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Component Based Software Engineering has one main sole motive of making the development process of software systems as easy as possible and to achieve this objective work needs to be done in previous systems to identify the concerns and limitations which can be overcome using this software engineering based approach. In this paper to support concept of component based system a domain is chosen that covers the GIS systems. GIS (Geographic Information Systems) are commonly used for development of map based applications these systems are widely used across the web and in various organizations. With the development and deepening of GIS, traditional GIS systems showed the challenges on isolation, sealing, interoperability and the limitations, thereby hindering further development and application of GIS technology. In this paper framework for component based GIS system is proposed. This framework is having rich graphical interface and user data can be easily retrieved from the connected database and displayed in the browser.

Clustering improves network stability and scalability significantly by efficiently handling the fast topological change in VANET. Multi-hop clustering is introduced in VANET to handle some significant issues observed in the single-hop clustering, i.e., low cluster coverage, high number of clusters formed, and inefficient bandwidth utilization in routing decisions and internetwork data processing. Among many, cluster forming based on neighbourhood following strategy is the most recent and advanced way to form multi-hop clustering. By exploring the weaknesses and limitations of the existing neighbourhood following strategy, this paper introduced an advanced multi-hop clustering (AMC) algorithm. The proposed algorithm is empowered with an efficient cluster formation procedure that helps the vehicle to select and follow the most suitable target vehicle from one-hop neighbours. The suggested CH selection and cluster merging procedure contribute to strengthening stability. Further, we suggested the routing protocol of AMC to check the data transmission performance. Here, one vehicle appointed as a cross node at the road junction assigns the weight to the road segments by proper connectivity analysis. The lowest weight route signifies the best data forwarding path. The algorithm performance is tested in the realistic type scenario where vehicles’ speed and arrival time are randomized. The detailed simulation result shows the strength of the AMC algorithm.

Powdery mildew is the most commonly observed disease of tomato plants, which affects its quality and productivity. On-time treatment with an optimized amount of fungicides spray can improve the yield and quality of tomato fruit and reduce the effect of powdery mildew disease. Thus, an automated spray prediction model can be developed to deal with the tomato powdery mildew disease. In the current study, three spray prediction models have been proposed using different machine learning models viz. k-Nearest Neighbor (kNN), Support Vector Machine (SVM) and Naive Bayes (NB). This experiment was conducted using an imbalanced dataset named as tomato powdery mildew disease (TPMD) dataset. Synthetic minority over-sampling technique (SMOTE) has been used to balance the TPMD dataset. Results show that SVM based spray prediction model performed the best with 98.65% accuracy amongst all three models. An application version of spray prediction models can help the farmers for spraying fungicides in an efficient manner.

Introduction: Imaging studies have shown enlargement of pancreatic parts in children diagnosed with acute pancreatitis. The purpose here is to develop imaging based diagnostic evaluation criterion for acute pancreatitis in children. Materials and Methods: This study included 62 children of acute pancreatitis in the age range of 0.33 to 13 years, as reported in a single hospital center (1994-2019). A study was planned including 1116 normal healthy children in the age range of 0.16 to 18 years for pancreatic evaluation during 2016-17. Ultrasonography based measurement of three pancreatic parts were obtained for each individual in disease and normal groups. Age-adjusted receiver operating characteristics curve analysis was performed on each pancreatic part independently to derive respective cut-offs using a training set. These cut-offs were further referred to dichotomize the measurement data for each individual and was subjected to multiple logistic regression with presence/absence of acute pancreatitis as dependent variable. A probability score and accordingly the cut-off were obtained indicating a collective impression of enlargement of pancreas in disease condition independently for males and females. Result: On test data, the accuracy of age-adjusted cut-offs for three parts was near 80% for males, while it ranged between 81-85% for females. ROC analysis of probability score resulted into threshold value of 0.024 for males and 0.044 for females, with sensitivity of 94.11% and 90.91% respectively. The classification accuracy of score derived for males and females was nearly same (83%). Conclusion: The extent of enlargement of pancreas in acute pancreatitis in children can be determined using the MLR method along with hypoechogenicity. Keywords: Amylase, Computed Tomography, Gastroenterology, Lipase, Multiple logistic regression, Radiology and Receiver operating, characteristics curve.

The expeditious development in information technology visualizes that facilities for computing are one of the prime utilities of life after the other main essential utilities i.e., water, gas, and ...

Purpose Intensity-modulated radiotherapy (IMRT) provides higher dose to target volumes and limits the dose to normal tissues. IMRT may be applied using either simultaneous integrated boost (SIB-IMRT) or sequential boost (SEQ-IMRT) technique. The objectives of this study were to compare acute toxicity and objective response rates between SIB-IMRT and SEQ-IMRT in patients with locally advanced head and neck cancer. Materials and methods Total 110 patients with locally advanced carcinoma of oropharynx, hypopharynx, and larynx were randomized equally into the two arms (SIB-IMRT vs. SEQ-IMRT). Patients in SIB-IMRT arm received dose of 66 Gy in 30 fractions, 5 days a week, over 6 weeks. SEQ-IMRT arm's patients received 70 Gy in 35 fractions over 7 weeks. Weekly concurrent cisplatin chemotherapy was given in both arms. Patients were assessed for acute toxicities during the treatment and for objective response at 3 months after the radiotherapy. Results Grade 3 dysphagia was significantly more with SIB-IMRT compared to SEQ-IMRT (72% vs. 41.2%; p = 0.006) but other toxicities including mucositis, dermatitis, xerostomia, weight-loss, incidence of nasogastric tube intubation and hospitalization for supportive management were similar in both the arms. Patients in SIB-IMRT arm showed better treatment-compliance and had significantly less treatment-interruption compared to SEQ-IMRT arm (p = 0.028). Objective response rates were similar in both the arms (p = 0.783). Conclusion Concurrent chemoradiation with SIB-IMRT for locally advanced head and neck cancer is well-tolerated and results in better treatment-compliance, similar objective response rates, comparable incidence of mucositis and higher incidence of grade 3 dysphagia compared to SEQ-IMRT.

Syphilitic spinal disease is a rare condition caused by the spirochete Treponema pallidum, either from direct spirochete involvement of the cord, or as a consequence of indirect spirochete involvement of the meninges, blood vessels, or the vertebral column. Following the introduction of penicillin therapy in the 1940s, it has become an increasingly rare condition. We report three challenging cases of syphilitic spinal disease presenting as myelopathy - one with an extra-axial gumma of tertiary syphilis causing cord compression, and two with tabes dorsalis complicated by tabetic spinal neuroarthropathy - each presenting a diagnostic dilemma to their treating physicians. We also review the literature for updates on modern investigative modalities, and discuss pitfalls physicians need to avoid in order to arrive at the diagnosis.

The safety event information sharing among the vehicles in motion is the primary goal to design the vehicular ad hoc network (VANET). The shared safety event information assists vehicles to avoid road accidents and driving inconvenience. The advantages of safety event information sharing in VANET has become blunt due to the misbehavior of vehicles. The vehicle’s misbehavior like dissemination of false information, reply of bogus messages, etc., can create traffic hazards on the road and may result in the loss of property and human lives. In VANET, the detection of such misbehaving vehicles along with minimum time delay in flooding safety event information (i.e., incident delay) to others is challenging due to the high speed of vehicles. The formation of stable VANET topology is a feasible solution among many to improve the performance of misbehavior detection and reducing incident delay even with high speed of vehicles. In this paper, we propose an information based misbehavior detection algorithm (IBMDA) that effectively works in stable cluster based VANET. Our proposed IBMDA algorithm that runs on the selected cluster head vehicles is used to verify the content of received safety event messages. The identification of vehicles as malicious or non malicious depends on the result of verification at cluster heads. An illustrative example is given to explore our proposed algorithm easily and effectively. The highway scenario is considered to test the performance of our proposed IBMDA algorithm. The simulation is performed with a detailed comparative analysis using ns-3 simulator. It is observed that under the considered scenario, our proposed algorithm improves the misbehavior detection accuracy up to 6.46% and reduces average incident delay approximately up to 14.78% as compared to existing algorithms.

We retrospectively reviewed the charts of 180 children sedated for esophagogastroduodenoscopy (EGD) with ketamine or propofol-based regimens at our institution. Pre-EGD diagnoses and American Society of Anesthesiology physical status were similar in all subjects. Onset of action and recovery time for both regimens were not statistically significant (p > 0.05). Mean onset of sedation for all patients was 3.85 ± 3.04 minutes, mean Aldrete score was 6.31 ± 0.61, and mean recovery time was 51.85 ± 31.78 minutes (p > 0.05). Sedation-related adverse events observed include apnea, hypoxemia, bradycardia, hypotension, laryngospasm, skin rash, and wheezing. Deep sedation for pediatric EGD is safe if patients are carefully screened and properly monitored.

Tariffs on shrimps and a few other items imported from the USA are set to go up as the government of India has decided to impose retaliatory duties worth over $200 million on 29 products. This work focuses on shrimp which is the flagship item of seafood exports from India which accounts for 10% of the total exports and nearly 20% of the agricultural exports. Changing markets and product profile including exports diagnostics are explored. Backed by a strong market acceptance, international markets for Indian shrimp have been relatively steady but product profile needs to develop vertically. India needs to diversify its focus from frozen shrimp into value added products. It needs to develop resilience to foreign exchange rates volatility and retain, diversify and develop niche markets. Overall, strong and steady performance of Indian seafood exports overtime should not lead to complacence. Industry needs to be supported both physically and financially. National Bank for Agriculture and Rural Development (NABARD) finance and priority sector lending needs to broad base their outlook to enable industry to update and adopt latest processing technology to develop and market value added shrimp. The MPEDA and Coastal Aquaculture Authority (CAA) which govern aquaculture production and regulation need to look into restructuring shrimp farming in the country to eliminate negative externalities that limit full-scale exploitation of its potential. The steady contribution of seafood exports to the foreign exchange kitty of India should make it a priority sector for attention, in terms of financial, physical, human and social considerations. The newly announced MatsyaSampada Yojana of the government of India, should envisage strategies for sustainable exploitation of available open common use resources of fisheries and aquaculture in the light of climate change scenario and human resource development for sustaining the sector.

Background and objectives: Awake fiberoptic intubation (AFI) is the technique of choice for anticipated difficult intubation via nasal/oral route depending on the type of surgery. Various anesthetic techniques have been used to facilitate AFI, including regional anesthesia, topical anesthesia and sedation. There are few studies regarding the use of magnesium sulphate for this purpose. The aim of our study was to find the efficacy of magnesium sulphate as sedative in patients with maxillofacial trauma undergoing AFI and to establish the dose that provides better sedation.

To compare clinico-radiological pattern of non-cirrhotic versus cirrhotic HCC and correlate them with histopathological tumor grade. This prospective study was carried out on 94 patients enrolled following ultrasound diagnosis of a liver mass measuring > 3 cm. Multiphasic MDCT was performed on all treatment-naive cases and 56 cases with imaging pattern consistent with unifocal HCC were selected. Background liver parenchyma was assessed on ultrasound for cirrhosis and NAFLD. Cases were categorized into cirrhotic liver (CL) and non-cirrhotic liver (NCL) groups with 26 and 30 cases, respectively, and guided biopsy of each liver mass was performed. AFP levels were compared in both groups. Serum markers for hepatitis B and C were assessed. Masses in both groups were compared for morphology, attenuation on each phase and washout time. Presence of capsule, corona enhancement, satellite nodules and portal vein invasion was noted. AFP level was higher in CL group. HBV serum marker was raised in both groups. Most HCCs in NCL were moderately differentiated (histopathology), larger, had well-defined margins, showed mosaic pattern of enhancement, complete capsule and delayed phase washout. Majority in CL group were poorly differentiated, smaller, had ill-defined margins, showed heterogeneous enhancement, absent capsule and portal venous phase washout. Time of washout correlated with histopathological differentiation of masses, with earlier washout indicating poorer differentiation. HCCs in NCL have different clinico-radiological characteristics than HCCs in CL. Time of contrast washout correlates with histopathological grade of HCC. Non-cirrhotic NAFLD may require formulation of new screening guidelines for HCC.

OBJECTIVE. This study aims to compare the diagnostic accuracy of virtual unenhanced CT images derived from dual-source dual-energy contrast-enhanced CT with that of standard unenhanced CT images for evaluation of sialolithiasis. MATERIALS AND METHODS. All dual-energy CT studies of the neck performed during the preceding 5 years were reviewed for submandibular gland calculi. Only patients who had unenhanced CT and contrast-enhanced CT performed as part of the same evaluation were included in this study. This review yielded 30 patients. Virtual unenhanced CT images were derived from the dual-energy dataset and compared with the true unenhanced CT images by two separate radiologists who assessed the total number of calculi encountered, their location, the largest dimension, and the attenuation of the calculi. The radiation burden incurred for true unenhanced CT and virtual unenhanced CT and the total radiation burden were calculated. RESULTS. Our analysis revealed that measurements of stone size showed good interobserver agreement. The mean stone size was 7.9 ± 5.9 (SD) mm on virtual unenhanced CT and 8.4 ± 5.9 mm on true unenhanced CT (range, 2.0-31.8 mm); the difference was statistically insignificant. The mean stone attenuation differed considerably (p < 0.01) between the virtual and true unenhanced CT images (494.8 ± 187.5 HU and 924.4 ± 374.9 HU, respectively), but correlated well. The mean radiation dose for a dual-phase IV contrast-enhanced CT study was 23.13 mGy (volume CT dose index). The mean dose was 10.93 mGy for the true unenhanced CT phase, thereby suggesting a 47.25% reduction in administered radiation dose when a single-phase contrast-enhanced CT study with virtual unenhanced image reconstructions is performed. CONCLUSION. Virtual unenhanced CT images derived from dual-source dual-energy CT scans of the neck provide accurate assessment of sialolithiasis akin to that provided by conventional CT protocols but at only a fraction of the radiation dosage.

Diseases in plants harm the quantity of the overall food production as well as the quality of the yield. Early detection, diagnosis and treatment can greatly reduce losses, both economic and ecological. Intuitively, reduction in the use of agrochemicals due to timely detection of the disease, would greatly help in mitigating the environmental impact. In this paper, the authors have proposed an improved feature computation approach based on Squeeze and Excitation (SE) Networks, before processing by the original Capsule networks (CapsNet) for classification, for estimating the disease severity in plants. Two SE networks, one based on AlexNet and another on ResNet have been combined with Capsule networks. Leaf images for the devastating Late Blight disease occurring in the Tomato crop have been utilized from the PlantVillage dataset. The images, divided into four severity stages i.e. healthy, early, middle and end, are downscaled, enhanced and given as input to the SE networks. The feature maps generated from the two networks are separately given as input to the Capsule Network for classification and their performances are compared with the original CapsNet, on two image sizes 32X32 and 64X64. SE-Alex-CapsNet achieves the highest accuracy of 92.1% and SE-Res CapsNet achieves the highest accuracy of 93.75% with 64X64 image size, as compared to CapsNet that results in 85.53% accuracy. The classification accuracies of six state-of-the-art CNN models namely AlexNet, SqueezeNet, ResNet50, VGG16, VGG19 and Inception V3 are also presented for comparison purposes. Accuracy as well as precision, recall, F1-score, validation loss etc. measures have been recorded and compared. The findings have been validated by implementing the proposed approaches with another dataset, achieving similar resultant accuracy measures. The implementation was also accomplished with datasets after noise addition in six different variations, to verify the robustness of the proposed model. Based on the performances, the proposed techniques can be exploited for disease severity assessment in other crops as well and can be extended to other areas of applications such as plant species classification, weed identification etc. In addition to improved performance, with reduced image size, the proposed methodology can be utilized to create a mobile application requiring low processing capabilities, to be installed on reasonably priced smartphones for practical usage by farmers.

We present ORIS, a holistic autonomous traversal assist system. ORIS follows a multimodal architecture which includes navigation, environment sense, and on road vehicle and object detection. Introducing Level 4 autonomy within vehicles in India has been a challenge, with ambiguous road signs to unkept road markings, developing an autonomous system is hurdled with countless challenges. ORIS tackles the aforementioned challenges by introducing an efficient and minimalistic image-input based tagging solution over three different facets: Lane Segmentation and Centering, Road Sign Detection and Recognition, and Vehicles and On Road Object Detection.

In the current scenario, plant disease detection is seeking attention from many agricultural scientists. Plant diseases are deeply influenced by the weather conditions, and each disease has its individual weather requirements. The changes in weather parameters such as humidity, temperature, wind speed, etc., can cause many diseases in tomato plants. In the current empirical study, we have taken specific disease powdery mildew whose fungus is named as Leveillula Taurica which belongs to Leotiomycetes class, and it is responsible for the occurrence of this specific disease in tomatoes. In this research, three weather-based prediction models have been developed using k-nearest neighbor (kNN), decision tree (DT), and random forest (RF) algorithm for powdery mildew disease prediction in tomatoes at an early stage. Results indicate that the proposed model, based on RF algorithm, shows the best accuracy of 93.24% for tomato powdery mildew disease (TPMD) dataset. A real-time version of the proposed model can be used by the agricultural experts to take preventive measures in the most sensitive areas that are prone to powdery mildew disease based on the weather conditions. Hence, timely intervention would help in reducing the loss in productivity of tomato crops which will further benefit the global economy, agricultural production, and the food industry.

INTRODUCTION AND OBJECTIVE: During the COVID-19 pandemic our institution limited elective surgery including management of kidney stones. Additionally, patients themselves were reluctant to pursue elective surgery. This presented us a unique opportunity to reassess the natural history of symptomatic nephrolithiasis given potentially prolonged periods of conservative management. METHODS: A retrospective review was performed of patients presenting to the emergency room (ER) with flank pain secondary to nephrolithiasis from March to April of 2020 (COVID peak period of elective surgery limitations), and a comparative cohort from March and April 2019. Assessed outcomes included definitive stone treatment at initial presentation to the ER, rate of spontaneous stone passage, and time to elective surgery from initial ER presentation. Chi-square or Mann-Whitney U tests were utilized for dependent binary variables and continuous variables, respectively. A Kaplan Meier analysis was used to demonstrate differences in time to elective surgery between the two eras. RESULTS: Baseline characteristics did not differ between groups (Table). Patients discharged from the ER were more often offered medical expulsive therapy (71.6% vs 55.0%, p=0.026) during the COVID era. The rate of surgical stone management or stent placement at initial presentation did not differ, however, discharged patients waited longer from initial ER presentation to elective surgery (55.3 vs 33.1 days, p=0.02). Spontaneous stone passage rates were similar between groups despite the delay, and similar stone location and stone size between eras. CONCLUSIONS: During the height of the COVID pandemic, ER patients with symptomatic stones had similar characteristics at presentation but were more often offered MET. Spontaneous stone passage during the pandemic was no different than in 2019, despite a significant difference in time to elective surgery from initial presentation to the ER with flank pain .

In the current circumstances, plant disease prediction is drawing the attention of various scientists and agricultural experts. The prediction of plant diseases is the foundation of the early identification of diseases in plants efficiently using machine-learning algorithms. However, this area of agriculture science faces the challenge of the imbalanced dataset. Imbalanced datasets can bias the results of machine learning models towards the major class containing the largest number of samples of datasets. This problem can be dealt with the use of resampling techniques that balance the dataset to improve the efficiency of machine learning models. Hence, in the current study, the impact of resampling techniques such as Importance Sampling, Random over Sampling, Synthetic Minority Over-sampling Technique, and Random under Sampling has been evaluated on imbalanced plant disease datasets, i.e., Tomato Powdery Mildew Disease and Soybean Large using various machine-learning classifiers, i.e., Random Forest, Naive Bayes, Multinomial Logistic Regression and Bagged Classification and Regression Tree. The results of this evaluation show that amongst all the resampling techniques Random Over Sampling has performed the best with 99.24% accuracy for Tomato Powdery Mildew Disease dataset for Random Forest Classifier, whereas Synthetic Minority Over-sampling Technique performed the best with 98.53% accuracy for Soybean Large dataset in case of Bagged Classification and Regression Tree Classifier.

A modified analysis of different design and techniques of piezoelectric sensors for generating the power in the most efficient way in this paper. Comparison and observation of various arrangement of piezoelectric sensors. Comparison between the output of the piezoelectric sensor arranged in series, parallel and series-parallel are done in this paper. In this paper the piezoelectric sensor is compared for the objective to design and model a high energy efficient piezoelectric sensor so that it can be used broadly.

Vision-based detection of road accidents using traffic surveillance video is a highly desirable but challenging task. In this paper, we propose a novel framework for automatic detection of road accidents in surveillance videos. The proposed framework automatically learns feature representation from the spatiotemporal volumes of raw pixel intensity instead of traditional hand-crafted features. We consider the accident of the vehicles as an unusual incident. The proposed framework extracts deep representation using denoising autoencoders trained over the normal traffic videos. The possibility of an accident is determined based on the reconstruction error and the likelihood of the deep representation. For the likelihood of the deep representation, an unsupervised model is trained using one class support vector machine. Also, the intersection points of the vehicle’s trajectories are used to reduce the false alarm rate and increase the reliability of the overall system. We evaluated out proposed approach on real accident videos collected from the CCTV surveillance network of Hyderabad City in India. The experiments on these real accident videos demonstrate the efficacy of the proposed approach.

Demand for organic chemical-free formulations in food industry for the purpose of food preservation has triggered exploration of biosurfactants capable of nano-emulsification. Extraction protocol for saponin from fruit pericarp of Sapindus mukorossi was standardized. Saponin was characterized through FTIR and NMR. Solvents were found to have significant influence on the critical micelle concentration (CMC). Aqueous extraction yielded higher CMC (666.67 ppm) over 1:1 water: ethanol (416.67 ppm) and ethanolic extract (370.37 ppm). Aqueous extract of S. mukorossi (0.4%) was used as biosurfactant for nanoemulsification of basil oil through ultrasonication. DLS and TEM confirmed its efficacy for nanoemulsification based on droplet size of 37.7 and 57.6 nm and stability tests. The formulated nanoemulsion was investigated for its antimicrobial activity against common food spoilage fungi Penicillium chrysogenum and Aspergillus flavus. Basil oil thus nanoemulsified was found to have higher inhibitory activity (upto 20%) over its coarse emulsion. Formulated nanoemulsion at 1000 ppm was found to have 64–67% inhibitory activity over these pathogens compared to standard fungicide Carbendazim. Sapindus mukorossi can thus emerge as potential source of biosurfactant for formulating emulsion based preparations for food safety.

Aim: While delivering radiotherapy it is utmost important to minimize target motion to decrease margins in postoperative gynaecological patients. Hence certain bladder and rectum filling protocols are followed by each institute. During cone beam computed tomography (CBCT) verification, we observed that this motion was more affected by rectal filling. To verify, we retrospectively analysed the vaginal movement and its relation with bladder and rectum filling. Materials and Methods: We evaluated CBCTs of 15 patients of carcinoma endometrium. Bladder and rectum both were contoured offline on each scan. To assess the motion of vagina, two reference points were selected. Posterior movement of bladder and anterior movement of rectum were noted on these points on each scan. Results: Total 150 scans (135 KV-CBCT scans and 15 planning computed tomography) of 15 patients were studied. Stepwise regression analysis reported that bladder wall changes has a nonsignificant relationship with bladder volume among all the individuals. The significant rectal wall changes both at Point X and Y were observed in six patients and only at Point X in three patients. Rest of the patients showed no significant relationship in their CBCT scans. Thus 60% patients showed significant relation between rectal volume and rectal wall changes. Conclusion: Hence we suggest to advice our patients more regarding volume of rectum to decrease vaginal motion. Bladder volume is needed to decrease the dose to small intestine. However prospective data with large number of patients in the study is required to confirm these findings.

Vitamin D deficiency is a common disorder that is associated with morbidity and mortality in the general population. We conducted a cross-sectional study of 384 children admitted to paediatric intensive care to determine its prevalence and association with severity of illness and outcome in critically ill children. The severity of illness was evaluated using the paediatric risk of mortality score (PRISM III), on admission, at 24 and 48 h. Vitamin D deficiency was observed in 175 children (45.6%) and was associated with higher severity of illness, need for mechanical ventilation and increased mortality.

In general, AMBTC-based steganography techniques effectively utilize smooth blocks for secret data embedding but keeps complex blocks as it is. It is considered that any modification in the complex blocks may cause a substantiate distortion in an original image. Therefore, conventional techniques do not make any modification in complex blocks. In proposed paper, a technique is disclosed for secret data embedding in complex blocks in addition to smooth blocks. It shows that a cluster of bit-map blocks corresponding to complex blocks of multiple images are formed, and then most frequent 256 bit-map blocks are determined based on histogram analysis. Multiple images can be drawn from any standard image database. The most frequent 256 bit-map are block patterns which are indexed using 8 bits. Now, for secret data embedding, bit-map of each complex block is scanned one-by-one and is compared with the 256 bit-map block patterns. If the bit-map of scanned complex block is matched with any of the 256 block patterns, then first 8 bits of bit-map of scanned complex block is replaced with 8-bit index of matched block pattern and remaining 8 bits of bit-map of scanned complex block are replaced with secret data. Thus, this technique shows a way of embedding secret data in complex blocks using block patterns. Further, for the smooth blocks, present technique utilizes Ou et al. technique which is efficient for secret data embedding in smooth blocks. Results of the proposed scheme show increase in embedding capacity while preserving image quality.

The petrous apex, cerebellopontine angles and jugular foramen have many important neurovascular structures traversing in and around them. The lesions in these areas are unfortunately varied and tend to overlap considerably. This chapter provides a concise overview of the relevant anatomy and a systematic stepwise approach on how to differentiate these various lesions.

Deep Learning is acquiring momentum in the agricultural field for crop disease detection using image processing due to its computational power. Several deep learning techniques have been implemented in different domains and recently introduced in the field of agriculture to classify and predict the diseases of crops. Based on images of banana crops in the early stages of development, the objective of this research study is to create a prediction model using two types of Convolutional Neural Networks (CNN) architectures, namely, AlexNet and ResNet50. In order to carry out the empirical study, the PlantVillage dataset for the Banana plant with 510 images of banana leaves was used to train and test the networks. Results were analyzed using four parameters namely; training accuracy (TA), training loss (TL), validation accuracy (VA), and validation loss (VL). It was observed that ResNet50 outperformed the other one with better results at 88.54% when validation accuracy is considered as a performance evaluation measure. The results of this study will be useful for farmers as they can make timely interventions in the case of Banana Black Sigatoka (BBS) and Banana Bacterial Wilt (BBW) diseases.

Fixture design is the most critical aspect for the success of friction stir welding and processing (FSW/P). Nowadays, the machines that developed exclusively dedicated to FSW/P are very expensive, which motivated to design a new innovative fixture. This paper aims to design and develop a robust fixture that can be easy to use on a conventional vertical milling machine. The designed fixture and clamping devices rigidly hold the workpiece in such a way that it restricts lateral and longitudinal movement during processing. The later part carried out a detailed finite element analysis to check the robustness of fixture using Autodesk Fusion 360 software (Education license) and validated it in the actual condition. The fixture setup is installed and tested successfully on the universal vertical milling machine. The result shows that it withstands high forces generated due to relative motion between the tool and the workpiece.

Deep learning (DL) has rapidly become an essential tool for image classification tasks. This technique is now being deployed to the tasks of classifying and detecting plant diseases. The encouraging results achieved with this methodology hide many problems that are rarely addressed in related experiments. This study examines the main factors influencing the efficiency of deep neural networks for plant disease detection. The challenges discussed in the study are based on the literature as well as experiments conducted using an image database, which contains approximately 1,296 leaf images of the beans crop. A pre-trained convolutional neural network, EfficientNet B0, is used for training and testing purposes. This study gives and emphasizes on factors and challenges that may potentially affect the use of DL techniques to detect and classify plant diseases. Some solutions are also suggested that may overcome these problems.

With the emergence of platforms like Netftix which keep users engaged through their sophisticated recommendation algorithms, it is of growing importance to accurately predict the genre a movie belongs to, in order to push the right content to viewers. Until recently, content creators found their customers by communicating the plot of a movie through expressive posters or by publishing a short overview of the movie. For an algorithm to try and replicate the perceptions drawn by humans to predict which movies a consumer will be interested in, it is necessary to treat this as a multi-label problem and attribute multiple genres to a single movie. The principal aim of this paper is to reliably predict the different genres under which a movie can be classified, and further check whether a combination of poster and overview of a movie can provide more accurate results than a poster and overview used individually. We evaluate this task by performing a series of experiments that compare and contrast the importance of both, textual features from movie overviews and visual features from the poster. We propose that the ensemble use of both these features using a ResNet50 model and GloVe embeddings, outperforms conventional models that use these features individually, with an average F1-Score of 0.674.

Communication among vehicles should be secure and safe. Any vehicle or node that misbehaves or transmits wrong information needs to be revoked. One of the widely used scheme to revoke the malicious node’s certificate is a public key infrastructure (PKI). In PKI Certificate revocation list (CRL) is issued by the Certificate Authority (CA). CRL is distributed in the system after its creation by the CA. All the vehicles must receive a genuine copy of CRL so that they can stop receiving messages from malicious nodes. CRL should be distributed very fast and efficiently in the VANET system. We propose a new method for CRL distribution called Smart Certificate Revocation List Exchange (SCRLE). Both V2V and V2I communication is used by our method to distribute CRL pieces among the vehicles. Our method is inspired by C2CEpidemic method of CRL distribution and uses the idea of sharing the CRL list in peer to peer manner. With this approach, vehicles will be able to find their peers more appropriately. This approach uses a ranking mechanism by which vehicles will be paired with those vehicles which have more number of CRL pieces. This will increase the chance of exchanging of CRL pieces efficiently. OMNET++ along with Vein s framework is used to implement and evaluate the method. Sumo is used to generate traces of vehicles for large scale simulation. Results show that it performs better than C2CEpidemic method of CRL exchange.

The real-time detection of traffic rule violators in a city-wide surveillance network is a highly desirable but challenging task because it needs to perform computationally complex analytics on the live video streams from large number of cameras, simultaneously. In this paper, we propose an efficient framework using edge computing to deploy a system for automatic detection of bike-riders without helmet. First, we propose a novel robust and compact method for the detection of the motorcyclists without helmet using convolutional neural networks (CNNs). Then, we scale it for the real-time performance on an edge-device by dropping redundant filters and quantizing the model weights. To reduce the network latency, we place the detector module on edge-devices in the cameras. The edge-nodes send their detected alerts to a central alert database where the end users access these alerts through a web interface. To evaluate the proposed method, we collected two datasets of real traffic videos, namely, IITH_Helmet_1 which contains sparse traffic and IITH_Helmet_2 which contains dense traffic. The experimental results show that our method achieves a high detection accuracy of$\approx$95% while maintaining the real-time processing speed of $\approx$22fps on Nvidia-TXI.

Background: Since its initial start in December 2019 at Wuhan, China, the coronavirus disease 2019 (COVID-19) has been rapidly spreading and labelled as a pandemic by the World Health Organization. The rate of human to human transmission of COVID-19 is far higher than severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome coronavirus (MERS). With no drugs or vaccines approved for the treatment of the disease, physicians have been using pre-existing drugs to curb the disease. One potential anti-viral agent currently undergoing numerous clinical trials is remdesivir, a nucleotide analog that inhibits RNA-dependent RNA polymerase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Objective: In this mini-review, we provide an overview of remdesivir’s journey, mechanism of action, pharmacokinetics, used in patients with COVID-19 under compassionate use principle and clinical trials to understand the effect of remdesivir in the treatment of patients with COVID-19. Conclusion: Initially, remdesivir was granted an emergency use authorization (EUA) by the U.S. Food and Drug Administration for the treatment of COVID-19 with severe disease. But now, remdesivir has been granted for use under EUA to treat all hospitalized COVID-19 patients, irrespective of their severity of disease.

In this paper, semi-empirical models of the performance characteristics, i.e., material removal rate, average surface roughness, kerf top width and taper angle in abrasive water jet machining (AWJM) of AA6351 aluminum alloy are reported. These models are developed using the dimensional analysis based on Buckingham ∏ theorem, and the proposed models for the considered performance parameters are analyzed both qualitatively and quantitatively using experimental data. Likewise, artificial feed forward neural network has been attempted to predict the considered performance parameters of AWJM process. For this purpose, different numbers of neurons have been tried at the hidden layer to obtain the best neural network. The validity of semi-empirical models and artificial neural network is verified using the experimental results. Furthermore, software prototype graphical user interface (GUI) is developed which allows user to interact with the models to predict the considered performance characteristics. The GUI is tested and validated through the experimental results. The results proved the functional capability and effectiveness of the GUI in predicting the performance characteristics.

Cloud computing is a utility where compute resources are accessed on pay-per-use ground. It is enacted through virtualizationwhich is a central technology in cloud computing. Virtualization provides a secure and commendable environment for the functioning of cloud applications. Virtual machine (VM) plays a crucial role in the cloud data center when a host machine is exhausted due to tremendous network traffic, then the load is stabilized by shifting highly loaded VM to the under-utilized host machine. This review article recapitulates energy-efficient VM migration, VM migration techniques, objectives and issues related to it. The existing Swarm intelligence based VMmigration techniques are conferred and compared.

Seeds and propagating materials of plants are the primary source of pathogen inoculum to cause diseases. These materials also transmit the pathogen from one place to other places and establish the disease in a new area. Hence, it is an utmost requirement to detect the pathogen of a particular crop before transportation and sowing to ensure that no potentially damaging pathogens are introduced in the field through seeds and planting materials. This can be most effectively accomplished by keeping out pathogens from seed lots by either discarding or treating seeds with chemicals. Various conventional methods for the detection of pathogens such as visual examination, selective growth media, serological methods, and bioassay have been used commonly. But these methods have disadvantages like inefficiency, less specificity, less sensitivity, and more time-consuming. Now-a-days, polymerase chain reaction (PCR) has more potential to improve bacterial pathogen detection in seeds as well as planting materials. There are advanced techniques like BIO-PCR, immunomagnetic separation-PCR (IMS-PCR), and magnetic capture hybridization-PCR (MCH-PCR) which reduce inhibitory compounds during PCR, which further improve the detection level of bacterial pathogens from seeds and planting materials. IMS-PCR and MCH-PCR are more attractive due to their simple and universally applicable methods to test seeds for different culturable and non-culturable bacterial pathogens. However, it is difficult to adapt their applicability for routine testing of seed under the laboratory. It should be ensured that these methods should work and these methods must be validated in multi-laboratory tests thoroughly and these tests should be reproducible and repeatable before their commercialization.

Dysregulation of deubiquitination pathway is associated with poor prognosis in cancers such as hepatocellular carcinoma (HCC). The mammalian target of rapamycin, mTOR, has become an attractive cancer therapeutic target in HCC. However, whether and how aberrant expression of deubiquitination pathway regulates mTOR pathway has remained elusive. Here we report that ubiquitin-specific protease 10 (USP10) functions as a tumor suppressor which inhibits mTOR pathway by stabilizing PTEN and AMPKα in HCC cells. Mechanistically, USP10 interacts and stabilizes PTEN and AMPKα by inhibiting their polyubiquitylation. This stabilization in turn inhibits AKT phosphorylation and mTOR Complex1 (mTORC1) activation. In human liver cancer, USP10 expression is downregulated in HCC tumor tissues across three independent HCC cohorts, and lower-expression of USP10 will generate poor prognosis outcome. Collectively, our results uncover an undescribed mechanism where USP10, as a tumor suppressor, negatively regulates mTORC1 activation and AKT phosphorylation by stabilizing AMPKα and PTEN in HCC cells. This study sheds light on the theoretical basis of mTOR signaling pathway-oriented targeting treatment in clinic.

With the development of Internet of things (IOT), it is now possible to connect various heterogeneous devices together using Internet. The devices are able to share their information for various applications including health care, security and monitoring. IOT facilitates patients to self-monitor their physiological states invariably and doctors to monitor their patients remotely. Electroencephalography (EEG) provides a monitoring method to record such electrical activities of the brain using sensors. In this paper, we present an automatic age and gender prediction framework of users based on their neurosignals captured during eyes closed resting state using EEG sensor. Using EEG sensor, brain activities of 60 individuals with different age groups varying between 6 and 55 years and gender (i.e., male and female) have been recorded using a wireless EEG sensor. Discrete wavelet transform frequency decomposition has been performed for feature extraction. Next, random forest classifier has been applied for modeling the brain signals. Lastly, the accuracies have been compared with support vector machine and artificial neural network classifiers. The performance of the system has been tested using user-independent approach with an accuracy of 88.33 and 96.66% in age and gender prediction, respectively. It has been analyzed that oscillations in beta and theta band waves show maximum age prediction, whereas delta rhythm leads to highest gender classification rates. The proposed method can be extended to different IOT applications in healthcare sector where age and gender information can be automatically transmitted to hospitals and clinics through Internet.

In this paper, we propose a novel multimodal user identification and verification scheme combining two inter-linked biometric traits, i.e., signature and brain signals (Electroencephalography (EEG)). Moulding handwritten strokes for signature stimulates EEG signals in the brain. The response of the brain signals during signing is unique for each user and this brain signal-signature relation is utilized in the proposed scheme. To the best of our knowledge, there exist no authentication approach combining these two inter-linked biometric traits. The proposed multimodal scheme uses Hidden Markov Model (HMM) based sequential classifier to model features extracted from signatures and EEG signals individually. Pyramid Histogram of Orientation Gradients (PHOG) features are extracted from the signature-images and next PHOG features are used to build user specific signature-HMM models. Similarly, user wise EEG-HMM models are built with Daubechies-4 (DB4) wavelet analysis. Finally, a score combining classification scores of signature-HMM and EEG-HMM models is used to perform user identification and verification. To evaluate the effectiveness of the proposed scheme, we have developed a dataset collecting these two traits simultaneously using the Emotiv Epoc+ device and pen-paper for 70 individual subjects. Thereafter, user’s identification is performed with individuals’ signature and EEG signals as well as their combined traits. The identification accuracy of the proposed multimodal approach has been achieved upto 98.24%. The effectiveness of the verification scheme is validated by the Receiver Operating Characteristic (ROC) curves.