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Abstract Background Scurvy has been a rare condition in the developed world due to the increasing availability of adequate nutrition and easy access to fruits and vegetables. Scurvy is caused by severe vitamin C deficiency and has been historically associated with malnutrition in high-risk populations such as sailors. As it is an uncommon nutritional deficiency, scurvy is seldom suspected and usually under-diagnosed especially in the pediatric population. Case presentation We report a case of a pediatric patient with a history of autism spectrum disorder presenting with atypical symptoms of scurvy with gait abnormalities, diffuse rash, musculoskeletal deconditioning, and bleeding manifestations. After multiple extensive investigations and a prolonged hospitalization, a detailed dietary analysis revealed a severely restricted diet devoid of fruits and vegetables. Physical examination revealed specific signs of corkscrew hair with peri-follicular hemorrhages, hypertrophic, and bleeding gums with a concurrent low vitamin C level. Treatment with vitamin C supplementation was initiated and he was ambulant within 1 month of discharge. Conclusion This case highlights the importance of nutrition history for determining micronutrient deficiency in children with special needs. Enhanced physician education and higher index of suspicion regarding nutritional deficiencies will avoid over-investigations and delays for the institution of specific treatment.

Objective: The incidence of cardiovascular diseases (CVD) is rising rapidly worldwide. Some forms of CVD, such as stroke and heart attack, are more common among patients with certain conditions. Atherosclerosis development is a major factor underlying cardiovascular events, such as heart attack and stroke, and its early detection may prevent such events. Ultrasound imaging of carotid arteries is a useful method for diagnosis of atherosclerotic plaques; however, an automated method to classify atherosclerotic plaques for evaluation of early-stage CVD is needed. Here, we propose an automated method for classification of high-risk atherosclerotic plaque ultrasound images. Methods: Five deep learning (DL) models (VGG16, ResNet-50, GoogLeNet, XceptionNet, and SqueezeNet) were used for automated classification and the results compared with those of a machine learning (ML)-based technique, involving extraction of 23 texture features from ultrasound images and classification using a Support Vector Machine classifier. To enhance model interpretability, output gradient-weighted convolutional activation maps (GradCAMs) were generated and overlayed on original images. Results: A series of indices, including accuracy, sensitivity, specificity, F1-score, Cohen-kappa index, and area under the curve values, were calculated to evaluate model performance. GradCAM output images allowed visualization of the most significant ultrasound image regions. The GoogLeNet model yielded the highest accuracy (98.20%). Conclusion: ML models may be also suitable for applications requiring low computational resource. Further, DL models could be more completely automated than ML models.

Abstract Human Papillomavirus (HPV) is the most common sexually transmitted infection and is associated with cervical, anogenital, and oropharyngeal cancers. It is crucial to improve vaccination uptake in both genders as primary prevention for these conditions. Pediatricians play an active role in advocating for HPV vaccination and our study aims to assess the level of knowledge, attitudes, barriers, and practices among pediatric trainees and general pediatricians regarding HPV infection and vaccination. A survey‐questionnaire was administered to our target groups. It comprised 14 questions regarding demographics of the healthcare provider, knowledge of HPV infection and vaccination, practices and barriers of recommending HPV vaccination, and effective strategies for improving HPV vaccine uptake. Among survey respondents, majority did not recommend for HPV vaccination (66.7%) or receive any enquiry about it (80.6%) within the preceding 12 months. The most common perceived barrier was inadequate knowledge, which was consistent with the misconceptions regarding HPV infection and vaccination that were identified in this survey. Strategies which physicians felt would be most effective in increasing vaccine uptake include educating and providing resources for both physicians and caregivers as well as making the vaccine free. Our study revealed a low advocacy rate for HPV vaccination. Physicians need to be equipped with the knowledge, skills, and resources to better counsel caregivers as well as focus our efforts on vaccinating male patients in order to increase vaccine uptake in both genders.

In previous studies, the comprehensive scaling-up of nickel electroforming on a lab-scale rotating disk electrode (RDE) suggested that secondary current distribution could adequately simulate such a forming process. In this work, the use of a 3-D, time-dependent, secondary current distribution model, developed in COMSOL Multiphysics®, was examined to validate the nickel electroforming of an industrial mechanical vane, a low-tolerance part with a demanding thickness profile of great interest to the aerospace industry. A set of experiments were carried out in an industrial pilot tank with computations showing that the model can satisfactorily predict the experimental findings. In addition, these experiments revealed that the local applied current density was related to the surface appearance (shiny vs matt) of the electroform.Simulations of the process at applied current densities ≤5A/dm2 satisfactorily predicted the experimentally observed thickness distribution while, simulations of the process at applied current densities ≥5A/dm2 underpredicted the experimentally achieved thicknesses. Nevertheless, it is proposed that the model can be used for either quantitative or qualitative studies, respectively, depending on the required operating current density on a case-by-case basis. Scanning electron microscopy was used to determine the microstructure of the electroforms and determine the purity of nickel (i.e., if nickel oxide is formed), with imaging suggesting that pyramid-shaped nickel particles evolve during deposition. Another interesting observation revealed a periodicity in the deposit's growth mechanism which leads to “necklace”-like deposit layers at the areas where the electroforms presented the highest thickness.

Abstract Background Dengue fever, a viral illness spread mostly by Aedes mosquitoes, continues to pose a substantial public health issue in Dhaka city, Bangladesh. In Dhaka, climatic and socio-demographic factors like population density affect the spread of dengue. The dengue indexes are greatest in the residential mixed zone. Numerous environmental parameters, such as temperature, relative humidity, rainfall, and the air pollution index, have been linked to mosquito larvae, and dengue prevalence is correlated with urbanization, decreased vegetation, and population expansion. Methods By using an extensive dataset that encompasses a range of years, we use spatial and temporal analytic methodologies to investigate the correlation between land use attributes, climatic variables, and the occurrence of dengue fever. To better understand the dynamics of dengue, the built environment and climatic factors are treated as independent variables in this study. ArcPy is a Python package that facilitates here for geographic data analysis and ArcMap 10.7 also used for visualizing spatial data. Results The results of our study demonstrate that land use significantly influences the spatial patterns of Dengue incidence in Dhaka city. The dengue hotspot Thana are identified and these are Badda, Jatrabari, kadamtali, Mirpur, Mohammadpur, Sobujbagh, Shyampur, Tejgoan, Dhanmondi and Uttara. All of these areas’ population density and residential use as land use is higher than the other Thana of Dhaka city. There exists a significant correlation between climatic characteristics, such as temperature (0.25), rainfall (.803), specific humidity (0.74), relative humidity (0.76), wind speed (0.4) and Dengue incidence patterns. This research emphasizes the structural use and climatic relationship in Dengue epidemics, with climatic conditions playing a significant role as drivers of these variations. Conclusions This research demonstrates the complex relationship between land use, meteorological factors, and the spread of Dengue fever in Dhaka city. The results of this study have significant significance for several domains, including urban planning, public health measures, and vector control tactics. A comprehensive understanding of the temporal and geographical patterns of dengue transmission might aid in the development of accurate and effective prevention measures intended to lessen the effects of dengue in cities, such as Dhaka.

Background: Colorectal cancer (CRC) is the second leading cause of cancer fatalities and the third most common human disease. Identifying molecular subgroups of CRC and treating patients accordingly could result in better therapeutic success compared with treating all CRC patients similarly. Studies have highlighted the significance of CRC as a major cause of mortality worldwide and the potential benefits of identifying molecular subtypes to tailor treatment strategies and improve patient outcomes. Methods: This study proposed an unsupervised learning approach using hierarchical clustering and feature selection to identify molecular subtypes and compares its performance with that of conventional methods. The proposed model contained gene expression data from CRC patients obtained from Kaggle and used dimension reduction techniques followed by Z-score-based outlier removal. Agglomerative hierarchy clustering was used to identify molecular subtypes, with a P-value-based approach for feature selection. The performance of the model was evaluated using various classifiers including multilayer perceptron (MLP). Results: The proposed methodology outperformed conventional methods, with the MLP classifier achieving the highest accuracy of 89% after feature selection. The model successfully identified molecular subtypes of CRC and differentiated between different subtypes based on their gene expression profiles. Conclusion: This method could aid in developing tailored therapeutic strategies for CRC patients, although there is a need for further validation and evaluation of its clinical significance.

Abstract Highly transparent resistive-switching (RS) devices were fabricated by growing amorphous tantalum pentoxide (a-Ta2O5) and indium tin oxide (a-ITO) thin films on barium-borosilicate glass (7059) substrates, using electron beam evaporation. These layers exhibited the transmittance greater than ~ 85% in the full visible region and showed RS behavior and battery-like IV characteristics. The overall characteristics of RS can be tuned using the top electrode and the thickness of a-Ta2O5. Thinner films showed a conventional RS behavior, while thicker films with metal electrodes showed a battery-like characteristic, which could be explained by additional redox reactions and non-Faradaic capacitive effects. Devices having battery-like IV characteristics showed higher enhanced, retention and low-operation current.

The forthcoming era of industrialization is marked by intelligent production, with the manufacturing industry and the Industrial Internet of Things (IIoT) driving the creation and innovation of new concepts and systems. One innovative solution addressing the demands of industrial settings is the time-slotted channel hopping (TSCH) protocol, known for its ultra-low power consumption and reliable communication. However, the scheduling mechanism at the core of TSCH requires substantial time for every node. In this study, we propose a sustainable scheduling method for Industrial IoT networks called Linguistic Interval Type 2 Fuzzy Logic System (LIT2FLS) based Priority Aware TSCH (LIT2FLPAS). The suggested method aims to promote service diversification to extend battery life and reduce delay. The significance of each node is determined by the LIT2FLS method, which allocates the required number of slots based on priority and the volume of generated traffic. We compare our proposed LIT2FLPAS algorithm with two state-of-the-art scheduling functions, Scheduling Function-Zero (SF0) and Low Latency Scheduling Function (LLSF). The results demonstrate that LIT2FLPAS exhibits superior adaptability compared to SF0 and LLSF. It provides collision-free communications and significantly enhances the duty cycle by 8%, battery life by 30.32%, and packet loss ratio by 95% while reducing latency by 75.69% and collisions by 91%. These findings underscore the effectiveness of the LIT2FLPAS algorithm in addressing the specific challenges of industrial IoT networks, offering improved performance metrics and robust communication in dynamic industrial environments. The proposed method presents a promising solution for achieving efficient and reliable communication in the evolving landscape of intelligent industrial production.

An Exigency Vehicle (EV) has to go more quickly to increase the likelihood that someone in danger would survive. Construction projects, strikes, and accidents may all be avoided with an effective vehicle routing solution. The Internet of Things (IoT) network simulation for Exigency Vehicle routing is proposed in this research utilizing a linguistic interval type 2 fuzzy logic system (LIT2FLS) based data fusion approach. The predicted effectiveness of our model for intelligent city applications, including emergency vehicle routing, is well demonstrated by the LIT2FLS correlation coefficients of 0.994%. This data fusion approach determines the exact level of congestion for a given place by combining sensory data with crowd reactions. In addition, OSRM employs a road communication system to detect real-time traffic variations and choose the least congested route. Furthermore, a sensor station for gathering the speeds and pollutants of moving automobiles on the road has also been established. An Android app has been developed to collect public information on blockages A driver of an Exigency Vehicle (EV) is directed towards a medical facility with the quickest, congestion-aware path by means of the Application software service. We have created an IT2FLS system that assists with decisions to calculate traffic congestion. Analyze the ability to scale and quickness of response associated with the suggested routing strategy.

Background Nutrition education is one the essential components of any paediatric residency training. However, many paediatricians world-wide have suboptimal knowledge in core clinical nutritional topics. The purpose of our study is to assess the perceived effectiveness of nutrition training among physicians, including paediatric residents, paediatric trainees and paediatricians; identify areas of need for additional education resources and the preferred method of teaching for nutritional topics for medical education. Material and Methods A survey-questionnaire was sent to our physicians in our centre to assess general nutrition education via self-assessment of current nutritional knowledge across 12 distinct topics; importance of teaching amongst these topics and the preferred method of teaching. Results A total of 64 physicians completed the survey with an overall response rate of 88%. Most responders reported having average or above-average knowledge base in all nutritional topics except for nutritional aspects of special diets. Poorest knowledge base (either below average or poor) was reported in special diet ( n = 38, 59.4%), inflammatory bowel disease ( n = 17, 26.6%) and gastrointestinal allergy ( n = 16, 25.0%). There was strong interest in additional teaching needs for the topics of failure to thrive ( n = 38, 59.4%), nutritional assessment ( n = 34, 53.1%) and infant feeding ( n = 31, 48.4%). Most popular teaching methods were clinical case review (31.7%) followed by didactic lectures (29.8%) for overall clinical nutritional education. Conclusions Physicians identified gaps in their nutrition knowledge base and multiple topics were identified for additional nutrition education. Preferred methods of teaching for nutritional education were clinical case reviews and didactic lectures.

Abstract LaTe3 is a non-centrosymmetric material with time reversal symmetry, where the charge density wave is hosted by the Te bilayers. Here, we show that LaTe3 hosts a Kramers nodal line—a twofold degenerate nodal line connecting time reversal-invariant momenta. We use angle-resolved photoemission spectroscopy, density functional theory with an experimentally reported modulated structure, effective band structures calculated by band unfolding, and symmetry arguments to reveal the Kramers nodal line. Furthermore, calculations confirm that the nodal line imposes gapless crossings between the bilayer-split charge density wave-induced shadow bands and the main bands. In excellent agreement with the calculations, spectroscopic data confirm the presence of the Kramers nodal line and show that the crossings traverse the Fermi level. Furthermore, spinless nodal lines—completely gapped out by spin-orbit coupling—are formed by the linear crossings of the shadow and main bands with a high Fermi velocity.

Background: While common in the elderly population, abdominal aortic aneurysms (AAAs) are rare in the paediatric population and reported to be more common in patients with connective tissue disorders or certain genetic conditions such as tuberous sclerosis complex (TSC). Presenting symptoms of an AAA can be varied, and can range from being completely asymptomatic, to symptoms including a pulsatile abdominal mass, hypertension, back pain and even rupture. Aortic aneurysms in patients with TSC have been reported presenting at varying age groups. Case description: We report a case of TSC associated with an incidental finding of an abdominal aortic aneurysm in a child. Ultrasound of the abdomen revealed a renal aortic abdominal aneurysm. Computed tomographic angiogram and magnetic resonance imaging of the abdomen confirmed this finding with wall thinning over its right lateral aspect. The child underwent surgical repair of her abdominal aortic aneurysm and recovered uneventfully. Discussion: This case highlights the importance of monitoring patients with TS closely for signs and symptoms of aortic aneurysms and to proceed with screening scans for an early diagnosis. If left untreated, AAA carries a high-risk of morbidity and mortality in the event of a rupture.

Background: Recalcitrant dermatophytosis has become a menace in recent times. The perception of the hosts or patients regarding the disease gives a real-life picture of the deep-rooted host-factors and the way to overcome the socio-economic and cultural milieu that becomes a hindrance in preventing the relapse and recurrence of this infection. Aims: This qualitative study was conducted to assess the perception regarding the patients' understanding of causation and prevention of dermatophytosis, role of personal hygiene, and healthcare seeking behavior, and consequently, exploring the socio-economic and cultural milieu that contributes to the menace of recalcitrant dermatophytosis. Materials and Methods: Two focus group discussions (FGDs) consisting 12 patients with recalcitrant dermatophytosis, who speak the vernacular, were conducted with the help of a predetermined FGD-guide. Both FGD sessions were recorded, transcribed, and translated into English. These verbatims were thematically analyzed and emerging themes were identified with illustrative quotations. Free listing and pile sorted data obtained were analyzed by Visual Anthropac version 1.0 software for Smith's salience value, cognitive mapping with two-dimensional scaling and hierarchical cluster analysis. Results: Washing of clothes with soap antiseptics and drying the clothes under the sun prevents diseases, whereas, not changing undergarments for prolonged periods of time, bathing in ponds using mustard oil spreads the disease, furthermore, staying in wet clothes, working for more than five hours under sun, playing outdoors, household jobs cause tinea, etc. were the most common reported perceptions. Overall, five themes emerged after pile sorting the data, namely, ignorance about the disease, understanding of the predisposing factors of the disease, prevention, role of personal and traditional belief, and care-seeking behavior. Conclusions: There is a dire need to address the myths or traditional beliefs, which are not supported by scientific evidence. Expense of the treatment of dermatophytosis calls for price control of the commonly prescribed antifungals. Poor socio-economic conditions and professional responsibilities hinder people from implementing the preventive measures in spite of their knowledge.

This paper presents a robust colon cancer diagnosis method based on the feature selection method. The proposed method for colon disease diagnosis can be divided into three steps. In the first step, the images’ features were extracted based on the convolutional neural network. Squeezenet, Resnet-50, AlexNet, and GoogleNet were used for the convolutional neural network. The extracted features are huge, and the number of features cannot be appropriate for training the system. For this reason, the metaheuristic method is used in the second step to reduce the number of features. This research uses the grasshopper optimization algorithm to select the best features from the feature data. Finally, using machine learning methods, colon disease diagnosis was found to be accurate and successful. Two classification methods are applied for the evaluation of the proposed method. These methods include the decision tree and the support vector machine. The sensitivity, specificity, accuracy, and F1Score have been used to evaluate the proposed method. For Squeezenet based on the support vector machine, we obtained results of 99.34%, 99.41%, 99.12%, 98.91% and 98.94% for sensitivity, specificity, accuracy, precision, and F1Score, respectively. In the end, we compared the suggested recognition method’s performance to the performances of other methods, including 9-layer CNN, random forest, 7-layer CNN, and DropBlock. We demonstrated that our solution outperformed the others.

Introduction: Chronic urticaria is common and distressing dermatosis where the search for newer agents with improved effectiveness and tolerability profile is a felt need. Bepotastine, a second-generation antihistamine, with added effect on suppression of eosinophil migration has a prospect in the management of chronic urticaria. Aims: To assess and compare effectiveness and safety of bepotastine versus levocetirizine in chronic urticaria. Materials and Methods: Single-center, investigator-blind, randomized, active-controlled, parallel-group phase IV trial (CTRI REF/2018/04/019692) conducted on adult patients of chronic urticaria of either sex. Patients were randomized into receiving either bepotastine besilate 10 mg tablet twice daily or levocetirizine 5 mg tablet once daily with fortnightly follow-up for 6 follow-up visits after thebaseline evaluation. The primary outcome measures were Urticaria Activity Score 7 (UAS7) and Urticaria Total Severity Score (TSS). Routine hematological, biochemical tests, treatment-emergent adverse events were monitored for safety. Results: Thirty patients in the bepotastine group and 29 patients in the levocetirizine group were analyzed by modified-intention-to-treat criteria. The study groups were comparable at the baseline with respect to the severity of chronic urticaria. UAS7 and TSS reduced significantly (P < 0.001, Friedman's ANOVA) in both treatment groups from 1st follow-up visit and 2nd follow-up visits (P < 0.05, Post Hoc Dunn's test) At the test-of-cure visit, UAS7 (5.13 ± 8.21 vs 7.48 ± 8.96) and TSS (5.10 ± 4.06 vs 7.07 ± 4.48) were less with bepotastine than levocetirizine although not statistically significant (P = 0.188 and 0.073, respectively, Mann–Whitney U test). Sedation during daytime was found to be significantly more (P < 0.001, Fischer's exact test) with levocetirizine than bepotastine (73.3% vs 17.2%). Conclusion: Bepotastine is comparable to levocetirizine with respect to its effectiveness with an edge in terms of side-effect (sedation during day time); thus, it offers a new therapeutic option in chronic urticaria.

Polypropylene is a multi-reason thermoplastic resin with ample of scope for engineering applications. This article presents a very distinct and new methodology called linguistic Response Surface Methodology (RSM) for predicting the quality of polypropylene used in petrochemical industries. This model is framed on the basis of a huge quantity of data obtained from well-known Indian chemical factories. The quality of polypropylene depends on factors such as melt flow index and solubility of the product in xylene. The parameters controlling both the factors are the hydrogen flow, the donor flow, and, the pressure and temperature of the polymerization reactor. Using these four input and output parameters, a response surface method based on the different features of the variable is created. Then the simulation results are compared with the collected plant information and the most appropriate model is selected through a series of fact-finding. This linguistic surface response method, when compared with Type 1, and Type 2 fuzzy logic, was found to provide the maximum value of R. An ANNOVA analysis of the experimental data was done to determine the reliability, the repetition, and the efficiency of the constructed regression models. This method will not only save the expenses but also the time, generally being taken by the other models.

Electroforming is increasingly gaining recognition as a promising and sustainable additive manufacturing process of the “Industry 4.0” era. Numerous important laboratory-scale studies try to shed light onto the pressing question as to which are the best industry approaches to be followed towards the process’s optimisation. One of the most common laboratory-scale apparatus to gather electrochemical data is the rotating disk electrode (RDE). However, for electroforming to be successfully optimised and efficiently applied in industry, systematic scale up studies need to be conducted. Nowadays, well-informed simulations can provide a much-desired insight into the novelties and limits of the process, and therefore, scaling up modelling studies are of essence. Targeted investigations on how the size and geometry of an electroforming reactor can affect the final product could lead to process optimisation through simple modifications of the setup itself, allowing immediate time- and cost-effective adjustments within existing production lines. This means that the accuracy of results that any scaled up model provides, if compared to a successful, smaller scale version of itself, needs to be investigated. In this work a 3-D electrodeposition model of an RDE was used to conduct geometry and model sensitivity studies using a commercial software as is often done in industry. As a next step, a 3-D model of an industrial-scale electroforming reactor, which was 90 times larger in electrolyte volume compared to the RDE, was developed to compare, and identify the key model parameters during scale up. The model results were validated against experimental data collected in the laboratory for both cases to assess model validity.

Introduction: Ayurveda recommends standardization of different useful parts of herbal drug, either organized or unorganized, starting from its collection. The latex of snuhi is said to be collected at the end of Shishira ritu. This article highlights the result of scientific study on effect of season on collection of latex of Euphorbia caducifolia Haines. (EC), one of the botanicl source of Snuhi. Materials and Methods: The fresh crude latex of EC was collected in clean glass vials in all the six ritu, i.e. Hemanta-S1 (November–December), Shishira-S2 (January–February), Vasanta-S3 (March–April), Grishma-S4 (May–June), Varsha-S5 (July–August), and Sharada-S6 (September–October). The collection was done for 4 days in each ritu and 3 times in a day, i.e. before sunrise, 3 h after sunrise, and 6 h after sunrise (6AS), by incision of branch, incision of main stem, and Plucking of branch. The observations regarding volume, girth of plant parts, pH, viscosity, and specific gravity along with preliminary phytochemical analysis of latex were carried out in samples of crude latex collected in six seasons. The seasonal similarity and diversity was assessed by applying principal component analysis using Unscrambler software 9.7 version. Results and Conclusion: Rakta Snuhi latex collected during Hemanta ritu (November–December) was well differentiated from other ritu, with regard to volume and girth. Highest volume of latex was collected by plucking of branch. Data shows that the latex is having highest pH (5.29) in Varsha ritu (July–August), specific gravity (1.034) in Vasanta ritu (March–April), and the relative viscosity (2.444) in Shishira ritu (January–February)

Human papillomavirus type-16 (HPV16) is classified into lineages, A, B, C and D and 10 sub-lineages portraying variable infectivity, persistence, and cytological outcomes, however, with geographical variations. Our objective was to delineate the distinctive features of lineages among cervical squamous cell carcinoma (SCC) in the eastern region of India. A total of 145 SCC cases and 24 non-malignant specimens, harboring episomal HPV16, were included. The presence of higher proportion of lineage A over D was observed among SCC cases (86.89% A1, 8.97% D1 and 4.14% D2), while only A1 sub-lineage viruses were found among control specimens. Among the A1 viruses, an association of variants in the E5 (Y44L, I65V), E6 (L83V) genes and LCR: C7577T with SCC, with combined Odd's ratio (95% CI) of 20.5(4.61–91.25) was observed. Network analyses revealed the presence of 10 clades of lineage A viruses comprising of 64 HPV16 genomes harboring the risk alleles. High episomal HPV16 DNA copy numbers (adjusted p-value= 0.0271) and E7 mRNA expression (p-value=0.000017) predominated in SCC with lineage A, over D. Our study highlights the distinctive modalities of oncogenicity among different HPV16 lineages.

Higher education in India was caught completely unawares by the COVID-19 pandemic and the necessitated closure of educational institutions. Despite almost a decade of experience with online and distance learning at some top-tier and private institutions, the vast majority were unprepared and looked for quick solutions for different components of teaching–learning depending on the need of the hour. The immediate tool sought was a videoconferencing platform to substitute in-class lectures. With no access to a learning management system, faculty chose one platform for videoconferencing, one for interaction with students, and another for uploading class notes. Disparity in students’ access to devices and the internet presented challenges. Assessment of learning, which hitherto was largely pen and paper based, was delayed for lack of a viable solution. Experiences documented in this study demonstrate faculty resilience, but lack of institutional leadership and preparedness is starkly evident.

Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.

Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging.

The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.

Background: Ayurveda recommends collection of latex from some plants belonging to Upavisha (semi-poisonous) category in Sharada rutu. The latex from Calotropis gigantea and Calotropis procera (Arka species) finds a significant place, in different therapeutic formulations, with unique mention in management of mukhakaarshnya (hyper pigmentation of face). Aim: To study the impact of seasonal variation on lupeol content of two species of Arka. Material and methods: The latex was collected aseptically during six seasons from two species. After suitable sample preparation, it was used for HPTLC wherein lupeol was used as standard biomarker. Lupeol is a well known bioactive reported for its anti-inflammatory and anti-melanogenic effect on skin. Observations and Results: The lupeol content of Calotropis gigantea was found to be highest in latex sample collected in August-September (Varsha rutu) whereas it was highest in December-January (Hemanta rutu) in Calotropis procera. Conclusion: The findings of highest percentage of lupeol in Saumya rutu i.e Hemanta & Varsha rutu thus coincides with the concept of Kala. It thus proves that seasonal variation has an impact on lupeol content of latex.

An edge detection is important for its reliability and security which delivers a better understanding of object recognition in the applications of computer vision, such as pedestrian detection, face detection, and video surveillance. This paper introduced two fundamental limitations encountered in edge detection: edge connectivity and edge thickness, those have been used by various developments in the state-of-the-art. An optimal selection of the threshold for effectual edge detection has constantly been a key challenge in computer vision. Therefore, a robust edge detection algorithm using multiple threshold approaches (B-Edge) is proposed to cover both the limitations. The majorly used canny edge operator focuses on two thresholds selections and still witnesses a few gaps for optimal results. To handle the loopholes of the canny edge operator, our method selects the simulated triple thresholds that target to the prime issues of the edge detection: image contrast, effective edge pixels selection, errors handling, and similarity to the ground truth. The qualitative and quantitative experimental evaluations demonstrate that our edge detection method outperforms competing algorithms for mentioned issues. The proposed approach endeavors an improvement for both grayscale and colored images.

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.

Due to lack of robust data on childhood cystic fibrosis (CF) in Bangladesh we sought to evaluate their clinico-epidemiology. A cross-sectional observation was conducted adopting CF-foundation consensus-panel-diagnostic criteria in 3 tertiary-care-hospitals in Bangladesh from 2000 to 2017. Clinically suspected 95 CF-cases were subjected to sweat-chloride testing using locally-developed a fast, cheap and effective indigenously body-wrapped sweating technique measured by US-Easy Lyte-automated microprocessor-controlled analyzer marking ≥60 mmol/L as positive. Mean-age of CF-cases at disease-onset was 16.9 ± 26.6 months that significantly differed with age-at-diagnosis ( P

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.

Background: Leprosy is a disease having tremendous social implications due to ostracization. Despite continuous efforts made so far to eliminate leprosy, stigma/misbeliefs/adverse attitude toward leprosy still prevails among common people. Community perceptions and attitudes towards leprosy patients are critical and unique indicator of how society stereotypes leprosy. Aims: The qualitative study was conducted to explore the perceptions of leprosy patients towards leprosy. Materials and Methods: Two focused group discussions (FGDs) with 19 newly-diagnosed leprosy patients who can speak vernacular were conducted with help of a predetermined FGD-guide between April and June 2019 in the dermatology out-patient department of the tertiary-care center. Both FGD sessions were recorded, transcribed, and translated into English. These verbatims were thematically analyzed and emerging themes were identified with illustrative quotations. Free list and pile sort data obtained were analyzed by Visual Anthropac version 1.0 software for Smith's salience value Cognitive mapping with two-dimensional scaling and hierarchical cluster analysis. Results: Small size of wound, not giving importance, failure to realize, bad luck and scared of being isolated etc., were the most common reported perceptions. Overall, five themes emerged after pile sorting, namely ignorance about the disease, stigma and economic burden, positive perception after diagnosis, treatment-related suggestions, and myths and frequently asked questions. Conclusion: The study concludes that more IEC campaigns are required to abolish the stigma; and ASHA workers/front-line workers can play a pivotal role. The program (NLEP) can also utilize the role of satisfied cured-patient as peer-educator to improve the attitude of society towards this disease.

Abstract Background Psoriasis is a chronic inflammatory autoimmune skin disorder. Several studies suggested psoriasis to be a complex multifactorial disease, but the exact triggering factor is yet to be determined. Evidences suggest that in addition to genetic factors, epigenetic reprogramming is also involved in psoriasis development. Major histopathological features, like increased proliferation and abnormal differentiation of keratinocytes, and immune cell infiltrations are characteristic marks of psoriatic skin lesions. Following therapy, histopathological features as well as aberrant DNA methylation reversed to normal levels. To understand the role of DNA methylation in regulating these crucial histopathologic features, we investigated the genome-wide DNA methylation profile of psoriasis patients with different histopathological features. Results Genome-wide DNA methylation profiling of psoriatic and adjacent normal skin tissues identified several novel differentially methylated regions associated with psoriasis. Differentially methylated CpGs were significantly enriched in several psoriasis susceptibility (PSORS) regions and epigenetically regulated the expression of key pathogenic genes, even with low-CpG promoters. Top differentially methylated genes overlapped with PSORS regions including S100A9, SELENBP1, CARD14, KAZN and PTPN22 showed inverse correlation between methylation and gene expression. We identified differentially methylated genes associated with characteristic histopathological features in psoriasis. Psoriatic skin with Munro’s microabscess, a distinctive feature in psoriasis including parakeratosis and neutrophil accumulation at the stratum corneum, was enriched with differentially methylated genes involved in neutrophil chemotaxis. Rete peg elongation and focal hypergranulosis were also associated with epigenetically regulated genes, supporting the reversible nature of these characteristic features during remission and relapse of the lesions. Conclusion Our study, for the first time, indicated the possible involvement of DNA methylation in regulating the cardinal pathophysiological features in psoriasis. Common genes involved in regulation of these pathologies may be used to develop drugs for better clinical management of psoriasis.

Background: Ayurveda recommends collection of latex from some plants belonging to Upavisha (semi-poisonous) category in Sharada rutu. The latex from Calotropis gigantea and Calotropis procera (Arka species) finds a significant place, in different therapeutic formulations, with unique mention in management of mukhakaarshnya (hyper pigmentation of face). Aim: To study the impact of seasonal variation on lupeol content of two species of Arka. Material and methods: The latex was collected aseptically during six seasons from two species. After suitable sample preparation, it was used for HPTLC wherein lupeol was used as standard biomarker. Lupeol is a well known bioactive reported for its anti-inflammatory and anti-melanogenic effect on skin. Observations and Results: The lupeol content of Calotropis gigantea was found to be highest in latex sample collected in August-September (Varsha rutu) whereas it was highest in December-January (Hemanta rutu) in Calotropis procera. Conclusion: The findings of highest percentage of lupeol in Saumya rutu i.e Hemanta & Varsha rutu thus coincides with the concept of Kala. It thus proves that seasonal variation has an impact on lupeol content of latex.

Objective: Accurate identification of brain tumor and its heterogeneity is a critical task in planning for proper therapy. A reliable fully automatic detection and analysis method for the brain tumor is necessary for an efficient measurement of the tumors and its extent. This paper presents a computerized approach to brain tumor-edema detection and analysis from the MRI of brain sequences. Method: Computer-aided diagnosis systems are focused on several research activities and the ideas of the study of brain images with the diverse modality of heterogeneity by applying better image analysis algorithms. The proposed automated modern approach includes several stages of image segmentation, area and volume calculation, and its location findings using statistical and unsupervised clustering prediction method. Result: The outcome of the proposed computerized method is compared with reference images and gives very promising results. Performance of our proposed methodology is also assessed with the gold standard recent comparable method and our method gives better results in context to accuracy and error metrics. Conclusion: The proposed method is capable of improving the overall detection, segmentation, and quantification of a variety of tumors for different cases from multiple standard datasets. Keywords: Abnormality detection, Brain tumor, Segmentation, Heterogeneity, Magnetic resonance imaging, Accuracy estimation

Data is growing at an enormous rate in the present world. One of the finest and most popular technologies available for handling and processing that enormous amount of data is the Hadoop ecosystem. Enterprises are increasingly relying on Hadoop for storing their valuable data and processing it. However, Hadoop is still evolving. There is much vulnerability found in Hadoop, which can question the security of the sensitive information that enterprises are storing on it. In this paper, security issues associated with the framework have been identified. We have also tried to give a brief overview of the currently available solutions and what are their limitations. At the end a novel method is introduced, which can be used to eliminate the found vulnerabilities in the framework. In the modern era, information security has become a fundamental necessity for each and every individual. However, not everyone can afford the specialized distributions provided by different vendors to their Hadoop cluster. This paper presents a cost-effective technique that anyone can use with their Hadoop cluster to give it 3-D security.

In recent time, stock price prediction is an area of profound interest in the realm of fiscal market. To predict the stock prices, authors have proposed a technique by first calculating the sentiment scores through Naïve Bayes classifier and after that neural network is applied on both sentiment scores and historical stock dataset. They have also addressed the issue of data cleaning using a Hive ecosystem. This ecosystem is being used for pre-processing part and a neural network model with inputs from sentiment analysis and historic data is used to predict the prices. It has been observed from the experiments that the accuracy level reaches above 90% in maximum cases, as well as it also provides the solid base that model will be more accurate if it trained with recent data. The intended combination of sentiment analysis and Neural networks is used to establish a statistical relationship between historic numerical data records of a particular stock and other sentimental factors which can affects the stock prices.

Objective: Accurate identification of brain tumors and their heterogeneity is a critical task in planning for proper therapy. A reliable fully automatic detection and analysis method for the brain tumor is necessary for an efficient measurement of the tumors and their extent. This paper presents a computerized approach to brain tumor-edema detection and analysis from the MRI of brain sequences. Method: Computer-aided diagnosis systems are focused on several research activities, and the ideas of the study of brain images with the diverse modality of heterogeneity by applying better image analysis algorithms. The proposed automated modern approach includes several stages of image segmentation, area and volume calculation, and locational findings using statistical and an unsupervised clustering prediction method. Result: The outcome of the proposed computerized method is compared with reference images and gives very promising results. Performance of our proposed methodology is also assessed with the gold standard recent comparable method, and our method gives better results in the context of accuracy and error metrics. Conclusion: The proposed method is capable of improving the overall detection, segmentation, and quantification of a variety of tumors for different cases from multiple standard datasets. Keywords: Abnormality detection, Brain tumor, Segmentation, Heterogeneity, Magnetic resonance imaging, Accuracy estimation

The bulk electronic structures of two high-order quaternary approximants of F-type icosahedral (i)-Al-Pd-TM quasicrystal: Al-Pd-Cr-Fe and Al-Pd-Mo-Fe, having similar electron to atom (e/a) ratio as i-Al-Pd-Mn quasicrystal, have been investigated by hard x-ray photoelectron spectroscopy. We establish the presence of a well-formed pseudogap at the Fermi level in both the approximants. The pseudogap turns out to be deeper in the approximants compared to i-Al-Pd-Mn, and this is supported by specific heat data. Modifications in the line shape of Al 2s core-level main peak as well as the plasmon loss peaks indicate enhanced hybridization of Al sp and transition metal d states in the approximants, which could be one of the possible reasons for their pseudogap. The absence of magnetic exchange splitting in the Fe 2p core-level spectra establishes the nonmagnetic nature of the approximants.

Goal setting and grade loss aversion have individually been found to be effective motivators for student performance. We combine these two strategies to investigate, in a university setting, the effectiveness of grade endowment within a goal-setting framework. Our participants set grade goals which some must earn the traditional way, thus framed as 'gains', while others are awarded their target grade upfront, thus framed as 'losses'. We find a positive impact of goal setting on student performance. We also find that the effect of grade goals framed as 'losses' is numerically, but not statistically, greater than those framed as 'gains'.

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’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’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’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—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’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—Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)—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.