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The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings. [ABSTRACT FROM AUTHOR]

The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population. [ABSTRACT FROM AUTHOR]

Economic interventions have social consequences. In this paper, we explore one such relationship, between microfinance intensity and social distrust levels reported by the low-income people. We find a significant association between microfinance intensity in a country and distrust among the poor as well as ultra-poor in cross-section using World Values Survey & European Values Survey (WVS-EVS) Wave 7 (2017–2022). We supplement these findings using empirical Bayes on a panel extending back from 7th to the 4th WVS wave (1999–2004). To deal with potential endogeneity, we run 2SLS as well as weak instruments-robust conditional instrumental variable tests and find evidence showing microfinance prevalence intensity affects distrust levels among the poor and ultra-poor households. We find no association between microfinance and distrust levels in the rich in any of the tests, potentially because the rich are not exposed to microfinance. [ABSTRACT FROM AUTHOR]

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset. [ABSTRACT FROM AUTHOR]

Importance: The measurement of laboratory biomarkers plays a critical role in managing patients with COVID-19. However, to date most systematic reviews examining the association between laboratory biomarkers and mortality in hospitalized patients early in the pandemic focused on small sets of biomarkers, did not account for multiple studies including patients within the same institutions during overlapping timeframes, and did not include a significant number of studies conducted in countries other than China. Objective: To provide a comprehensive summary and an evidence map examining the relationship between a wide range of laboratory biomarkers and mortality among patients hospitalized with COVID-19 during the early phase of the pandemic in multiple countries. Evidence review: MEDLINE, EMBASE, and Web of Science were searched from Dec 2019 to March 9, 2021. A total of 14,049 studies were identified and screened independently by two raters; data was extracted by a single rater and verified by a second. Quality was assessed using the Joanna Briggs Institute (JBI) Case Series Critical Appraisal tool. To allow comparison across biomarkers, standardized mean differences (SMD) were used to quantify the relationship between laboratory biomarkers and hospital mortality. Meta-regression was conducted to account for clustering within institutions and countries. Results: Our systematic review included 94 case-series studies from 30 countries. Across all biomarkers, the largest and most precise SMDs were observed for cardiac (troponin (1.03 (95% CI 0.86 to 1.21)), and BNP/NT-proBNP (0.93 (0.52 to 1.34)), inflammatory (IL-6 (0.97 (0.67 to 1.28) and Neutrophil-to-lymphocyte ratio (0.94 (0.59 to 1.29)), and renal biomarkers (blood urea nitrogen (1.01 (0.79 to 1.23)) and estimated glomerular filtration rate (-0.96 (-1.42 to -0.50)). There was heterogeneity for most biomarkers across countries with studies conducted in China generally having larger effect sizes. Conclusions and relevance: The results of this study provide an early pandemic summary of the relationship between biomarkers and mortality in hospitalized patients. We found our estimated ESs were generally attenuated compared to previous systematic reviews which predominantly included studies conducted in China. Despite using sophisticated methodology to examine studies across countries, heterogeneity in reporting of case-series studies early in the pandemic limits clinical interpretability. [ABSTRACT FROM AUTHOR]

The study explores the causal relationship between monetary policy effectiveness and financial inclusion in developed and under-developed countries. Structural Vector Auto-regressive techniques have been inducted to explore the relationship between monetary policy effectiveness and financial inclusion. The study covers the secondary data of 10 developed and 30 underdeveloped countries throughout 2004–2018. It is concluded that monetary policy effectiveness and financial inclusion do not have a contemporaneous impact on each other. Nevertheless, the reduced-form Vector Auto-regressive witness the reverse causality between financial inclusion and monetary policy effectiveness in developed countries. Thus, effective monetary policy enhances financial inclusion in a country, and a higher degree of financial inclusion lowers the inflation rate and makes monetary policy effective. One way causality from monetary policy effectiveness to financial inclusion can be observed in under-developed countries. Using the Structural Vector auto-regressive technique and financial inclusion index composed of three-dimension to examine the relationship of monetary policy effectiveness and financial inclusion in developed and developing countries is considered the study's significant contribution. [ABSTRACT FROM AUTHOR]

Historically, the database search algorithms have been the de facto standard for inferring peptides from mass spectrometry (MS) data. Database search algorithms deduce peptides by transforming theoretical peptides into theoretical spectra and matching them to the experimental spectra. Heuristic similarity-scoring functions are used to match an experimental spectrum to a theoretical spectrum. However, the heuristic nature of the scoring functions and the simple transformation of the peptides into theoretical spectra, along with noisy mass spectra for the less abundant peptides, can introduce a cascade of inaccuracies. In this paper, we design and implement a Deep Cross-Modal Similarity Network called SpeCollate, which overcomes these inaccuracies by learning the similarity function between experimental spectra and peptides directly from the labeled MS data. SpeCollate transforms spectra and peptides into a shared Euclidean subspace by learning fixed size embeddings for both. Our proposed deep-learning network trains on sextuplets of positive and negative examples coupled with our custom-designed SNAP-loss function. Online hardest negative mining is used to select the appropriate negative examples for optimal training performance. We use 4.8 million sextuplets obtained from the NIST and MassIVE peptide libraries to train the network and demonstrate that for closed search, SpeCollate is able to perform better than Crux and MSFragger in terms of the number of peptide-spectrum matches (PSMs) and unique peptides identified under 1% FDR for real-world data. SpeCollate also identifies a large number of peptides not reported by either Crux or MSFragger. To the best of our knowledge, our proposed SpeCollate is the first deep-learning network that can determine the cross-modal similarity between peptides and mass-spectra for MS-based proteomics. We believe SpeCollate is significant progress towards developing machine-learning solutions for MS-based omics data analysis. SpeCollate is available at https://deepspecs.github.io/. [ABSTRACT FROM AUTHOR]

This paper investigates how banking competition and capital level impact on the risk-taking behavior of banking institutions in the Middle East and North Africa (MENA) region. The topic is perceived to be of significant importance during the COVID-19 pandemic. We use data for more than 225 banks in 18 countries in the MENA region to test whether increased competition causes banks to hold higher capital ratios. Employing panel data techniques, and distinguishing between Islamic and conventional banks, we show that banks tend to hold higher capital ratios when operating in a more competitive environment. We also provide evidence that banks in the MENA region increase their capitalization levels in response to a higher risk and vice versa. Further, banking concentration (measured by the HH-index) and credit risk have a significant and positive impact on capital ratios of IBs, whereas competition does play a restrictive role in determining the level of their capital. The results hold when controlling for ownership structure, regulatory and institutional environment, bank-specific and macroeconomic characteristics. Our findings inform regulatory authorities concerned with improving the financial stability of banking sector in the MENA region to strengthen their policies in order to force banks to better align with capital requirements and risk during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The Acknowledgements section is missing in the paper. The correct Acknowledgements is as follows: The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.By Muhammad Usman Tariq; Shuhaida Binti Ismail; Muhammad Babar and Ashir AhmadReported by Author; Author; Author; Author [Extracted from the article]

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model's efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare. [ABSTRACT FROM AUTHOR]

Given the recent trends in the MPPT converters in PV systems, which have been researched extensively to improve design, modified closed-loop converter technology based on SoC is presented here. This paper aims to provide detailed information on the modern-day solar Maximum Power Point Tracking (MPPT) controller and Battery Management System (BMS). Most MPPT controller examination researched in the past is suitable only for fixed-rated battery capacity, which limits the converter capability and applications. The proposed paper uses the distributed energy management control technique to dispatch multi-battery charging based on the State of Charge (SoC). The converter construction is modified here as per the prerequisite of the model. The system hardware is developed and tested using Atmega2560 low power RISC based high-performance microcontroller. The batteries' SoC level and State of Health (SoH) are calculated using embedded sensors and communication platforms through the IoT platform and Global System Monitoring (GSM) technology. The GSM and IoT technology ensure that the different batteries are monitored periodically, and any irregularities are immediately addressed through the distributed energy management control technique. This ensures a safe, reliable, and effective charging of multiple batteries with increased accuracy, thereby maximizing battery life and reducing operational costs. [ABSTRACT FROM AUTHOR]

The intensive search of new and cleaner energy catches interest in recent years due to huge consumption of fossil fuels coupled with the challenge of energy and environmental sustainability. Production of renewable and environmentally benign energy from locally available raw materials is coming in the frontline. In this work, conversion of the combined biomass (cotton gin trash, cow manure, and Microalgae [Nannochloropsis oculata]) through batch pyrolysis has been investigated. The effect of temperature to the production of energy fuels such as bio-oil, char, and biogas have been simulated considering the yield and energy content as responses. Result of the investigation generally revealed that the proportions of the different biomass did not significantly affect the product yield and energy recovery. Significant effect of temperature is evident in the simulation result of energy recovery whereby maximum conversion was achieved at 400°C for char (91 wt%), 600°C for syngas (22 wt%), and 551°C for bio-oil (48 wt%). Overall energy conversion efficiency of 75.5% was obtained at 589°C in which 15.6 MJ/kg of mixed biomass will be elevated to pyrolysis products. [ABSTRACT FROM AUTHOR]

Background: Vitamin D is postulated to decrease the risk of breast cancer by inhibiting cell proliferation via the vitamin D receptor (VDR). Two common single nucleotide polymorphisms (SNPs) in the VDR gene, rs1544410 (BsmI) and rs2228570 (FokI), are inconsistently associated with breast cancer risk in Caucasian populations, while data for Asians are scarce. Here, we investigated the possible contribution of these SNPs to breast cancer risk in Pakistani breast cancer patients and in controls participating in a hospital-based breast cancer case-control study (PAK-BCCC). Methods: Genotyping of the BsmI and FokI SNPs was performed by PCR-based restriction fragment length polymorphism (RFLP) analysis of 463 genetically enriched female breast cancer cases with known BRCA1/2 status and in 1,012 controls from Pakistan. The association between SNP genotypes and breast cancer risk was investigated by logistic regression adjusted for potential breast cancer risk factors and stratified by BRCA1/2 status and family history. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported. Results: The b allele of the BsmI was associated with an increased breast cancer risk (per b allele OR 1.28, 95% CI 1.09–1.49, P = 0.003). Subgroup analysis revealed that this effect was restricted to BRCA1/2 non-carriers (per b allele OR 1.33, 95% CI 1.11–1.59, P = 0.002) and was stronger in those who reported a positive family history of breast and/or ovarian cancer (per b allele OR 1.64, 95% CI 1.20–2.22, P = 0.002). No association with breast cancer risk was detected for the FokI SNP. Conclusions: The BsmI polymorphism in the VDR gene may be associated with an increased breast cancer risk in Pakistani women negative for BRCA1/2 germline mutations. [ABSTRACT FROM AUTHOR]

Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices. [ABSTRACT FROM AUTHOR]

Ivermectin (IVM) is an anti-parasitic drug which is used for treating parasitic infestations. It has been used in humans for treating intestinal strongyloidiasis and onchocerciasis however, currently researchers are investigating its potential for treating coronavirus SARS-CoV-2. Due to its broad-spectrum activities, IVM is being used excessively in animals which has generated an interest for researchers to investigate its toxic effects. Cytotoxic and genotoxic effects have been reported in animals due to excessive usage of IVM. Therefore, this study aims to evaluate the cytotoxic and genotoxic effects of IVM on the Madin-Darby-Bovine-Kidney (MDBK) cell line by examining the expression of a DNA damage-responsive gene (OGG1). Cytotoxicity of IVM was tested using an assay (MTT 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide), whereas the genotoxicity was evaluated using comet assay along with micronucleus assay. Moreover, the gene expression of DNA damage response gene (OGG1) was measured by qRT-PCR, after extraction of RNA from the MDBK cell line using the TRIzol method and its conversion to cDNA by reverse-transcriptase PCR. During the experiment, cell viability percentage was measured at different doses of IVM i.e., 25%, 50%, 75%, along with LC50/2, LC50 and LC50*2. It was observed that the gene expression of OGG1 increased as the concentration of IVM increased. It was concluded that IVM has both cytotoxic and genotoxic effects on the MDBK cell line. Furthermore, it is recommended that studies related to the toxic effects of IVM at molecular level and on other model organisms should be conducted to combat its hazardous effects. [ABSTRACT FROM AUTHOR]

The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients. A new research direction is gaining increasing attention that involves the use of hybrid approaches based on natural language processing and computer vision techniques to create auto medical report generation systems. The auto report generator, producing radiology reports, will significantly reduce the burden on doctors and assist them in writing manual reports. Because the sensitivity of chest X-ray (CXR) findings provided by existing techniques not adequately accurate, producing comprehensive explanations for medical photographs remains a difficult task. A novel approach to address this issue was proposed, based on the continuous integration of convolutional neural networks and long short-term memory for detecting diseases, followed by the attention mechanism for sequence generation based on these diseases. Experimental results obtained by using the Indiana University CXR and MIMIC-CXR datasets showed that the proposed model attained the current state-of-the-art efficiency as opposed to other solutions of the baseline. BLEU-1, BLEU-2, BLEU-3, and BLEU-4 were used as the evaluation metrics. [ABSTRACT FROM AUTHOR]

China-Pakistan Economic Corridor (CPEC) has become a "game-changer" not only for policymakers but also for common citizens of China and Pakistan because of its potential benefits in the economic prosperity and sustainable development in the lives of individuals. Recently, scholars have shown a great interest in researching the eminence of CPEC. Despite prodigious efforts of scholars, the question "how CPEC would influence living standards of rural women and what they perceive?" has been remained unanswered. The present study is the first attempt to unearth the perceptions of rural areas' women towards CPEC. We collected empirical evidence through a structured questionnaire from 302 educated rural women and interviewing 32 uneducated rural women. The following major conclusions are drawn through structural equation modeling via AMOS 21: the rural women significantly directly perceive new opportunities through CPEC; however, they perceive that the CPEC would not directly influence their quality of life and self-enhancement, but through the development of these rural areas. Rural development partially mediates the relationship between CPEC development and perceived opportunities while it fully mediates the relationships between CPEC development and quality of life as well as between CPEC development and self-enhancement. Policymakers need to emphasize on development of rural areas that would improve living standards of poor communities. The government needs to meet the expectations of poor communities and rural women to ensure their sustainable development. [ABSTRACT FROM AUTHOR]

A salinity gradient solar pond (SGSP) is capable of storing a significant quantity of heat for an extended period of time. It is a great option for providing hot water at a reduced energy cost. Additionally, SGSP is used in low-temperature industrial applications such as saltwater desalination, space heating, and power generation. Solar pond thermal performance is dependent on a variety of operational variables, including the soil conditions, the climate of the particular site, the thickness of the solar pond layers, the depth of the water table, and the salt content of the pond. As such, this study examines the thermal performance of a solar pond under a variety of operational conditions. The solar pond model is used to test the thermal performance by simulating two-dimensional heat and mass transport equations. The equations are solved using the finite difference technique utilizing MATLAB® scripts. Salt distributions and temperature profiles are computed for a variety of factors influencing SGSP's thermal performance. The main distinguishing variables influencing the thermal performance of SGSP are soil conditions, such as soil texture, types, the moisture level in soil, and water table depth. The final findings indicated that the fine sand dry soil performed better than the other soil types owing to its poor heat conductivity. The economic results indicated that the period of return (POR) of the intended system is around 2 years. The solar pond construction costs such as excavation, transportation, salt and lining, were considered based on the local prices. This modeled study extracted the greatest possible energy is 110W/m2, with the fine sand dry at 62.48°C lowest temperature. This study suggested that the climatic conditions of Lahore is better than climatic conditions of Islamabad. Additionally, deeper water tables are suggested for improved thermal performance of the pond. [ABSTRACT FROM AUTHOR]

To explore the potential role of polymers in the development of drug-delivery systems, this study investigated the use of β-cyclodextrin (β-CD), carboxymethyl cellulose (CMC), acrylic acid (AA) and N’ N’-methylenebis-acrylamide (MBA) in the synthesis of hydrogels for controlled drug delivery of acyclovir (ACV). Different proportions of β-CD, CMC, AA and MBA were blended with each other to fabricate hydrogels via free radical polymerization technique. Fourier transform infrared spectroscopy (FTIR) revealed successful grafting of components into the polymeric network. Thermal and morphological characterization confirmed the formation of thermodynamically stable hydrogels having porous structure. The pH-responsive behaviour of hydrogels has been documented by swelling dynamics and drug release behaviour in simulated gastrointestinal fluids. Drug release kinetics revealed controlled release behaviour of the antiviral drug acyclovir in developed polymeric network. Cross-linked β-cyclodextrin and carboxymethyl cellulose hydrogels can be used as promising candidates for the design and development of controlled drug-delivery systems. [ABSTRACT FROM AUTHOR]

One of the renewable energy resources, wind energy is widely used due to its wide distribution, large reserves, green and clean energy, and it is also an important part of large-scale grid integration. However, wind power has strong randomness, volatility, anti-peaking characteristics, and the problem of low wind power prediction accuracy, which brings serious challenges to the power system. Based on the difference of power prediction error and confidence interval between different new energy power stations, an optimal control strategy for active power of wind farms was proposed. Therefore, we focus on solving the problem of wind power forecasting and improving the accuracy of wind power prediction. Due to the prediction error of wind power generation, the power control cannot meet the control target. An optimal control strategy for active power of wind farms is proposed based on the difference in power prediction error and confidence interval between different new energy power stations. The strategy used historical data to evaluate the prediction error distribution and confidence interval of wind power. We use confidence interval constraints to create a wind power active optimization model that realize active power distribution and complementary prediction errors among wind farms with asymmetric error distribution. Combined with the actual data of a domestic (Cox's Bazar, Bangladesh) wind power base, a simulation example is designed to verify the rationality and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Drought stress is a major limitation in wheat production around the globe. Organic amendments could be the possible option in semi-arid climatic conditions to mitigate the adverse effects of drought at critical growth stages. Wheat straw biochar (BC0 = Control, BC1 = 3% biochar and BC2 = 5% biochar) was used to alleviate the drought stress at tillering (DTS), flowering (DFS), and grain filling (DGFS) stages. Drought stress significantly reduced the growth and yield of wheat at critical growth stages, with DGFS being the most susceptible stage, resulting in significant yield loss. Biochar application substantially reduced the detrimental effects of drought by improving plant height (15.74%), fertile tiller count (17.14%), spike length (16.61%), grains per spike (13.89%), thousand grain weight (10.4%), and biological yield (13.1%) when compared with the control treatment. Furthermore, physiological parameters such as water use efficiency (38.41%), stomatal conductance (42.76%), chlorophyll a (19.3%), chlorophyll b (22.24%), transpiration rate (39.17%), photosynthetic rate (24.86%), electrolyte leakage (-42.5%) hydrogen peroxide (-18.03%) superoxide dismutase (24.66%), catalase (24.11%) and peroxidase (-13.14%) were also improved by biochar application. The use of principal component analysis linked disparate scales of our findings to explain the changes occurred in wheat growth and yield in response to biochar application under drought circumstances. In essence, using biochar at 5% rate could be a successful strategy to promote wheat grain production by reducing the hazardous impacts of drought stress. [ABSTRACT FROM AUTHOR]

Background: Amongst the pre-analytical, analytical, and post-analytical phase of laboratory testing, pre-analytical phase is the most error-prone. Knowledge gaps in understanding of pre-analytical factors are identified in the clinical years amongst undergraduate students due to lack of formal teaching modules on the pre-analytical phase. This study was conducted to seek experts' consensus in Clinical Chemistry on learning objectives and contents using the Delphi technique with an aim to develop an asynchronous virtual classroom for teaching pre-analytical factors of laboratory testing. Methods: A mixed method study was conducted at the Aga Khan University. A questionnaire comprising of 16 learning objectives and their associated triggers was developed on Google Docs for developing the case vignettes. A four-point Likert Scale, which included strongly agree, agree, disagree and strongly disagree, was utilized for the learning objectives. An open-ended question was included for experts to suggest new items for inclusion. A cut off of at least 75% agreement was set to establish consensus on each item. A total of 17 Chemical Pathology faculty from 13 institutions across Pakistan were invited to participate in the first round of Delphi. Similar method of response was used in round two to establish consensus on the newly identified items suggested by the faculty in round 1. Later, the agreed-upon objectives and triggers were used to develop interactive scenarios over Moodle to concurrently test and teach medical students in a nonchalant manner. Results: A total of 17 responses were received in Round 1 of the Delphi process (response rate = 100%), while 12 responses were received in Round 2 (response rate = 71%). In round 1, all 16 learning objectives reached the required consensus (≥ 75%) with no additional learning objectives suggested by the experts. Out of 75 triggers in round 1, 61 (81.3%) reached the consensus to be included while 39 were additionally suggested. In 2nd round, 17 out of 39 newly suggested triggers met the desired consensus. 14 triggers did not reach the consensus after two rounds, and were therefore eliminated. The virtual classroom developed using the agreed-upon learning objectives and triggers consisted of 20 items with a total score of 31 marks. The questions included multiple choice questions, fill in the blanks, drag and drop sequences and read-and-answer comprehensions. Specific learning points were included after each item and graphs and pictures were included for a vibrant experience. Conclusion: We developed an effective and interactive virtual session with expert consensus on the pre-analytical phase of laboratory testing for undergraduate medical students which can be used for medical technologist, graduate students and fellows in Chemical Pathology. [ABSTRACT FROM AUTHOR]

The major goal of this project was to formulate iodine-based self nano-emulsifying drug delivery system to provide improve antimicrobial activity and enhanced mucosal residence time via mucus penetration. Iodine SNEDDS (Self nano-emulsifying drug delivery system) with different concentration were formulated using castor oil as the oil phase, cremophor ethoxylated (CrEL) as a surfactant and after screening a number of vehicles, PEG 400 was employed as co-surfactant. Self-emulsification time, thermodynamic stability tests, robustness to dilution, percent transmittance, droplet size, and drug release were measured. Ternary phase diagrams were plotted to determine the area of emulsification. When compared to the commercial formulation, dissolving experiments revealed that the iodine from the SNEDDS enhanced aqueous solubility. In-vitro iodine release was determined to be around 15% per hour, with muco-adhesive and, muco-penetrating characteristics showing a 38-fold improvement. Furthermore, SNEDDS demonstrated significant antibacterial efficacy against Escherichia coli and Staphylococcus aureus. Similarly, when compared to marketed drugs, in-vitro drug absorption profile from the manufactured SNEDDS shown to be much higher. According to these results iodine containing SNEDDS could be a useful new formulation for iodine mucosal usage. [ABSTRACT FROM AUTHOR]

The amount of soil contaminated with heavy metal increases due to urbanization, industrialization, and anthropogenic activities. Quinoa is considered a useful candidate in the remediation of such soil. In this pot experiment, the phytoextraction capacity of quinoa lines (A1, A2, A7, and A9) against different nickel (Ni) concentrations (0, 50, and 100 mg kg-1) were investigated. Required Ni concentrations were developed in polythene bags filled with sandy loam soil using nickel nitrate salt prior to two months of sowing and kept sealed up to sowing. Results showed that translocation of Ni increased from roots to shoots with an increase in soil Ni concentration in all lines. A2 line accumulated high Ni in leaf compared to the root as depicted by translocation factor 3.09 and 3.21 when grown at soil having 50 and 100 Ni mg kg-1, respectively. While, in the case of root, A7 accumulated high Ni followed by A9, A1, and A2, respectively. There was a 5–7% increased seed yield by 50 mg kg-1 Ni in all except A1 compared to control. However, growth and yield declined with a further increase in Ni level. The maximum reduction in yield was noticed in A9, which was strongly linked with poor physiological performance, e.g., chlorophyll a, b, and phenolic contents. Ni concentrations in the seed of all lines were within the permissible value set (67 ppm) by FAO/WHO. The result of the present study suggests that quinoa is a better accumulator of Ni. This species can provide the scope of decontamination of heavy metal polluted soil. The screened line can be used for future quinoa breeding programs for bioremediation and phytoextraction purpose. [ABSTRACT FROM AUTHOR]

Studying the progress and trend of the novel coronavirus pneumonia (COVID-19) transmission mode will help effectively curb its spread. Some commonly used infectious disease prediction models are introduced. The hybrid model is proposed, which overcomes the disadvantages of the logistic model's inability to predict the number of confirmed diagnoses and the drawbacks of too many tuning parameters of the SEIR (Susceptible, Exposed, Infectious, Recovered) model. The realization and superiority of the prediction of the proposed model are proven through experiments. At the same time, the influence of different initial values of the parameters that need to be debugged on the hybrid model is further studied, and the mean error is used to quantify the prediction effect. By forecasting epidemic size and peak time and simulating the effects of public health interventions, this paper aims to clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviours are critical to slow down the epidemic. [ABSTRACT FROM AUTHOR]

Sepsis is a syndromic response to infections and is becoming an emerging threat to the public health sector, particularly in developing countries. Salmonella Typhi (S. Typhi), the cause of typhoid fever, is one primary cause of pediatric sepsis in typhoid endemic areas. Extensively drug-resistant (XDR) S. Typhi is more common among pediatric patients, which is responsible for over 90% of the reported XDR typhoid cases, but the majority of antibiotic resistance studies available have been carried out using S. Typhi isolates from adult patients. Here, we characterized antibiotic-resistance profiles of XDR S. Typhi isolates from a medium size cohort of pediatric typhoid patients (n = 45, 68.89% male and 31.11% female) and determined antibiotic-resistance-related gene signatures associated with common treatment options to typhoid fever patients of 18 XDR S. Typhi representing all 45 isolates. Their ages were 1–13 years old: toddlers aging 1–2 years old (n = 9, 20%), pre-schoolers aging 3–5 years old (n = 17, 37.78%), school-age children aging 6–12 years old (n = 17, 37.78%), and adolescents aging 13–18 years old (n = 2, 4.44%). Through analyzing blaTEM1, dhfR7, sul1, and catA1genes for multidrug-resistance, qnrS, gyrA, gyrB, parC, and parE for fluoroquinolone-resistance, blaCTX-M-15 for XDR, and macAB and acrAB efflux pump system-associated genes, we showed the phenotype of the XDR S. Typhi isolates matches with their genotypes featured by the acquisitions of the genes blaTEM1, dhfR7, sul1, catA1, qnrS, and blaCTX-M-15 and a point mutation on gyrA. This study informs the molecular basis of antibiotic-resistance among recent S. Typhi isolates from pediatric septicemia patients, therefore providing insights into the development of molecular detection methods and treatment strategies for XDR S. Typhi. [ABSTRACT FROM AUTHOR]

Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help in providing an optimal gaming experience by improving video game design. To this end, we present a framework for classifying the game player's expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert and novice players' brain activity is different, which can be classified using frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach lead us in identifying two significant EEG channels, i.e., AF3 and P7, among fourteen channels available in Emotiv EPOC headset. In particular, features extracted from these two EEG channels contributed the most to the video game player's expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying game player's expertise level with a classification accuracy of up to 98.04% (without data balancing) and 98.33% (with data balancing). [ABSTRACT FROM AUTHOR]

The thriving adoption of drones for delivering parcels, packages, medicines, etc., is surging with time. The application of drones for delivery services results in faster delivery, fuel-saving, and less energy consumption. Giant companies like Google, Amazon, Facebook, etc., are actively working on developing, testing, and improving drone-based delivery systems. So far, a lot of work has been done for improving the design, speed, operating range, security of the delivery drones, etc. However, very limited work has been done to ensure a complete and reliable last-mile delivery from the merchant's store to the hands of the actual customer. To ensure a complete and reliable last-mile delivery, a drone must authenticate the consumer before dropping the package. Therefore, in this work, we propose a consumer authentication (Consumer-Auth) hybrid computing framework for drone delivery as a service to make sure that the parcel is perfectly delivered to the intended customer. The proposed Consumer-Auth framework enables a drone to reach the exact destination by using the GPS coordinates of the customer autonomously. After reaching the exact location, the drone waits for the customer to come to the specific pinned location then it starts a two-factor consumer authentication process, i.e., one-time password (OTP) verification and face Recognition. The experimental results manifest the effectiveness of the proposed Consumer-Auth framework to ensure a complete and reliable drone-based last-mile delivery. [ABSTRACT FROM AUTHOR]

Colonization of extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae as animal gut microbiota is a substantial global threat. This study aimed to determine the molecular characterization of blaSHV, blaTEM, and blaCTX-M variants in animals, as well as to evaluate the antimicrobial resistance conferred by these genes. We prospectively analyzed 1273 fecal specimens of farm and domestic animals for the isolation of enterobacteria that had the ESBL phenotype by using biochemical methods. The extracted genes were amplified by polymerase chain reaction and sequenced for the characterization of blaSHV, blaTEM, and blaCTX-M variants. The drug-resistance spectrum and hierarchical clusters were analyzed against 19 antibacterial agents. Out of 245 (19.2%) ESBL enterobacteria, 180 (75.5%) Escherichia coli and 34 (13.9%) Klebsiella pneumoniae were prevalent species. A total of 73.9% blaCTX-M, 26.1% blaTEM, and 14.2% blaSHV were found among the enterobacteria; however, their association with farm or domestic animals was not statistically significant. The distribution of bla gene variants showed the highest number of blaCTX-M-1 (133; 54.3%), followed by blaCTX-M-15 (28; 11.4%), blaTEM-52 (40; 16.3%), and blaSHV-12 (22; 9%). In addition, 84.5% of the enterobacteria had the integrons intI1. We observed ±100% enterobacteria resistant to cephalosporin, 7 (2.9%) to colistin (minimum inhibitory concentration breakpoint ≥4 μg/mL), 9 (3.7%) to piperacillin-tazobactam, 11 (4.5%) to imipenem, 14 (5.7%) to meropenem, and 18 (7.3%) to cefoperazone-sulbactam, without statistically significant association. Animal gut microbiota contain a considerable number of blaCTX-M, blaTEM, blaSHV, and integrons, which are a potential source of acquired extensive drug resistance in human strains and leaves fewer therapeutic substitutes. [ABSTRACT FROM AUTHOR]

In the continuous development of computer network technology, multimedia technology and information technology, digitization has become the main means of displaying information, thus facilitating the storage, copying and dissemination of digital multimedia information. In this context, there are no restrictions on arbitrary editing, copying, modification, and dissemination of digital images, music, etc., which leads to various social problems such as information security, copyright disputes, and piracy. With the advancement of networks and multimedia, digital watermarking technology has received worldwide attention as an effective method of copyright protection. Improving the anti-geometric attack ability of digital watermarking algorithms using image feature-based algorithms have received extensive attention. This paper proposes a novel robust watermarking algorithm based on SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform), namely blind watermarking. The algorithm firstly uses the affine transformation with a feature matrix and chaotic encryption technology to preprocess the watermark image, enhance the confidentiality of the watermark, and perform block and DCT coefficients extraction on the carrier image, and then uses the positive and negative quantization rules to modify the DCT coefficients. The embedding of the watermark is completed, and the blind extraction of the watermark realized. Experiments show that the algorithm has good invisibility and strong robustness against conventional and geometric attacks and can effectively protect the security of images with NC value more than 90%. [ABSTRACT FROM AUTHOR]

The red palm weevil Rhynchophorus ferrugineus (Olivier) is an important pest of date palms in many regions of the world. This paper reports the first survey of insecticide resistance in field populations of R. ferrugineus in Pakistan which were collected from seven date palm growing areas across Punjab and Khyber Pakhtunkhwa (KPK) provinces, Pakistan. The resistance was assessed by the diet incorporation method against the formulated commonly used chemical insecticides profenophos, imidacloprid, chlorpyrifos, cypermethrin, deltamethrin, spinosad, lambda-cyhalothrin and a fumigant phosphine. Elevated levels of resistance were recorded for cypermethrin, deltamethrin and phosphine after a long history of insecticide use in Pakistan. Resistance Ratios (RRs) were 63- to 79-fold for phosphine, 16- to 74-fold for cypermethrin, 13- to 58-fold for deltamethrin, 2.6- to 44-fold for profenophos, 3- to 24-fold for chlorpyrifos, 2- to 12-fold for lambda-cyhalothrin and 1- to 10-fold for spinosad compared to a susceptible control line. Resistant R. ferrugineus populations were mainly found in southern Punjab and to some extent in KPK. The populations from Bahawalpur, Vehari, Layyah and Dera Ghazi Khan were most resistant to chemical insecticides, while all populations exhibited high levels of resistance to phosphine. Of the eight agents tested, lower LC50 and LC90 values were recorded for spinosad and lambda-cyhalothrin. These results suggest that spinosad and lambda-cyhalothrin exhibit unique modes of action and given their better environmental profile, these two insecticides could be used in insecticide rotation or assist in phasing out the use of older insecticides. A changed pattern of both insecticides can be used sensibly be recommended without evidence of dose rates and frequencies used. [ABSTRACT FROM AUTHOR]

Global Mobility Networks(GLOMONETs) in wireless communication permits the global roaming services that enable a user to leverage the mobile services in any foreign country. Technological growth in wireless communication is also accompanied by new security threats and challenges. A threat-proof authentication protocol in wireless communication may overcome the security flaws by allowing only legitimate users to access a particular service. Recently, Lee et al. found Mun et al. scheme vulnerable to different attacks and proposed an advanced secure scheme to overcome the security flaws. However, this article points out that Lee et al. scheme lacks user anonymity, inefficient user authentication, vulnerable to replay and DoS attacks and Lack of local password verification. Furthermore, this article presents a more robust anonymous authentication scheme to handle the threats and challenges found in Lee et al.’s protocol. The proposed protocol is formally verified with an automated tool(ProVerif). The proposed protocol has superior efficiency in comparison to the existing protocols. [ABSTRACT FROM AUTHOR]