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Religious involvement in humanitarian aid and development takes countless forms, varying by country, traditions, and sector. Research and operational evaluations have expanded in recent years, with some sectors far better documented than others, notably health and peacebuilding. There are, however, substantial gaps in evidence, most significantly regarding local and non-formal involvement. Pandemic preparedness and response are critical issues ready for further development. Family planning, a USAID priority, exemplifies sensitivities but also positive examples and potential. Experience of religious engagement on women's empowerment including specific issues like gender-based violence (GBV) and child marriage, can be complex, but their importance warrants particular attention. Child protection poses similar issues. [ FROM AUTHOR] Copyright of Review of Faith & International Affairs is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all Abstracts.)

This essay introduces a special, open-access issue of this journal focused on the intersection of religion and relief & development. It describes the discussions—of terminology and tangibility, of ...

Introduction Pyogenic granulomas are benign vascular lesions of the skin and mucosa which are often a source of concern because of their recurrent bleeding even with minimal trauma. Current treatment for pyogenic granuloma is ablative; no medical therapy is standardized to date. Timolol, due to its vasoconstrictive effect, vascular growth factor inhibition and apoptosis promotion properties, is a potential therapeutic option. Objectives: To assess the effectiveness and safety of topical timolol in the treatment of pyogenic granulomas. Methods A two-centre, double-blind and placebo-controlled trial (Registration CTRI/2019/04/018581) was conducted. Patients of either sex were recruited with pyogenic granuloma lesions of less than eight weeks duration. Topical treatment with 0.5% timolol or matching glycerin placebo was continued for six weeks. Changes in color, size, bleeding tendency, physicians’ and patients’ global assessments and adverse events were assessed. Results Forty subjects were randomized between the two groups which were comparable in age, sex, duration of illness and baseline lesion size.Significant improvement was noted with timolol, with color change from first follow-up onwards and lesion size reduction from second follow-up onward. Patients’ assessment of bleeding tendency also showed imrovement from the second visit onward. Between-group comparison showed significant difference with respect to percentage reduction in size (timolol 40.9%, placebo 3.4%; P = 0.002). Rescue treatment (electrosurgery) was required in five patients on placebo and in one in the timolol group (P = 0.182). Complete resolution occurred in 2 (10%) patients with timolol and in no patients on placebo (P = 0.231). Limitations: We observed effects of treatment for only six weeks. Conclusion Topical timolol may be a treatment option for early pyogenic granulomas but complete resolution is unlikely in six weeks. Studies of longer duration are required to assess resolution and recurrence rates.

Background: Leprosy is an infectious disease which faces diagnostic challenges. Slit-skin smear (SSS) is an age simple diagnostic technique, yet not commonly applied by health care providers. The study aimed to determine the effectiveness of SSS in terms of validity, diagnostic accuracy, and percentage agreement against punch biopsy in diagnosing leprosy among leprosy patients who were diagnosed with leprosy on clinical grounds only (i.e., number of skin lesions and/or peripheral nerve thickening). Methods: An evaluation study of diagnostic tests with a cross-sectional design was conducted at a tertiary care center of Bankura. In general, 70 new untreated leprosy patients, diagnosed solely by clinical grounds (i.e., count of skin lesions and/or thickening of the nerve) and attending the dermatology outpatient department (From February 2019 to January 2020) were enrolled in this study. After excluding pure neuritic, relapse, and seriously ill patients by consecutive sampling, they were subjected to both SSS and punch biopsy using a standard process. SPSS for Windows (Version 16.0., Chicago, SPSS Inc.) was used to analyze data. Z test, chi-square test, and kappa test were conducted to test the statistical significance between the effectiveness of SSS and punch biopsy. Results: The sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and diagnostic accuracy of SSS were 81.81%, 95.83%, 90%, 92%, 20%, 0.19%, 102.87%, and 91.42%, respectively. Based on the results, SSS could detect acid-fast bacilli (AFB) in clinically diagnosed leprosy cases slightly less than punch biopsy, but it was statistically insignificant (Z=0.3689, P=0.71138, df=1). Finally, Cohen’s Kappa coefficient was 0.796, representing substantial agreement between SSS and punch biopsy (95% CI: 0.641-0.951). Conclusions: Overall, SSS is more or less equally effective as compared to punch biopsy in confirming leprosy cases. Interest and training on SSS among resident doctors should be emphasized accordingly.

Purpose We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. Methods TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUVmean, SUVmax, and lean body mass-normalized SULpeak measures. Results Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUVmean, SUVmax, and SULpeak measures. Conclusions We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial.

Human detection and activity recognition is very important research area in the healthcare, video surveillance, pedestrian detection, intelligent vehicle system and home care center. Among the various human activity detection frameworks, the statistical based approach were most intensively studied and used in practice in which pattern recognition was traditionally formulated. More recently, supervised learning based techniques and methods imported from statistical learning theory have deserved increasing attention. Many new supervised learning methods such as transfer learning, multi-instance learning, and the new trends in deep learning techniques have used for the formulation of solutions to the human activity detection. This paper reviews the automatic human detection and their activity recognition in the video sequences and static images. We explain several problems of human detection and activity recognition in different steps such as processing, segmentation of human features extraction and classification. Moreover, discuss the problems in each step and provide the recent state- of-the-art methods, gaps between recent methods, technical difficulties, applications and their challenges. Several features extraction techniques and corresponding problems for human classification have been discussed in details. Special emphasis have been given on convolution neural network that solves the problem of human segmentation, efficient classification and activity recognition. The objective of this review paper is to summarize and review related of the established and recent methods used in various stages of a human detection and activity classification system and identify research topics and applications that are at the forefront of this exciting and challenging field. Further, the evaluation protocols (i.e. datasets and simulation tools) and possible solution of current limitation have been discussed briefly in this survey.

Colloidal quantum dots (QDs) are promising materials for optoelectronic devices. In this paper, monodispersed and environment stable cobalt (Co)-doped CdS QDs have been synthesized and characterize...

In Bangladesh, 4 million students study in 64000 madrasha, which represent 7% of all students, most of these are unregistered. There is little evaluation of helminthic infestation by any authority. It is believed that madrasha students came from vulnerable part of society. : Present Sheikh Hasian government declared on equivalency of their certificate therefore it is important to study on them and evaluate their helminthic infestation. May be this is one of the first study on helminthic infestation on grass root level madrasha students in Bangladesh. We conducted the study to evaluate on helminthiasis to find out current situations, to identify the risk factors and for intervention to control of helminthic infestation. This cross sectional study was conducted on 164 from 1000 residential students by simple random sampling. Face to face interview and anthropometric measurement were conducted by semistructured open ended questionnaire from those students. Out of hundred-sixty four students all were male, age range from 06-18 years, ova found 75% students in their stool sample, 71% have multiple helminthasis, Ascaris Lumbricoids (AL) was the most (28%) prevalence, in polyparasitism 58% were Ascaris Lumbricoids and Trichuris Trichuria (AL+TT), anal itching found 68% students which indicate pin worm, no antihelminthic intake 76% students within 6 months. Teachers and parent’s health education help to prevent helminthasis. Regular survey, evaluation is needed to identify the risk factors of helminthasis for intervention, monitoring, guidance and training of students and teachers to improve their personal hygiene practice. Moreover need to intake of regular antihelminthic for dewarming to build a healthy green Bangladesh. Bangladesh Med J. 2019 May; 48 (2): 44-47

The recent COVID-19 pandemic caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a significant impact on human life and the economy around the world. A reverse transcription polymerase chain reaction (RT-PCR) test is used to screen for this disease, but its low sensitivity means that it is not sufficient for early detection and treatment. As RT-PCR is a time-consuming procedure, there is interest in the introduction of automated techniques for diagnosis. Deep learning has a key role to play in the field of medical imaging. The most important issue in this area is the choice of key features. Here, we propose a set of deep learning features based on a system for automated classification of computed tomography (CT) images to identify COVID-19. Initially, this method was used to prepare a database of three classes: Pneumonia, COVID-19, and Healthy. The dataset consisted of 6000 CT images refined by a hybrid contrast stretching approach. In the next step, two advanced deep learning models (ResNet50 and DarkNet53) were fine-tuned and trained through transfer learning. The features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization approach. For each deep model, the Rao-1 algorithm and the PSO algorithm were combined in the hybrid approach. Later, the selected features were merged using the new minimum parallel distance non-redundant (PMDNR) approach. The final fused vector was finally classified using the extreme machine classifier. The experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%. Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework.

In the medical field, image segmentation is a paramount and challenging task. The head and vertebral column make up the central nervous system (CNS), which control all the paramount functions. These include thinking, speaking, and gestures. The uncontrolled growth in the CNS can affect a person’s thinking of communication or movement. The tumor is known as the uncontrolled growth of cells in brain. The tumor can be recognized by MRI image. Brain tumor detection is mostly affected with inaccurate classification. This proposed work designed a novel classification and segmentation algorithm for the brain tumor detection. The proposed system uses the Adaptive fuzzy deep neural network with frog leap optimization to detect normality and abnormality of the image. Accurate classification is achieved with error minimization strategy through our proposed method. Then, the abnormal image is segmented using adaptive flying squirrel algorithm and the size of the tumor is detected, which is used to find out the severity of the tumor. The proposed work is implemented in the MATLAB simulation platform. The proposed work Accuracy, sensitivity, specificity, false positive rate and false negative rate are 99.6%, 99.9%, 99.8%, 0.0043 and 0.543, respectively. The detection accuracy is better in our proposed system than the existing teaching and learning based algorithm, social group algorithm and deep neural network.

In the Industry 4.0 Era, big data and automation will require precise knowledge that allows one to control, monitor and predict a process. Electroforming, which is the fabrication of free-standing components using electrodeposition, is rapidly gaining acceptance as a sustainable additive manufacturing technology. However, current knowledge of electroforming is based on empirical data, and academic engagement in this area has been limited. This article throws light on some of the complex issues surrounding the electrochemical and chemical behaviour during electroforming, which are yet unresolved. The differences between cathodic reactions in sulfamates and sulfates, ambiguities related to the role of boric acid and paucity of data on anode reactions are highlighted.

Nowadays, the importance of continuous connectivity of wireless network has increased because of increase in the number of applications in handheld devices. For offering continuous connectivity, seamless mobility management is very much necessary. Many mobility management schemes have been proposed. The techniques can broadly classified as, tunnel based scheme and routing based scheme. The tunnel based techniques has the advantage of limited route management packet transmission. But disadvantage is that besides source and destination addresses each of the data packets carry an extra header for routing. In case of routing based scheme, advantage is that the data packets do not carry any extra header for routing. But the disadvantage is that during handoff lots of route management packets are transmitted. As the mesh client speed increases, these problems further increase. Hybrid mobility management technique has been proposed in this paper for minimizing the problems of both tunnel based scheme and routing based scheme. It has been designed to handle highly mobile mesh clients. The proposed scheme routes internet packets using routing table entries. On the other hand, it uses tunneling to route the intranet packets to the destination. The hybrid scheme and some of the existing schemes have been analyzed and compared. Performance comparison shows that the hybrid scheme outperforms these techniques.

Nowadays, the web series has turned out to be part and parcel of viewers entertainment world. At present when people can’t find out so much time from their busy schedule to take a correct decision over enormous options and happiness depends on others suggestions, they prefer to select or purchase a new product based on the feed-back from other users who have used a similar or similar type of products. Instead of doing these time-consuming tasks manually, a recommender system needs to be implemented to do all these tasks automatically. In this project, a Machine learning-based web Series recommendation system has been designed to suggest web series to the users which they have not watched yet. In this recommendation system at first, some parameters have been taken as features of tagging, and all the elements have been mapped with their respective id created separately for each of the selected para-meters and using tagging, we have got different classes for a list of web series. After tagging all the features of web series have been mapped and a recommendation system has been used to recommend unseen but preferable movies to the users. Different types of analysis on parameters which are used in our system and their results and different graphs prove the robustness of our recommendation system.

Aim Reversal of hartmanns is still an operation associated with significant morbidity. Although various studies have tried to assess the best time for attempting reversal after the primary operation, there is still no consensus. The aim of our study was to look at our experience over the last three years to find any possible factors which determine the duration between primary operation and reversal and compare laparoscopic vs open approach. Methods Prospective cohort including consisting of patients who underwent hartmanns reversal in 3 years was analysed (January-2017 to December-2019). Data was collected retrospectively from clinical notes. Results Among the patients (n = 50) there was equal distribution of males (52%) and females (48%). Although the initial operation was open in 74% patients, reversal was attempted laparoscopically in 46% with a conversion rate of 43.4%. Median duration between initial operation and reversal was 433 days. There was no significant association between duration before reversal and patient comorbidities or the indication for the index procedure. Also there was no statistical difference in postoperative hospital stay or immediate postoperative morbidity between laparoscopic and open hartmanns reversal. Conclusion There was no difference in immediate postoperative outcomes between patients who had laparoscopic or open hartmanns reversal. There was no determining factor which made the surgeon prefer laparoscopic over open approach nor was there a difference in duration between primary operation and reversal based of patient factors or method of approach. Hence timing of hartmanns reversal and the method of approach still depends on surgeon preference and experience.

Currently, the pulmonary route has obtained a lot of research attention as a parallel non-invasive way for drug delivery to both local tissue and systemic circulation. Different devices for delivering the bioactive through pulmonary routes are available in the market, but none of them possess the required qualities for efficient and optimal pulmonary delivery. Recently the massive development in the area of pharmaceutical nanotechnology has opened the door of opportunity for fabrication and clinical implementation of inhalable nanomedicines with the ability to target specific sustained and controlled pulmonary delivery of drugs. Several studies suggest the ability of liposomes and solid lipid nanoparticles (SLNs) as lucrative nanocarriers for delivering the drugs through the pulmonary route. But still, we have a fragmented understanding regarding how the drug carriers and the individual parts of the carriers (as opposed to the loaded drugs) are disposed of from the lungs. In this way, it becomes significant to clarify the kinetics of the carriers in the lungs after pulmonary administration. This chapter possesses an aim to give a clear understanding regarding the mechanism and pharmacokinetic attributes of the clearance of liposomes and SLNs from lungs after administering through pulmonary route.

Simple Summary Co-clinical trials are an emerging area of investigation in which a clinical trial is coupled with a corresponding preclinical trial to inform the corresponding clinical trial. The preclinical arm aids in assessing therapeutic efficacy, patient stratification, and designing optimal imaging strategies. There is much interest in harmonizing preclinical and clinical quantitative imaging pipelines. Radiomics is widely explored in clinical imaging to predict response to therapy. In preclinical imaging, high-throughput radiomic analysis is limited by manual delineation of tumor boundaries, which is labor intensive with poor reproducibility. Our proposed deep-learning-based system was trained to automatically segment tumors from multi-contrast MR images and extract radiomic features. The proposed method is highly reproducible with significant correlation in radiomic features. The deployment of this pipeline in the preclinical arm would provide high throughput and reproducible radiomic analysis. Abstract Preclinical magnetic resonance imaging (MRI) is a critical component in a co-clinical research pipeline. Importantly, segmentation of tumors in MRI is a necessary step in tumor phenotyping and assessment of response to therapy. However, manual segmentation is time-intensive and suffers from inter- and intra- observer variability and lack of reproducibility. This study aimed to develop an automated pipeline for accurate localization and delineation of TNBC PDX tumors from preclinical T1w and T2w MR images using a deep learning (DL) algorithm and to assess the sensitivity of radiomic features to tumor boundaries. We tested five network architectures including U-Net, dense U-Net, Res-Net, recurrent residual UNet (R2UNet), and dense R2U-Net (D-R2UNet), which were compared against manual delineation by experts. To mitigate bias among multiple experts, the simultaneous truth and performance level estimation (STAPLE) algorithm was applied to create consensus maps. Performance metrics (F1-Score, recall, precision, and AUC) were used to assess the performance of the networks. Multi-contrast D-R2UNet performed best with F1-score = 0.948; however, all networks scored within 1–3% of each other. Radiomic features extracted from D-R2UNet were highly corelated to STAPLE-derived features with 67.13% of T1w and 53.15% of T2w exhibiting correlation ρ ≥ 0.9 (p ≤ 0.05). D-R2UNet-extracted features exhibited better reproducibility relative to STAPLE with 86.71% of T1w and 69.93% of T2w features found to be highly reproducible (CCC ≥ 0.9, p ≤ 0.05). Finally, 39.16% T1w and 13.9% T2w features were identified as insensitive to tumor boundary perturbations (Spearman correlation (−0.4 ≤ ρ ≤ 0.4). We developed a highly reproducible DL algorithm to circumvent manual segmentation of T1w and T2w MR images and identified sensitivity of radiomic features to tumor boundaries.

Numerous recent works highlight the limited utility of established tumor cell lines in recapitulating the heterogeneity of tumors in patients. More realistic preclinical cancer models are thought to be provided by transplantable, patient-derived xenografts (PDXs). The inter- and intratumor heterogeneity of PDXs, however, presents several challenges in developing optimal quantitative pipelines to assess response to therapy. The objective of this work was to develop and optimize image metrics for 18F-FDG PET to assess response to combination docetaxel and carboplatin therapy in a co-clinical trial involving triple-negative breast cancer PDXs. We characterized the reproducibility of standardized uptake value (SUV) metrics to assess response to therapy, and we optimized a preclinical PERCIST paradigm to complement clinical standards. Considerations in this effort included variability in tumor growth rate and tumor size, solid tumors versus tumor heterogeneity and a necrotic phenotype, and optimal selection of tumor slices versus whole tumor. Methods: A test-retest protocol was implemented to optimize the reproducibility of 18F-FDG PET SUV thresholds, SUVpeak metrics, and preclinical PERCIST parameters. In assessing response to therapy, 18F-FDG PET imaging was performed at baseline and 4 d after therapy. The reproducibility, accuracy, variability, and performance of imaging metrics to assess response to therapy were determined. We defined an index called the Quantitative Response Assessment Score to integrate parameters of prediction and precision and thus aid in selecting the optimal image metric to assess response to therapy. Results: Our data suggest that a threshold of 25% of SUVmax (SUV25) was highly reproducible (

Children mostly suffer from respiratory illnesses around the world. Situation is not different in Bangladesh as suggested by many studies on childhood illness. But most of these studies have been done in tertiary level hospitals located in urban areas and very few were done in rural setting. This study was conducted to estimate the frequency and to determine the pattern of respiratory morbidity among children and also to assess the impact of passive smoking on the magnitude of acute respiratory tract infections (ARI) among children. This was an observational study. It was carried out on 1002 children over a period of about 180 days, who attended the Tungipara Upazila Health Complex (UHC), Gopalganj, Bangladesh between January 2018 and June 2018. Data were collected through face to face interview, physical examination, relevant investigations and collected data in the questionnaire for respiratory illness and others then respiratory data were not collected. Out of 1002 children, more then half (54%) were male and the age range was new born to 168 months. Among them upper respiratory tract infections (URTI) were 98.5% as against only 1.5% of lower respiratory tract infections (LRTI). In this study, 38% fathers were smoker and no mother found as smoker. Families of 63% patients used smoke producing substances for cooking. Respiratory illness was more prevalent among the children whose fathers used to smoke tobacco χ2 (1) 380 P 0.01. As well as babies more vulnerable for respiratory morbidity where smoke producing fuel used for cooking χ2 (1) 170 P 0.01. Children suffered more from diseases affecting the respiratory system than diseases of other systems. Simple cough related illness was the most common URTI. Under 05 children suffered most. Respiratory illness was more common among those whose fathers smoked tobacco. Children were more vulnerable to respiratory morbidity, where smoke producing fuel used for cooking. There need multicentric study to observe the real picture of respiratory morbidity. Bangladesh Med J. 2019 Jan; 48 (1): 1-4

This paper presents an efficient medical image indexing and retrieval method using two new proposed feature descriptors named as threshold local binary AND pattern (TLBAP) and local adjacent neighborhood average difference pattern (LANADP). In basic local binary pattern (LBP), every center pixel is considered as a threshold to generate the binary pattern, whereas in the proposed method a threshold value is calculated using the highest pixel intensity of the neighboring pixels to construct the threshold local binary pattern (TLBP). Thereafter, logical AND operation is performed between LBP and TLBP pattern to produce TLBAP pattern. The objective of the other feature descriptor named here as LANADP is to explore the relationship of neighboring pixels with its adjacent neighbors in vertical, horizontal and diagonal directions. In the proposed work, both TLBAP and LANADP features are concatenated in the form of the histograms to generate the final features vector and the performance of the system is evaluated. To test the effectiveness of the proposed method, three publicly available medical image databases, namely OASIS-MRI brain images, NEMA-CT images and VIA/ELCAP-CT images, are used. Two measures, viz. average retrieval precision and average retrieval rate, have been used to evaluate the performance of the method proposed which is further compared with some existing local pattern-based methods. The experimental results show that the proposed methods give better results as compared to the other existing methods considered in this study.

Single copy Distributed Software Transactional Memory protocol maintains only one replica of each object in the system and is therefore prone to failures in large scale dynamically changing network. In this paper we propose a replication model using quorum system for transactional memory protocol where communication among the nodes takes place using gossip. The previous protocols demand a static structure over the network. Maintenance of a static structure for a dynamic network requires a significant overhead. Our method executes on an unstructured network which does not require adaption in case of node joining and node leaving. The algorithm maintains the coherence of the objects and aims to achieve low communication cost while reducing execution time of the transactions. The algorithm achieves a message complexity of $$ {\text{O}}\left( {\sqrt n } \right) $$ and time complexity of $$ {\text{O}}\left( {\log \sqrt n } \right) $$ which is an improvement over previous replication protocols for distributed transactional memory. Simulation results shows that the method exhibits better fault tolerance and requires less number of messages than existing approaches.

The appearance and structure of blood vessels in retinal fundus image is a fundamental part of diagnosing different issues related with such as diabetes and hypertension. The proposed blood vessel segmentation in fundus image using Clifford Algebra approach is divided into three steps. Image vectorization as a first step helps to convert the image space into Clifford space. Next step introduces Clifford matched filter as a proposed mask which works for retinal blood vessel extraction. The third and final step of this method is Clifford convolution operation with the help of Clifford convolution. This mask generates edge points along the boundaries of the blood vessels. The edge points are represented as a Grade-0 vector or scalar unit. Discrete edge points along the boundary of blood vessels are the edge pixels instead of continuous edges. The output of this method differs in the representation of vessel tree compare to other existing methods. The output image can be defined as the edge point set. This method achieves blood vessel segmentation accuracy of 94.88% and 92.95% on two publicly available datasets STARE and DRIVE respectively in less than 0.5 s per image. The proposed matched filter and the segmentation technique opens many windows of reliable and faster processing for further image processing steps on retinal fundus images.

Copy-move forgery is one of the most popular tampering artefacts in digital images. However, tampering effect in digital images makes the authentication of the processing as untrustworthy. In this ...

The networks (SDN) poses many potential security threats because of its principle of dissociating the network management interface from the underneath networking components and making a programmable networking system. Many security countermeasures have been proposed regarding this current trending environment, but none of the research has considered the existing vulnerabilities in SDN entities. This paper demonstrates the vulnerabilities using CVSS in the first place and then identifies the mutual relationship between the vulnerabilities using Bayesian network methodology.

The Vehicular Ad Hoc Network (VANET) plays a vital role in the development of smart cities, especially in ensuring vehicles' safety on roads. However, VANET wireless-based networks face some challenges such as security, stability, communication, and reliability. To resolve these issues, we propose a fuzzy cluster head selection scheme in Cognitive Radio (CR) VANET, which uses the CR technology for the spectrum sensing algorithm. In this technology, the free spectrums of the primary user are utilized by secondary users without any correlation. Moreover, we have considered some input parameters such as vehicles' average velocity, distance, network connectivity level, lane weight and trustworthiness for the fuzzy system based CR VANET in this research. The selected cluster head provides stability and reliability to the cluster compared to the state of art techniques. Extensive experiments were conducted in order to evaluate the effectiveness of the proposed approach. However, simulation results authenticate more stable and secure cluster formation using the proposed fuzzy logic based CR VANET.

PurposeWe sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to optimize robust radiomic features associated with response to therapy in the context of a co-clinical trial and implement PDX-optimized image features in the corresponding clinical study to predict and assess response to therapy using machine-learning (ML) algorithms.MethodsTNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging study to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUVmean, SUVmax, and lean body mass normalized SULpeak measures.ResultsSixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUVmean, SUVmax, and SULpeak, measures.ConclusionsWe optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in a context of a co-clinical imaging trial.DECLARATIONSFundingThis work was supported by NCI grants U24CA209837, U24CA253531, and U54CA224083; U2CCA233303, and K12CA167540; Siteman Cancer Center (SCC) Support Grant P30CA091842; and Internal funds provided by Mallinckrodt Institute of Radiology.Conflicts of interest/Competing interests.None.Availability of data and materialAll the co-clinical data will be available for download through the Washington University School of Medicine Co-Clinical Imaging Research Resource web portal at https://c2ir2.wustl.edu/, co-clinical database (CCDB).Code availabilityNot applicable.Authors’ contributionsConceptualization: SR, FOA, KIS; Methodology: SR, TDW, SL, KIS; Formal analysis and investigation: SR, KIS; Writing - original draft preparation: SR; Writing - review and editing: RLW, FD, KIS; Funding acquisition: RWL, FOA, SL, KIS; Resources: SL; Supervision: FD, KIS. All authors read and approved the final manuscript.Ethics approvalAll studies were performed with approval from the Washington University Humans subjects research committee and animal studies committee.Consent to participateInformed consent to participate in the study was obtained from all participants.Consent for publicationNot applicable.

Higher education institutions (HEIs) in India were caught completely unawares by the Covid-19 pandemic and necessitated lockdown. Despite almost two decades of experimentation with online and distance learning by top-tier and private institutions, the vast majority were unprepared and looked for standalone solutions for different components of teaching and learning. Valuable lessons have been learned based on which a more comprehensive solution must be sought for the post-Covid-19 environment. The lockdown has provided the much-needed impetus to reshape higher education in India. Calls for the adoption of blended learning (BL) have been made on prior occasions; this chapter renews that call and stresses its urgency. It is imperative that educational institutions amplify the momentum gained during the lockdown and transition to a BL model supported by the adoption and use of learning management systems (LMSs). Government should support this initiative by providing a centralized LMS. Corporations must “adopt an HEI,” channeling their unused corporate social responsibility funds to support information and communication technology needs at educational institutions. All stakeholders must work together to transform the country to a digitally empowered society and knowledge economy as envisioned in the National Education Policy 2020.

Background Radiomics analyses has been proposed to interrogate the biology of tumour as well as to predict/assess response to therapy in vivo. The objective of this work was to assess the sensitivity of radiomics features to noise, resolution, and tumour volume in the context of a co-clinical trial. Methods Triple negative breast cancer (TNBC) patients were recruited into an ongoing co-clinical imaging trial. Sub-typed matched TNBC patient-derived tumour xenografts (PDX) were generated to investigate optimal co-clinical MR radiomic features. The MR imaging protocol included T1-weighed and T2-weighted imaging. To test the sensitivity of radiomics to resolution, PDX were imaged at three different resolutions. Multiple sets of images with varying signal-to-noise ratio (SNR) were generated, and an image independent patch-based method was implemented to measure the noise levels. Forty-eight radiomic features were extracted from manually segmented 2D and 3D segmented tumours and normal tissues of T1- and T2- weighted co-clinical MR images. Findings Sixteen radiomics features were identified as volume dependent and corrected for volume-dependency following normalization. Features from grey-level run-length matrix (GLRLM), grey-level size zone matrix (GLSZM) were identified as most sensitive to noise. Radiomic features Kurtosis and Run-length variance (RLV) from GLSZM were most sensitive to changes in resolution in both T1w and T2w MRI. In general, 3D radiomic features were more robust compared to 2D (single slice) measures, although the former exhibited higher variability between subjects. Interpretation Tumour volume, noise characteristics, and image resolution significantly impact radiomic analysis in co-clinical studies.

Traditionally, searching for videos on popular streaming sites like YouTube is performed by taking the keywords, titles, and descriptions that are already tagged along with the video into consideration. However, the video content is not utilized for searching of the user&rsquo, s query because of the difficulty in encoding the events in a video and comparing them to the search query. One solution to tackle this problem is to encode the events in a video and then compare them to the query in the same space. A method of encoding meaning to a video could be video captioning. The captioned events in the video can be compared to the query of the user, and we can get the optimal search space for the videos. There have been many developments over the course of the past few years in modeling video-caption generators and sentence embeddings. In this paper, we exploit an end-to-end video captioning model and various sentence embedding techniques that collectively help in building the proposed video-searching method. The YouCook2 dataset was used for the experimentation. Seven sentence embedding techniques were used, out of which the Universal Sentence Encoder outperformed over all the other six, with a median percentile score of 99.51. Thus, this method of searching, when integrated with traditional methods, can help improve the quality of search results.

Hiding data in electrocardiogram signals are a big challenge due to the embedded information that can hamper the accuracy of disease detection. On the other hand, hiding data into ECG signals provides more security for, and authenticity of, the patient&rsquo, s data. Some recent studies used non-blind watermarking techniques to embed patient information and data of a patient into ECG signals. However, these techniques are not robust against attacks with noise and show a low performance in terms of parameters such as peak signal to noise ratio (PSNR), normalized correlation (NC), mean square error (MSE), percentage residual difference (PRD), bit error rate (BER), structure similarity index measure (SSIM). In this study, an improved blind ECG-watermarking technique is proposed to embed the information of the patient&rsquo, s data into the ECG signals using curvelet transform. The Euclidean distance between every two curvelet coefficients was computed to cluster the curvelet coefficients and after this, data were embedded into the selected clusters. This was an improvement not only in terms of extracting a hidden message from the watermarked ECG signals, but also robust against image-processing attacks. Performance metrics of SSIM, NC, PSNR and BER were used to measure the superiority of presented work. KL divergence and PRD were also used to reveal data hiding in curvelet coefficients of ECG without disturbing the original signal. The simulation results also demonstrated that the clustering method in the curvelet domain provided the best performance&mdash, even when the hidden messages were large size.

Digital image watermarking aims to protect the information in an image without significantly affecting visual quality. In this paper, a new image watermarking technique has been proposed that uses Gaussian filters and first-order partial differential matrix to extort the edge surface of a host image. This paper influence on the edge surface curvelet coefficients as human eyes are not equally sensitive to a smooth and an edged surface. To preserve the quality of the artwork and to increase the resistance against attacks, the author utilizes the edge surface area of an image, coarse levels of curvelet transform, and strength parameters. The selection of host coefficients are conforming to the human visual system (HVS) is the uniqueness of the research. The exploitation of the Gaussian filters and first-order partial differential coarse curvelet coefficients and the watermark strength parameter offers robustness against image processing attacks. The standard visual quality perception of HVS evaluation metrics are used to measure the superiority of the presented work.

Smart agriculture systems with combinations of advanced technologies are used in an attempt to increase the competence of certain farming activities and the standard of living for farm employees by reducing significant labor and tedious tasks. Internet-of-things-based sensors are capable of providing such information about smart agriculture and then acting upon predictions using data analysis. The proposed methodology works alongside a cloud-based server and a mobile-based device (ideally an Android/iOS device) to assist the user in regulating the standing of the plant as monitored by a mix of software packages and hardware devices. Our system detects changes in the moisture, temperature, and light intensity conditions in and around the plant and performs a learning-based call to supply necessary irrigation and illumination to plants. It permits the user to update, manage, and monitor using wireless sensing element networks. The sensors measure the aforementioned parameters and store the data within the cloud, which users can access at any time from anywhere. Farmers will have access to the most up-to-date knowledge so that they can act accordingly and make modifications as needed. This smart planting has become a core tool associated with cost-effective technology in agricultural modernization technologies. The proposed smart modern agriculture tool can be used to monitor climatic factors such as temperature, moisture, and virtually all environmental parameters relevant to the growth of plants.

An entity&rsquo, s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models&mdash, Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)&mdash, detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.

Early-onset neonatal sepsis (EOS) is a major cause of neonatal death and long-term neurodevelopmental disabilities among survivors. The common pathogens causing EOS are group B streptococcus (GBS) and Escherichia coli. Haemophilus influenzae (H. influenzae) is a Gram-negative coccobacillus that can cause severe invasive disease and can be divided into either typeable or non-typeable strains. H. influenzae serotype b (Hib) is the most virulent and the major cause of bacterial meningitis in young children prior to routine immunization against Hib. Hib infection rates have dramatically reduced since then. However, a number of studies have reported an increasing incidence of non-typeable H. influenzae (NTHi) sepsis in neonates worldwide and concluded that pregnant women may have an increased risk to invasive NTHi disease with poor pregnancy outcomes. We present a case of fulminant neonatal sepsis caused by NTHi in an extremely preterm infant and discuss potential preventative measures to reduce its re-emergence.

Bengali is the sixth most popular spoken language in the world. Computerized detection of handwritten Bengali (Bangla Lekha) character is very difficult due to the diversity and veracity of characters. In this paper, we have proposed a modified state-of-the-art deep learning to tackle the problem of Bengali handwritten character recognition. This method used the lesser number of iterations to train than other comparable methods. The transfer learning on Resnet-50 deep convolutional neural network model is used on pretrained ImageNet dataset. One cycle policy is modified with varying the input image sizes to ensure faster training. Proposed method executed on BanglaLekha-Isolated dataset for evaluation that consists of 84 classes (50 Basic, 10 Numerals and 24 Compound Characters). We have achieved 97.12% accuracy in just 47 epochs. Proposed method gives very good results in terms of epoch and accuracy compare to other recent methods by considering number of classes. Without ensembling, proposed solution achieves state-of-the-art result and shows the effectiveness of ResNet-50 for classification of Bangla HCR.

IEEE 802.11s was designed to implement Wireless Mesh Network (WMN). Hybrid Wireless Mesh Protocol (HWMP) is the routing protocol used in IEEE 802.11s. But external station’s (STA) mobility is not considered by IEEE 802.11s. Therefore, for handling both Internet and intranet packets a Session-to-Mobility Ratio (SMR)-based technique is implemented in this paper. The scheme efficiently handles mobility of external STA by reducing propagation of route management packets. Performance analysis and comparison of SMR-based scheme and IEEE 802.11s have been carried out. It has been observed that SMR-based scheme outperforms IEEE 802.11s.

Thiamine (vitamin B1) was the first B vitamin which has been identified. It serves as a cofactor for several enzymes involved in energy metabolism. The thiamine-dependent enzymes are important for the biosynthesis of neurotransmitters and for the production of reducing substances used in oxidant stress defenses, as well as for the synthesis of pentose used as nucleic acid precursors. Thiamine also plays a central role in cerebral metabolism. There are 2 major manifestations of thiamine deficiency(TD): cardiovascular disease (wet beriberi) and nervous system disease (dry beriberi and Wernicke–Korsakoff syndrome). In wet beriberi, cardiomyopathy with edema and lactic acidosis and in dry beriberi, peripheral neuropathy occurs. Manifestations of Wernicke–Korsakoff syndrome, consist of nystagmus, ophthalmoplegia and ataxia evolving into confusion, retrograde amnesia, cognitive impairment and confabulation. Thiamine deficiency is now very rare in developed countries, but still common in South East Asia specially in developing countries like Bangladesh. It is an important public health problem with potentially fatal consequences. Now a days highly polished rice (Minicut rice) is a popular staple food and other primary dietary sources of thiamine are in short supply. In wet beriberi myocardial disease is prominent which causes a high cardiac output with peripheral vasodilation and warm extremities. Before heart failure, tachycardia, a wide pulse pressure, sweating, warm skin and lactic acidosis develop leading to salt and water retention by the kidneys. The resulting fluid overload leads to edema of the dependent extremities. If it is left untreated the severity of potential outcome will be increased even up to death. Therefore, makes it essential for physician, cardiologists and Paediatrician to have an understanding of this condition and its optimal treatment. Patients on a strict thiamine-deficient diet display a state of severe depletion within 18 days. The most common cause of thiamine deficiency in affluent countries is either alcoholism or malnutrition in nonalcoholic patients especially in children. Treatment by thiamine supplementation is beneficial for diagnostic and therapeutic purposes. Northern International Medical College Journal Vol.10(1) Jul 2018: 351-354

Primary immunodeficiency diseases (PIDs) are inherited defects of the innate or adaptive arms of the immune system that differs from secondary immunodeficiencies. Respiratory disorders are significant causes of morbidity and the leading causes of death (30% -65%) in both children and adults with PIDs. The spectrum of respiratory manifestations is extremely wide due to PIDs. PIDs are broadly classified according to the components of the immune system that is primarily disrupted. Predominant antibody deficiency disorders are the most frequent and comprise approximately 70-75% of all PIDs. The most common clinical manifestations are infections involving the respiratory tract e.g. rhinosinusitis, otitis media, bronchitis, bronchiectasis and recurrent pneumonia (30% -65%). Recurrent respiratory infections are often the first warning sign. Timely diagnosis and appropriate therapy can improve or at least decelerate the progression of these complications. Infectious and non-infectious respiratory complications determinate the patient’s prognosis. These complications are associated with significant morbidity and mortality in PID patients. Appropriate awareness of these manifestations is essential, especially for the pulmonologist to reduce morbidity and mortality in PID patients.Bangladesh J Child Health 2018; VOL 42 (2) :86-93

Congenital pulmonary airway malformation (CPAM) is a very rare congenital cystic lung disease in children. Frequent respiratory tract infections (RTI) are major concerns in these patients. When a child having recurrent episodis of RTI, CPAM could be the underlying pathology. Surgical excision is recommended to make a definite diagnosis and exclude hidden malignancies and is also the treatment of choice. Here in, we report a 6 months old girl presenting with an acute respiratory infection for the first time. An routine USG of abdomen incidentally showed multiple cystic lessions in the left lower lung. CT scan of chest also revealed similar lessions in some area. After receiving treatment for pneumonia surgical excision was performed and she was doing well after surgeryBangladesh J Child Health 2017; VOL 41 (2) :129-134

The concept of software-defined networking is introduced to the networking domain to enhance networking capabilities to cope with network resources’ fast-growing requirements. Initially, security in Software Defined Network (SDN) is not considered as a priority concern, but later many research surveys taken place for determining the security loopholes in SDN architecture. To date, no algorithm exists for determining information security in contrast to SDN architecture or SDN based architecture. Here, a mechanism is introduced to determine the information risk associated with SDN or SDN based architecture. The SDN properties influence themselves; thus, the Multi-Criteria Decision Making (MCDM) method is incorporated in the proposed algorithm as a comprehensive mechanism while determining the information risk. To be specific, this proposed algorithm is divided into three different phases wherein; the first phase incorporates the Pythagorean Fuzzy Decision-Making Trial and Evaluation Laboratory method (PFDEMATEL) for determining the influences among SDN properties and vulnerabilities. The second phase implements the Pythagorean Fuzzy Analytical Hierarchical Process (PFAHP) for evaluating the priority of the severity weights of SDN properties along with vulnerabilities. Lastly, phase 3 implements information entropy to determine the overall SDN system risk. Our proposition is introduced in the first place to help the network administrator to determine associated risk in their SDN or SDN based architecture before the actual implementation and take proper countermeasures while implementing.

Objectives: In this paper, we focus our research on detecting brain tumours for various diagnostic purposes in medical field. Methods/Statistical Analysis: The method applied here is soft computing for image segmentationto detect the brain tumour from a particular MRI image which is important for various diagnostic purposes in medical field. For the purpose of identifying abnormal cells in the MRI images, which are collected from various real time situations, have been passed through a de-noising algorithm followed by a clustering approach. Findings: A new Fuzzy C Means clustering method followed by multilevel thresholding and level set algorithm have been adopted to recognise the tumour affected areas. This method has been compared against existing two techniques like multilevel thresholding and K-means algorithm. K-means algorithm is more efficient regarding time but this improved technique of image segmentation ensures more precise result. Application/improvements: This algorithm is fully tested with various medical images like MRI images and also working nicely to achieve orientation of accurate shape and size of brain tumor. Keywords: Erosion, Fuzzy C-Mean (FCM), Image Segmentation, K-Means, Level Set, Multilevel Thresholding