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Gaucher disease (GD) is a rare autosomal recessive metabolic disorder. It is characterized by a deficiency of lysosomal glucocerebrosidase, which results in the accumulation of glycosphingolipid substrates, primarily glucosylceramide, in the phagocyte system. In GD Type 1, the liver, spleen, and bone marrow are typically affected. We report the case of a 7-year-old female with GD Type 1 who presented with hepatosplenomegaly detected incidentally following a motor vehicle accident. She was found to have concomitant thrombocytopenia and Erlenmeyer flask deformities of her lower limbs. Diagnosis was made on the basis of very low leukocyte β-glucocerebrosidase activity and elevated plasma chitotriosidase. DNA mutation studies revealed both c.1226A>G and c.116_1505 deletion (exons 3-11). The patient is currently managed with biweekly intravenous imiglucerase (Cerezyme) replacement therapy. She demonstrated resolution of thrombocytopenia and hepatosplenomegaly at 2-year follow-up. Physicians must consider this rare diagnosis in children presenting with hepatosplenomegaly to prompt timely management.

The leading cause of mortality is due to cardio vascular disease (CVD) globally. CVD is the major cause of death all over the world for the past years because an estimation of 17.5 million people d...

BACKGROUND: Less than two-thirds of children with abdominal pain in the emergency department receive analgesia. We sought to determine whether hyoscine butylbromide was superior to acetaminophen for children with nonspecific colicky abdominal pain. METHODS: We randomly allocated children aged 8–17 years with nonspecific colicky abdominal pain who presented to the pediatric emergency department of London Health Sciences Centre, London, Ontario to receive hyoscine butylbromide, 10 mg given orally, or acetaminophen, 15 mg/kg given orally (maximum 975 mg). We considered the minimal clinically important difference for the primary outcome (self-reported pain at 80 min) to be 13 mm on a 100 mm visual analogue scale. Secondary outcomes included administration of rescue analgesia, adverse effects and pain score less than 30 mm at 80 minutes. RESULTS: A total of 236 participants (120 in the hyoscine butylbromide group and 116 in the acetaminophen group) were included in the trial. The mean visual analogue scale scores at 80 minutes were 29 mm (standard deviation [SD] 26 mm) and 30 mm (SD 29 mm) with hyoscine butylbromide and acetaminophen, respectively (adjusted difference 1, 95% confidence interval −7 to 7). Rescue analgesia was administered to 4 participants (3.3%) in the hyoscine butylbromide group and 1 participant (0.9%) in the acetaminophen groups (p = 0.2). We found no significant differences in rates of adverse effects between hyoscine butylbromide (32/116 [27.6%]) and acetaminophen (28/115 [24.3]) (p = 0.5); no serious adverse effects were observed. The proportion with a pain score less than 30 mm at 80 minutes was 66 (55.0%) with hyoscine butylbromide and 63 (54.3%) with acetaminophen (p = 0.9). INTERPRETATION: Hyoscine butylbromide was not superior to acetaminophen in this setting. Both agents were associated with clinically important pain reduction, and either can be considered for children presenting to the emergency department with nonspecific colicky abdominal pain. Trial registration:Clinicaltrials.gov, no. NCT02582307

Background Eosinophilic esophagitis (EoE) is a chronic disorder treated by food elimination diet (FED), topical steroids and/or proton-pump inhibitors (PPI). Serial endoscopies and biopsies assess response to therapy. EoE management has evolved as guidelines are updated. Aims To identify practice variation among Canadian paediatric gastroenterologists (PG) who care for children with EoE. Methods An online survey using REDCap about decision-making in children with EoE was distributed to PG in Canada in November 2020. Results 62 PG completed the survey (response rate 69%, 62/94). The majority work in academic centres (92%). 3 centers indicated an accrual of >50 new patients per year; 9/16 centres have >100 patients in follow-up. An EoE Clinic is present in 5 centres. Diagnosis: Familiarity with the 2018 AGREE and 2020 AGA EoE guidelines was found to be 57% and 67% respectively. Criteria required to diagnose EoE according to current guidelines were correctly indicated by 42% of PG. (Figure 1). Endoscopy: The majority of PG (95%) adhere to guidelines in terms of required number and location of biopsies for the initial diagnosis. Ideal timing of repeat endoscopy after change in therapy in patients who are not in histological remission was 8–12 weeks by 67% of PG, timing in stable patients on maintenance therapy varied (33% only if patient is symptomatic). 25% used the EREFS Score in reporting endoscopic findings. Therapy: Improvement of symptoms was the highest ranked goal (64%), followed by remission of histologic findings (30%). A treatment algorithm was in place in 4 centers. The majority routinely assess adherence to therapy (73%) and consult a dietitian for FED (77%). Most (87%) do not consult an allergist for initial management. Preferred choices of 1st-line therapy varied among PG (Figure 2). When FED was selected, 32% of PG started with 1 food, 32% started with 2 foods, most frequently excluding dairy, followed by wheat. 14 (26%) start with ≥6 FED. Prescription of budesonide slurry was consistent among PG with doses of 1 and 2 mg/day in children 10 years, respectively. Conclusions The is the first Canadian study to assess the variation in management of children with EoE by PG. Overall, PG demonstrated good adherence to the guidelines in terms of initial diagnosis, but differences in maintenance therapy choice and timing of endoscopies. The results highlight a need for standardized management algorithms to deliver uniform care to this growing group. Grounding these guidelines in evidence will warrant a significant investment in further paediatric EoE research. Funding Agencies None

In the present digital age and with the huge volume of data floating around, the information security has become utmost importance. Intrusion detection is the process of analysing various events in a system/network for possible presence of intrusion. The aim of an intrusion detection system is to protect the system from unauthorized access. Design of intrusion detection systems (IDS) has gained lots of importance in the recent years and has become the standard component of in network security. Intrusion detection systems operate either using anomaly based or signature based and in some cases IDS operate in a hybrid way. The data growth rate and the higher bandwidth and network speed makes it very difficult to process the data in real-time. Many researchers have focused in this area and have used data mining techniques for detecting the intrusions. Classification is a data mining technique used to predict group membership for each data instance. Classification is being used by various researchers for detection intrusions. Lot of classification algorithms have been developed for intrusion detection with respective strengths and weaknesses. This paper presents a novel classification algorithm based on distance measure and Relief-F feature weighting. The performance measures of intrusion detection are compared with the commonly used classification algorithms such as Naive Bayes, Decision Tree and Support Vector Machine (SVM) and the proposed algorithm outperforms the above mentioned algorithms in terms of Detection Rate, Accuracy, False Alarm Rate, F-Score and Mathews Correlation Coefficient. The proposed algorithm is tested using a benchmark dataset (KDDcup99 dataset) and a real traces dataset (Kyoto 2006 + dataset). This study also intend to compare the execution time for various classifiers and the parallel performance of NADA since NADA outperforms all the other classifiers in terms of serial execution time. The algorithm is parallelized and the results are presented in terms of execution time with various data size, speed up and efficiency.

The aim of this study is to examine the awareness, accessibility and use of e-databases at Market research organizations because huge money is invested for the subscription, growth, and management of e-databases. The main aim is to determine the awareness of e-databases; and to find out their extent of use, level of satisfaction, problems, and instructions/help in accessing of e-databases.

Large scale Neural Network (NN) accelerators typically have multiple processing nodes that can be implemented as a multi-core chip, and can be organized on a network of chips (noise) corresponding to neurons with heavy traffic. Portions of several NoC-based NN chip-to-chip interconnect networks are linked to further enhance overall nerve amplification capacity. Large volumes of multicast on-chip or cross-chip can further complicate the construction of a cross-link network and create a NN barrier of device capacity and resources. In this paper, this refer to inter-chip and inter-chip communication strategies known as neuron connection for NN accelerators. Interconnect for powerful fault-tolerant routing system neural NoC is implemented in this paper. Regarding intra-chip communication, this recommend crossbar arbitrage placement, virtual interrupt and path-based parallelization strategies for virtual channel routing, leading to higher NoC output with lower hardware costs. For multicast-based traffic. Regarding inter-chip communication, this propose a lightweight NoC compatible chip-to-chip interconnection scheme to allow efficient interconnection for NoC-based NN chips. In addition, this will test the proposed methods with four Field Programmable Gate Arrays (FPGAs) on four hardwired Deep Neural Network (DNN) chips. The experimental results show that the proposed interconnection network can effectively handle data traffic with high thr

Sentiment and Emotion detection in social media conversations remains a challenge and analyzing the people emotion emerged as an important task in this unprecedented time of COVID-19. People sentiment and emotions are affected by lockdowns, social distancing, travel, work-from-home, wearing mask, reading social media posting. Most of them are feeling sad, anger, depressed, and some of them are neutral and happy. The most recent Sentiment Analysis (SA) researches are done using Twitter dataset (short-text) and rule-based (sentiment lexicon) approach, the outcome of these SA models' results is not showing the consistent prediction of people sentiment about COVID-19. To mitigate and overcome limitations of lexicon approach, processing unstructured social media long text posting, getting context based sentiment score, model overfitting, performance problems of sentiment models, authors' proposed and built a novel multi-class SA model using extension of Bidirectional LSTM (SAB-LSTM) with additional layers. In this experiment SAB-LSTM model has been used to process long text of social media posting, news articles text dataset. Experiment result showed, SAB-LSTM model performance is better than traditional LSTM and BLSTM. Compared SAB-LSTM performance metric of Precision, Recall, F1 Score and sentiment score with traditional LSTM and BLSTM. For this experiment collected COVID-19 related dataset from various social media sources such as Twitter, Facebook, YouTube, News articles blogs and collected data from friends and family.

Crohn’s disease exacerbations can often be associated with bacterial infections causing gastroenteritis. We report a child who experienced exacerbation of his Crohn’s disease associated with a positive stool culture forEdwardsiella tarda (E. tarda). Endoscopy showed features of moderate inflammation similar to exacerbation of Crohn’s disease. The patient was treated simultaneously with intravenous steroids and antibiotics, and his symptoms resolved. This case report highlights the importance of clinicians being able to promptly recognize and treat concurrent bacterial infections in Crohn’s disease exacerbations.

Given the exponential growth of Internet and increased availability of bandwidth, Intrusion Detection has become the critical component of Information Security and the importance of secure networks has tremendously increased. Though the concept of Intrusion Detection was introduced by James Anderson J. P. in the year 1980, it has gained lots of importance in the recent years because of the recent attacks on the IT infrastructure. The main objective of this study is to examine the existing literature on various approaches for Intrusion Detection in particular Anomaly Detection, to examine their conceptual foundations, to taxonomize the Intrusion Detection System (IDS) and to develop a morphological framework for IDS for easy understanding. In this study a detailed survey of IDS from the initial days, the development of IDS, architectures, components are presented.

With the increased use of Internet and Internet of Things (IoT), data is being shared/generated instantaneously between/by various devices that range from small sensors to various appliances. Though this offers lots of tangible benefits, there are certain concerns such as the requirement of faster networks, higher bandwidth and huge storage etc., are there and the major concern is security of the data. The rate of information generation/exchange has increased the significance secure networks. As the network speed and bandwidth are ever increasing, Anomaly detection has attracted the attention of researchers to overcome the difficulties faced in signature based intrusion detection where detecting new attacks are not possible and the other factors which affect intrusion detection such as detection rate and the time required to detect intrusions. In this study a novel algorithm for network anomaly detection based on distance and initial classification of data based on 'protocol type' is proposed. The algorithm is tested with Kyoto University's 2006+ Benchmark dataset (new version of data). The results of the proposed algorithm outperform all the known/commonly used classification algorithms with respect to Detection Rate, False Alarm Rate, Recall and F-score.

Diabetes is a leading cause of micro vascular complications such as neuropathy and peripheral vascular disease. It is also associated with an accelerating atherosclerosis. Peripheral arterial disease (PAD) is manifestation of atherosclerosis which can pre date the diagnosis of diabetes. PAD is prevalent in diabetes and has a silent nature of progression. ABI is used as a reproducible and accurate non-invasive method for measurement and detection of PAD and the determination of disease complexity. Toe pressure and Toe brachial index (TBI) is useful in the evaluation of people with medial arterial calcification, where the ABI is considered as less accurate. Hence the aim is to develop a device which can assist humans to predict diabetic vascular disorder and treat them. The proposed system uses the toe brachial index (TBI) for detection of PAD. The normal range of TBI is from 0.5-0.95, below this value the person is diagnosed for PAD and above this value would be considered as unclassified due to calcification of blood vessel. The proposed device is a microcontroller based system that can monitor TBI and accordingly treat the vascular issue by providing compression.

BACKGROUND: Focal active colitis (FAC) is a histopathologic finding of uncertain clinical significance in individual patients. In adult cases, infection accounts for approx. 50%, Crohn’s disease (CD) for 0–13%, and 20–30% are idiopathic (likely related to bowel preparation). One previous study of 29 cases of pediatric FAC showed a 28% rate of CD. Histologic features to distinguish between idiopathic FAC and inflammatory bowel disease (IBD) related FAC have not been determined. AIMS: Our study aimed to review a large cohort of pediatric patients with FAC to determine what proportion of them had IBD, and whether there was an amount or pattern of inflammation that predicted IBD. METHODS: Sixty eight biopsy sets from 68 patients aged ≤18 years with FAC were identified and reviewed retrospectively. Patients with a prior diagnosis of IBD or chronic colitis in the index biopsies were excluded. Original slides were assessed for a number of inflammatory criteria. Clinical data including presenting symptoms, medication history and final diagnoses were recorded. Data were analysed using Pearson correlations and Fisher’s exact chi-square analyses. RESULTS: Sixteen patients (24%) had a final diagnosis of IBD. When cases with terminal ileal inflammation and / or granulomas were excluded 6 of 54 remaining patients had a final diagnosis of IBD (11%). A final diagnosis of IBD was significantly associated with the presence of crypt abscesses in patients with and without terminal ileal inflammation (Fisher’s exact=5.67, p=0.027 and Fisher’s exact=7.99, p=0.025) and the presence of one or more elevated serum inflammatory markers (Fisher’s exact=11.44, p=0.001 and Fisher’s exact=15.02, p=0.001). IBD was significantly associated with TI inflammation (Fisher’s exact=20.27, p

BACKGROUND: Abdominal pain is one of the most frequent reasons for an emergency department (ED) visit. Most cases are functional and no therapy has proven effective. The purpose of this study is to identify an effective therapeutic agent for children who present to the ED with presumed functional abdominal pain. OBJECTIVES: We sought to determine if hyoscine butylbromide (HBB) (BuscopanTM) is effective for children who present to the ED with functional abdominal pain. DESIGN/METHODS: We conducted a randomized, blinded, superiority trial comparing HBB 10 mg plus acetaminophen placebo to oral acetaminophen 15 mg/kg (max 975 mg) plus HBB placebo using a double-dummy approach. We included children 8–17 years presenting to the ED with colicky abdominal pain rated > 40 mm on a 100 mm visual analog scale (VAS). The primary outcome was VAS pain score at 80 minutes post-administration. Secondary outcomes included adverse effects; caregiver satisfaction with pain management using a five-item Likert scale; recidivism and missed surgical diagnoses within 24-hours of discharge. Analysis was based on intention to treat. RESULTS: 236 participants were randomized (116 acetaminophen; 120 HBB). The mean (SD) age was 12.4 (3.0) years and 153/236 (64.8%) were female. The median (IQR) duration of pain prior to enrollment was 2 (4.5) hours and analgesia was provided to 129/236 (54.7%) of participants. The mean (SD) pre-intervention pain scores in the acetaminophen and HBB groups were 62.3 (16.5) mm and 60.3 (17.9) mm, respectively. At 80 minutes, the mean (SD) pain scores in the acetaminophen and HBB groups were 30.1 (28.8) mm and 29.4 (26.4) mm, respectively and there were no significant differences adjusting for pre-intervention scores (p=0.96). The median (IQR) caregiver satisfaction was high in the acetaminophen [5 (2)] and HBB [5 (1)] groups (p=0.79). The median (IQR) length of stay between acetaminophen [236 (99.75)] and HBB [230.5 (102.5)] was not significantly different (p=0.91). In the acetaminophen and HBB groups, 6 and 8 participants returned to a health provider, of which 4/6 and 6/8 returned with abdominal pain, respectively. There were no missed surgical diagnoses. The most common adverse effect was nausea (9% per group) and there were no significant differences in adverse effects between acetaminophen (28/116, 24.1%) and HBB (32/120, 26.7%) (p=0.57). CONCLUSION: For children with presumed functional abdominal pain who present to the ED, both acetaminophen and HBB produce a clinically important (VAS

Background/Objectives: Data Mining has been used to analyze large datasets and establish useful classification and patterns in the datasets. The efficient analysis of data in different format becomes a challenging work. Methods/Statistical Analysis: This work proposed a novel Soil Profile Feature Reduction Model using Principal Component Analysis for data reduction. The proposed model uses the method of k-Means clustering and PC Aapproach for feature reduction which initially applies PCA to acquire reduced uncorrelated attributes showing maximal eigenvalues in the dataset with minimum loss of information. Again proposed model uses k-Means on the PCA reduced dataset to find out discriminative features that will be the most sufficient ones for classification. Findings: The weight by PCA generates attribute weights of the soil profile dataset using a component created by the PCA. The component is mentioned by the component number parameter. The normalize weight parameter is usually set to true to spread the weights between 0 and 1. The attribute weights reflect the relevance of the attributes with respect to the class attribute. The higher weight of an attribute is more relevant, it is considered. This is a combination of clustering approach with feature reduction to get a minimal set attributes relating a suitably high accuracy in describing the original features. The result of clustering is same after reducing the attributes using PCA. The experimental results prove that proposed model is reducing number of initial attributes, reducing computational complexity and improving predictive accuracy in High Dimensional Datasets. Applications/Improvements: The same soil profile feature is implemented by using the other techniques instead of PCA algorithm in future.

Threats on the Internet are posting high risk to information security and network anomaly detection has become an important issue/area in information security. Data mining algorithms are used to find patterns and characteristic rules in huge data and this is very much used in Network Anomaly Detection System (NADS). Network traffic has several attributes of qualitative and quantitative nature, which needs to be treated/normalized differently. In general, a model is built with the existing data and the system is trained with the model and then used to detect intrusions. The major and important issue with such NADS is that the network traffic changes over time; in such cases, the system should get trained automatically or retrained. This paper presents an adaptive algorithm that gets trained according to the network traffic. The presented algorithm is tested with Kyoto University’s 2006+ Benchmark dataset. It can be observed that the results of the proposed algorithm outperform all the known/commonly used classifiers and are very much suitable for network anomaly detection.

Osteoporosis is a skeletal disorder which leads to bone mineral loss and fracture of bone. Its mainly cause to elderly population and postmenopausal women. Bone mineral density (BMD) is related to bone mineral loss and it is measured by Dual energy X-ray absorptiometry (DXA) which is currently available instrument for the measurement of BMD. The main objective of the study includes measurement of cortical thickness in south Indian women population for the evaluation of osteoporosis. The study was concluded with the variable women population (n=25) age range from 25 to 81 years (Mean ± SD age = 48.64 ± 17.13 years). The cortical thickness and percentage cortical thickness of the second metacarpal region of the right forearm were measured. The standard protocol was followed with DXA output and comparison was made accordingly. The semi-automated approach was carried out to measure the bone mass indices. The correlation matrix for a different variable with N-BMD and T-BMD was obtained. Similarly, the correlation matrix of post menopausal women (n=14) and total women (n=25) with N-BMD and T-BMD was obtained with the significance level of (p

The macula is the part of the eye that helps to provide us with our central vision. Diabetic Maculopathy is when the macula sustains some form of dames. DM is a blurring of one central vision. In this paper we have proposed algorithm for macula on retinal images by using fuzzy c-means (FCM) algorithm. The pre-processing stages of macula detection including image resizing, noise removal is performed. Feature extraction based on gray level cooccurrence matrix (GLCM) techniques is implemented. Finally the retinal images and classification is developed using FCM classifier and CNN classifier.

There is desperate need to find an alternate power source to run the vehicles. This reasons leads to work on electric vehicles. In India, it has been noticed major part of urban air pollution is caused by auto rickshaw emissions as they are not maintained properly. So switching from conventional rickshaw to electric will significantly reduce this problem in urban areas. Continuously variable transmission (CVT) transfers power uninterrupted due to its infinite gear ratios. Moreover, it allows the motor to run at its optimum speed which ultimately reduces the current consumption. CVT available in current market is design to work efficiently with internal combustion engines. This CVT can be made compatible with pure electric power train by CVT tuning. This paper aims to study and analyses the effect on performance of CVT by varying the mass of flyweights. The driver and driven pulley RPM are measured using DAQ system. This data is then use to obtain gear ratio. The iterations are performed using two different mass of flyweights. The gear ratio verses pulley rpm graphs are compared. The result infers that as the flyweight mass is increase the engagement rpm decreases and up-shift rate increases. This enhances performance of CVT.

Introduction: Abdominal pain is one of the most frequent reasons for an emergency department (ED) visit. Most cases are functional and no therapy has proven effective. Our objective was to determine if hyoscine butylbromide (HBB) (BuscopanTM) is effective for children who present to the ED with functional abdominal pain. Methods: We conducted a randomized, blinded, superiority trial comparing HBB 10 mg plus acetaminophen placebo to oral acetaminophen 15 mg/kg (max 975 mg) plus HBB placebo using a double-dummy approach. We included children 8-17 years presenting to the ED at London Health Sciences Centre with colicky abdominal pain rated >40 mm on a 100 mm visual analog scale (VAS). The primary outcome was VAS pain score at 80 minutes post-administration. Secondary outcomes included adverse effects; caregiver satisfaction with pain management using a five-item Likert scale; recidivism and missed surgical diagnoses within 24-hours of discharge. Analysis was based on intention to treat. Results: We analyzed 225 participants (112 acetaminophen; 113 HBB). The mean (SD) age was 12.4 (3.0) years and 148/225 (65.8%) were females. Prior to enrollment, the median (IQR) duration of pain prior was 2 (4.5) hours and analgesia was provided to 101/225 (44.9%) of participants. The mean (SD) pre-intervention pain scores in the acetaminophen and HBB groups were 62.7 (15.9) mm and 60.3 (17.3) mm, respectively. At 80 minutes, the mean (SD) pain scores in the acetaminophen and HBB groups were 30.1 (28.8) mm and 29.4 (26.4) mm, respectively and there were no significant differences adjusting for pre-intervention scores (p = 0.96). The median (IQR) caregiver satisfaction was high in the acetaminophen [5 (2)] and HBB [5 (1)] groups (p = 0.79). The median (IQR) length of stay between acetaminophen [235 (101)] and HBB [234 (103)] was not significantly different (p = 0.53). The proportion of participants with a return visit for abdominal pain was 4/112 (3.5%) in the acetaminophen group and 6/113 (5.3%) in the HBB group. The most common adverse effect was nausea (9% in each group) and there were no significant differences in adverse effects between acetaminophen (26/112, 23.2%) and HBB (31/113, 27.4%) (p = 0.52). There were no missed surgical diagnoses. Conclusion: For children with presumed functional abdominal pain who present to the ED, both acetaminophen and HBB produce a clinically important (VAS < 30 mm) reduction in pain and should be routinely considered in this clinical setting.

Background: Handwritten signature is a person's unique identity. Signature verification is an economical biometric method with online and offline schemes. This paper deals with the offline verification of signatures found in bank checks. Method: Extracting feature is the most vital part of a signature verification process. An efficient feature extraction method, Concentric Circles Masking Method, is used to extract robust, scale invariant and rotation invariant features. The extracted feature values are normalized and fed to a feedforward back propagation neural network for classification of the signatures into genuine or forged ones. The feature's performance is measured with various training functions of the neural network. The system modeled is tested with the well-known CEDAR database. Findings: Experimental Analysis shows that the features extracted by this method prove to be efficient. The scanned signature is covered by concentric circles and the pixel distribution ratio in each circle is calculated and used for verification purpose. Since a circle is used, the extracted features are scale and rotation invariant which makes the feature robust. The neural network's training, validation and testing ratio are varied and the performance of various training functions is studied. It is inferred that conjugate gradient back propagation with Fletcher-Reeves updates (traincgf) training function has the maximum average accuracy of 97.89% for the CCMM features.

Craniofacial defects (CFA) are a diverse group of deformities in the skull and facial bones. The cause is multifactorial though genes, food and environmental factors are the major causes. Cleft lip and palate is the most common congenital craniofacial defect. The objective of this work is to develop a three dimensional (3D) finite element (FE) model of human skull with complete unilateral cleft lip, alveolus and palate anomaly for clinical studies. The computed tomography (CT) images was used to construct the 3D-FE model of the human skull with the craniofacial defect. The model is constructed using image processing software MIMICS and analysis was done in ANSYS. These 3D models can then be used for a variety of engineering applications and surgery planning. The complete left sided unilateral cleft lip, alveolus and palate with all the features and 3D-FE analysis was done. The load of 250N is given on each side along the two molar regions. The preliminary results reveal that cleft halve experiences more displacement comparatively to normal halve, this proves that cleft halve is prone to deformation and complications.

This article throws light on the usage pattern and the reasons behind low sensitivity of packaged dairy products. This paragraph obtains information concerning consumers' preferences and knowledge of processing organic/dairy products. It also identifies those consumers who are most likely to get processed organic/dairy products. The objectives of this study were to examine the usage pattern, user preferences, and scopes for products change, with respect to the packaged (and branded) dairy products in the town; Bettiah, West Champaran, Bihar. The researcher has used a descriptive research study using a structured questionnaire for collecting the necessary details for research analysis. Descriptive frequencies analysis used to determine the user or household characteristics that affect the purchase order related to packaged dairy products. The study also showed that, the family size and families with higher earnings were more concerned with quality of packaged dairy product. Further, this study also revealed that, majority of the population living in rural areas still uses the unprocessed, unbranded local dairy products only, which are neither healthy nor enriched with nutrients.

Content-Based Image Retrieval (CBIR), also known as query by image content (QBIC) Content-based image retrieval (CBIR) system helps users to retrieve relevant images based on their contents. Content Based Image Retrieval (CBIR) technologies provide a method to find images in large databases by using unique descriptors from a trained image. The image descriptors include texture, color, intensity and shape of the object inside an image. In this paper, we compare the several feature extraction techniques viz., Gabor, Wavelet and Histogram over color and texture features applied in a novel way for color image and used for CBIR. The experiments which show that the Gabor filters advice promising results are reported. General Terms Image Processing, CBIR.

Background: Aorto-iliac occlusion is an advanced and late manifestation of the atherosclerotic vascular disease. Patients with this complicated and frequently multi-level disease can present with debilitating symptoms ranging from life-limiting claudication to limb-threatening ischemia. To study the results of Surgical management and complications of Aortic surgery in a patient with chronic, aortoiliac occlusion.Methods: Our study of 22 cases, 12 patient underwent aortic bi femoral bypass, 08 cases underwent aortobi-iliac bypass and 2 of them aortic endarterectomy. This entire patient had a 64 slice CT angiogram of aorto with bilateral lower limb and offered surgical treatment after explaining the risk and complication of surgery. All patients followed post op with the clinical examination, ABI, duplex once a month.Results: 19 patients had a patent graft at the end of the study period. The primary patency of 86%. 2 patient had graft thrombectomy, 1 patient had graft thrombectomy with extension bypass, hence the secondary patency was 95%.Conclusions: The spectrum of aortoiliac occlusive disease ranges from short, simple stenosis to long, complete occlusions, with treatment options that vary accordingly from minimally invasive endovascular procedures to major open surgical intervention.

The C4-C6 region of the cervical spine is a common area of injury in the spinal column. Most of the injuries are caused due to accidents, sports etc. These injuries with increase in age can lead to slight, moderate and severe disc degeneration. As technology has advanced over the years, biomechanical finite element models are used instead of cadavers to study effects of progressive degeneration on the stress distribution. This study was conducted to develop a three dimensional finite element model of the C4-C6 human cervical spine structure using computed tomography scans and was validated against experimental data published earlier. The model was then used to produce progressive disc degeneration at the C4-C6 region by varying the material properties. Slight degeneration and moderate degeneration is obtained by modifying the material properties of nucleus pulposus and annulus fibrosus respectively. Severe degeneration was achieved by taking the material properties for maximum degeneration. The degenerated models were subjected to flexion, extension and lateral bending and the stress distribution on the anterior and posterior region were determined and compared with the intact model. Based on this study, the anterior region showed more increase in stress as compared to the posterior region with progressive degeneration.

Finger deformities are a major concern among the Indian population, where the increase of risk factors are higher for people suffering from Rheumatoid Arthritis. The deformities hinder the movements in the finger, affecting their day to day activities. Finger splint is a device which is used to support and correct this deformity in order to improve function. Three-dimensional modeling and 3D printing techniques are the standard measures used. The proposed methodology involves 3D modeling which was done using Solidworks 2013, along with standard measurements taken from the patients with deformities due to Rheumatoid Arthritis. The measurements were obtained using a vernier caliper. The 3D printing was done using Fused Deposition Modeling (FDM) and the materials needed for the same are Acrylonitrile Butadiene Styrene (ABS) and flex Polylactic Acid (PLA). The 3D printer used for the same is Flashforge Dreamer 3D printer. The volunteers were fitted with the custom finger splint. The finger splint is light-weight, easy to maintain and clean, with an inventive design based on the finger deformity. It is comfortable and helps support the patients during daily activities. It serves as an easy slip-on. Since it is well-ventilated, swelling of the finger does not occur. Future research will focus on the correction of the deformity, in addition to the biomechanical aspect of finger deformities.

In this modern world, especially on the Internet, user might have more and more usernames or IDs and passwords, which contains his/her private information. There are too many for a user to remember and it is unsafe to write them down on the notebook. To solve this problem, this paper proposes a novel design of User data Management System (UMS), by which user can manage his/her private information's efficiently. With the fast evolution in sensor technology biometric authentication system becomes more popular in daily lives. The biometrics is improving the capability to recognize the persons. The construction of Bio-Capsule from biometrics is used generally to secure the system. The biometrics used in this paper is fingerprint and iris. These two features are combined with the help of fusion algorithm. From the combined features, Bio-Capsule is generated which used for authenticating User data Management Systems.

Summary The information content (relative entropy) of transcription factor binding sites (TFBS) is used to classify the transcription factors (TFs). The TF classes are clustered based on the TFBS clustering using information content. Any TF belonging to the TF class cluster has a chance of binding to any TFBS of the clustered group. Thus, out of the 41 TFBS (in humans), perhaps only 5 -10 TFs may be actually needed and in case of mouse instead of 13 TFs, we may have actually 5 or so TFs. The JASPAR database of TFBS are used in this study. The experimental data on TFs of specific gene expression from TRRD database is also coinciding with our computational results. This gives us a new way to look at the protein classification- not based on their structure or function but by the nature of their TFBS.

This study seeks to investigate the various training functions with non-linear auto regressive eXogenous neural network (NARXNN) to forecasting the closing index of the stock market. An iterative approach strives to adjust the number of hidden neurons of a NARXNN model. This approach systematically constructs different NARXNN models from simple architecture to complex architecture with different training functions and finds the optimum NARXNN model. The effectiveness of the proposed approach was seen to be a step ahead of Bombay Stock Exchange (BSE100) closing stock index of the Indian stock market. This approach has identified optimum neuron counts in the hidden layer for every training function with NARXNN, which reduces neural network (NN) structure and training time and increases the convergence speed. The experimental result reveals that neuron counts in the hidden layer cannot be identified by some rule of thumb.

Background: Children with abdominal pain in the emergency department (ED) are at particular risk of suboptimal analgesia due to fears of missing appendicitis and absent guidelines. Many still experience pain at discharge. Acetaminophen is the most commonly used analgesic and efficacy of hyoscine butylbromide (HBB) is supported by adult evidence. However, no evidence exists for either agent in children with abdominal pain. Objective: To determine if HBB is superior to acetaminophen for abdominal pain in children. Methods: We will consecutively recruit children 8-17 years presenting to the ED with presumed non-surgical abdominal pain rated >4/10 on the Faces Pain Scale – Revised (FPS-R) and described as colicky, excluding:-Suspected appendicitis or bowel obstruction-Anticholinergic, analgesic, or antispasmodic five years. Secondary outcomes include:-Pain scores at 15, 30, 45, 60, 80, 100, and 120 minutes post-intervention (FPS-R and 100 mm visual analog scale)-Discharge pain score-Rescue analgesia-Time to achieve a 20% reduction in pain-Adverse effects-Recidivism Importance: Our findings will guide evidence-based analgesic choices for children with non-surgical abdominal pain in the ED.

In recent years, Multiply-Accumulate (MAC) unit is developing for various high performance applications. MAC unit is a fundamental block in the computing devices, especially Digital Signal Processor (DSP). MAC unit performs multiplication and accumulation process. Basic MAC unit consists of multiplier, adder, and accumulator. In the existing MAC unit model, multiplier is designed using modified Radix-2 booth multiplier. In this paper, MAC unit model is designed by incorporating the various multipliers such as Array Multiplier, Ripple Carry Array Multiplier with Row Bypassing Technique, Wallace Tree Multiplier and DADDA Multiplier in the multiplier module and the performance of MAC unit models is analyzed in terms of area, delay and power. The performance analysis of MAC unit models is done by designing the models in Verilog HDL. Then, MAC unit models are simulated and synthesized in Xilinx ISE 13.2 for Virtex-6 family 40nm technology.

This paper preferences extraction and ranking functions in mobile search that captures the users' preferences in the form of concepts by mining their click through data. Due to the importance of location information in mobile search, it classifies these concepts into content concepts and location concepts. In addition, users' locations (positioned by GPS) are used to supplement the location concepts. The user preferences are organized in an ontology-based, multi-facet user profile, which are used to adapt a personalized ranking function for rank adaptation of future search results. To characterize the diversity of the concepts associated with a query and their relevance's to the users need, four entropies are introduced to balance the weights between the content and location facets. Based on the client-server model, also present a detailed architecture and design for implementation of preferences extraction and ranking functions in mobile search. In our design, the client collects and stores locally the click through data to protect privacy, whereas heavy tasks such as concept extraction, training and re-ranking are performed at the server. Moreover, here address the privacy issue by restricting the information in the user profile exposed to the server with two privacy parameters. To prototype preferences extraction and ranking functions in mobile search on the Google Android platform.

This paper investigates the various training algorithm with Multi-Layer Perceptron Feed Forward Neural Network (MLPFFNN) and identify the best training algorithm for Indian Stock Exchange Market especially for BSE100 and NIFTY MIDCAP50. The forecasting accuracy is analyzed and measured with reference to an Indian stock market index such as Bombay Stock Exchange (BSE) and NIFTY MIDCAP50 in this study and it is found that the best training algorithm is Levenberg-Marquardt. All Training algorithms uses the 1-5-1 MLPFFNN architecture and its various parameter such as epochs, learning rate, etc are studied and the results are tabulated for the data division ratio 60%, 20% and 20% which represents training, validating and testing for all training algorithm.

We have entered the 21st century at a time when the demography of our small town and rural population is changing significantly to drive organized retail growth. Thus, 85 per cent of the retail boom which was focused only in the metros has started to infiltrate towards smaller cities and towns. These smaller cities and towns where organized retail is still in its early stages are classified as emerging Indian markets (Ali et al., 2010). Literature indicates that store choice has been widely studied across the world. Interestingly Sinha et al. (2002) identified that it would be interesting to study the influence of purchase characteristics of shoppers on store choice. Even after 10 years, research publications related to this area are very little. Recently Ali et al. (2010) indicated that emerging markets especially where organized retail is still in its early stages are less researched. Hence in this research paper we have studied the influence of purchase characteristics on store choice in Kadapa where organized retail is still in its early stages.

Quality is an important concept which play significant role in today's manufacturing market. From customers' point of view quality is very important because the extent of quality of the procured item (or product) influences the degree of satisfaction of the consumers during usage of the procured goods. So, every manufacturing unit must concern about the quality of the product. Apart from quality, there exists another criterion, called productivity which is directly related to the profit level and also good will of the organization. So as days gets enrolled manufacturing Industry started aiming at producing a large number of products within relatively lesser conservation of energy. In the production unit, lathe is one of the important protection machines. This paper focuses on producing a quality product in lathe machine with less power consumption. In order to achieve that, a special setup is developed in the lathe machine for simultaneous turning and finishing of the components, to achieve quality product and also to improve the productivity. As a result of this new approach, profuse amount of energy can be saved, quality product can be obtained and tool life can be increased. The study aimed at evaluating the best process environment which could simultaneously satisfy requirements of both quality and as well as productivity. By conducting many experiments it was found that this special setup process improvers the quality and also reduces the power consumption as compared with the existing process.

Increased use of computer networks, internet and online transactions pose higher risk of intrusions and protecting the information from the hackers/intruders is a new area in computers and network security. The major factors which affect intrusion detection are the system's detection rate and time required to detect intrusions. Many researchers have focused in this area and have used data mining techniques for detecting the intrusions. This paper proposes to classify the dataset initially based on 'protocol type' feature and the performance improvements over traditional way of considering the full data without initial classification. This paper does not advocate any techniques or algorithms, but establishes the fact that by splitting the dataset on Protocol Type feature enhances performance with respect to detection rate and time to build model for intrusion detection. In this study, the well-known KDD Cup 99 intrusion dataset has been tested with the proposed approach. The computational study reveals that the initial classification based on protocol type' attribute increases the performance with respect to rate of detection and time to build model.

Apparently weed is a major menace in crop production as it competes with crops for nutrients, moisture, space and light which resulting in poor growth and development of the crop and finally yield. Yield loss accounts for even more than 70 % when crops grown under unweeded condition with severe weed infestation. There are several weed control measures being practiced in crop production, they are physical, mechanical, biological and chemical methods. Weed Management plays vital role in agriculture and horticulture production and economic benefits derived by agricultural industry. Weed is controlled mainly by application of herbicides. Weeds are not uniformly distributed in the crop and uncropped fields and mostly they are found in patches. With the help of Color and growth parameters, the weeds and crops may not be distinguished in the fields for the reasons of imbalance in availability of nutrients, water and other environmental resources. Weed control need to be done at the early stage of the crop growth. The management of weeds with in the field is imperative. Weed management practices using chemical tools propose to apply herbicide in the dosage strictly necessary based on weed infestation and location or position. Currently research is carried out relating to identification of weed spices and the location of the weed occurrence with the aims to allow accurate weeding and apply herbicides based on the weed density. Machine vision system, remote sensing and aerial imaging techniques are used for control weeds. Sensor attached electromagnetic system, imaging spectra radiometer and spectrometer can also be used to identify weeds for effective weed control. Almost all the existing weed detection methods process the captured image by segmentation of vegetation against background (soil), detection of weed vegetation pixels. Further, classification of feature extraction of weeds is done by color, shape and texture. The various methods studied and concepts used for crop and weed discrimination by the various researchers are discussed in this paper.

Clustering is the process of partitioning a set of objects into a distinct number of groups or clusters, such that objects from the same group are more similar than objects from different groups. Clusters are the simple and compact representation of a data set and are useful in applications, where we have no prior knowledge about the data set. There are many approaches to data clustering that vary in their complexity and effectiveness due to its wide number of applications. K-means is a standard and landmark algorithm for clustering data. This multi-pass algorithm has higher time complexity. But in real time we want the algorithm which is time efficient. Hence, here we are giving a new approach using wiener transformation. Here the data is wiener transformed for k-means clustering. The computational results shows that the proposed approach is highly time efficient and also it finds very fine clusters.

Inadequate and inappropriate knowledge of handling biomedical waste (BMW) among health care personnel may have serious health consequences and a significant impact on the environment as well. In view of this, present study was planned to assess knowledge, practices and attitude (KPA) regarding BMW management among our hospital staff. A questionnaire containing 32 questions based on KPA regarding BMW management was filled by 125 study participants [25 each of doctors, interns, nurses, technicians, class IV employees]. Data was analyzed using Stata 11.0.Chi-square test was applied and p value was obtained. Only 15.2% participants could correctly answer about BMW categories. Doctors were found to have significantly better knowledge than auxiliary staff about measures to be taken following accidental exposure to infected blood or sharps (p=0.007). Less than 65% of them could correctly answer use of each colour coded bags. A favorable positive attitude was found among study participants on BMW management. Our study revealed that although attitude about BMW management was high among health care personnels of our hospital, the knowledge and practice was comparatively low.Therefore, all health care personnel must undergo regular training in BMW management. This should be coupled with effective implementation of rules and regular