As of 26 March 2020, Pakistan had 1179 cases of COVID-19, with most 421 cases from Sindh, 394 cases, 131 cases, 123 cases, 84 cases, 25 cases and 01 cases from Punjab, Balochistan, Khyber Pakhtunkhwa, Gilgit-Baltistan, Islamabad Capital Territory, and Azad Jammu and Kashmir respectively. Travel-related cases were the main source of SARS-CoV-2 infection during the early phase of the pandemic in Pakistan. Nevertheless, cases of local virus transmission are increasing day by day. As of 26 March 2020, nine deaths have been reported from COVID-19. The case fatality rate is 0.8%, which is less compare to China, Italy, USA, and Iran. The SIR (Susceptible-Infected-Recovered) model of epidemiological analysis predicts that almost 90 million population will be infected in the coming days with 5% critical cases that need health care facilities. However, the Pakistan health care system cannot provide services to this much population. Hence, we need to act timely to reduce this number by restricting local transmission of the disease. This can be done by mass testing, quarantine, isolation and social distancing of the active coronavirus cases in Pakistan. Moreover, better communication between the authorities is very much required to control disease transmission.

This study aims to summarize the literature on sustainable global supply chain management so that researchers and practitioners can see the trends in the area in a single place. Systematic literature review (SLR) or Content analysis is used as a methodology of this literature review, studies that are published within the time frame of 2010 to 2020 are included in this literature. Dimensions to analyze each article includes; year of publications, methodology of research, data collection type, unit of analysis, industry, a country in which data is being collected, respondents’ types, the theory that is used in the study, dimensions of the sustainability and finally the purpose of study either it is to test the already existing theory or building up a new theory. I found that sustainable global supply chain management is an emerging field and there is potential in the area for researchers to explore the area.

The introduction of the AI-powered chatbot ChatGPT by OpenAI has sparked much interest and debate among academic researchers. Commentators from different scientific disciplines have raised many concerns and issues, especially related to the ethics of using these tools in scientific writing and publications. In addition, there has been discussions about whether ChatGPT is trustworthy, effective, and useful in increasing researchers’ productivity. Therefore, in this paper, we evaluate ChatGPT’s performance on tasks related to bibliometric analysis, by comparing the output provided by the chatbot with a recently conducted bibliometric study on the same topic. The findings show that there are large discrepancies and ChatGPT’s trustworthiness is low in this particular area. Therefore, researchers should exercise caution when using ChatGPT as a tool in bibliometric studies.

The aim of this articles is to investigate the entropy optimization in unsteady MHD flow Darcy-Forchheimer nanofluids towards a stretchable sheet. The surface we tend to think about is porous and stretchy under acceleration. Flow occurs due to the stretching of the surface. Four distinct types of aqueous nanostructures are taken in this examination where copper oxide ( ), copper ( ), titanium dioxide ( ) and aluminum oxide ( ) are the nanoparticles. Irreversibility analysis are discussed through second law of thermodynamics. The expression of energy is mathematically designed and discussed according to heat generation / absorption, dissipation, thermal radiation, and joule heating. The nonlinear PDE (partial differential conditions) is first changed to ODE (normal differential conditions) through appropriate similarity variables. Here we used the numerically embedded solution technique to develop a numerical result for the obtained nonlinear flow expression. Influence of various flow parameter velocity temperature distribution and entropy generation are discussed. Reduction occurs in velocity profile for larger porosity and magnetic parameters. An enhancement in entropy generation and temperature distribution is seen for Brinkman number. An opposite effect is noticed in velocity and temperature through solid volume friction.

Modern language models are designed to generate text so realistic that it could easily be mistaken for human-authored content. Additionally, they can engage in conversations with humans in a manner that appears coherent and reasonable. The epitome of this technological advancement is ChatGPT, a model based on OpenAI's Generative Pretrained Transformer (GPT) language model. With its ability to produce high-quality content in mere seconds, ChatGPT surpasses other chatbots in terms of capabilities, drawing significant interest and excitement from both the business community and academic researchers. This study offers a comprehensive review of current research on ChatGPT, delving into its technological foundation, supportive mechanisms, and findings regarding its benefits across various fields and application areas. We evaluate and discuss the strengths and weaknesses of ChatGPT based on this review and suggest potential avenues for future research.

Hong Kong is the biggest consumer of eggs in the world and ranks on the top with an average consumption of 24.52kg/capita/year. The egg consumption trend in Hong Kong has been increasing specifically; a drastic increase of about 100% has been observed from 2008 to 2020. One of the reasons behind this significant increase is the increase in the gross national income of Hong Kong in addition to the population growth rate. However, there was a slight decline in egg consumption trend in the year 2020 amid the COVID-19 pandemic. It has been estimated that there was a bulk of 17,100 tons of recoverable waste eggshells in 2019 (pre-COVID-19 situation), having a daily yield of 47 tons per day. Based on the linear regression, it may be expected that if the pre-COVID-19 trend of egg consumption remains sustained, there will be a big bulk of more than 45,000 tons per year and up to 125 tons per day of waste eggshells till 2050. In addition, a public survey was conducted across the city targeting individuals, families, and restaurants majorly on egg consumption and reuse. The survey outcomes revealed that Chinese eggs have an extensive market in Hong Kong, and people have a positive attitude towards eggshell recycling. Also, a massive population disposes of the waste eggshells while only a trace population reuses them. Therefore, Hong Kong has a very high potential to improve the circular economy by reusing waste eggshells on a commercial level in cementitious products.

Most historical buildings and structures in Oman were built using unreinforced stone masonry. These structures have deteriorated due to ageing of materials, environmental degradation, and lack of maintenance. This research investigates the physical, chemical, and mechanical properties of local building materials and the results of an experimental study on the out-of-plane bending effectiveness of an innovative strengthening technique applied to existing masonry walls. The technique consists of the application on one face of the walls of a basalt textile-reinforced sarooj mortar (TRM). Four-point bending tests of full-scale masonry samples (1000 mm width, 2000 mm height, and 350 mm depth) were carried out using one unreinforced specimen and three different cases of reinforced specimens. The performance of unreinforced and reinforced specimens was analyzed and compared. The strengthened specimens were able to resist moments of out-of-plane bending 2.5 to 3 times greater than those of unreinforced specimen (160%- 233% increase). Moreover, the strengthened walls were able to sustain higher deformations (deflections) than the unreinforced specimen ranging from 20%-130%. The results showed that using TRM was effective for the out-of-plane strengthening of stone masonry using a local material (sarooj) that is compatible with existing stone masonry building materials.

The global COVID-19 pandemic has affected all spheres of human life, resulting in millions of deaths and overwhelming medical facilities. Moreover, the world has witnessed great financial hardship because of job losses resulting in economic havoc. Many sections of society have contributed in different ways to slow the spread of the virus and protect public health. For example, medical scientists are praised for their efforts to develop COVID-19 vaccines. Clinical trials have shown that the COVID-19 vaccines are highly effective in preventing symptomatic COVID-19 infections. However, many people around the world have been hesitant to get vaccinated. Vaccine misconceptions have emerged and increased due to a combination of factors, including the availability of information on the Internet and the influence of celebrities and opinion leaders. In this context, we have analyzed ChatGPT responses to relevant queries on vaccine misconceptions. The positive responses and supportive opinions provided by the AI chatbot could be instrumental in shaping people’s perceptions of vaccines and in encouraging users to get vaccinated and reduce misconceptions.

The primary distribution systems are comprised of power lines delivering power to utility feeders from substations. The inspection and maintenance of damaged and broken power system insulators are of paramount importance for continuous power supply and public safety. hence, to identify any faults and defects in advance a periodic inspection of power line insulators and other components be ensured beforehand. To automate the process and reduce operational cost and risk Unmanned Aerial Vehicles (UAVs) are being extensively utilized. As they present a safer and efficient way to examine the power system insulators and their components without closing the power distribution system ensuring continuous supply to the end-users. To achieve these objectives in this work a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. Deep Laplacian pyramids based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low light images a low light image enhancement technique is used for the robust exposure correction of the training images. Using computer vision-based object detection techniques to identify faults and classify them according to classes they belong. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. To improve the faults detection several flight path strategies are proposed for efficient inspection. Such strategies overcome the shuttering effect of insulators along with providing a less complex, time, and energy-efficient approach for capturing video stream of the power system components. Performance of different object detection models is presented for selecting the suitable one for fine-tuning on the specific faulty insulator dataset. Our proposed approach gives a less complex and more efficient flight strategy along with better results. For defect detection, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust faults recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat Pakistan.

There is a lack of knowledge concerning the effects of ambient heat exposure on morbidity in Northern Europe. Therefore, this study aimed to evaluate the relationships of daily summer-time temperature and heatwaves with cardiorespiratory hospital admissions in the Helsinki metropolitan area, Finland. Methods: Time-series models adjusted for potential confounders such as air pollution were used to investigate the associations of daily temperature and heatwaves with cause-specific cardiorespiratory hospital admissions, during summer months of 2001-2017. Daily number of hospitalizations was obtained from the national hospital discharge register, weather information from the Finnish Meteorological Institute. Results: Increased daily temperature was associated with decreased risk of total respiratory hospital admissions and asthma. Heatwave days were associated with 20.5% (95% CI: 6.9, 35.9) increased risk of pneumonia admissions and during long or intense heatwaves also with total respiratory admissions in the oldest age group (≥ 75 years). There were also suggestive positive associations between heatwave days and admissions due to myocardial infarction and cerebrovascular diseases. In contrast, risk of arrhythmia admissions was decreased 20.8% (95% CI: 8.0, 31.8) during heatwaves. Conclusions: Heatwaves, rather than single hot days, are a health threat affecting the morbidity even in a Northern climate.

In this report we have discussed the important role of molecular modeling, especially the use of the molecular dynamics method, in investigating transport processes in nanoporous materials such as membranes. With the availability of high performance computers, molecular modeling can now be used to study rather complex systems at a fraction of the cost or time requirements of experimental studies. Molecular modeling techniques have the advantage of being able to access spatial and temporal resolution which are difficult to reach in experimental studies. For example, sub-Angstrom level spatial resolution is very accessible as is sub-femtosecond temporal resolution. Due to these advantages, simulation can play two important roles: Firstly because of the increased spatial and temporal resolution, it can help understand phenomena not well understood. As an example, we discuss the study of reverse osmosis processes. Before simulations were used it was thought the separation of water from salt was purely a coulombic phenomenon. However, by applying molecular simulation techniques, it was clearly demonstrated that the solvation of ions made the separation in effect a steric separation and it was the flux which was strongly affected by the coulombic interactions between water and the membrane surface. Additionally, because of their relatively low cost and quick turnaround (by using multiple processor systems now increasingly available) simulations can be a useful screening tool to identify membranes for a potential application. To this end, we have described our studies in determining the most suitable zeolite membrane for redox flow battery applications. As computing facilities become more widely available and new computational methods are developed, we believe molecular modeling will become a key tool in the study of transport processes in nanoporous materials.

Precise association of serum leptin with body mass index (BMI) and blood pressure (BP; both systolic blood pressure (SBP) and diastolic blood pressure (DBP) is not well known for understanding their involvement in health and disease. Hence, the present study was planned to be conducted for investigating the association of BP, BMI and serum leptin levels in young normal weight (NW) and overweight (OW) male Saudi students. The NW (n: 198) and OW male subjects (n: 192) in the age range of 18-20 years were consulted. Blood pressure (BP) was measured with a mercury sphygmomanometer. Leptin Human ELISA Kits were employed for the determination of the serum leptin levels. The mean ± SD values of BMI (kg/m2), serum leptin (ng/ml), SBP (mmHg), and DBP (mmHg) all showed significant difference for young OW vs. NW male university students as: 27.52± 1.42 vs. 21.49± 2.03; 10.70± 4.67 vs. 4.68± 1.91; 121.37±2.59 vs.118.51±1.54 and 81.44±1.97 vs. 78.79±1.44 respectively. All associations (among BMI, leptin, SBP and DBP) showed positive linear and significant correlation except nonsignificant correlation of BMI and SBP for NW group. The present study in the young Saudi male students, presents significant variations for BP and serum leptin levels and significant positive linear association among serum leptin, BMI and BP.

This study aims at the sustainable valorization of eggshells (ES) from different countries of origin in cement concrete as a cement replacement for Hong Kong. For this purpose, sixteen different eggs from different regions across the world were selected from the local market. Firstly, the extent of uniformity in the weight and mineral content of sampled eggshells were assessed by a specific gravity test and thermogravimetric analysis (TGA), respectively. It was found that specific gravity varies between 2.02 and 2.39, with an average value of 2.20 ± 0.01, whereas CaCO₃ varies between 94.65% and 97.23%, with an average value of 96.33%. These values were a bit lower than those of extra-pure limestone (LS) because of the organic part and the porous structure of eggshells. Secondly, three different eggshells were selected for cement replacement in cement concrete, both in the uncalcined and calcined states, based on having the highest, medium, and lowest CaCO₃ content, respectively. To get the calcined eggshells, the eggshells were calcined at 800^oC for three hours. It was found that the strength of eggshells varies with CaCO₃, but the variation was acceptable. The calcined eggshells showed comparatively more compressive strength and were close to the LS because of the free CaO and the absence of an organic part. In general, the variation in the basic properties of eggshells from different regions across the world is negligible and suitable to use as a cement replacement with acceptable variation in strength in Hong Kong.

Deep Neural Network (DNN) inference demands substantial computing power, resulting in significant energy consumption. A large number of negative output activations in convolution layers are rendered zero due to the invocation of the ReLU activation function. This results in a substantial number of unnecessary computations that consume significant amounts of energy. This paper presents ECHO: Energy-efficient Computation Harnessing Onilne Arithmetic - A MSDF-based accelerator for DNN inference, designed for computation pruning, utilizing an unconventional arithmetic paradigm known as online/ most-significant digit first (MSDF) arithmetic which performs computations in a digit-serial manner. The MSDF digital serial computation of online arithmetic enables overlapped computation of successive operations leading to substantial performance improvements. The online arithmetic, coupled with a negative output detection scheme, facilitates early and precise recognition of negative outputs. This, in turn, allows for the timely termination of unnecessary computations, resulting in a reduction of energy consumption. The implemented design has been realized on the Xilinx Virtex-7 VU3P FPGA and subjected to a comprehensive evaluation through a rigorous comparative analysis involving widely used performance metrics. Experimental results demonstrate promising power and throughput improvements compared to contemporary methods. In particular, the proposed design achieved an average improvement in power consumption of up to 81%, 82.9%, and 40.6% for VGG-16, ResNet-18, and ResNet-50 workloads compared to the conventional bit-serial design, respectively. Furthermore, significant average speedups of 2.39×, 2.6×, and 2.42 were observed when comparing the proposed design to conventional bit-serial designs for VGG-16, ResNet-18, and ResNet-50 models respectively.

During December 2019, a novel coronavirus named as 2019-nCoV, has emerged in Wuhan, China. The human to human transmission of this virus has also been established. Untill now the virus has infected more than seven thousand people and has spread to fifteen countries. The World Health Organization (WHO) has declared 2019-nCoV as global health emergency due to its outburst well beyond China. There is need to develop some vaccines or therapeutics to control or prevent 2019-nCoV infections. The bottleneck with current conventional approaches is that these require longer time for vaccine development. However, computer assisted approaches help us to produce effective vaccine in short time compared with conventional methods. In this study, bioinformatics analysis was used to predict B cell and T cell epitopes of surface glycoprotein of 2019-nCoV that could be suitable to trigger significant immune response. The sequence of surface glycoprotein was collected from the database and analyzed to identify the immunogenic epitope. Both B cell and T cell epitopes were analyzed so the predicted epitopes can stimulate both cellular and humoral immune responses. We predicted 13 B cell and 05 T cell epitopes that later on were joined with GPGPG linker to make a single peptide. This computational approach to design a multi epitope peptide vaccine against emerging 2019-nCoV allows us to find novel immunogenic epitopes against the antigen targets of surface 2019-nCoV surface glycoprotein. This multi epitope peptide vaccine may prove effective to combat 2019-nCoV infections.

This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By analyzing data from the Scopus database, 533 relevant articles were identified for analysis. The findings reveal the prominent publication venues, influential authors, and countries contributing to ChatGPT research. Collaborative networks among researchers and institutions are visualized, highlighting patterns of co-authorship. The application domains of ChatGPT, such as customer support and content generation, are examined. Moreover, the study identifies emerging keywords and potential research areas for future exploration. The methodology employed includes data extraction, bibliometric analysis using various indicators, and visualization techniques such as Sankey diagrams. The analysis provides valuable insights into ChatGPT's influence in academia and offers researchers guidance for further advancements. This study stimulates discussions, collaborations and innovations to enhance ChatGPT's capabilities and impact across domains.

Hypertension (HT) is a very common risk factor and a frequent cause of morbidity and mortality worldwide mainly due to much increase in overweight (OW) and obesity that manifests in various forms of cardiovascular disease (CVD). There are a number of factors that may cause overweight hypertension (OHT). However, we planned to conduct the present work to study the HT and OHT men for finding the variation, interrelationship and association of important cytokines mainly serum leptin (Lep) and tumor necrosis factor-alpha (TNF-α), serum testosterone (ST), total cholesterol (TC) and inflammatory status by measuring interleukin-6 (IL-6). Three groups of subjects, the normal controls (NC; n:98; age range: 51-60 years), HT (n: 97; age range: 51-60 years) subjects, and OHT (n: 97; age range: 51-60 years) subjects were studied. Comparisons for Lep, ST, TNF-α, IL-6 and TC levels for the subject groups showed significant variations. The ST indicated significant difference only for NC vs. OHT. The IL-6 gave significant difference for NC vs. OHT and HT vs. OHT. The one-way ANOVA showed significant difference among groups for BMI, TC, IL-6, TNF-α, ST and Lep levels. Significant positive linear correlation for all groups among Lep, TNF-α, IL-6 and TC were found. The ST against TC for HT and OHT groups presented non-significant negative linear correlations. The ST showed significant negative linear correlation with other variables. Conclusively, the present study uncovers the interactive impact of serum leptin, TNF-α, testosterone, total cholesterol and IL-6 in hypertensive and overweight-related hypertensive men.

Cerebrospinal fluid contacting neurons (CSF-cNs) are a specific type of neurons located around the ventricles in the brain and the central canal in the spinal cord and have been demonstrated to be intrinsic sensory neurons in the central nervous system (CNS). One of the important channels responsible for the sensory function is polycystic kidney disease 2-like 1 (PKD2L1) channel. Most of the studies concerning the distribution and function of the PKD2L1-expressing CSF-cNs in the spinal cord have previously been performed in non-mammalian vertebrates. In the present study immunohistochemistry was performed to determine the distribution of PKD2L1-immunoreactive (IR) CSF-cNs in the spinal cords of 4 mammalian species: mouse, rat, cat, and macaque monkey. Here, we found that PKD2L1-expressing CSF-cNs were present at all levels of the spinal cord in these animal species. Although the distribution pattern was similar across these species, differences existed. Mice and rats presented a clear PKD2L1-IR cell body labeling, whereas in cats and macaques the PKD2L1-IR cell bodies were more weakly labeled. Ectopic PKD2L1-IR neurons away from the ependymal layer were observed in all the animal species although the abundance and the detailed locations varied. The apical dendritic protrusions towards the lumen of the central canal were clearly seen in all the animal species, but the sizes of protrusion bulbs were different among the species. PKD2L1-IR cell bodies/dendrites were co-expressed with doublecortin, MAP2 (microtubule-associated protein 2) and aromatic L-amino acid decarboxylase, but not with NeuN (neuronal nuclear protein), indicating their immature property and ability to synthesis monoamine transmitters. In addition, in-situ hybridization performed in rats revealed PKD2L1 mRNA expression in the cells around the central canal. Our results indicate that the intrinsic sensory neurons are conserved across non-mammalian and mammalian vertebrates, and that this may play an essential role in the regulation of motor and sensory functions as well as in the maintenance of homeostasis in the CNS.

Introduction: Exams are a relatively stressful period for all students, especially undergraduate medical students. Exams bring anxiety and stress for the students. Some students experience such high stress that it hinders their academic productivity and reduces their exam performance. The aim of this study is to assess the effects of pre-exam anxiety on the academic performance of medical students. Methods: Two hundred and twenty-five final year medical students completed Westside Test Anxiety (WTA) Scale one month before their exams. Grade Point Average (GPA) of these students was noted when the results were announced. Data was processed and analyzed using SPSS v 22.0Results: The mean anxiety score on WTA scale was 3.46 ± 0.87. All students (100%) who scored ≤ 2.50 GPA were highly-extremely highly anxious. In the 2.51-3.00 GPA group, 46% were highly-extremely highly anxious, 32% were moderately anxious, and 21% had low to normal anxiety. In the 3.01-3.50 GPA group, 30% were highly-extremely highly anxious, 30% were moderately anxious, and 39% had low to normal anxiety. In the 3.51-4.00 GPA group, 29% were highly-extremely highly anxious, 23% were moderately anxious, and 47% had low to normal anxiety. The correlation coefficient between GPA and test anxiety of students was -.314 which shows inverse relationship.Conclusion: Pre-exam anxiety and stress imparts negative effects on the exam performance of final year medical students. Poor academic performance was associated with high to extremely high pre-exam anxiety while high achievers had relatively lower anxiety levels.

During December 2019, a novel coronavirus named SARS-CoV-2 has emerged in Wuhan, China. The human to human transmission of this virus has also been established. The virus has so far infected more than 2 million people and spread over 200 countries. The World Health Organization (WHO) has declared COVID-19 a global health emergency due to its spread well beyond China. It has been established that this virus originates from bats and uses an intermediate host for transfer to humans. The knowledge about the intermediate host is important to find the virus shuttle mechanism to stop future outbreaks. For this, the genetic and structural analysis of coronaviruses spike proteins was performed using a computer-assisted approach.To conduct the In silico analysis, 43 sequences of spike protein belong to different species were retrieved from the NCBI nucleotide database. Pairwise and multiple sequence alignments were performed to check the similarities and differences of the retrieved sequences. Moreover, to highlight relationships among different species, phylogenetics analysis was performed using the MEGA software tool. In the end, protein structure alignment (superimposition) was performed against the reference structure by UCSF Chimera software. The results highlighted that the maximum similarity of human protein was found against Bat and Pangolinsequences. Moreover, among Bat and Pangolin, the highest similarity was found against pangolin based on phylogenetics analysis. These results suggest that SARS-CoV-2 transfers from bats to humans through pangolins.

Introduction: COVID-19 pandemic has affected HCPs in multiple way. It has caused psychological impact in form of anxiety, depression, and insomnia. In this study, we aim to study and compare the stress level, anxiety and depression among HCPs who are posted in special COVID-19 units with the HCPs who are not posted in COVID-19 units.Methods: This cross-sectional study was conducted in June 2020, at various hospitals of Karachi, Pakistan. All health care professionals (HCPs) were invited to participate. A total of 301 HCPs completed this study, who were divided into two groups; those who are posted in COVID-19 ward (Group A) and those who are not (Group B). Psychological Impact was English version of the Depression Anxiety Stress Scale - 21 (DASS-21).Results: In Group A, 70.5% had moderate, severe, or extremely severe depression compared to 49.2% in group B. In Group A, 75.4% had moderate, severe, or extremely severe anxiety compared to 44.7% in group B. In Group A, 80.3% had moderate, severe or extremely severe stress compared to 54.2% in group B. Anxiety, depression and stress were significantly higher in HCPs who were posted in COVID-19 ward compared to those who were not posted in COVID-19 wardConclusion: There was significantly higher anxiety, stress and depression in health care professionals posted in COVID-19 ward. Both the government and health care agencies should take responsibility for protecting the psychological well-being of health care communities all over the world and ensuring a healthy work environment.

Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

Background. Type 2 diabetes mellitus (T2DM) is becoming a major global health concern, especially in poorer nations. The high prevalence of obesity and the ensuing diabetes is attributed to rapid economic progress, physical inactivity, consumption of high-calorie foods, and changing lifestyles. Objectives. We investigated the role of pro-inflammatory chemokines; CCL1, 2, 4 and 5 with varying levels of obesity in T2DM in the Asir region of Saudi Arabia. Materials and methods. 170 confirmed T2DM patients and a normal control group were enrolled. The demographic data, serum levels CCL-1, 2, 4 and 5 and the biochemical indices were assessed in patients and control groups by standard procedures. Results. T2DM patients were divided into four groups: A (normal body weight), B (Overweight), C (obese), and D (highly obese). n controls. We observed that male and female control patients had similar mean serum concentrations of pro-inflammatory chemokines CCL 1, 2, 4, and 5. Chemokines CCL1, CCL2, and CCL4 in the serum of Type 2 Diabetes Mellitus patients with normal or overweight body weights were significantly higher than the control group, regardless of gender. In T2DM individuals with obesity and severe obesity, the rise was most significant. There was a progressive rise in the concentrations of CCL1, 2, and 4 in T2DM) patients with increasing BMI. Serum CCL5 levels increased significantly in all T2DM patient groups. The increase in CCL5 was more predominant in normal-weight people as compared to overweight and obese T2DM patients. Conclusions. Male and female control patients had similar serum levels of pro-inflammatory chemokines CCL 1, 2, 4, and 5. The progressive rise in blood concentrations of three Pro-inflammatory chemokines; CCL1, 2, and 4 in T2DM patients with increasing BMI supports the idea that dyslipidemia and obesity contribute to chronic inflammation and insulin resistance. Serum CCL5 levels increased significantly in all T2DM patient groups. The selective and more pronounced increase of CCL5 in the T2DM group with normal BMI as compared to patients with varying degrees of obesity, was rather surprising. Further research is needed to determine if CCL5 under-expression in overweight and obese T2DM patients is due to some unexplained counterbalancing processes.

Background. Type 2 diabetes mellitus (T2DM) is becoming a major global health concern, especially in poorer nations. The high prevalence of obesity and the ensuing diabetes I attributed to rapid economic progress, physical inactivity, consumption of high-calorie foods, and changing lifestyles. Objectives. We investigated the role of interleukins-10 , 19 and 22 with varying levels of obesity in T2DM in the Asir region of Saudi Arabia. Materials and methods. 170 confirmed T2DM patients and a control group were enrolled. The demographic data, serum levels of IL-10, IL-19, and IL-22, and biochemical indices were assessed in patients and control groups by standard procedures. Results. T2DM patients were divided into four groups: A (normal body weight), B (Overweight), C (obese), and D (highly obese). Both male and female T2DM subjects in Group A showed significant decreases in IL-10 levels as compared to controls; however, there were insignificant changes in IL-10 levels in Groups C and D. T2DM patients in groups C and D, in both males and females, depicted very significant (p 0.001) increases in IL-19 levels as compared to controls and group A. Patient groups A to D displayed a progressive elevation of Il-22 levels irrespective of gender, although significant alterations were seen only in groups B to D, with p 0.05 for group B and p 0.01 for groups C and D respectively, as compared to healthy controls. Conclusions. IL-10 showed a strong relationship with T2DM in males with varying degrees of obesity, but females depicted relatively higher IL-10 levels in obese and highly obese groups, pointing to a protective phenomenon. IL-19 levels showed significant increases in all four groups, irrespective of gender. IL-22 appears to be unrelated to T2DM per se but shows an association with varying degrees of obesity.

Four new zoonotic coronaviruses outbreak occurred in the past two decades. The first coronavirus outbreak happened in 2003 and was named Severe Acute Respiratory Syndrome Coronavirus-1 (SARS-CoV-1), followed by Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in 2013, and by Swine Acute Diarrhoea Syndrome (SADS) in 2017. In late December 2019, a patient from Wuhan Seafood Wholesale Market, China, was admitted to Huang's hospital with respiratory distress syndrome (RDS) of apparently unknown etiology, which later was diagnosed with SARS-CoV-2-induced coronavirus disease-19 (COVID-19). SARS-CoV-2 was identified from another clade within the subgenus Sarbecovirus of the genus Betacoronavirus. This new CoV variant showed 96.2% similarity at whole-genome level to bat coronavirus. Based on genetic studies, pangolin, bats, and snakes are suspected to be a possible intermediate host of this virus, but further research is in progress to determine how this virus appeared and what is the exact source of infection. The SARS-CoV-2 outbreak has higher transmissibility, contagiosity and pathogenicity compared to that of SARS-CoV-1 and MERS-CoV. SARS-CoV-2 swiftly spread from continent to continent and is persistently causing a shocking global public health concern albeit the rate of infected cases and mortality is relatively decreasing over time. In this review, we provide an update on the origin, transmissibility, etiology, epidemiology, and evolution of COVID-19. We also highlight the current diagnostics, therapies, and prognostics for SARS-CoV-2. Eventually, we report and provide our humble opinion about the preventive strategies to control the ongoing related pandemic situation at global and individual levels, considering the international public health emergency and possible future epidemics and pandemics.