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AbstractStaphylococcus pseudintermedius is a significant bacterial pathogen that frequently colonizes different body sites and mucous membranes of pets. The objectives of the cross-sectional study were to estimate the prevalence, antimicrobial resistance pattern, and detection of diverse resistance as well as virulence genes of S. pseudintermedius in cats. A standard bacteriological method, species-specific gene and different antimicrobial resistance as well as virulence genes were confirmed by PCR assay. A total of 233 swab samples were collected from different body sites of 102 cats, among them 146 swabs from 73 healthy cats, and 87 from 29 diseased cats. Overall, prevalence of S. pseudintermedius in cats was 12.01%, while dermatitis and otitis affected cats were 26.08% and 33.33%, respectively. The highest antimicrobial resistance was observed against penicillin (96.42%) followed by streptomycin (85.71%) and erythromycin (78.57%). Moreover, 89.28% of S. pseudintermedius isolates exhibit multi-drug resistance (MDR) (≥ 3 classes’ antimicrobial resistant). In addition, 17.86% isolates harbored the mecA gene; thus, were classified as methicillin-resistant S. pseudintermedius (MRSP). Furthermore, the erythromycin resistance genes ermA and ermB were harbored by 25% and 10.71% of isolates, while 42.86% and 17.86% of isolates carried tetK and tetL (tetracycline resistance) genes, respectively. In virulence profiling, 32.14% (sea) and 10.71% (seb) of isolates were found positive for enterotoxin genes, whereas, the toxic shock syndrome toxin-1 (tst-1) gene and the Panton-Valentine leukocidin gene (pvl) were detected in 25% and 14.29% of isolates, respectively. To our knowledge, this is the first report of cats in Bangladesh for MDR S. pseudintermedius, MRSP, and their virulence profiling.

Abstract. Background. The incidence of prostate cancer is increasing worldwide. A significant proportion of patients develop metastatic disease and are initially prescribed androgen deprivation therapy (ADT). However, subsequent sequences of treatments in real-world settings that may improve overall survival remain an area of active investigation. Materials and methods. Data were collected from 384 patients presenting with de novo metastatic prostate cancer from 2011 to 2015 at a tertiary cancer center. Patients were categorized into surviving (n = 232) and deceased (n = 152) groups at the end of 3 years. Modified sequence pattern mining techniques (Generalized Sequential Pattern Mining and Sequential Pattern Discovery using Equivalence Classes) were applied to determine the exact order of the most frequent sets of treatments in each group. Results. Degarelix, as the initial form of ADT, was uniquely in the surviving group. The sequence of ADT followed by abiraterone and docetaxel was uniquely associated with a higher 3-year overall survival. Orchiectomy followed by fosfestrol was found to have a unique niche among surviving patients with a long duration of response to the initial ADT. Patients who received chemotherapy followed by radiotherapy and those who received radiotherapy followed by chemotherapy were found more frequently in the deceased group. Conclusions. We identified unique treatment sequences among surviving and deceased patients at the end of 3 years. Degarelix should be the preferred form of ADT. Patients who received ADT followed by abiraterone and chemotherapy showed better results. Patients requiring palliative radiation and chemotherapy in any sequence were significantly more frequent in the deceased group, identifying the need to offer such patients the most efficacious agents and to target them in clinical trial design.

Aspergillus westerdijkiae is a major producer of ochratoxin A (OTA), a highly toxic and carcinogenic mycotoxin found in various food and feed products. A. westerdijkiae produces excessive amount of OTA under various water activity (aw) conditions that occur during food and feed storage. The biosynthetic gene clusters associated with OTA production include OTAbZIP, which plays a key role in controlling mycotoxin production in response to environmental conditions. This study explored the regulation of OTA biosynthesis in A. westerdijkiae fc-1, focusing on the OTAbZIP gene’s influence under aw stress. The mycelium growth of A. westerdijkiae fc-1 wild type and OTAbZIP mutant strains increased by 40.7% and 50.5% under high water activity (0.96 aw) respectively, at 6 days post-inoculation (dpi), indicating a stress on A. westerdijkiae fc-1. While OTAbZIP mutant did not produce OTA under both high and moderate aW conditions. The wild type produced OTA and OTA biosynthetic gene expression levels were downregulated under high (0.96 aw) and moderate (0.91 aw) water activity. The expression level of hog1 gene in OTAbZIP mutant was significantly lower than in the wild type. Pathogenicity tests revealed that deletion of OTAbZIP did not significantly affect disease infection. This study shows that deleting OTAbZIP gene greatly reduces OTA production, affecting the strain’s adaptability to water activity stress.

Injecting drug use driving the HIV epidemic is currently a major global public health concern. However, the early epidemic among injecting drug users (IDUs), more than three decades ago, was only concentrated in few places. The HIV epidemic among the IDUs in New York, USA; Edinburgh, Scotland, UK; and northern Italy provides examples of where the recorded prevalence exceeded 50% within a very short period of time. This brief review highlights historical perspectives of HIV transmission risk among IDUs during the early stages of the epidemic. Salient features and related experiences during this period might provide valuable insights for current HIV prevention. Our overview of the selected locations reemphasizes the importance of early prevention. The discussion also introduces to new researchers the early situation associated with the HIV epidemic in IDUs and highlights some crucial components need to be included during current HIV prevention activities.

Abstract Requirements elicitation is one of the fundamental sub-processes of requirements engineering which is used to find the needs of stakeholders. There are several activities in this sub-process, i.e., identification of stakeholders and their requirements, software requirements prioritization, and analysis. Recommendation systems have been intertwined with the requirements elicitation process to predict the stakeholders’ requirements based on their preferences for functional and non-functional requirements. A number of systematic literature reviews (SLRs) have been carried out in the area of requirements elicitation. These SLRs do not support the applications of the recommendation systems during the requirements elicitation process. To deal with this issue, we present an SLR on recommendation systems-based software requirements elicitation processes, from 2009 to 2022, undertaking four research questions: (a) What are the different activities of the software requirements elicitation methods? (b) What are the applications of recommendation systems in the identification of the software requirements? (c) How the recommendation systems can facilitate the identification of stakeholders in the requirements elicitation process? (d) What are the ways to automate the selection of requirements elicitation techniques? The aim of this study is to identify the research gaps in the area of recommendation systems-based requirements elicitation processes and suggest future research directions.

AbstractThis paper describes deep neural network (DNN) models based on hyperparameter optimization for the prediction of the compressive strength of concrete. The novelty of this research lies in the implementation of optimized hyperparameters to train the DNN models with the aim to enhance their predictive accuracy. Utilizing the Keras Tuner library, the most effective hyperparameters for the DNN models were identified. These models are then trained and evaluated using an experimental dataset of 1030 instances, encompassing nine quantitative variables that include various concrete mix ingredients and age. The target variable for all models is the compressive strength of concrete. The assessment of model performance, based on metrics like mean squared error (MSE) and R2 values, was conducted on previously unseen test data. The optimal configuration for the hidden layers in the model was identified as five layers, containing 12, 16, 16, 40, and 26 neurons, respectively. The optimal learning rate for the model was 0.001. With this set of optimal hyperparameters, the best DNN model achieved an MSE of 28.76 and an R2 value of 0.89 on the testing data. The results demonstrate a significant improvement in the DNN models' performance when trained with the optimized hyperparameters. In comparison to the regression model, the performance of the DNN models was significantly better. Adopting the predictive models developed in this research offers the potential for substantial cost and time savings by circumventing the need for labour-intensive and time-consuming laboratory tests.

Online information has increased tremendously in today’s age of the Internet. As a result, the need has arisen to extract relevant content from the plethora of available information. Researchers are widely using automatic text summarization techniques for extracting useful and relevant information from voluminous available information. The summary obtained from the automatic text summarization often faces the issues of diversity and information coverage. Earlier researchers have used graph-based approaches for ranking and optimization. This research work introduces a probabilistic approach named as ClusRank for summary extraction, comprising of a two-stage sentence selection model involving clustering and then ranking of sentences. The initial stage involves clustering of sentences using a proposed overlapping clustering algorithm on the weighted network, and later selection of salient sentences using the introduced probabilistic approach. In the analysis of real-world networks, community structure development is essential because it provides strategic insights that help decision-makers make well-informed choices. Furthermore, methodologically strict community detection algorithms are required due to the occurrence of discontinuous, overlapping, and nested community patterns in such networks.This research work, an algorithm is presented for detecting overlapping communities based on the concept of rough set and granular information on links. The sentence selection algorithm based on budget maximum coverage approach supports the assumption that larger sub-topics in a document are of more importance than smaller subtopics. The performance of the proposed probabilistic ClusRank is validated on DUC2001, DUC 2002, DUC2004, and DUC 2006 data sets.

Post-harvest fruit rot caused by Alternaria species is one of the most important threats to the fruit industry. Post-harvest rot on sweet cherry (Prunus avium) fruit was observed in the fruit markets of the Haidian district of Beijing, China. The fungal isolates obtained from the infected sweet cherry fruits matched the descriptions of Alternaria alternata based on the morphology and multi-gene (ITS, endo-PG, and Alta1) sequence analysis. Pathogenicity tests indicated that ACT-3 was the most virulent isolate, exhibiting typical post-harvest fruit rot symptoms. Physiological studies revealed that the optimal conditions for the growth of ACT-3 were temperature of 28°C, water activity of 0.999, and pH of 8 with 87, 85, and 86 mm radial growth of ACT-3 on a potato dextrose agar (PDA) medium, respectively, at 12 days post-inoculation (dpi). Moreover, the fungus showed the highest growth on a Martin agar medium (MAM) modified (85 mm) and a PDA medium (84 mm) at 12 dpi. The proliferation of the fungus was visualized inside the fruit tissues by confocal and scanning electron microscope (SEM), revealing the invasion and destruction of fruit tissues. Alternaria mycotoxins, tenuazonic acid (TeA), and alternariol (AOH) were detected in five representative isolates by HPLC analysis. The highest concentrations of TeA (313 μg/mL) and AOH (8.9 μg/mL) were observed in ACT-6 and ACT-3 isolates, respectively. This study is the first to present a detailed report on the characteristics and proliferation of A. alternata associated with sweet cherry fruit rot and the detection of toxic metabolites.

Abstract A global hazard to public health has been generated by the coronavirus infection 2019 (COVID-19), which is spreading quickly. Pomegranate is a strong source of antioxidants and has demonstrated a number of pharmacological characteristics. This work was aimed to analyze the phytochemicals present in ethanolic pomegranate seed extract (PSE) and their in vitro antioxidant potential and further in-silico evaluation for antiviral potential against crystal structure of two nucleocapsid proteins i.e., N-terminal RNA binding domain (NRBD) and C-terminal Domain (CTD) of SARS-CoV-2. The bioactive components from ethanolic extract of PSE were assessed by gas chromatography-mass spectroscopy (GC–MS). Free radical scavenging activity of PSE was determined using DPPH dye. Molecular docking was executed through the Glide module of Maestro software. Lipinski’s 5 rule was applied for drug-likeness characteristics using cheminformatics Molinspiration software while OSIRIS Data Warrior V5.5.0 was used to predict possible toxicological characteristics of components. Thirty-two phytocomponents was detected in PSE by GC–MS technique. Free radical scavenging assay revealed the high antioxidant capacity of PSE. Docking analysis showed that twenty phytocomponents from PSE exhibited good binding affinity (Docking score ≥ − 1.0 kcal/mol) towards NRBD and CTD nucleocapsid protein. This result increases the possibility that the top 20 hits could prevent the spread of SARS-CoV-2 by concentrating on both nucleocapsid proteins. Moreover, molecular dynamics (MD) simulation using GROMACS was used to check their binding efficacy and internal dynamics of top complexes with the lowest docking scores. The metrics root mean square deviation (RMSD), root mean square fluctuation (RMSF), intermolecular hydrogen bonding (H-bonds) and radius of gyration (Rg) revealed that the lead phytochemicals form an energetically stable complex with the target protein. Majority of the phytoconstituents exhibited drug-likeness with non-tumorigenic properties. Thus, the PSE phytoconstituents could be useful source of drug or nutraceutical development in SARS-CoV-2 pathogenesis.

Objective: The study aimed to assess two portable glucometers and an automated hematology analyzer for monitoring diabetes in cats. Glucose concentrations in the cats' blood samples were determined, and the factors influencing the glucose level were identified in this study. Material and Methods: The study was conducted at the Teaching Veterinary Hospital of Chattogram Veterinary and Animal Sciences University with 30 cats. Blood samples were collected, and their glucose levels were measured using two portable glucometers (Glucotest Prime® and Accu-Chek®) and an automated hematology analyzer (BIOBASE-Silver, BIOBASE). Results: The study showed that local and exotic breeds showed almost the same percentage of diabetes cases with all monitoring devices. Older (Glucotest Prime: before meal = 78.6%, after meal = 85.8%), (Accu-Chek: before meal = 71.5%, after meal = 71.5%), (Hematology analyzer: before meal = 71.5%, after meal = 78.6%), and obese (p = 0.002; before meal = 91%, after meal = 91% with all monitoring devices) animals had a higher occurrence of diabetes than young and non-obese animals. Both male and female cats showed almost equal numbers of diabetes cases. Conclusion: Several factors, like breed, older age, obesity, and gender, are prominent risk factors for diabetes in cats. The study concludes that feline diabetes is of similar concern to human diabe¬tes and can be regularly monitored with a portable glucometer and hematology analyzer. [Vet. Res. Notes 2023; 3(3.000): 11-15]

The presence of pathogenic fungi and contamination of mycotoxins in food and feed pose significant threats and challenging issues to food in the world [...]

The application of natural dye in the textile industry is gaining popularity because of the increasing awareness of environmental effects, the ecological effects, and the pollution caused by synthetic dyes. Rose flowers, which are one of the best sources of natural dye, have been selected for fabric dyeing due to the presence of anthocyanin pigment. Dyeing of wool is done at optimal conditions such as pH, temperature, time, and salt. It is found that at different percentages (1%, 3%, 5%, 7%, and 9%) chemical and bio mordants mediums gave a variety of shades of good color and better fastness properties onto natural fabrics. By using different chemical and bio mordants and their combinations to analyze the color strength and fastness properties could be enhanced. Bio-mordants gave shades with good color strength and acceptable for the universe. The plant extract has therapeutic values like antioxidant, anti-inflammatory, and antibacterial activity against some gram-positive and gram-negative pathogenic bacteria.

Effluent load produced by synthesis and application of synthetic dyes has led global community to empty sustainable tools during textile processing. Current study is the application of ultrasonic treatment for fabric pre-treated before desizing, before bleaching and before mercerization followed by dyeing using Direct Red 31 dye. It has been found that ultrasonic treatment to bleached fabric for 45 min has given high color strength using direct dye. FTIR analysis shows that US did not cause any chemical change but modify the surface of bleached fabric to make it more substantive for dye. Response surface methodology was employed for the optimization of dyeing conditions to make coloring process more sustainable under mild conditions. The statistical analysis reveals that under reduced optimal conditions shade made has not only show improvement in colorfastness rating but also enhanced the sustainability of fabric to uptake dye to much extend.

Effluent load eluted from textile processing have forced traders and industrialists to employ sustainable tool; which not only save energy, cost, and labor but also make the process cleaner. This study is concerned with the improvement of the dyeing of proteinous fabrics using Acid Violet 3B dye under microwave radiations. It has been found that dyeing of irradiated silk at 60°C for 60 min using 30 mL of irradiated dye bath of 2 pH containing 7 g/100 mL of salt has given excellent result whereas for dyeing of irradiated wool 40 mL of irradiated dye bath of 2 pH contain 5 g/100 mL salt at 60°C for 60 min has given the best result. ISO standards for colorfastness applied at 1–6% shade made at optimal condition showed that microwave treatment has enhanced the rating from good to excellent. The result verifies that this clean heating tool has excellent efficiency to improve the dyeing process and functional behavior of fabrics under mild conditions.

Environment-friendly textile processing is now-a-days worldwide demand. The current study has been concerned with the utilization of suitable tool i.e. microwave radiation for the improvement of dyeing behavior of natural fabrics using Acid Red 138 dye. Microwave treatment up to 6 min has been given to both dye solution and fabrics. Microwave-treated and un-treated fabrics were used to dye under-treated and un-treated solutions. Different dyeing parameters were employed and shade made at optimized conditions were as per ISO fastness standards and it was found that un-heated silk has given excellent results when dyed at 65◦C for 65 min under irradiated dye solution of 4 pH containing 4 g/100 mL of salt. But irradiated wool has given good results when the solution of 1 pH contaminating 4 g/100 mL salt was irradiated to dye fabrics at 65 ◦C for 65 min. However, silk fabric has shown more promising behavior toward treated dye solution as compared to wool fabric under optimum conditions. It is concluded that microwave treatment has a good potential not only to make acid dyeing process more sustainable for the coloring behavior of silk more than wool under mild condition.

Design of bridge piers and abutments is significantly impacted by hydrodynamic processes that cause scouring of the foundation. Although, many empirical formulae are available in the literature to estimate the depth of scouring, but they suffer from several limitations. A major limitation of empirical formulae is that they are largely applicable to the hydraulic conditions for which they have been derived. In this research, a deep neural network (DNN) has been developed and applied to predict the depth of scour around bridge piers and abutments. The practicality of the proposed model has been demonstrated using the experimental data sets consisting of 211 data points. The novelty of the DNN model applied herein lies in the use of Adam Optimizer for optimizing the parameters of the DNN model. The performance of the DNN model was evaluated for each parameter set using statistical indicators such as the coefficient of determination, root mean square error, and mean absolute error. A regression equation based upon the available data set has also been proposed. Based upon the values of the statistical parameters, the DNN model has been found to be significantly better than the regression model. The model proposed herein performs better than the regression model. A distinct practical advantage of the model proposed herein is that it eliminates the need of hit and trial procedure to determine the optimal parameter set for the model.

In 2014–15, Pakistan’s provincial government of Khyber Pakhtunkhwa (KPK) launched a decentralised power generation initiative, installing over 356 small hydro power units to provide electricity to over 2.5 million people in its mountainous regions. This initiative built on the foundation of prior community-based mini/micro hydro-power projects (MHPPs) deployed in the region since the 1980s that have experienced mixed success; by 2020, over 150 units were non-operational. This paper investigates the socio-technical factors influencing the success and failure of these projects, focusing on outcomes and processes integral to community MHPPs. The study analyses the critical implications of MHPPs for socio-economic welfare, gender equity and democratic governance through extensive primary research including field observations at 40 sites, questionnaire surveys with community residents in three in-depth case-studies, and expert interviews with local policymakers and project developers. Analysis shows that electricity provision from MHPPs has had positive outcomes for the communities in terms of improved education, poverty alleviation and economic growth, alongside enhanced access to information and gains in women’s entrepreneurship and health. Nonetheless, the study also identifies significant shortcomings in the processes involved in community MHPPs related to inadequate technical assessments, monitoring, and evaluation, as well as challenges arising from undemocratic governance mechanisms and inequitable participation. By teasing out the multiple socio-technical dimensions that shape infrastructure resilience of MHPPs in KPK, this paper contributes to the broader discourse on sustainable community energy projects and informs policy for decentralised energy transitions that ensure democratic alignment and inclusive design.

Abstract Background Irritable bowel syndrome (IBS) is a very common gastrointestinal disorder worldwide, but research regarding this disease is rare in Bangladesh. This study aimed to assess the prevalence of IBS and its associated risk factors among university students in Bangladesh. Methods This is a cross‐sectional study. A total of 300 randomly selected participants were included in this study. By using a structured questionnaire and anthropometric methods, we collected all the required data for our study. The diagnosis of IBS was based on Rome III criteria. Results The overall prevalence of IBS was 39.3%, but the majority (77.3%) had no basic awareness of IBS. In our study, anxiety and depression (χ2 = 6.817; odds ratio [OR] = 1.910; 95% confidence interval [CI] = 1.172, 3.113; P = 0.011) had a significant relationship with IBS and IBS had a significant (P

Replicator Neural Network (RNN) is a popular algorithm for anomaly detection, but finding optimal number of hidden layers and then finding optimal number of neurons in each hidden layer is quite a challenging and time-consuming task. Extreme Learning Machines (ELM) are neural networks with single-hidden layer but the learning algorithm is different and faster than back-propagation. ELM-based RNNs solve our problem of determining the number of hidden layers and the learning algorithm is also faster than gradient-descent based RNN. The problem of identifying the optimal number of neurons in the hidden layer can be solved by Garson algorithm. In this work, the author propose an optimal Replicator Neural Network which is optimized using ELM learning and Garson algorithm for anomaly detection. The experimental results show that the proposed method is fast as well as highly accurate.

The use of synthetic fungicides against postharvest Alternaria rot adversely affects human health and the environment. In this study, as a safe alternative to fungicides, Bacillus subtilis strain Y17B isolated from soil exhibited significant antifungal activity against Alternaria alternata. Y17B was identified as B. subtilis based on phenotypic identification and 16S rRNA sequence analysis. To reveal the antimicrobial activity of this strain, a PCR-based study detected the presence of antifungal lipopeptide (LP) biosynthetic genes from genomic DNA. UPLC Q TOF mass spectrometry analysis detected the LPs surfactin (m/z 994.64, 1022.68, and 1026.62), iturin (m/z 1043.56), and fengycin (m/z 1491.85) in the extracted LP crude of B. subtilis Y17B. In vitro antagonistic study demonstrated the efficiency of LPs in inhibiting A. alternata growth. Microscopy (SEM and TEM) studies showed the alteration of the morphology of A. alternata in the interaction with LPs. In vivo test results revealed the efficiency of LPs in reducing the growth of the A. alternata pathogen. The overall results highlight the biocontrol potential of LPs produced by B. subtilis Y17B as an effective biological control agent against A. alternata fruit rot of cherry.

Clostridium perfringens is a ubiquitous spore-forming anaerobic pathogen that is frequently associated with enteric disease in chickens. Moreover, enterotoxin-producing C. perfringens has high zoonotic potential as well as serious public health concerns due to the emanation of food-borne intoxication. The present study was designed to isolate, identify, and toxinotype C. perfringens from both healthy and cases of necrotic or ulcerative enteritis chickens. A total of 110 samples were collected from July 2019 to February 2021. Among the samples, 38 (34.5%, 95% CI: 26.39–43.83) were positive for C. perfringens and were obtained from broiler 21 (33.3%, 95% CI: 22.91–45.67), Sonali 9 (34.6%, 95% CI: 19.31–53.88), and layer 8 (38%, 95% CI: 20.68–59.20). C. perfringens was highly prevalent (35.7%, 95% CI: 25.48–47.44) in enteritis chickens compared with healthy ones. In multiplex PCR toxinotyping, 34 (89.4%) isolates were identified as C. perfringens type A by the presence of the alpha toxin gene (cpa). Moreover, in addition to the cpa gene, 3 (14.3%, 95% CI: 4.14–35.48) broiler and 1 (11.1%, 95% CI: 0.01–45.67) Sonali isolates harbored the enterotoxin gene (cpe) and were classified as type F. However, none of the isolates carried genes encoding beta (cpb), epsilon (etx), iota (iap), or beta-2 (cpb2) toxins. Multivariable logistic regression analysis identified the following variables such as; “previously used litter materials” (OR 21.77, 95% CI 2.22–212.66, p≤0.008); intestinal lesions, “presence of ulceration” (OR 30.01, 95% CI 3.02–297.91, p≤0.004); “ballooned with gas” (OR 24.74, 95% CI 4.34–140.86, p≤0.001) and “use of probiotics” (OR 5.24, 95% CI 0.74–36.75, p≤0.095) act as risk factors for C. perfringens colonization in chicken gut. This is the first study of molecular toxinotyping of C. perfringens from healthy and enteric-diseased chickens in Bangladesh, which might have a potential food-borne zoonotic impact on human health.

Introduction: Air pollution, one of the biggest environmental risks to health, is a severe problem in Bangladesh. The Lancet ''Commission on Pollution and Health'' emphasized the importance of research on health effects of ambient air pollution. This study explored the negative health impacts of air pollution on pregnancy outcomes - preterm births (PTB) and low birth weight (LBW). Methods: The study assessed air quality in terms Air Quality Index (AQI) and quantified the association with LBW and PTB. Pregnancy outcome data were collected from the Maternal and Child Health Training Institute in Dhaka, and the AQI data from the Clean Air and Sustainable Environment Project of the Department of Environment. A total 3,206 birth outcome records were assessed within the period from 2014 to 2017. Results: Air pollution levels are alarmingly high in Dhaka, with ‘Unhealthy’ to ‘Extremely Unhealthy’ levels for almost half of the year. An increase in the prevalence of LBW and PTB was found with increasing cumulative air pollution exposure. LBW increased from 20.6% to 36.0% and PTB increased from 9.0% to 15.2% respectively between the lowest and highest category AQI value exposure. For every 10,000 AQI value increase in cumulative exposure, LBW and PTB increased by 4% and 2%, respectively. There is significant gender differentiated impact on LBW and PTB due to air pollution where female fetuses are at higher risk of LBW and males are more prone to PTB. Air pollution exposure during the second trimester increased LBW and PTB more compared to first and third trimesters, suggesting it is potentially the most vulnerable period of pregnancy. Conclusion: Air pollution contributes to adverse pregnancy outcomes. To reduce this effect, proper interventions to reduce air pollution levels need to be urgently implemented.

Background and Aim: A vaccine program for coronavirus illness (coronavirus disease [COVID-19]) is currently underway in numerous regions of the world, including Bangladesh, but no health data on those who have been vaccinated are available at this time. The study aimed to investigate the health condition of people who had received their first dose of the Oxford- AstraZeneca vaccine and were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Materials and Methods: To detect SARS-CoV-2, a standard virological approach, real-time reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR), was used. Several health indicators from vaccinated patients were collected using pre-structured questionnaires during the infection phase. Results: A total of 6146 suspicious samples were analyzed, and 1752 were found to be positive for SARS-CoV-2, with 200 people receiving the first dose of the COVID-19 vaccine. One hundred and sixty-five (82.5%) were not hospitalized among the vaccinated people, and 177 (88.5%) did not have any respiratory problems. Only 8% of patients required further oxygen support, and 199 (99.5%) did not require intensive care unit intervention. Overall, oxygen saturation was recorded at around 96.8% and respiratory difficulties did not extend more than 5 days during the infection period. Among the vaccinated COVID-19-positive people, 113 (56.5%) and 111 (55.5%) had typical physiological taste and smell. Surprisingly, 129 (64.5%) people had diverse comorbidities, with high blood pressure (27.9%) and diabetes (32 [24.8%]) being the most common. The major conclusion of the current study was that 199 (99.5%) of vaccinated patients survived in good health and tested negative for RT-qPCR. Conclusion: According to the findings of this study, administering the first dose of the Oxford-AstraZeneca vaccine considerably reduces health risks during the COVID-19 infection period.

Mycotoxin contamination of food and feed is a major concern in sub-Sahara African countries, particularly Nigeria. It represents a significant limit to health of human, livestock as well as the international trade. Aflatoxins, fumonisins, ochratoxin, zearalenone, deoxynivalenol and beauvericin are the major mycotoxins recognised in the aetiology of food safety challenges that precipitated countless number of diseases. In Nigeria, aflatoxins and fumonisin found in nearly all crops are the most common mycotoxins of economic and health importance such as sorghum, maize and groundnuts. Thus, consumption of food contaminated with mycotoxins are inevitable, hence the need for adequate regulation is necessary in these frontier economies as done in many developed economies to ensure food safety for human and animals. In low and middle-income countries, especially Nigeria, there is lack of awareness and sufficient information on the risk associated with consequent of mycotoxin contamination on wellbeing of human, animals health and the economy. It is based on the foregoing that this paper summarized the status of mycotoxin present in Nigerian food and feeds relative to the global regulatory standards. This aimed at preventing consuming mycotoxin contaminated food stuff while confronting its associated challenges. Suggestions on some possible control strategies to mitigate vending mycotoxin food and feeds were made.

Background and Aim: Coronavirus disease (COVID-19) has been announced as a life-threatening, highly transmissible infectious novel emerging disease worldwide. Rapid detection and epidemiological information are desperately needed to overcome the existing pandemic situation and alleviate national and international crises. Still, to date, there is no significant epidemiological study of COVID-19 available in Bangladesh, especially in the Chattogram division. However, the current study focuses on molecular detection, prevalence, and risk factors associated with COVID-19 in Southern Bangladesh. Materials and Methods: Standard diagnostic protocols through real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) were performed for molecular confirmation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Different patient demographics were analyzed for exploring the relationship of four factors – region, sex, age, and symptoms with the accumulated number of COVID-19 cases in the Southern Bangladesh during the period of May 13, 2020, to June 12, 2020. Results: A total of 2954 samples were tested where the cumulative prevalence of circulating SARS-CoV-2 was 29.76% (n=879; 95% CI: 28.11-31.44) in the selected study region. Among the risk factors, the present study revealed that flatland people (35.62%, 95% CI 33.61-37.67, OR=3.13) were more vulnerable to getting infected by SARS-CoV-2 than the people living in hill tracts (13.04%, 95% CI 10.73-15.63). People older than 50 years (34.68%, 95% CI 30.38-39.18) were designated the highest risk than other different age groups. A higher number of COVID-19 cases were confirmed in patients (36.0%, 95% CI 33.77-38.29, OR=1.76) with typical symptoms, but interestingly a significant number of asymptomatic carriers (20.39%, 95% CI 18.13-22.80) also positive for SARS-CoV-2. Conclusion: To the best of our knowledge, this is the first epidemiological report in the Southern Bangladesh for COVID- 19. The study's findings will contribute to health professionals and the policy-makers to take preventive measures against the next outbreak emergency for Bangladesh.

Background: Old age is one of the vulnerable and prone stages in terms of health status. So this study aimed to assess the nutritional status and common health complications of older people. Methods: Simplified Nutritional Appetite Questionnaire (SNAQ), Anthropometric measurements, Diet History Method, and Mini Nutritional Assessment (MNA) tools were used to measure the nutritional status. Data were analyzed by using Statistical Package for Social Science (SPSS) version 16. Results: Out of the total 320 elderly participants the mean ± SD value for the age of male and female was 67.25 ± 6.5 and 67.32 ± 7.7 years respectively. According to BMI classification, it was noticed that with advancing age the percentage of underweight was also increased such as for 60–75 years old age group the underweight percentage was 30.0% where for 76 to 85 and >85 years old age group the underweight percentage was 45.0% and 60.0% respectively. According to the MNA score, 97 elderly respondents were malnourished and a total of 172 respondents had SNAQ scores below 14. This study found a statistically significant (P < 0.05) correlations among various health complications with nutritional status according to MNA score. In addition 56.6% (OR = 1.24, 95% CI = .799–1.939), 63.8% (OR = 1.18, 95% CI = .745–1.857) and 64.7% (OR = 1.14, 95% CI = .720–1.804) respondents had diabetes mellitus, hypertension and cardiovascular disease respectively. The risk of musculoskeletal pain (OR = 1.073, 95% CI = .684–1.681), bedsore (OR = 1.884, 95% CI = .903–3.934) and decreased sense of thirst (OR = 1.278, 95% CI = .821–1.991) were higher among females than males. A little number of the elderly used to take milk, meat, and fish daily. Conclusion: During this cross-sectional study, significant correlations among nutritional changes with health complications were determined. To prevent malnutrition among the elderly a proper health policy as well as periodical nutritional screening should be conducted.

This paper proposed a new method for learning to rank documents using enumerative feature subsetting in the presence of the implicit user feedback of the various classes of users. The objective of this research was to provide an alternative method for learning ranking functions using important subsets of the LETOR (Learning to Rank) features based on the feedback of various classes of the users identified from active subsets of the features. This research, unlike other feature engineering approaches, do not force the learner to drop the inactive features permanently; instead, it allows to learn ranking function on currently active feature subsets while keeping inactive subsets of the features in the training process. The proposed model allows the search engine to dynamically utilize the implicit user feedback of the various classes of the users in learning ranking models repeatedly. The experiments performed on the LETOR MQ2008 dataset shows that the proposed model gives better NDCG (Normalized Discounted Cumulative Gain) scores for the subsets of users in the ensemble settings. Results also show that the variance of the predicted ranking can be controlled by controlling the hyper-parameters like the probability of the selection of a subset, feedback on the subsets and the weights of each subset used in training the low-level ranker. We have used cross-entropy pairwise learner RankNet and the maximum margin type svmRank as low-level rankers, but their objective functions are modified for the feature subsets. Results obtained by bagging and boosting based ensemble methods shows that the proposed method is flexible enough to model a family of the feedback weights on the individual models and can be used to provide personalized rank learning functions to the selected subsets of the users.

Abstract Minimizing total harmonic distortion (THD) with less system complexity and computation time is a stringent constraint for many power systems. The multilevel inverter can have low THD when switching angles are selected at the fundamental frequency. For low-order harmonic minimization, selective harmonic elimination (SHE) is the most adopted and proficient technique but it involves the non-linear transcendental equations which are very difficult to solve analytically and numerically. This paper proposes a genetic algorithm (GA)-based optimization technique to minimize the THD of cascaded H-bridge multilevel inverter. The GA is the finest approach for solving such complex equations by obtaining optimized switching angles. The switching angles are calculated by the genetic algorithm by solving the nonlinear transcendental equations. This paper has modeled and simulated a five-level inverter in MATLAB Simulink. The THD comparison is carried out between step modulation method and optimization method. The results reveal that THD has been reduced from 17.88 to 16.74% while third and fifth harmonics have been reduced from 3.24%, 3.7% to 0.84% and 3.3%, respectively. The optimization method along with LC filter significantly improves the power quality providing a complete sinusoidal signal for varying load.

Recently in literature many families of distributions have been introduced to study the skewness, kurtosis and to explore the shape of the distribution more intensely. These families of distribution have wider applicability in variety of fields. In this paper, we introduce a five-parameter distribution, called the Kumaraswamy generalized Kappa distribution which extends the three-parameter Kappa distribution. The new distribution is more flexible and is applicable in the study of the highly-skewed data. Some mathematical properties of the proposed distribution are studied that includes the explicit expression for generating functions, moments, inequality indices, and entropies. The maximum likelihood estimates are computed using the numerical procedure. An application of the Kumaraswamy generalized Kappa distribution is illustrated using a real data set on stream flow. Keywords: Entropy, Inequality indices, Kumaraswamy distribution, Kappa distribution, Maximum likelihood

The COVID-19 pandemic has led to an incredible amount of fake news and conspiracy theories around the world. Calls for the integration of COVID-19 and fake news-related research have been advanced in various fields. This paper aims to unpack a structured overview of previous research topics and findings and identify gaps. Our goal in this systematic review is to (a) synthesize the selected earlier studies, (b) offer researchers a structural framework for future COVID-19 and fake news research, and (c) recommend relevant areas for future research. In this study, we focus on eighty conceptual and empirical studies on misinformation of COVID-19-related news on social media. We identify vital publications and methodological and theoretical approaches that exist in the COVID-19 literature. The articles were systematically analyzed, focusing on the research context and time frame, data collection/analysis procedures, and equivalence issues. While COVID-19 research has been advancing significantly over the past couple of months, numerous questions remain unexplained in the domain of the social media landscape. For example, our review suggests that researchers should begin to concentrate on a process framework blending Artificial Intelligence (AI) to curb the fake news problem. This can be achieved in all three phases, e.g., the study of individual decisions and experiences, the experiences of groups and organizations and the interactions between them, and finally, the interactions at the broadest level (micro, meso, and macro stages).

The objective of the study was to find major correlates of decline in the physical functionality of the geriatric population. The main focus was the evaluation of demographic, social, economic, and health-related factors that have a potential impact on physical functionality. A sample of 423 elderly people were selected from district Faisalabad, Pakistan by using a snowball sampling approach. Statistical analysis was performed using frequency analysis and some tests for association like chi-square, Cramer’s V, and gamma coefficient. It was observed that 42.8% of the respondents were independent in doing their all-routine work and the physical functional status decline was observed partially in 35.2% and completely in 22.0% of the respondents. Using frequency analysis and association measures, age, education, marital status, living with family, having an independent source of income, level of social network and support, having exercise routine were found to be significantly associated with functional status of elders. The study concluded that besides age and other demographic factors that cannot be controlled, the socio-economic factors can be improved to substantially reduce the decline in the physical functionality of the geriatric population.

The patient of Parkinson's disease (PD) is facing a critical neurological disorder issue. Efficient and early prediction of people having PD is a key issue to improve patient's quality of life. The diagnosis of PD specifically in its initial stages is extremely complex and time-consuming. Thus, the accurate and efficient diagnosis of PD has been a significant challenge for medical experts and practitioners. In order to tackle this issue and to accurately diagnosis the patient of PD, we proposed a machine-learning-based prediction system. In the development of the proposed system, the support vector machine (SVM) was used as a predictive model for the prediction of PD. The L1-norm SVM of features selection was used for appropriate and highly related features selection for accurate target classification of PD and healthy people. The L1-norm SVM produced a new subset of features from the PD dataset based on a feature weight value. For the validation of the proposed system, the K-fold cross-validation method was used. In addition, the metrics of performance measures, such as accuracy, sensitivity, specificity, precision, F1 score, and execution time, were computed for model performance evaluation. The PD dataset was in this paper. The optimal accuracy achieved the best subset of the selected features that might be due to various contributions of the PD features. The experimental findings of this paper suggest that the proposed method can be used to accurately predict the PD and can be easily incorporated in healthcare for diagnosis purpose. Currently, the computer-based assisted predictive system is playing an important role to assist in PD recognition. In addition, the proposed approach fills in a gap on feature selection and classification using voice recordings data by properly matching the experimental design.

INTRODUCTION: In order to protect the owner’s visual multimedia data, various watermarking methods have introducedin the last few years. In image watermarking techniques, a secret watermark image inserted into another image.OBJECTIVES: The main aim of this paper is to implement an image watermarking technique having high security fromstealing the information hidden in image and make the resultant image more robust against various attacks.METHODS: In this paper, frequency domain based techniques like DWT, DCT has used with 2 Level SVD and a new 3Ddiscrete hyper chaotic map. The digital signature and other image parameters have appends in the watermark image toensure the security and copyright protection of the original image using RSA, Arnold transforms, and SHA-1, techniques.At the receiver side, digital signature and information parameters have retrieved from the watermark image and identify todetect whether an unauthorized person attacks the watermark image or not. To verify the originality of image, the extractedinformation parameter, and the hash value of the extracted watermark have compared with the original value of theparameters and source hash value of the watermark image, respectively. The secret key used in the proposed scheme thatmakes the system more robust and adds security to the system. It helps to authenticate the originality of the receivedimage.RESULTS: We compare the result with the present scheme proposed by Lin at el. The experiments have conducted tocheck various standard image processing attacks, and the watermark extraction process generates a high-quality imageafter different attacks. The primary purpose of the suggested watermarking scheme is to ensure reversibility and highsecurity.CONCLUSION: The experimental outcomes confirmed that not only the recommended scheme attained higher robustnessand imperceptibility in comparison to our earlier scheme. The Watermark image also recovered successfully with theknown secret key.

Semantic Relatedness computation has been a fundamental as well as an essential step for domains likeInformation Retrieval, Natural Language Processing, Semantic Web, etc. Many techniques for SemanticRelatedness calculation in a single domain have been proposed. However, these techniques give inappropriateresults for the massive multidomain dataset because they provide a relation between concepts across differentdomains, which are not related to each other. Their similarities should be minimized. In this paper, anovel method, "modified Balanced Mutual Information(MBMI)," to calculate the semantic relatedness ofmultidomain data has been proposed. In this proposed method, to get semantic relatedness, concepts areextracted, followed by a fuzzy vector from a given corpus. A comparison of the proposed method with otherexisting methods has been performed. We used medical and computer science articles as our dataset. Theproposed method shows better results for multidomain data.

Objective: To find out the indication and frequency of permanent pacemaker implantation in complete heart block patients (CHB). Study Design: Prospective cohort study. Place and Duration of Study: Rawalpindi Institute of Cardiology, Rawalpindi for a period of six months from Mar 2016 to Sep 2016. Material and Methods: Total 153 patients of both genders coming to Rawalpindi Institute of Cardiology with complete heart block were included in the study. The patients fulfilling the inclusion criteria were evaluated for the etiology of complete heart block by history, physical examination, electrocardiography, blood tests, chest x-ray and echocardiography. Later their outcome (pacemaker implantation/medical treatment/death) was determined. Results: Myocardial infarction was found out to be the cause for CHB in 32.1% patients. Out of 153 patients, 12 patients died. In the remaining 141 patients, 77 were implanted with a pacemaker. Conclusions: Myocardial infarction accounted for 32.1% cases of complete heart block and a significant number of patients were implanted with a permanent pacemaker. Hence the trend of pacemaker use is on a rise.

ABSTRACT: INTRODUCTION: Incidence of Pancreatitis is increasing day by day in our country and gallstone disease is a major cause of this disease. OBJECTIVE: To assess the treatment and outcome of these patients and compare it to other similar studies done at national and international level. MATERIALS AND METHODS: It is a case series conducted in surgical department of Madina Teaching Hospital Faisalabad having acute pancreatitis due to gall stone disease during the period of Jan 2015 to Jun 2016. RESULTS: Out of 50 patients in our study, 32(64%) patients were female. Patient ages were in range of 17 to 67 years, most in the fourth decade (34%). Out of these, 37(74%) patients were having mild attack according to ranson's criteria, whereas 10(20%) had moderate and 3(6%) patients had severe attack of pancreatitis.4 (8%) patients had CBD stones at presentation. 6(12%) patients underwent laparotomy due to unclear diagnosis and worsening clinical condition. 3(6%) patients expired in our study, two after laparotomy and one patient expired having worsening of pancreatitis after ERCP. CONCLUSION: Gallstones are a major factor in etiology of acute pancreatitis here in Pakistan so symptomatic gallstone surgery should not be delayed unnecessarily. Early diagnosis based on clinical assessment, lab tests and CT scan is very important for appropriate treatment and to avoid undue laparotomies.

The article proposes three modified percentile estimators for parameter estimation of the Pareto distribution. These modifications are based on median, geometric mean and expectation of empirical cumulative distribution function of first-order statistic. The proposed modified estimators are compared with traditional percentile estimators through a Monte Carlo simulation for different parameter combinations with varying sample sizes. Performance of different estimators is assessed in terms of total mean square error and total relative deviation. It is determined that modified percentile estimator based on expectation of empirical cumulative distribution function of first-order statistic provides efficient and precise parameter estimates compared to other estimators considered. The simulation results were further confirmed using two real life examples where maximum likelihood and moment estimators were also considered.

This study was focused on survival analysis of heart failure patients who were admitted to Institute of Cardiology and Allied hospital Faisalabad-Pakistan during April-December (2015). All the patients were aged 40 years or above, having left ventricular systolic dysfunction, belonging to NYHA class III and IV. Cox regression was used to model mortality considering age, ejection fraction, serum creatinine, serum sodium, anemia, platelets, creatinine phosphokinase, blood pressure, gender, diabetes and smoking status as potentially contributing for mortality. Kaplan Meier plot was used to study the general pattern of survival which showed high intensity of mortality in the initial days and then a gradual increase up to the end of study. Martingale residuals were used to assess functional form of variables. Results were validated computing calibration slope and discrimination ability of model via bootstrapping. For graphical prediction of survival probability, a nomogram was constructed. Age, renal dysfunction, blood pressure, ejection fraction and anemia were found as significant risk factors for mortality among heart failure patients.

Increasing large-scale integration of renewables in conventional power system has led to an increase in reserve power requirement owing to the forecasting error. Innovative operating strategies are required for maintaining balance between load and generation in real time, while keeping the reserve power requirement at its minimum. This research work proposes a control strategy for active power balance control without compromising power system security, emphasizing the integration of wind power and flexible load in automatic generation control. Simulations were performed in DIgSILENT for forecasting the modern Danish power system with bulk wind power integration. A high wind day of year 2020 was selected for analysis when wind power plants were contributing 76.7% of the total electricity production. Conventional power plants and power exchange with interconnected power systems utilize an hour-ahead power regulation schedule, while real-time series are used for wind power plants and load demand. Analysis showed that flexible load units along with wind power plants can actively help in reducing real-time power imbalances introduced due to large-scale integration of wind power, thus increasing power system reliability without enhancing the reserve power requirement from conventional power plants.

This paper presents, with a live field experiment, the potential of increasing wind farm power generation by optimally yawing upstream wind turbine for reducing wake effects as a part of the SmartEOLE project. Two 2MW turbines from the Le Sole de Moulin Vieux (SMV) wind farm are used for this purpose. The upstream turbine (SMV6) is operated with a yaw offset ( α ) in a range of − 12 ° to 8° for analysing the impact on the downstream turbine (SMV5). Simulations are performed with intelligent control strategies for estimating optimum α settings. Simulations show that optimal α can increase net production of the two turbines by more than 5%. The impact of α on SMV6 is quantified using the data obtained during the experiment. A comparison of the data obtained during the experiment is carried out with data obtained during normal operations in similar wind conditions. This comparison show that an optimum or near-optimum α increases net production by more than 5% in wake affected wind conditions, which is in confirmation with the simulated results.

This paper presents the effectiveness of the interior search algorithm in economic power scheduling of a grid-tied DC microgrid with renewable generation (wind and photovoltaic) and battery energy storage. The study presents the modelling and simulation of various DC/DC converters for tracking maximum power from wind and photovoltaic sources and the bidirectional power flow of battery energy storage. The DC microgrid and its controllers were modelled and simulated in MATLAB/Simulink. The generating units were dispatched economically using the interior search algorithm with the objective to minimize the operating cost of the microgrid. The simulated results verify the effectiveness of the interior search algorithm as the daily cost of microgrid operation was reduced by 11.25%.

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines particle swarm optimisation (PSO) with a turbulence intensity⁻based Jensen wake model (TI⁻JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ( C P ) or deflecting wakes by applying yaw-offsets for maximising net farm production. Firstly, TI⁻JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimised strategies are evaluated using simulations based on TI⁻JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 s for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.

Background: The present study was conducted to investigate the antinociceptive activity of methanol extract of Macaranga denticulata (Met.MD) in an animal model, followed by molecular docking analysis. Methods: Antinociceptive activity was determined by acetic acid-induced writhing and formalin-induced licking test in mice. Then, molecular docking study was performed to identify compounds having maximum activity against the COX-1 enzyme using Schrödinger Maestro (version 10.1) to determine docking fitness. Results: A preliminary phytochemical analysis of Met.MD revealed that it contained alkaloids, carbohydrates, phenols, flavonoids, tannins, and terpenoids. Met.MD exhibited a dose-dependent and statistically significant antinociceptive activity in the acetic acid and formalin test at the doses of 200 and 400 mg/kg. In addition, our docking study showed that macarangin had the best fitness score of −5.81 with COX-1 enzyme among six major compounds of M. denticulata. Conclusions: Results of the present study confirmed the potential antinociceptive activity of M. denticulata leaf extract in both in vivo and in silico models.

Background: Tinea pedis is the most common fungal infection especially prevalent in certain groups. The objective of this study was to determine the frequency of tinea pedis and its predisposing factors in the military recruits. Material & Methods: This cross-sectional study was conducted at Combined Military Hospital, D.I.Khan, from March 2012 to September 2012. A random sample of 350 individuals was selected out of individuals who were undergoing initial military training. Clinical examination, microscopic examination of scrapings from suspected lesions to see fungal hyphae and subsequent culture was done for species identification. Findings were recorded on a performa. Data was analyzed using SPSS version 10.0. Results: The mean age of subjects was 18.57±1.02 (16-23) years. On clinical examination tinea pedis was suspected in 34 (9.71%) out of 350 subjects. A total of 10 (2.8%) individuals were found to have positive fungal hyphae while 14 (4%) were culture positive for trichophyton. Out of 14 positive cultures, 8 yielded growth of Trichophyton mentagrophytes, 5 Trichophyton interdigitale and one yielded Trichophyton rubrum. Predisposing factors were occlusive footwear for >8 hours/ day in (96%), history of excessive sweating in (7.7%), history of fungal infection of feet during last 6 months in (3.4%) and fungal infection of nails during the last 6 months in (2.8%). Conclusion: The prevalence of tinea pedis in Pakistani military recruits is low. Occlusive footwear for more than 8 hours a day is the major risk factor. Excessive sweating and history of previous fungal infection are also risk factors.