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The importance of predicting crop yields has increased due to growing concerns of surrounding food security. Early forecasting of crop yields holds a pivotal role in avoiding starvations by estimating the food supply available for the expanding global population. Several Deep Learning (DL) and Machine Learning (ML) techniques are involved to develop effective and accurate crop yield prediction model. Nevertheless, existing models faces some limitations such as less accuracy, high error rate because of noisy data, high training time and extracted less effective features for prediction. To overcome these issues, the novel DL methodology is introduced for attaining high accurate crop yield prediction. Initially, the soil, weather and other resources big data are collected from the various agriculture field. In data collection phase, the input data of larger size are stored in the Hadoop platform for the purpose of storing as well as processing the entire data in a distributed manner. The data are pre-processed through the utilization of Missing value imputation and z-score based data normalization. From the pre-processed data, the optimal features are considered using Integrated Correlation Random recursive elimination (InCorRe) approach. Based on the previous soil and weather information, the suitable yield of crops can be predicted using Multi head Residual dilated convolution assisted gated unit (MResGat) model. Finally, the losses of the network model can be optimized using African vulture optimization algorithm (AVO). The proposed method is evaluated using the several performance metrics, which achieved 0.023% of MSE value and 0.036% of MAE values. [ABSTRACT FROM AUTHOR]

Polycystic Ovarian Disease or Polycystic Ovary Syndrome (PCOS) is becoming increasingly communal among women, owing to poor lifestyle choices. According to the research conducted by National Institutes of Health, it has been observe that PCOS, an endocrine condition common in women of childbearing age, has become a significant contributing factor to infertility. Ovarian abnormalities brought on by PCOS carry a high risk of miscarriage, infertility, cardiac problems, diabetes, uterine cancer, etc. Ovarian cysts, obesity, menstrual irregularities, elevated amounts of male hormones, acne vulgaris, hair loss, and hirsutism are some of the symptoms of PCOS. It is not easy to determine PCOS because of its different combinations of symptoms in different women and various criteria needed for diagnosis. Taking biochemical tests and ovary scanning is a time-consuming process and the financial expenses have become a hardship to the patients. Thus, early prognosis of PCOS is crucial to avoid infertility. The goal of the proposed work is to analyse PCOS symptoms based on clinical data for early diagnosis and to classify into PCOS affected or not. To achieve this objective, clinical features dataset and ultrasound imaging dataset from Kaggle is utilized. Initially 541 instances of 45 clinical features such as testosterone, hirsutism, family history, BMI, fast food, menstrual disorder, risk etc. are considered and correlation-based feature extraction method is applied to this dataset which results in 17 features. The extracted features are applied to various machine learning algorithms such as Logistic Regression, Naïve Bayes and Support Vector Machine. The performance of each method is evaluated based on accuracy, precision, recall, F1-score and the result shows that among three models, Support Vector Machine model achieved high accuracy of 94.44%. In addition to this, 3856 ultrasound images are analysed by CNN based deep learning algorithm and VGG16 transfer learning algorithm. The performance of these models is evaluated using training accuracy, loss and validation accuracy, loss and the result depicts that VGG16 outperforms than CNN model with validation accuracy of 98.29%. [ABSTRACT FROM AUTHOR]

Background of the Study: Low back pain is one of the most common health problem and 80% of people experiences low back pain in their lifetime. Majority of low back pain is due to the muscle imbalance. Muscle imbalance occurs due to shortened or lengthened in structure and it leads to injury which causes changes in motor programming. Objectives: To compare the effects of Muscle Energy Technique, Positional Release Technique and Conventional Therapy on improving pain, range of motion and functional ability in patients with mechanical low back pain. Subjects and Methods: Twenty four patients of complaining of mechanical low back pain participated in the study. Patients were randomly allocated using computer generator software into three groups: group A which received Muscle Energy Technique, group B which received Positional Release Technique and group C which received Conventional Therapy. Treatment sessions were given three times per week for three weeks. Patients were assessed before and after treatment using Numerical Pain Rating Scale(NPRS),Modified Oswestry Disability Index (MODI), and Modified Schobers Test to assess pain severity, functional ability, and range of motion of lumbar spine respectively. Results: Based on this statistical analysis, Group A showed better statistically significance in NPRS,MODI and ROM mean values as compared to Group B and C. Hence alternate hypothesis is accepted and null hypothesis is rejected. This we conclude that Muscle Energy Technique is more beneficial compared to Positional Release Technique and Conventional Therapy group. Conclusion: Muscle Energy Technique and Conventional therapy group shows significant improvement on Pain and Lumbar Extension Range of Motion compared to Positional Release Technique, whereas there was no significant difference in MODI on Muscle Energy Technique, Positional Release Technique and Conventional therapy. [ABSTRACT FROM AUTHOR]

Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate diagnosis and treatment. This study addresses critical challenges in the diagnostic imaging of lung and colon cancers, which are among the leading causes of cancer-related deaths worldwide. Recognizing the limitations of existing diagnostic methods, which often suffer from overfitting and poor generalizability, our research introduces a novel deep learning framework that synergistically combines the Xception and MobileNet architectures. This innovative ensemble model aims to enhance feature extraction, improve model robustness, and reduce overfitting. Our methodology involves training the hybrid model on a comprehensive dataset of histopathological images, followed by validation against a balanced test set. The results demonstrate an impressive classification accuracy of 99.44%, with perfect precision and recall in identifying certain cancerous and non-cancerous tissues, marking a significant improvement over traditional approach. The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Mapping (Grad-CAM), the model offers enhanced interpretability, allowing clinicians to visualize the diagnostic reasoning process. This transparency is vital for clinical acceptance and enables more personalized, accurate treatment planning. Our study not only pushes the boundaries of medical imaging technology but also sets the stage for future research aimed at expanding these techniques to other types of cancer diagnostics. [ABSTRACT FROM AUTHOR]

Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis. Leveraging the HAM10000 dataset, a comprehensive collection of dermatoscopic images encompassing a diverse range of skin lesions, this study introduces a sophisticated CNN model tailored for the nuanced task of skin lesion classification. The model's architecture is intricately designed with multiple convolutional, pooling, and dense layers, aimed at capturing the complex visual features of skin lesions. To address the challenge of class imbalance within the dataset, an innovative data augmentation strategy is employed, ensuring a balanced representation of each lesion category during training. Furthermore, this study introduces a CNN model with optimized layer configuration and data augmentation, significantly boosting diagnostic precision in skin cancer detection. The model's learning process is optimized using the Adam optimizer, with parameters fine-tuned over 50 epochs and a batch size of 128 to enhance the model's ability to discern subtle patterns in the image data. A Model Checkpoint callback ensures the preservation of the best model iteration for future use. The proposed model demonstrates an accuracy of 97.78% with a notable precision of 97.9%, recall of 97.9%, and an F2 score of 97.8%, underscoring its potential as a robust tool in the early detection and classification of skin cancer, thereby supporting clinical decision-making and contributing to improved patient outcomes in dermatology. [ABSTRACT FROM AUTHOR]

Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitations such as subjectivity in interpretation and constraints in handling complex image features. This research paper proposes an integrated deep learning approach utilizing pre-trained models—VGG16, ResNet50, and InceptionV3—combined within a unified framework to improve diagnostic accuracy in medical imaging. The method focuses on lung cancer detection using images resized and converted to a uniform format to optimize performance and ensure consistency across datasets. Our proposed model leverages the strengths of each pre-trained network, achieving a high degree of feature extraction and robustness by freezing the early convolutional layers and fine-tuning the deeper layers. Additionally, techniques like SMOTE and Gaussian Blur are applied to address class imbalance, enhancing model training on underrepresented classes. The model's performance was validated on the IQ-OTH/NCCD lung cancer dataset, which was collected from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases over a period of three months in fall 2019. The proposed model achieved an accuracy of 98.18%, with precision and recall rates notably high across all classes. This improvement highlights the potential of integrated deep learning systems in medical diagnostics, providing a more accurate, reliable, and efficient means of disease detection. [ABSTRACT FROM AUTHOR]

Rationale: Sinonasal tumours are rare occurrences accounting for 3–4% of tumours (benign/malignant/co-existing) in the head-and-neck region, making it difficult to render an absolute diagnosis and treatment, thereof keeping in mind the tumour presentation and quality of life of the patient. Patient Concerns: The patient reported with complaints of swelling over the right side of the face associated with recent onset of pain in the same region. Diagnosis: Upon clinical, radiographic and histopathological examination, it was diagnosed as right Sinonasal inverted papilloma (SNIP). Treatment: The clinicians in the study approached the SNIP by opting for tumour resection with subtotal maxillectomy and reconstruction using a prosthetic obturator. Outcomes: Patient was operated and relived of the pathology and followed up for 18 months. She is now taken up for prosthetic rehabilitation. Take-away Lessons: The authors advocate the choice for open surgeries in cases where minimally invasive techniques do not meet the needs of desired results. [ABSTRACT FROM AUTHOR]

Rationale: Osteochondroma, a benign bony neoplasm that can develop in any part of the body, commonly appears in the ends of the long bones. It can also occur in the craniofacial region such as mandibular condyle and coronoid region. We describe the clinical presentation, diagnostic process, surgical management and post-operative outcomes in a 34-year-old male patient. Only few cases are reported in literature till now and often are mistaken as bony exostosis. Patient Concerns: The patient sought medical attention due to the cosmetic concerns. Diagnosis: Through clinical palpation and radiological interpretation. Provisional diagnosis of bony exostosis was made, which was later confirmed as osteochondroma on histopathological examination. Treatment: Surgical removal of the bony growth was performed. Outcomes: Follow-up was uneventful with no evidence of recurrence. Take-away Lessons: The report aims to raise awareness about this manifestation and to encourage further research to better understand aetiology, management and outcomes of osteochondroma in unusual locations. [ABSTRACT FROM AUTHOR]

These days, diabetes is an incurable disease, with millions of people suffering from it worldwide. Several variables namely lack of education, crowded living conditions, obesity and improper diet are among the causes of this recent upsurge in diabetes cases. They are identified by the name of infections induced by bacteria or viruses, harmful compounds in food, autoimmune reactions, obesity, unhealthy lifestyles, and pollution in the environment. Excessive and sight-threatening diabetic retinopathy (DR) is the most common retinal micro-vascular dysfunction that is characterized by the occurrence of a disorder of retinal blood vessels resulting in impaired vision. The IoT-based work is conducted in this work on the machine learning (ML) techniques, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The classification of diabetic retinopathy is a topic that is under research. The range of activities of the processes of downsampling, labelling, image flattening, and format conversion is all within the dataset preparation process. An advanced prognosis model is designed which follows a combination of two machine learning techniques such as SVM and KNN. This approach classifies the images of diabetic retinopathy into five segments (subclasses), thus facilitating in-depth analysis. Our solution proposal in this case is a superior one because of its higher classification accuracy and faster processing speed as the findings showed. The robustness and accuracy that the SVM is known for are ensured by the convergence of the KNN to the SVM. The paper also proves a close linkage of clinical symptoms and blood sugar readings to an algorithmic DM prediction system that is based on IoT and ML approaches. This is another advantage of this method that it outperforms the existing classification methods. Amongst all the classifiers that we used in this project, the KNN ML classifier turned out to be the most accurate one with an accuracy rate of 93%. It was found that the algorithm performed with a 79% accuracy rate after tough testing and training and it was consistently providing number one quality DM predictions. [ABSTRACT FROM AUTHOR]

The exponential growth of the Internet of Things (IoT) usage for real-time applications has made it necessary to implement stringent security measures to protect sensitive data that is communicated and stored by IoT devices. These procedures are designed to prevent unauthorized access. In this research work, we investigate how the Internet of Things (IoT) could benefit from the implementation of a revolutionary lightweight Cipher-Text Attribute-Based Encryption (CP-ABE) system that is rooted in lattice cryptography. We dive into the complexities of lattice-based cryptography and evaluate its viability in contexts where there is limited access to resources related to the Internet of Things (IoT). In addition, we carry out a thorough analysis of the ABE methods that are currently in use, paying particular attention to how effective they are and how easily they may be scaled. Our contribution consists of the development of a novel lightweight lattice-based CP-ABE system that is specifically optimized for Internet of Things installations. According to the findings of our research, it has the potential to considerably bolster the security of the Internet of Things. We provide empirical data that is drawn from applications that take place in the actual world to back up these arguments. Our major purpose is to present a revolutionary CP-ABE approach that was designed to improve the security of the Internet of Things, and this objective will be supported by compelling experimental data in terms of encipherment and decipherment times, key generation times of existing models with the proposed model. According to the results, we have concluded that the computational overhead of the proposed model is roughly 30% lower than that of the existing models, and that the efficiency of providing security is 20% higher in the proposed model than in the existing models. [ABSTRACT FROM AUTHOR]

Introduction: Although diarrheal diseases are highly preventable, they remain one of the leading causes of mortality and morbidity in the pediatric population in developing countries. Knowledge of etiological agents is essential to implement public health programs. Viral infections are the most common cause of acute infectious diarrhea among children. There is a paucity of information regarding viral enteropathogens responsible for diarrhea in India, as many hospitals do not routinely carry out diagnostic tests for viral detection. Aim and Objectives: To estimate the prevalence of viral enteropathogens in children under five years of age presenting with acute diarrhea using real-time multiplex polymerase chain reaction (PCR) and its correlation with clinico-demographic features. Methodology: A cross-sectional study was carried out in a tertiary care center in Maharashtra from January 2016 to June 2019. Our study included 300 children who were under five years old with acute diarrhea. Standard protocols were advocated for the collection and transportation of stool samples. All samples were subjected to real-time multiplex PCR (Light cycler 480 Roche) using Fast Track Diagnostic kits for viral gastroenteritis. Data analysis was done using descriptive and analytical methods. Results: In our study, 57% of samples were positive for viral enteropathogens. Rotavirus (32%) was the most common isolate, followed by Norovirus Genogroup II (GII) (17%). The majority of pathogens were isolated from children less than 2 years of age. Conclusion: There is a need to test stool specimens of clinically confirmed diarrheal patients for enteric virus. This will avoid blind antibiotic treatment. [ABSTRACT FROM AUTHOR]

This research paper explores the implications of technology in promoting wellness tourism in Thrissur, Kerala. Thrissur, renowned for its cultural heritage and natural beauty, has emerged as a significant destination for wellness tourism, offering Ayurvedic retreats, yoga centers, and holistic healing practices. In recent years, technological advancements have reshaped the tourism industry, presenting opportunities for enhanced marketing strategies, personalized experiences, and improved accessibility. Through a mixed-methods approach combining quantitative analysis of online marketing data and qualitative insights from tourists, wellness practitioners, and local stakeholders, this study aims to assess the effectiveness of technology-driven promotional strategies in attracting wellness tourists to Thrissur. It also analyzes the impact of digital platforms on visitor experiences, identifies challenges and opportunities for leveraging technology in wellness tourism promotion, and provides recommendations for stakeholders. The findings contribute to the understanding of the role of technology in shaping wellness tourism destinations and offer practical insights for destination marketers, policymakers, and businesses in Thrissur. [ABSTRACT FROM AUTHOR]

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process. This research introduces an integrated approach using ResNet50, a deep learning model, combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to offer a transparent and explainable framework for brain tumor detection. We employed a dataset of MRI images, enhanced through data augmentation, to train and validate our model. The results demonstrate a significant improvement in model performance, with a testing accuracy of 98.52% and precision-recall metrics exceeding 98%, showcasing the model's effectiveness in distinguishing tumor presence. The application of Grad-CAM provides insightful visual explanations, illustrating the model's focus areas in making predictions. This fusion of high accuracy and explainability holds profound implications for medical diagnostics, offering a pathway towards more reliable and interpretable brain tumor detection tools. [ABSTRACT FROM AUTHOR]

Background: Rheumatoid arthritis (RA) is a chronic inflammatory disorder involving the joints along with various other extra-articular manifestations. Mortality due to coronary artery disease is higher in patients with RA when compared to the general population. Hence, this study is undertaken to assess the predictors of sub-clinical atherosclerosis in patients with RA, so that early prompt management can prevent cardiovascular complications. Methods: A case-control study with 42 patients of RA and 42 healthy age- and sex-matched controls was undertaken. Relevant investigations were done. Disease Activity Score (DAS)-28 was calculated. Carotid artery doppler was performed on both sides to examine the common carotid artery. Results: The mean carotid intima-media thickness (CIMT) (mm) was significantly higher in RA patients compared to controls on the right side (0.57 ± 0.11 Vs 0.40 ± 0.03; P < 0.001) as well as left side (0.56 ± 0.11 Vs 0.39 ± 0.06; P < 0.001). Only age (P = 0.006) and DAS-28 (P = 0.002) showed a positive correlation with CIMT. Conclusions: CIMT can be used as a marker for the detection of subclinical atherosclerosis and the risk of atherosclerosis increases with the increasing age and increasing disease severity. [ABSTRACT FROM AUTHOR]

Rationale: Mesiodens are supernumerary teeth located in the pre-maxilla between the two central incisors with a prevalence rate of 0.15–1.9%. An impacted mesiodens may cause delayed eruption of permanent incisors, ectopic eruption, pathological disturbances and malalignment of teeth. Patient Concerns: The patient complained of missing upper front teeth associated with pain in the same region for two weeks. Diagnosis: Upon clinical and radiographic examination, it was diagnosed as triple impacted mesiodens with impacted permanent maxillary central incisors. Treatment: The clinicians in the study approached the issue by surgical removal of the impacted mesiodens and orthodontic treatment for extrusion of the impacted permanent maxillary central incisors. Outcomes: The patient was relieved of the pre-operative concerns and followed up regularly for the eruption of permanent maxillary central incisors. Take-away Lessons: The authors advocate the importance of timely diagnosis and proper management of such rare pathologies. [ABSTRACT FROM AUTHOR]

Introduction: During orthodontic treatment, temporary anchoring devices (TADs) are used to restrain tooth movement. They are a relatively recent addition to the dental toolkit. Aim: As TADs have limitations, Dr. Eric Lieu of Taiwan developed Infra Zygomatic Crest (IZC) screws which are placed between the maxillary second premolar at the bony crest. Treatment Planning: The goal of this case study is to emphasize the value of anatomy, site selection, and IZC retrieval in the event of an accident. Cone beam computed tomography was used as a diagnostic tool for the precise location of the displaced IZC and immediate surgical retrieval was done under local anesthesia from the infratemporal space to prevent further complications. Take-away Lessons: Orthodontists knowledge of soft tissue and hard tissue anatomy and precise positioning is crucial for successful TAD implantation. [ABSTRACT FROM AUTHOR]

Rationale: Cancrum oris, also known as noma, is a rare and rapidly progressing gangrenous infection affecting the oral cavity, commonly seen in malnourished children. We discuss the clinical presentation, diagnostic dilemma and management in a 54-year-old male human immunodeficiency virus-positive patient with oral ulceration clinically resembling squamous cell carcinoma (SCC). Patient Concerns: The patient had severe oral ulceration with pain and difficulty eating food. Diagnosis: Histopathological examination was misleading as it revealed features akin to early invasion of SCC. Immunohistochemistry findings were not in favour of SCC. Treatment: Antiretroviral therapy was started owing to the decreased CD4 cell count. Outcomes: Lesions began to show signs of healing on follow-up. Take-away Lessons: This case aims to highlight the unique challenges of diagnosing and emphasises the importance of considering opportunistic infections in immunocompromised patients presenting with oral ulcerations to prevent misdiagnosis and maltreatment. [ABSTRACT FROM AUTHOR]

In today's society, time is considered more valuable than money, and researchers often have limited time to find relevant papers for their research. Identifying and accessing essential information can be a challenge in this situation. To address this, the personalized suggestion system has been developed, which uses a user's behavior data to suggest relevant items. The collaborative filtering strategy has been used to provide a user with the top research articles based on their queries and similarities with other users' questions, thus saving time by avoiding time-consuming searches. However, when rating data is abundant but sparse, the usual method of determining user similarity is relatively straightforward. Furthermore, it fails to account for changes in users' interests over time resulting in poor performance. This research proposes a new similarity measure approach that takes both user confidence and time context into account to increase user similarity computation. The experimental results show that the proposed technique works well with sparse data, and improves accuracy by 16.2% compared to existing models, especially during prediction. Furthermore, it enhances the quality of recommendations. [ABSTRACT FROM AUTHOR]

Rationale: Gorlin‑Goltz syndrome (GGS) has a wide range of expressivity, with a majority of cases being first diagnosed from the oral findings. Early intervention can reduce its severity. Patient Concerns: The primary complaints of all the patients were pain and swelling. Clinical examination with radiological and histopathological evaluation confirmed the diagnosis. Diagnosis: This series presents the six cases of GGS treated over a time frame of five years (2018–2022). Treatment: The treatments range from enucleation, chemical cauterisation and peripheral ostectomy to aggressive modalities such as resection and reconstruction. Outcomes: This series comprises of six patients with ages ranging from 12 to 42 years, four of which were female and two were male presenting minimal expression to highly aggressive forms and its unpredictable frequent manifestation. Take-Away Lessons: This emphasises the significance of long‑term periodic follow‑up and genetic screening for early detection, thereby reducing the intensity and aggressiveness of the disease. [ABSTRACT FROM AUTHOR]

Introduction: Disimpaction is one of the most common operations done by oral and maxillofacial surgeons around the world. Ketamine is a well‑known general anaesthetic and short‑acting intraoperative analgesic. The aim of this study was to measure the efficacy of anaesthesia using combined treatment with local anaesthetic plus a subanaesthetic dose of ketamine and local anaesthetic alone in bilaterally impacted mandibular third molar surgery. Methods and Materials: A total of 24 patients who consented were taken up for a split‑mouth study. In the control group, surgical extraction of the impacted lower third molar was done using local anaesthesia (lignocaine 2% with 1:80,000 adrenaline) only, and in the study group, local anaesthesia with ketamine extraction was done using ketamine (0.2 mg/ kg) along with local anaesthesia (LA). The time of onset and the duration of anaesthesia intraoperatively were recorded using a digital stopwatch. The depth of anaesthesia was noted on the 10th, 30th and 60th min. After extraction, the post‑operative pain on the first, fourth, eighth and 12th hour was evaluated using a Visual Analogue Scale (VAS) score rating of 1–10. The pulse was also noted and compared for any differences in either of the groups. Results: A statistically significant (P < 0.005) difference in result was obtained for the onset, duration, depth and pain score after surgical extraction in both the evaluated groups. The pain index score by the ketamine group was significantly low as compared to the local anaesthesia‑only group. Intraoperative onset, duration and depth of anaesthesia obtained had a significant difference. Discussion: Ketamine can be used as a viable option for surgical third molar extractions with reduced discomfort and post-operative pain. [ABSTRACT FROM AUTHOR]

Background: Coronavirus has been found to interact with and affect the cardiovascular system leading to myocardial damage and cardiac and endothelial dysfunction. Respiratory symptoms are worse in COVID-19 affected patients with pre-existing cardiac ailments; however, new-onset cardiac dysfunction is common in this subset. Most patients with COVID-19 have cardiac symptoms like chest pain, palpitations along respiratory symptoms. Methods: It is a single-centre observational study. This study was conducted in tertiary health care from May 2020 to December 2020(9 months) with COVID-19 infection having cardiac symptoms. The patient's clinical features, electrocardiogram (ECG), Echocardiography, cardiac biomarkers, haematological and biochemical parameters are studied in detail. Result: The mean age of the patients was 48±16 years. Male: Female ratio was 7:3. The percentage of onset of cardiac dysfunction was more in the less than 40year age group compared to other age groups. Nearly 46.7% of people have co-morbidities such as diabetes mellitus, hypertension and COPD. In the study population, elevated levels of biomarkers were noted. Abnormal electrocardiogram and echocardiogram was observed. The recovery rate from COVID-19 with cardiac manifestation was observed to be 76.7% in our study population. Conclusion: Cardiac injury is a common condition among patients hospitalized with COVID-19, and it is associated with a higher risk of in-hospital mortality. A combined evaluation of cardiac biomarkers with ECG and ECHO findings is likely to give a better picture of the severity of cardiac function in newly onset of cardiac dysfunction due to COVID-19 infection for better management and prognosis. [ABSTRACT FROM AUTHOR]

Camera gimbal systems pervade various applications such as navigation, target tracking, security and surveillance. The need for higher steering rate (rotation angle per second) of gimbal often results in motion blur in the captured video frames. Motion deblurring in real-time is difficult with existing blind restoration methods which incur large execution times while attempting to retrieve latent images from blurry inputs using high-dimensional optimization. On the other hand, deep learning methods for motion deblurring, though fast, do not generalize satisfactorily with domain shifts. In this work, we address the problem of real-time motion deblurring in infrared (IR) images captured by a real gimbal-based system. We propose two blur-kernel estimation methods and reveal how a priori knowledge of the blur-kernel can be used in conjunction with non-blind deblurring methods to achieve real-time performance. We experimentally show that, in comparison to the state-of-the-art techniques in deblurring, our method is better-suited for practical gimbal-based imaging systems. [ABSTRACT FROM AUTHOR]

Cryptocurrencies are now well-established and widely accepted as a form of alternative trading money. Because they have invaded nearly all accounting operations, cryptocurrency trading is often regarded as popular and propitious sorts of successful investments. A reliable forecasting and prediction model is essential for predicting the high volatility caused by the increase in financial market. This current work summarises various optimisation techniques by shedding light on the volatility factors like internal competition, market pressure, financial problems, social and political conflicts, etc. A comprehensive study is presented on how to predict the volatility of cryptocurrency and formulate their inter-connections with the parent Cryptocurrencies using various deep learning frameworks. Further, this work focuses on utilising sophisticated models to improve the forecasting performance of cryptocurrency. We are hopeful that this work will provide broader insights in the future research of cryptocurrency. [ABSTRACT FROM AUTHOR]

Background: Using the CBCT, this technique has the ability to identify the proper dimensions of the mesiodistal width of the tooth and other clearing techniques that in the past have been of higher value to the other conventional techniques that can be implemented to give a three-dimensional perspective of the complete morphological details. With this, analysing the mesiodistal width of mandibular central incisors between male and female based on CBCT can be accomplished. Methods: One hundred Cone Beam Computed Tomography (CBCT) images from different patients are selected randomly from Department of Oral Radiology of Saveetha Dental College and Hospital, India. All the CBCT images of patients are examined for the inclusion criterias in the mesiodistal width of the mandibular incisors between different genders. All mesiodistal width of mandibular central incisors between different genders are investigated and compared. Conclusion:When both the genders were compared in terms of the number of canals, there was a significant difference seen (p-value: 0.000002208). With the p-value calculated, it was found that there was a significant difference between the genders in terms of the distance between the occlusal pit to the pulp chamber (p-value: <0.0000001). Overall, there is no significant difference between the two genders at the level of the cervical third (p-value: 0.6554). [ABSTRACT FROM AUTHOR]

Background: Using the CBCT, this technique has the ability to identify the root canal morphology as detailed as using the root canal staining and other clearing techniques that in the past have been of higher value to the other conventional techniques that can be implemented to give a three-dimensional perspective of the complete morphological details. With this, analysing the variation in root canal morphology of human maxillary second premolar between different gender in Chennai population can be done. Methods:One hundred Cone Beam Computed Tomography (CBCT) images from different patients are selected randomly from Department of Oral Radiology of Saveetha Dental College and Hospital, India. All the CBCT images of patients are examined for the inclusion criteria in maxillary second premolars between different genders based on Vertucci classification. All root canal configurations of maxillary second premolars between different genders are investigated and compared withVertucci Classification. Conclusion: Of all the 100 maxillary second premolars, the most common type of canal configuration for both the genders is Type I (41%), Type III (22%), and Type V (22%). In females, the most common type of canal configuration discovered is Type I (35%), Type III (63.6%) and Type V (83.3%). In males, the most common type of canal configuration determined is Type I (65%) and Type III (36.4%). [ABSTRACT FROM AUTHOR]

Dual-lens (DL) cameras capture depth information, and hence enable several important vision applications. Most present-day DL cameras employ unconstrained settings in the two views in order to support extended functionalities. But a natural hindrance to their working is the ubiquitous motion blur encountered due to camera motion, object motion, or both. However, there exists not a single work for the prospective unconstrained DL cameras that addresses this problem (so called dynamic scene deblurring). Due to the unconstrained settings, degradations in the two views need not be the same, and consequently, naive deblurring approaches produce inconsistent left-right views and disrupt scene-consistent disparities. In this paper, we address this problem using Deep Learning and make three important contributions. First, we address the root cause of view-inconsistency in standard deblurring architectures using a Coherent Fusion Module. Second, we address an inherent problem in unconstrained DL deblurring that disrupts scene-consistent disparities by introducing a memory-efficient Adaptive Scale-space Approach. This signal processing formulation allows accommodation of different image-scales in the same network without increasing the number of parameters. Finally, we propose a module to address the Space-variant and Image-dependent nature of dynamic scene blur. We experimentally show that our proposed techniques have substantial practical merit. [ABSTRACT FROM AUTHOR]

Purpose: The negligence of oral health combined with barriers in accessing adequate oral care is more commonly encountered in children with sensory deficits. In a developing country like India, there is a severe lacuna in data regarding the oral health status and treatment needs in this group of population. The purpose of this study is to assess the oral health status and treatment needs of children with sensory deficits, using WHO criteria of Basic Oral Health Survey Methods, 1997. Materials and Methods: A descriptive cross‑sectional study was conducted using the WHO criteria (Basic Oral Health Survey, 1997); questionnaire data regarding the demographic profile, oral hygiene status, degree of sensory impairment were recorded and tabulated. The Chi‑square test was used to determine whether there existed a significant difference in the oral health status. The confidence interval was set at 95% and alpha error was assumed at 0.05. Results: Among the 742 sensory deficit children examined, 371 children are visually impaired and 371 are hearing impaired. Gingival bleeding and poor oral hygiene is diagnosed in more than 70% of the visually impaired children. The prevalence of trauma is estimated to be 8% in children, who are visually impaired. In the hearing impairment group, gingival bleeding because of inadequate oral hygiene is seen in 58% of the population examined. There is no statistically significant difference in the dental caries status between visually impaired and hearing impaired children (P > 0.05). There is also no statistically significant difference in the restorative treatment need and trauma status between visually impaired and hearing impaired children (P > 0.05). Conclusion: The present study shows a high prevalence of gingival diseases and dental caries in the special health care group children. The study signifies a wide spread presence of unmet treatment needs among children in this study population. [ABSTRACT FROM AUTHOR]

Today's business environment, survival and making profit in market are the prime requirement for any enterprise due to competitive environment. Innovation and staying updated are commonly identified two key parameters for achieving success and profit in business. Considerably supply chain management is also accountable for profit. As a measure to maximize the profit, supply chain process is to be streamlined and optimized. Appropriate grouping of various suppliers for the benefit of shipment cost reduction is proposed. Data relating to appropriate attributes of supplier logistics are collected. A methodology is proposed to optimize the supplier logistics using clustering algorithm. In the proposed methodology data preprocessing, clustering and validation process have been carried out. The Z-score normalization is used to normalize the data, which converts the data to uniform scales for improving the clustering performance. By employing Hierarchical and K-means clustering algorithms the supplier logistics are grouped and performance of each method is evaluated and presented. The supplier logistics data from different country is experimented. Outcome of this work can help the buyers to select the cost effective supplier for their business requirements. [ABSTRACT FROM AUTHOR]

The purpose of the study is to examine factors that contribute to the competency development of business education students. The aim is to develop a model wherein there are input factors of competency development and then relate them to competency parameters. Study uses 77 statements on Likert scale as input dimensions. Factor analysis is used as a dimension-reduction tool. The outcome measures are three dependent variables such as Knowledge, Attitude and Skill-development of students that are captured through 18 statements on Likert scale. Descriptive statistical analysis, exploratory factor analysis and reliability analysis were also conducted. The results indicate the competency development among male and female genders during a 2 year programme from different respondents' perspective. Factor analysis generates seven input factors of competencies. Result also highlights that among different competencies, knowledge competency develops most followed by skills and the least development is in attitude in a 2 years business education. The results may be helpful for educational institutions to understand how stakeholders rate the B-school's role in competency development. Thus, the findings can be used as a guide for decision-makers to improve overall input to achieve desirable competencies among students. [ABSTRACT FROM AUTHOR]

The article presents case study of 36 years old female, with history of insidious onset of tender nodule and mobility of mandibular anterior teeth, and treatment for mandibular swelling. Examinations revealed growth at mandibular anterior premolar region and mucosa appeared stretched and pale pink. Diagnosis of odontogenic tumor was made.

The article examines the historical development of Indian scripts and the computer processing of Indian languages. It is stated that Indian languages belong to the Indo-European family of languages and undergone significant mixing and cross-fertilization. It mentions the challenges in the design of Indian scripts and the structural similarity that led to a unified solution. Moreover, understanding the Indian scripts processing to Indian language processing relies on the evolution of Devanagari script and Hindi language.

Congenital epulis, a benign tumor of the oral cavity, is an extremely rare condition in newborn. It may lead to mechanical obstruction, therefore resulting in respiratory distress and difficulty in feeding. Addressing the problem may need a multidisciplinary team approach at the time of birth. Antenatal ultrasonography and perinatal magnetic resonance imaging are an adjunct to treatment planning. Prenatal diagnosis remains difficult as the findings are nonspecific due to the late development of the tumor. Surgical excision is, therefore, the treatment of choice. Our report discusses this condition and the treatment thereafter on a newborn, with an epulis originating from the upper alveolar ridge discovered at birth. Histological examination confirmed the diagnosis of large polygonal granular cells. The mass was excised under general anesthesia, and the outcome was good after surgery allowing regular feeds on the second postoperative day. [ABSTRACT FROM AUTHOR]