crisis period. Given this new reality, we seek to investigate the initial volatility and the sectoral return. In this study, we analyse data for eight sectors such as, transportation, pharmaceuticals, healthcare, energy, food, real estate, telecommunications and technology of the Australian stock market. In doing so, we obtain data from Australian Securities Exchange (ASX) and analysed them based on 'Event Study' method. Here, we use the 10-days window for the event of official announcement of the COVID-19 outbreak in Australia on 27 February 2020. The findings of the study show that on the day of announcement, the indices for food, pharmaceuticals and healthcare exhibit impressive positive returns. Following the announcement, the telecommunications, pharmaceuticals and healthcare sectors exhibit good performance, while poor performance is demonstrated by the transportation industry. The findings are vital for investors, market participants, companies, private and public policymakers and governments to develop recovery action plans for vulnerable sectors and enable investors to regain their confidence to make better investment decisions.
  10. Khan A, Khan A, Khan MM, Farid K, Alam MM, Su'ud MBM
    Diagnostics (Basel), 2022 Oct 26;12(11).
    PMID: 36359438 DOI: 10.3390/diagnostics12112595
    Cardiovascular disease includes coronary artery diseases (CAD), which include angina and myocardial infarction (commonly known as a heart attack), and coronary heart diseases (CHD), which are marked by the buildup of a waxy material called plaque inside the coronary arteries. Heart attacks are still the main cause of death worldwide, and if not treated right they have the potential to cause major health problems, such as diabetes. If ignored, diabetes can result in a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Machine learning methods can be used to identify and diagnose diabetes and other illnesses. Diabetes and cardiovascular disease both can be diagnosed using several classifier types. Naive Bayes, K-Nearest neighbor (KNN), linear regression, decision trees (DT), and support vector machines (SVM) were among the classifiers employed, although all of these models had poor accuracy. Therefore, due to a lack of significant effort and poor accuracy, new research is required to diagnose diabetes and cardiovascular disease. This study developed an ensemble approach called "Stacking Classifier" in order to improve the performance of integrated flexible individual classifiers and decrease the likelihood of misclassifying a single instance. Naive Bayes, KNN, Linear Discriminant Analysis (LDA), and Decision Tree (DT) are just a few of the classifiers used in this study. As a meta-classifier, Random Forest and SVM are used. The suggested stacking classifier obtains a superior accuracy of 0.9735 percent when compared to current models for diagnosing diabetes, such as Naive Bayes, KNN, DT, and LDA, which are 0.7646 percent, 0.7460 percent, 0.7857 percent, and 0.7735 percent, respectively. Furthermore, for cardiovascular disease, when compared to current models such as KNN, NB, DT, LDA, and SVM, which are 0.8377 percent, 0.8256 percent, 0.8426 percent, 0.8523 percent, and 0.8472 percent, respectively, the suggested stacking classifier performed better and obtained a higher accuracy of 0.8871 percent.
  11. Wahaj Z, Alam MM, Al-Amin AQ
    Environ Sci Pollut Res Int, 2022 Mar;29(11):16739-16748.
    PMID: 34989992 DOI: 10.1007/s11356-021-18402-5
    Pandemics leave their mark quickly. This is true for all pandemics, including COVID-19. Its multifarious presence has wreaked havoc on people's physical, economic, and social life since late 2019. Despite the need for social science to save lives, it is also critical to ensure future generations are protected. COVID-19 appeared as the world grappled with the epidemic of climate change. This study suggests policymakers and practitioners address climate change and COVID-19 together. This article offers a narrative review of both pandemics' impacts. Scopus and Web of Science were sought databases. The findings are reported analytically using important works of contemporary social theorists. The analysis focuses on three interconnected themes: technology advancements have harmed vulnerable people; pandemics have macro- and micro-dimensions; and structural disparities. To conclude, we believe that collaborative effort is the key to combating COVID-19 and climate change, while understanding the lessons learnt from the industrialised world. Finally, policymakers can decrease the impact of global catastrophes by addressing many socioeconomic concerns concurrently.
  12. Khalid A, Ahmad P, Khan A, Khandaker MU, Kebaili I, Alam MM, et al.
    RSC Adv, 2022 Feb 22;12(11):6592-6600.
    PMID: 35424596 DOI: 10.1039/d2ra00300g
    Boron nitride (BN) nanomaterials are rapidly being investigated for potential applications in biomedical sciences due to their exceptional physico-chemical characteristics. However, their safe use demands a thorough understanding of their possible environmental and toxicological effects. The cytotoxicity of boron nitride nanotubes (BNNTs) was explored to see if they could be used in living cell imaging. It was observed that the cytotoxicity of BNNTs is higher in cancer cells (65 and 80%) than in normal cell lines (40 and 60%) for 24 h and 48 h respectively. The influence of multiple experimental parameters such as pH, time, amount of catalyst, and initial dye concentration on percentage degradation efficiency was also examined for both catalyst and dye. The degradation effectiveness decreases (92 to 25%) as the original concentration of dye increases (5-50 ppm) due to a decrease in the availability of adsorption sites. Similarly, the degradation efficiency improves up to 90% as the concentration of catalyst increases (0.01-0.05 g) due to an increase in the adsorption sites. The influence of pH was also investigated, the highest degradation efficiency for MO dye was observed at pH 4. Our results show that lower concentrations of BNNTs can be employed in biomedical applications. Dye degradation properties of BNNTs suggest that it can be a potential candidate as a wastewater and air treatment material.
  13. Murad MW, Abdullah ABM, Islam MM, Alam MM, Reaiche C, Boyle S
    J Public Health Policy, 2023 Jun;44(2):230-241.
    PMID: 37117262 DOI: 10.1057/s41271-023-00413-w
    We investigated the macroeconomic determinants of neonatal, infant, and under-five mortalities in Bangladesh for the period 1991-2018 and discuss implications of the United Nations' Sustainable Development Goal 3 (SDG 3) and Millennium Development Goal 4 (MDG 4) for developing countries. We used annual time series data and the econometric techniques of Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) regressions for analysis. Determinants most effective in combating neonatal, infant, and under-five mortalities include variables such as 'protecting newborns against tetanus', 'increasing healthcare expenditure', and 'making sure births are attended by skilled healthcare staff'. Employing more healthcare workers and assuring more and improved healthcare provisions can further reduce the neonatal, infant, and under-five mortalities. Developing countries with similar macroeconomic profiles can achieve similar SDG 3 and MDG 4 outcomes by emulating the policies and strategies Bangladesh applied to reducing child mortalities over the last three decades.
  14. Khalid A, Ahmad P, Khan A, Muhammad S, Khandaker MU, Alam MM, et al.
    Bioinorg Chem Appl, 2022;2022:9459886.
    PMID: 35873731 DOI: 10.1155/2022/9459886
    Environmental problems with chemical and biological water pollution have become a major concern for society. Providing people with safe and affordable water is a grand challenge of the 21st century. The study investigates the photocatalytic degradation capabilities of hydrothermally prepared pure and Cu-doped ZnO nanoparticles (NPs) for the elimination of dye pollutants. A simple, cost-effective hydrothermal process is employed to synthesize the Cu-doped ZnO NPs. The photocatalytic dye degradation activity of the synthesized Cu-doped ZnO NPs is tested by using methylene blue (MB) dye. In addition, the parameters that affect photodegradation efficiency, such as catalyst concentration, starting potential of hydrogen (pH), and dye concentration, were also assessed. The dye degradation is found to be directly proportional to the irradiation time, as 94% of the MB dye is degraded in 2 hrs. Similarly, the dye degradation shows an inverse relation to the MB dye concentration, as the degradation reduced from 94% to 20% when the MB concentration increases from 5 ppm to 80 ppm. The synthesized cost-effective and environmentally friendly Cu-doped ZnO NPs exhibit improved photocatalytic activity against MB dye and can therefore be employed in wastewater treatment materials.
  15. Aktar MA, Alam MM, Harun M
    PMID: 35028847 DOI: 10.1007/s11356-021-18257-w
    Reduced electricity demand through the implementation of an energy efficiency policy is a central pillar of the Malaysian government's energy strategy. Energy efficiency first emerged as part of Malaysia's energy policy agenda in 1979 but only came into force during the 2000s. Initially, it was seen from global fears about the shortage of fossil fuels, then as a way of combating climate change. This paper offers a comprehensive review of Malaysia's energy policies with a focus on adopting policies to improve energy efficiency. Starting with Malaysia's preliminary policy in response to the OPEC-driven global oil crisis in 1973, the paper discusses how policymakers are considering energy efficiency from Malaysia's sustainable development perspective and what relevant government efforts have been made to improve it. The review evaluates the progress that has been made over the past 25 years to address energy efficiency in the economy and highlights the achievements and remaining difficulties. Findings show that the level of energy efficiency while having shown improvement during 1990-2015 was lower than expected. In terms of electricity intensity of GDP, Malaysia has a relatively large position among the ASEAN countries and the world's largest electricity consumers. Researchers, scientists, and practitioners will benefit from the extensive review material of this study, which will help them better understand energy efficiency and the sustainability strategy implemented in Malaysia to date.
  16. Rawindran H, Khoo KS, Ethiraj B, Lim JW, Liew CS, Goh PS, et al.
    Environ Res, 2024 Mar 16;251(Pt 2):118687.
    PMID: 38493853 DOI: 10.1016/j.envres.2024.118687
    The current study had conducted the life cycle analysis (LCA) to assess the environmental impact of microalgal wastewater treatment via an integrated membrane bioreactor. The functional unit selected for this analysis was 1 kg of treated microalgal wastewater with contaminants eliminated by ultrafiltration membrane fabricated from recycled polyethylene terephthalate waste. Meanwhile, the applied system boundary in this study was distinguished based on two scenarios, namely, cradle-to-gate encompassed wastewater treatment only and cradle-to-cradle which included the reutilization of treated wastewater to cultivate microalgae again. The environmental impacts and hotspots associated with the different stages of the wastewater treatment process had clearly elucidated that membrane treatment had ensued the highest impact, followed by microalgal harvesting, and finally cultivation. Among the environmental impact categories, water-related impact was found to be prominent in the following series: freshwater ecotoxicity, freshwater eutrophication and marine ecotoxicity. Notably, the key performance indicator of all environmental impact, i.e., the global warming potential was found to be very much lower at 2.94 × 10-4 kg CO2 eq as opposed to other literatures reported on the LCA of wastewater treatments using membranes. Overall, this study had proffered insights into the environmental impact of microalgal wastewater treatment and its stimulus for sustainable wastewater management. The findings of this study can be instrumental in making informed decision for optimizing microalgal wastewater treatment and reutilization assisted by membrane technology with an ultimate goal of enhancing sustainability.
  17. Umar B, Alam MM, Al-Amin AQ
    Environ Sci Pollut Res Int, 2021 Jan;28(2):1973-1982.
    PMID: 32862348 DOI: 10.1007/s11356-020-10641-2
    The increasing level of greenhouse gas carbon emission currently exacerbates the devastating effect of global warming on the Earth's ecosystem. Energy usage is one of the most important determinants that is increasing the amount of carbon gases being released. Simultaneously, the level of energy usage is derived by the price, and therefore, this study examines the contribution of energy price to carbon gas emissions in thirteen African nations for the period spanning 1990 to 2017. It does this by utilising the cross-sectional dependence (CD), augmented mean group (AMG) and pooled mean group (PMG) panel modelling methods. The findings of the AMG model suggest that a 1% increase in energy price leads to a 0.02% decrease in carbon emission. The results further reveal that a 1% increase in energy intensity and technological innovation leads to 0.04% and 3.65% increase in carbon emission, respectively, in the selected African countries. Findings will help policymakers to implement effective energy price policies to reduce carbon emissions and achieve sustainable development goals especially in the emerging economies of Africa.
  18. Asghar MZ, Albogamy FR, Al-Rakhami MS, Asghar J, Rahmat MK, Alam MM, et al.
    Front Public Health, 2022;10:855254.
    PMID: 35321193 DOI: 10.3389/fpubh.2022.855254
    Deep neural networks have made tremendous strides in the categorization of facial photos in the last several years. Due to the complexity of features, the enormous size of the picture/frame, and the severe inhomogeneity of image data, efficient face image classification using deep convolutional neural networks remains a challenge. Therefore, as data volumes continue to grow, the effective categorization of face photos in a mobile context utilizing advanced deep learning techniques is becoming increasingly important. In the recent past, some Deep Learning (DL) approaches for learning to identify face images have been designed; many of them use convolutional neural networks (CNNs). To address the problem of face mask recognition in facial images, we propose to use a Depthwise Separable Convolution Neural Network based on MobileNet (DWS-based MobileNet). The proposed network utilizes depth-wise separable convolution layers instead of 2D convolution layers. With limited datasets, the DWS-based MobileNet performs exceptionally well. DWS-based MobileNet decreases the number of trainable parameters while enhancing learning performance by adopting a lightweight network. Our technique outperformed the existing state of the art when tested on benchmark datasets. When compared to Full Convolution MobileNet and baseline methods, the results of this study reveal that adopting Depthwise Separable Convolution-based MobileNet significantly improves performance (Acc. = 93.14, Pre. = 92, recall = 92, F-score = 92).
  19. Aktar MA, Alam MM, Al-Amin AQ
    Sustain Prod Consum, 2021 Apr;26:770-781.
    PMID: 33786357 DOI: 10.1016/j.spc.2020.12.029
    The COVID-19 pandemic has emerged as one of the deadliest infectious diseases on the planet. Millions of people and businesses have been placed in lockdown where the main aim is to stop the spread of the virus. As an extreme phenomenon, the lockdown has triggered a global economic shock at an alarming pace, conveying sharp recessions for many countries. In the meantime, the lockdowns caused by the COVID-19 pandemic have drastically changed energy consumption patterns and reduced CO2 emissions throughout the world. Recent data released by the International Monetary Fund and International Energy Agency for 2020 further forecast that emissions will rebound in 2021. Still, the full impact of COVID-19 in terms of how long the crisis will be and how the consumption pattern of energy and the associated levels of CO2 emissions will be affected are unclear. This review aims to steer policymakers and governments of nations toward a better direction by providing a broad and convincing overview on the observed and likely impacts of the pandemic of COVID-19 on the world economy, world energy demand, and world energy-related CO2 emissions that may well emerge in the next few years. Indeed, given that immediate policy responses are required with equal urgency to address three things-pandemic, economic downturn, and climate crisis. This study outlines policy suggestions that can be used during these uncertain times as a guide.
  20. Ahmad SF, Han H, Alam MM, Rehmat MK, Irshad M, Arraño-Muñoz M, et al.
    Humanit Soc Sci Commun, 2023;10(1):311.
    PMID: 37325188 DOI: 10.1057/s41599-023-01787-8
    This study examines the impact of artificial intelligence (AI) on loss in decision-making, laziness, and privacy concerns among university students in Pakistan and China. Like other sectors, education also adopts AI technologies to address modern-day challenges. AI investment will grow to USD 253.82 million from 2021 to 2025. However, worryingly, researchers and institutions across the globe are praising the positive role of AI but ignoring its concerns. This study is based on qualitative methodology using PLS-Smart for the data analysis. Primary data was collected from 285 students from different universities in Pakistan and China. The purposive Sampling technique was used to draw the sample from the population. The data analysis findings show that AI significantly impacts the loss of human decision-making and makes humans lazy. It also impacts security and privacy. The findings show that 68.9% of laziness in humans, 68.6% in personal privacy and security issues, and 27.7% in the loss of decision-making are due to the impact of artificial intelligence in Pakistani and Chinese society. From this, it was observed that human laziness is the most affected area due to AI. However, this study argues that significant preventive measures are necessary before implementing AI technology in education. Accepting AI without addressing the major human concerns would be like summoning the devils. Concentrating on justified designing and deploying and using AI for education is recommended to address the issue.
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