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This study was conducted to evaluate the effect of different monochromatic light-emitting diode colours on the behaviour and welfare of broiler chicks. A total of 750 one-day-old chicks were used and lighting was set up as follows: pure blue (PB, 440–450 nm), bright blue (460–470 nm), sky blue (480–490 nm), greenish blue (500–510 nm), and green (530–540), while fluorescent white (400–700 nm) was used as a control. Birds were placed into 30 independent light proof pens and each light treatment was replicated five times with 25 birds in each pen. Video was recorded and behaviour was evaluated twice per day and observed five consecutive days in a week. Broiler welfare was evaluated using the characteristics of gait score, tibia dyschondroplasia, tonic immobility duration, and heterophil:lymphocyte ratio. In results, sitting, walking, and ground pecking behaviour were influenced by the light colour from 0 to 7 d. Extending the rearing period from 8 to 21 d resulted in increased sitting behaviour and decreased walking and pecking behaviour in chicks in the PB treatment (P

Dengue fever is a mosquito-borne disease that claims the lives of millions of people around the world. A number of factors like disease's non-specific symptoms, increased viral mutation, growing antiviral drug resistance due to reduced susceptibility, unavailability of an effective vaccine for dengue, weak immunity against the virus, and many more are involved. Dengue belongs to the Flaviviridae family of viruses. The two species of the vector transmitting dengue are Aedes aegypti and Aedes albopictus, with the former one being dominant. Serotypes 2 of dengue fever are spread to the human body and cause severe illness. Recently, dengue has imposed an aggressive effect synergistically with the COVID-19 pandemic. As a result, we concentrated our efforts on finding a potential therapeutic. For this, we chose natural compounds to fight dengue fever, which is currently regarded as successful among many drug therapies. Following this, we started the in silico experiment with 922 plant extracts as lead compounds to fight serotype 2. In this study, we used SwissADME for analyzing ligand drug-likeness, pkCSM for designing an ADMET profile, Autodock vina 4.2 and Swissdock tools for molecular docking, and finally Desmond for molecular dynamics simulation. Ultimately 45 were found effective against the 2'O methyltransferase protein of serotype 2. CHEMBL376820 was found as possible therapeutic candidates for inhibiting methyltransferase protein in this thorough analysis. Nevertheless, more in vitro and in vivo research are required to substantiate their potential therapeutic efficacy.

Bangladesh is prone to recurring natural hazards due to its geographical position and topography. The country has suffered casualties and damage to homes, agriculture, and the economy as a result of tropical cyclones. Effective disaster management approaches are required to reduce the risk of disaster and loss. The Constitution of Bangladesh plays an active role in implementing these approaches at the national and sub-national level. This article analyzes parts of the Constitution addressing disaster management by ensuring disaster governance and adaptive governance. To examine the theoretical aspects of disaster management from a global and Bangladesh perspective, the current institutional role for disaster management, the difference in service delivery for specific organizations, and human rights and humanitarian aspects, a study was conducted based on secondary data and information. Bangladesh's supreme law supports all phases of the disaster management cycle. Consequently, it is said that the Constitution would be an essential document for effective disaster management at all levels.

The empirical study involves detecting vulnerable images in social media. Use a vulnerable image detection dataset with a 78% data accuracy rate using a graph neural network. The study investigates that sharing vulnerable pictures on social networks might cause cyberbullying or stalking victims leads to many stressful events. The inexcusable behavior by many people is encompassing in the digital world. This phenomenon leads to threats of violence, slander, or even leakage of personal images may be included. The study associates vulnerability detection with the pictures posted on social media. In this way, the people of the society will be able to identify the vulnerable images and help to build social awareness. The ability of GNN to model the dependence between nodes in a network allows for a breakthrough in graph analysis research. A graph autoencoder framework can help GNNs solve the network embedding challenge. The approach associate's construction of a custom dataset with two classes, involve Vulnerable and Nonvulnerable images. We declare image size and batch size. After creating the GNN model data is trained using 10 epochs and then plot the accuracy and loss curve. The significance of the accuracy rate reached a satisfactory level. For the statistical approach, a confusion matrix is constructed to compare the actual and prediction data this helps to understand the performance of the proposed model. Graph neural network is very effective to classify images and other things. The findings constructed a Graph Neural Networks (GNNs) is an effective framework for representation learning of graphs.

The manifestation of COVID-19 severity and mortality has been associated with dysregulation of the immune response, often influenced by racial disparities and conferred by changes in hematological and immunological parameters. The biological and hematological parameters as well as the cytokine profiles have been investigated in a cohort of 61 COVID-19-positive patients (categorized into mild, moderate, and severe groups) from Bangladesh by standard analytical methods. Data reported that IL-4 and IL-6 levels were significantly increased while the levels of IFN-γ were significantly reduced in severe COVID-19 patients (p0.05) comparing mild and/or moderate COVID-19 patients. The extent of ESR, Neutrophil, Ferritin, CRP, and D'dimer (p0.05) were found to be significantly increased whereas WBC, Lymphocytes, Eosinophil, and platelets (p0.05) were observed significantly reduced in severe COVID-19 patients comparing to two other groups. Our study exhibited a significantly higher IL-6/lymphocyte ratio in severe COVID-19 patients compared to mild and moderate patients. The calculated Neutrophil-lymphocyte ratio (NLR), IL-6-lymphocyte ratio, Platelet-lymphocyte ratio (PLR), Lymphocyte-monocyte ratio (LMR), and Ferritin-ESR ratio was increased significantly in severe patients. The increase of IL-4 and IL-6 along with CRP and D'dimer may envisage a hyperinflammatory environment and an immune dysregulation which contribute to prolonged viral persistence leading to the severity of the disease. However, the reduced level of IFN-γ can be attributed to less fatality toll in Bangladesh compared to the rest of the parts of the world.

Background: There are variety of established causes of bradycardia (Heart rate

In this era of COVID19, proper diagnosis and treatment of pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite challenging and time-consuming to diagnose accurately due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgement. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network-principal component analysis (CNN-PCA) based feature extraction, and CNN-PCA-ELM with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc.

Systemic lupus erythematosus or SLE is a chronic autoimmune disorder of multiple systems that affects blood vessels and connective tissues. Children with SLE generally have a more severe and aggressive disease course than patients with adult-onset disease. Assess the hematological manifestations of systemic lupus erythematosus (SLE), amely leucopenia, thrombocytopenia, anaemia-with special reference to autoimmune haemolytic naemia (AIHA). Co-relation among different antibodies and clinical and hematological findings In-patients and Out-patients Department (OPD) of Paediatric Medicine, and out-patient Department (OPD) of Dept. of Rheumatology IPGME&R and SSKM Hospital, Kolkata 700020, a tertiary care center in West Bengal. Period of study-1.5 years (February 2018-July 2019). Inpatient Department and Out patient Department(OPD) of Department of Paediatric Medicine, IPGMER& SSKM Hospital. Depending upon the previous records, available patients eligible for the study , 30 patients were taken as study population. Definition of population- Children upto 12 years of age, attending the Department of Paediatric Medicine of IPGME&R and SSKM Hospital, who were diagnosed cases of Systemic Lupus Erythematosus satisfying inclusion and exclusion criteria for this study. Lupus nephritis was seen in more than half of the pediatric SLE cases. Larger number of study population needed to find statistically significant association between different parameters in this study

The clinical presentation of COVID-19 and the specific antibody responses associated with SARS-CoV-2 variants have not been investigated during the emergence of Omicron variants in Bangladesh. The Delta and Omicron variants were identified by post-PCR melting curve analysis of the spike (S) protein receptor binding domain amplicons. Anti-S-protein immunoglobulin-G anti-nucleocapsid (N)-protein immunoglobulin-G and immunoglobulin-A levels were measured by ELISA. The Delta variant was found in 40 out of 40 (100%) SARS-CoV-2 RT-PCR positive COVID-19 patients between 13 September and 23 October 2021 and Omicron variants in 90 out of 90 (100%) RT-PCR positive COVID-19 patients between 9 January and 10 February 2022. The Delta variant associated with hospitalization (74%, 80%, and 40%) and oxygen support (60%, 57%, and 40%) in the no vaccine, dose-1, and dose-2 vaccinated cases, respectively, whereas the Omicron COVID-19 required neither hospitalization nor oxygen support (0%, p < 0.0001). Fever, cough, and breathlessness were found at a significantly higher frequency among the Delta than Omicron variants (p < 0.001). The viral RNA levels of the Delta variant were higher than that of the Omicron variants (Ct median 19.9 versus 23.85; p < 0.02). Anti-spike protein immunoglobulin-G and anti-N-protein immunoglobulin-G within 1 week post onset of Delta variant COVID-19 symptoms indicate prior SARS-CoV-2 infection. The Delta variant and Omicron BA.1 and BA.2 breakthrough infections in the Dhaka region, at 240 days post onset of COVID-19 symptoms, negatively correlated with the time interval between the second vaccine dose and serum sampling. The findings of lower anti-spike protein immunoglobulin-G reactivity after booster vaccination than after the second vaccine dose suggest that the booster vaccine is not necessarily beneficial in young Bangladeshi adults having a history of repeated SARS-CoV-2 infections.

In the last decades, emerging and re-emerging epidemics such as AIDS, measles, SARS, HINI influenza, and tuberculosis cause death to millions of people each year. In response, a large and intensive research is evolving for the design of better drugs and vaccines. However, studies warn that the new pandemics such as Coronavirus (COVID-19) and even deadly pandemics can emerge in the future. The existing confinement approaches rely on large amount of available data to determine policies. Such dependencies could cause an irreversible effect before proper strategies are developed. Furthermore, the existing approaches follow a one-size fits all approach, which might not be effective. In contrast, we develop a game-theory inspired approach that considers societal and economic impacts and formulates the epidemic control as a non-zero sum dynamic game. Further, the proposed approach considers the demographic information leading to providing a tailored solution to each demography. We explore different strategies including masking, social distancing, contact tracing, quarantining, partial-, and full-lockdowns and their combinations and present demography-aware optimal solutions to confine a pandemic with minimal history information and optimal impact on economy.

Background: Children with disabilities and special needs present unique challenges for oral health professionals in the planning and carrying out of dental treatment. oral health care workers need to take cognizance of when preparing treatment plans for children with special needs. Children with disabilities are considered to be a high risk group for dental diseases specially dental caries and periodontal disease. High rates of dental caries, missing teeth, periodontal disease and malocclusion are all indicators of poor oral health of children with disabilities. Objectives:The aim of the study was to assess the oral health status of disabled children in special schools of Dhaka city. Study design: This was a cross sectional study. Study settings and period: The study was conducted in two elementary schools at mirpur area of Dhaka city in the period between of between february 2014 to January 2015. Subjects: 200 disabled children with the age range from 6 to 14 belonging to six different disability groups were examined. Participants were grouped according to their type of disability autistic children, Down syndrome, Cerebralpulsy, mute and deaf, blind and physical disability. Methods: Data was collected by using semi structured quationnaire and checklist.Datawere analyzed by statistical package of social science(SPSS- 20). Results: According to disability the participants autistic 44%, cerebral pulsy 37%, down syndrome 35%, blind 28%, deaf and mute 30% and physical disability 30%.The average number of decayed tooth was found 6.89%. Mean DMFT value was 4.75. Average number of missing tooth was 1.89%.Prevalence of dental caries highest 71% in autistic children then other cetagory of disabilty .Oral hygiene practice of the respondent found very poor.76% children brush teeth in the morning and once in a day.49% respondent use toothpick to clean food from in between teeth.37% of the respondent never visited to dentist. 34.5% respondents had normal gingiva, 49% had mild gingivitis and 16.5% had marked gingivitis. Highest numbers of trauma cases were recorded in visually impaired group. 98.7% children required some form of dental treatment. Conclusion: Oral health status of children with disabilities was poor and it is important to concentrate on a preventive approach and provide proper dental education to parents of disabled childrens. More attention should be paid to the oral hygiene practice of disabled children Update Dent. Coll. j: 2019; 9 (2): 32-35

The study was conducted to determine growth performances and bioaccumulation of Cr, Cu, Cd, Pb and Ni in fish organs such as intestines, muscle and gills of study species after culturing them for twelve weeks by using different commercial fish feeds. The highest average daily gain (ADG, g/day) and specific growth rate (SGR; %/day) were found in fish fed quality feed and lowest values were found in the fish fed mega feed. Best feed conversion ratio (FCR) value was found in fish fed quality feed among five different feeds. In cultured Koi, the concentration of heavy metals were found higher than initial concentration in fish feeds and collected fingerling samples. Heavy metal levels in cultured fish was Cu > Pb > Cr > Cd > Ni and levels of bioaccumulation in individual organs was intestines > gills > muscle. The concentration of Cu, Pb and Cr exceed the tolerable limit approved by FAO in fishes cultured with the experimental feeds. Considering the present study, Koi cultured with these experimental feeds may not safe for human consumption. Bangladesh J. Zool. 47(1): 77-88, 2019

This study investigated the restorative effect of Leea macrophylla ethanol root extract (LMERE) in carbon tetrachloride (CCl4) induced hepatic injury. It also tried to unfold the underlying mechanism through ligand-receptor interactions. Prior to conduct the CCl4 induced animal model study, the in vitro antioxidative capacities of LMERE were investigated. Gas chromatography mass spectroscopy (GC–MS) was accomplished to identify the prevalent bioactive compounds. The molecular docking was performed using Schrodinger Suites 2017-1. Results showed the promising antioxidative potentials of LMERE in in vitro models. Upon treatment of CCl4 intoxicated animals with LMERE, serum ALT and AST were found to be significantly (p

The ever-increasing demand for sophisticated cyber- physical systems (CPS), combined with the fabless model, led to vulnerability exploits in the IC supply chain, especially the insertion of hardware Trojans (HTs). The HTs are malicious modifications made to an authentic design to disrupt the functioning of the integrated circuits (ICs). In this work, we present a survey of the state-of-the-art HT designs focusing on the Trojan design, the risk level in the supply chain, and the targeted platform, followed by a discussion based on our observations. We categorize the HT designs based on the targeted platforms such as ML accelerators, IoT devices, FPGAs, ASICs, memory devices, CPU, and Cryptographic cores.

Pneumonia is a fatal contagious agent that causes respiratory disorders. The methodology utilized by an advisor to evaluate pneumonia through chest X-ray images is tedious and pricey. However, it also requires an experienced and skilled radiologist to correctly interpret the chest x-ray images. Moreover, the diagnosis can be confusing some times. Subsequently, researchers should pay attention to deploying a PC supported framework to recognize pneumonia. Again, clinical and experimental data can come from many different laboratories following different protocols and so databases are usually particularly disordered and noisy. To solve this issue, We have utilized EDA (Exploratory Data Analysis) to interpret the data set to clarify its basic traits. The class imbalance issue is reduced and data augmentation is also applied. A recently designed CNN (convolutional neural network) model EfficientNet is used to train the classifier by transfer learning and fine-tuning. The model has achieved a test accuracy of 96.33% and an AUC(Area Under Curve) of 0.991. We have also calculated other strategic objectives, such as precision, recall, and F1 score, to demonstrate the efficacy of our approach in finding pneumonia in the exclusion of trained physicians.

Online business is one of the rapidly growing business sectors of current world. Now-a-days people purchase a lot of things from online shopping sites. Sales of online products are most often review driven. Thus, detecting deceptive reviews is getting more importance day by day. Sentiment analysis has great importance in fake review detection system. This paper introduces a sentiment analysis model that can separate positive and negative sentimental reviews efficiently. It shows an analysis of sentiment distribution for fake and truthful reviews. Also, the proposed sentiment model is used to find the impact of probabilistic sentiment score in fake online review detection using a hotel review dataset.

In the last few decades, several epidemic diseases have been introduced. In some cases, doctors and medical physicians are facing difficulties in identifying these diseases correctly. A machine can perform some of these identification tasks more accurately than a human if it is trained correctly. With time, the number of medical data is increasing. A machine can analyze this medical data and extract knowledge from this data, which can help doctors and medical physicians. This study proposed a lightweight convolutional neural network (CNN) named ChestX-ray6 that automatically detects pneumonia, COVID19, cardiomegaly, lung opacity, and pleural from digital chest x-ray images. Here multiple databases have been combined, containing 9,514 chest x-ray images of normal and other five diseases. The lightweight ChestX-ray6 model achieved an accuracy of 80% for the detection of six diseases. The ChestX-ray6 model has been saved and used for binary classification of normal and pneumonia patients to reveal the model's generalization power. The pre-trained ChestX-ray6 model has achieved an accuracy and recall of 97.94% and 98% for binary classification, which outweighs the state-of-the-art (SOTA) models.

The following paper represents a new and improved wall climbing mechanism that can climb on flat and spherical surfaces. The design methodology of the robot was solely directed towards heavy lifting purposes; as the robot uses a redesigned mechanism to endure an enhanced workload. The robot was specially designed to climb any given vertical surface with the help of the Reverse Propulsion technology; which provides backward thrust as a convenient heavy-duty suction mechanism. The robot was built keeping overall body weight at a minimum and consisted an array of powerful brushless motors for the suction and navigation. The robot can be put to multiple use based on its operation such as surveillance, inspection, cleaning etc.

Additional file 2: Supplementary information 2. Additional tables provided as supplementary information. Table S2.1. Equations used as ratio-to-size estimators to estimate the biophysical indicators. Table S2.2. Total Carbon density by land cover in different zones. Table S2.3. Comparison between the classes identified from the land cover map and field observations.

Additional file 1: Supplementary information 1. R scripts for the analysis of the first cycle of the Bangladesh Forest Inventory. 1. R-scripts for the data importation. 2. R-scripts for the preparation of databases. 3. R-scripts for the ratio estimations.

The number of publicly known cyber-security vulnerabilities submitted to the National Vulnerability Database (NVD) has increased significantly. However, it is cumbersome to explore useful information from this large corpus of unstructured data to find meaningful trends over time without proper tools. Prior works with this purpose have mainly focused on the software vulnerabilities and fail to provide a storytelling framework that can extract useful information about the relationship and trends within the CVE and CWE databases over time. Additionally, hardware attacks on IoT devices are evolving very rapidly due to the recent proliferation of computing devices in mobile and IoT domains. Thus, herein, we focus on IoT hardware vulnerabilities and develop an Ontology-driven Storytelling Framework (OSF) which aims to identify similar patterns of vulnerabilities over time, to help mitigate the impacts of vulnerabilities or predict and prevent future vulnerabilities.

Additional file 3: Supplementary information 3. An illustration of differences between land class identification and delineation using the land cover map and field observations. Figure S3.1. An illustration of differences between land class identification and delineation using the land cover map and field observations.

With increasing use of internet, online business platforms are becoming the largest market place of the world. Purchase of online product is heavily dependent on user reviews. Some dishonest groups of people misuse this fact by posting fake reviews to promote their own products or demote their competitors. Detection of fake online reviews can be considered as a binary classification task that models a classifier to tell whether a review is fake or true. In this paper, we have developed an effective supervised machine learning approach to classify fake online reviews using a dataset that contains hotel reviews from online websites.

The increased global incidence of hepatitis E virus (HEV) infections, warrants accurate and affordable diagnostics across different geographical regions. The soluble and highly conserved HEV open reading frame 2 (ORF2) capsid antigen (HEV-Ag) is detectable in self-limited acute enteric hepatitis by HEV-Ag ELISA which is a promising serological assay in settings where HEV-RNA testing is not feasible. Our aim was to assess the HEV-Ag biomarker in an HEV outbreak in a low income country.A prospective single center longitudinal study during HEV outbreaks in the Chittagong, Bangladesh region between October 2018 and October 2019 was conducted based on recruitment of acute jaundice cases with clinical signs and symptoms of suspect HEV infections. Acute HEV infection was defined as a positive test result for anti-HEV IgM antibodies.Forty four of the 51 enrolled enteric hepatitis cases (86 %) were confirmed HEV by anti-HEV IgM ELISA at day 0 hospital entry. The anti-HEV-IgM and IgG were positive in all patients and did not reveal significant differences; neither between the time points day 0 and follow-up hospitalization on day 2-6 or day 7-10 nor between RNA-positive (n = 36) versus RNAnegative (n = 8) HEV groups. The HEV-Ag positivity was higher in viral RNA-positive (29/36, 81 %) than the viral RNA-negative (1/8, 12 %) group, p0.001 and the HEV-Ag levels positively correlated with viremia, r = 0.77, p0.0001. All non-HEV cases; n = 7 tested negative anti-HEV IgM and HEV-Ag and 5 of 7 (71 %) tested anti-HAV IgM positive.The HEV-Ag ELISA is a reliable and practical diagnostic tool in this acute HEV outbreak.

Logic obfuscation emerged as an efficient solution to strengthen the security of integrated circuits (ICs) from multiple threats including reverse engineering and intellectual property (IP) theft. Emergence of Boolean Satisfiability (SAT) attacks and its variants have shown to circumvent the security mechanisms such as obfuscation and a plethora of its variants. A plethora of advanced security defenses to thwart the SAT attacks are introduced. Despite the effectiveness, the imposed overheads in terms of area and power are unacceptably high. In contrast, our current work focuses on devising an iterative, dynamic and intelligent SAT-hard clause generator for a given SAT-prone problem, termed as SATConda. The SATConda is a SAT-hard clause generator that utilizes a bipartite propagation based neural network model. The utilized model comprises multiple layers of artificial neural networks to extract the dependencies of literals and variables, followed by long short term memory (LSTM) networks to validate the SAT hardness. The SATConda is trained with conjunctive normal form (CNF) of the IC netlist that are both SAT solvable and SAT-hard. Further, the SATConda is equipped with a SAT-clause generator to convert a CNF from satisfiable (SAT) to unsatisfiable (unSAT) with minor perturbation (which translates to minor overheads) so that the SAT-attack cannot decrypt the keys. To the best of our knowledge, no previous work has been reported on neural network based SAT-hard clause or CNF translator for circuit obfuscation. We evaluate our proposed SATConda's empirical performance against MiniSAT, Lingeling and Glucose SAT solvers on ISCAS'85 benchmark circuits.