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Tin oxide is an impressive metal oxide for optoelectronic applications and its properties can be easily augmented with doping. In current research work, undoped and zinc-doped SnO2 nanoparticles annealed at 500 °C were prepared by a solid-state reaction method. The rutile tetragonal structure for undoped and zinc-doped SnO2 nanoparticles was proved by the analysis of their x-ray diffraction (XRD) patterns. Scanning electron microscope (SEM) images confirm the spherical, smooth, and dense morphology. The presence of Sn, O, and Zn ions are supported by energy dispersive x-ray study (EDX). The transmission spectrum obtained by UV–Visible spectroscopy shows a blue shift and the energy band gap (Eg) for undoped and zinc-doped SnO2 nanoparticles changes from 3.03 to 3.18 eV. Photoluminiscence (PL) spectra of synthesized samples shows one emission peak at 430 nm whose intensity increases with zinc-doping. The electrical resistivity was studied by Hall-effect measurement and it revealed that the resistivity decreases with zinc doping.

Surgical approaches to the lateral skull base often lead to tearing of vessels and piecemeal removal of the tumour. This study is aimed to delineate exact relationship of the various foramina at the lateral skull base. The coronal dimensions of the jugular foramina are larger as compared to sagittal with right sided dominance also noticed in the case of carotid canal. The width of “Keel” separating the carotid and jugular foramina normally varies from 0.4 to1.4 centimetres and may not always suggest the erosion of the foramen of skull base scans, unless the erosion is associated with irregularity or demineralization the thickness of this keel really depends upon relative size of the vessels and location of foramina. Area between stylomastoid foramen, carotid canal and jugular foramen is roughly wedge shaped. The angle subtended by carotid and jugular at the stylomastoid foramen is about 36.84whereas the location of stylomastoid foramen and internal carotid axis pose an angle of 83:16. The angle subtended by stylomastoid and jugular at carotid on an average 59:31. The space between these structures is measured to be 0.642centimetres which can be verified on tomograms. By using these measurements, the precise location of the upper end of the vessels could be predicted, whereas the superior stump could be clamped with minimal exposure of the skull base and identification and location of the last four cranial nerves is found out. This could avoid injuries and subsequent morbidity while carrying out surgery in this region.

Background: Tuberculosis (TB) is the most prevalent health disorder in developing nations and is linked with decreased immunity. Vitamin D is one of the most important vitamins required to boost immune response as it is required to maintain innate immune response via its actions on TLRs and macrophages. Risk of Tuberculosis has been associated with decrease level of vitamin D in serum in various studies. Despite of these studies some discrepancies have been shown in different studies conducted in different regions which further potentiates the role of various factors like diet, ethnicity, and seasonal variations influencing the prevalence of T.B in vitamin D deficient and non-deficient population. This study has been conducted to evaluate and establish the link of serum vitamin D in TB and non TB population residing in Mathura region of U.P. Materials and Methods: Total studied population was 80. 40 participants were taken in patient group with newly diagnosed Tuberculosis both pulmonary and extrapulmonary and 40 were enrolled in healthy control group. Serum sample was collected twice first before the start of treatment in patient group and then after the three months of effective treatment initiation. Results: Among the two groups i.e., control and patient serum level of vitamin D in patient group was found to be significantly low as compared to the control group (22.66±15.17 vs73.03±25.6 ng/mL: P?0.68. Conclusions: Healthy population has higher serum levels of vitamin D as compared to patients with tuberculosis. Keywords: Chemokines, Interferon-gamma, Vitamin D, Tuberculosis.

It is a well known fact the magnitude of skin disorders has been rising in our country, India. Various data showing pattern of skin diseases in different countries and within the various regions of a country have been reported so far. The pattern of skin diseases varies with different countries and with different zones of a country due to socio -economic, racial and environmental factors. So far none of the study has been done in our country for the Mathura region, so we take the privilege to undertake a retrospective study based on our hospital data i.e K.D Medical College and hospital, Akbarpur Mathura. Our study will include all the newly diagnosed OPD cases attending the Dermatology and Venereology department in K.D Medical college and hospital from the period starting from 1st January 2018 to 31st December 2018. Diagnosis was based on the clinical expertise and laboratory investigations wherever it was required like KOH preparation, slit skin smear for AFB, Tzank smear, wood's lamp examination, VDRL test, skin biopsy, culture etc. Analysis of the clinical data records of the patients attended the OPD of dermatology and venereology department of our college during the period of 1st January 2018 to 31st December 2018 shows that Eczema (21.8%), fungal infections-dermatophytosis (19.37%) were among the most common skin diseases followed by scabies (17.51%) and pyodermas (7.62%). (0.73%) accounts for herpes progenitalis (STD). Genodermatoses formed the least number of cases in our study. Keywords: Eczema, Pyoderma, Dermatophytosis.

People have the ability to make sensible assumptions about other people's emotional states by being sympathetic, and because of our common sense of knowledge and the ability to think visually. Over the years, much research has been done on providing machines with the ability to detect human emotions and to develop automated emotional intelligence systems. The computer's ability to detect human emotions is gaining popularity in creating sensitive systems such as learning environments, health care systems and real-world. Improving people's health has been the subject of much research. This paper describes the formation as conceptual evidence of emotional acquisition and control in intelligent health settings. The authors of this paper aim for an unconventional approach with a friendly look to get emotional scenarios from the system to establish a functional, non-intrusive and emotionally-sensitive environment where users can do their normal activities naturally and see the program only when pleasant mood activating services are received. The context-sensitive system interacts with users to detect and differentiate emotions through facial expressions or speech recognition, to make music recommendations and mood color treatments with the services installed on their IoT devices.

Predicting and analyzing solar resource has been a prerequisite requirement to promote solar as an effective sustainable energy source. Solar radiation knowledge incident on inclined surface is the fundamental prerequisite for solar technology deployment and monitoring. Suggestions for efficient methods to capture and utilize the sun’s energy up to the maximum extent is important to perform Active Energy Management. Tilt and orientation angle play an important part in collecting the maximum amount of solar radiation. An effective solution to reduce the cost of generating power by adjusting the tilt angle and orientation angle based on various factors such as time, season and location. It is important to estimate irradiance of random custom orientations and tilt angles, as the orientations of the installation and tilt angles may differ at different locations. Maximizing the potential power generation by the solar panels has been the key engineering by proper positioning and placement of the Solar panels. Hence, an effort has been made to determine optimum tilt and orientation angles on basis of solar irradiance data for single and dual axis tracking system.

Based on the generalized definition of a network, we know that it is made up of a number of nodes. Sending and receiving of data takes place via these nodes. This process is characterized by the permission provided and requirement of sharing data. Basically, networking helps the nodes across the globe to connect and enable data transfer. Software Defined Networking (SDN) is a smart networking technique. It brings together other disciplines with networking for example, programming, research etc. The SDN model is predominantly controlled by a central unit called controller. All the communication takes place via this controller; however, it has a disadvantage. If the controller anyhow fails or is hacked, the entire system will either fail or get corrupted. In this paper disadvantage of the SDN has overcome with the relevant solution.

The task of recognising an object and estimating its 6d pose in a scene has received considerable attention in recent years. The accessibility and low-cost of consumer RGB-D cameras, make object recognition and pose estimation feasible even for small industrial businesses. An example is the industrial assembly line, where a robotic arm should pick a small, textureless and mostly homogeneous object and place it in a designated location. Despite all the recent advancements of object recognition and pose estimation techniques in natural scenes, the problem remains challenging for industrial parts. In this paper, we present a framework to simultaneously recognise the object&rsquo, s class and estimate its 6d pose from RGB-D data. The proposed model adapts a global approach, where an object and the Region of Interest (ROI) are first recognised from RGB images. The object&rsquo, s pose is then estimated from the corresponding depth information. We train various classifiers based on extracted Histogram of Oriented Gradient (HOG) features to detect and recognize the objects. We then perform template matching on the point cloud based on surface normal and Fast Point Feature Histograms (FPFH) to estimate the pose of the object. Experimental results show that our system is quite efficient, accurate and robust to illumination and background changes, even for the challenging objects of Tless dataset.

Pulse Shaping Network (PSN) conditions the electrical pulse in amplitude and timing to meet the input requirements of subsequent systems. High Power Microwave (HPM) sources require high voltage, high current pulse of short duration as its input for operation. This requirement of HPM sources is met through specialized configurations of PSN. It conditions/ shapes output of current source to a form suitable for driving high voltage pulse power loads. A typical configuration of PSN consists of Electrically Exploding Wire Opening Switch (EEW-OS)/ Fuse with inductor in series. A high current is passed through circuit. The fuse transforms from solid to vaporization phase by virtue of very high current flowing through it and its resistance increases sharply. This results in sharp fall in current leading to development of sharp rising high voltage pulse across inductor/fuse. The dynamic behavior of EEW-OS/ Fuse and its effect on modifying the input current is a complex relationship which governs the output characteristics of the circuit. This paper covers the implementation of dynamic behavior (dynamic resistivity/ resistance) of fuse along with complete circuit modelling and simulation involving current source, fuse and load, done using MATLAB/SIMULINK at Terminal Ballistics Research Laboratory (TBRL). Benchmarking of the model has been done against the published results in literature and experimental results.

Pneumonia is an infection that causes inflammation of lungs and can be deadly if not detected on time. The commonly used method to detect Pneumonia is using chest X-ray which requires careful examination of chest X-ray images by an expert. The method of detecting pneumonia using chest X-ray images by an expert is time-consuming and less accurate. In this paper, we propose different deep convolution neural network (CNN) architectures to extract features from images of chest X-ray and classify the images to detect if a person has pneumonia. To evaluate the effect of dataset size on the performance of CNN, we train the proposed CNN’s using both the original as well as augmented dataset and the results are reported.

Parkinson’s Disease (PD) is one of the most common neurological disorders in the world, affecting over 6 million people globally. In recent years, Diffusion Tensor Imaging (DTI) biomarkers have been established as one of the leading techniques to help diagnose the disease. However, identifying patterns and deducing even preliminary results require a neurologist to automatically analyze the scan. In this paper, we propose a Machine Learning (ML) based algorithm that can analyze DTI data and predict if the person has PD. We were able to obtain a classification accuracy of 80% and an F1 score of 0.833 using our approach. The method proposed is expected to reduce the number of misdiagnosis by assisting the neurologists in making a decision.

Image compression is an ever-evolving problem with different approaches coming into prominence at different times. Data analysts are still contemplating about the best approach with compression ratio, visual quality and complexity of the architecture as the main criteria of evaluation. Most recent one is the advancements in machine learning and its applications on image classification. Researchers are currently exploring the possibility of using this advanced computing power to enhance the quality of images with the availability of large datasets. Here, we present a deep learning Convolutional neural network (CNN) implemented and trained for image recognition and image processing tasks, to understand semantics of an image. We achieve higher visual quality in lossy compression by increasing the complexity of an encoder by a slight margin to generate semantic maps of image. The semantic maps are tailored to make the encoder content-aware for a given image. In the proposed work, we present a novel architecture developed specifically for image compression, which generates a semantic map for salient regions so that they can be encoded at higher quality as compared to background regions. Experiments are conducted on the Kodak PhotoCD dataset and the results are compared with other state-of-the-art models. Results of the conducted experiment reveal an increase in compression ratio by more than 17% compared to the referred state-of-the-art model while maintaining the visual quality.

Vision assisted navigation is an active area of research to assist pilots during bad weather conditions. However, these systems are not completely accurate. We propose 3D models to synthesize accurate 2D representations of the airport and the runway. A synthesized image sequence obtained from a 3D model of a view would be effective in conveying 3-D characteristics of the vanishing point (intersection between the horizon line and runway axis) and the beginning of the runway to the pilot. This can help to improve the pilot’s visual perception of the surroundings under adverse weather conditions, leading to a safer landing. We propose a system to generate 2D images of runway captured during takeoff and landing to provide better tracking. We analyze the results by segmenting the runway and comparing it with actual data captured by the aircraft.

Fabrication of mono, hybrid and nanocomposites is becoming an emerging field nowadays. The fabricated MMCs are used in the manufacturing of aircraft, aerospace, and aeronautics due to their better mechanical and tribological properties in comparison with the traditional fiber-reinforced composites. For fabricating metal matrix composites, friction stir processing is an evolving technique. In this study, the effect of vibrations on the properties of friction stir processed alloy is reviewed. A literature review related to the wear resistance analysis of friction stir resistance analysis of friction stir processed metal matrix composite is presented.

Multiple patient-reported outcomes (PROs) are currently being used in multiple sclerosis (MS) but their application is inconsistent and guidance on the appropriateness of each tool is lacking. The objective of our study was to identify MS-specific PROs and systematically to assess the development process and the reliability and validity of various instruments. A systematic literature search was conducted on multiple data sources, including MEDLINE, Embase (using the Ovid platform) and Google Scholar, from 1996 to March 2015. Search terms included combinations of MS, PROs and quality of life. Randomized controlled trials or observational studies conducted on patients with MS and published in English were included. In addition, the PROQOLID database was explored. The MS-specific PROs were systematically assessed using the Evaluating the Measurement of Patient-Reported Outcomes tool. In total, 8094 articles were screened and 405 PROs were identified from 1102 relevant articles. PROs were classified into MS-specific (n = 82) and non-MS-specific (n = 323). The results for the eight PROs that are most commonly used in MS clinical trials are presented here. For these eight PROs, the overall summary scores ranged between 50.1 and 68.7. The Multiple Sclerosis Impact Scale-29 had the best overall mean score (68.7), followed by the Leeds Multiple Sclerosis Quality of Life (67.0). This is the first study to provide a standardized assessment of all PROs for MS. There is a lack of data on content validity for PROs used in MS research, which indicates the need for a robust instrument in MS developed according to the US Food and Drug Administration guidelines.

Background Associations between disease characteristics and payer-relevant outcomes can be difficult to establish for rare and progressive chronic diseases with sparse available data. We developed an exploratory bridging model to predict premature mortality from disease characteristics, and using inclusion body myositis (IBM) as a representative case study. Methods Candidate variables that may be potentially associated with premature mortality were identified by disease experts and from the IBM literature. Interdependency between candidate variables in IBM patients were assessed using existing patient-level data. A Bayesian survival model for the IBM population was developed with identified variables as predictors for premature mortality in the model. For model selection and external validation, model predictions were compared to published mortality data in IBM patient cohorts. After validation, the final model was used to simulate the increased risk of premature death in IBM patients. Baseline survival was based on age- and gender-specific survival curves for the general population in Western countries as reported by the World Health Organisation. Results Presence of dysphagia, aspiration pneumonia, falls, being wheelchair-bound and 6-min walking distance (6MWD in meters) were identified as candidate variables to be used as predictors for premature mortality based on inputs received from disease experts and literature. There was limited correlation between these functional performance measures, which were therefore treated as independent variables in the model. Based on the Bayesian survival model, among all candidate variables, presence of dysphagia and decrease in 6MWD [m] were associated with poorer survival with contributing hazard ratios (HR) 1.61 (95% credible interval [CrI]: 0.84–3.50) and 2.48 (95% CrI: 1.27–5.00) respectively. Excess mortality simulated in an IBM cohort vs. an age- and gender matched general-population cohort was 4.03 (95% prediction interval 1.37–10.61). Conclusions For IBM patients, results suggest an increased risk of premature death compared with the general population of the same age and gender. In the absence of hard data, bridging modelling generated survival predictions by combining relevant information. The methodological principle would be applicable to the analysis of associations between disease characteristics and payer-relevant outcomes in progressive chronic and rare diseases. Studies with lifetime follow-up would be needed to confirm the modelling results.

Urine samples collected from 20 donors with unknown blood group and secretor status had been determined from saliva. ABO typing on the concentrated samples was successfully performed after 1 month of storage. Urine stained clothing samples are often submitted to forensic science laboratories for ABH blood group antigen determination. The serogenetic markers of urine stains submitted can be used to determine the origin of any of these samples. ABH blood group substances have previously been identified from urine. ABH blood group substance is low in urine in comparison with other body fluids.

INTRODUCTION: Porokeratosis is an autosomal dominant genodermatosis with variable penetrance, characterized by a clonal disorder of keratinization with one or more atrophic patches surrounded by a clinically and histologically distinctive hyperkeratotic ridge like border called the cornoid lamella. Multiple clinical variants of porokeratosis are recognized. The original description of this entity was given in 1893 by Mibelli.[1]

INTRODUCTION: Dominant dystrophic Epidermolysis Bullosa (EB) is a subtype of dystrophic type of Epidermolysis Bullosa. Dystrophic EB are caused by mutations in a single gene, COL7A1, which encodes the anchoring fibril protein, type VII collagen. Immunofluorescence staining of the skin of patients using antitype VII collagen antibodies showed that the normal bright linear staining is absent in severe generalized recessive dystrophic EB, but present in dominant dystrophic EB.[1-3] The main feature of dominant dystrophic EB is that the skin is generally less fragile than in severe generalized recessive dystrophic EB. Blisters usually follow sharp knocks or glancing blows rather than mild friction, Blisters mainly occur in skin overlying bony prominences, such as the knees, ankles and dorsa of the hands or feet. The most consistent findings are localized scarring with milia formation and dystrophic nails. Nail dystrophy is probably the most important diagnostic feature of the disease, especially in adults, because many patients have only limited scarring, which becomes less noticeable with age.

Purpose – The purpose of this paper is to compare the importance and performance of managerial training in India among different industrial sectors in the light of asynchronous industrial growth in last two decades which has unevenly touched various business organizations. Design/methodology/approach – For the purpose of analysis organizations are divided at two levels. At the first level, they are divided between multinational and Indian origin category and at the second level between service and manufacturing category. A 2×2 full factorial design with unequal numbers is used to analyze main as well as interaction impacts. A sample size of 542 is taken which comprises training professionals, top/general managers and trainee employees. Findings – Study concludes that Indian origin organizations fall short in terms of importance given and performance of training function compared to multinational companies. Further, it is found that service companies have an edge over manufacturing companies in terms of importance and performance of training. Research limitations/implications – Study is mainly focussed on large organizations in India and may not be representative of small organizations. The major implication is that Indian origin organizations as well as manufacturing organizations have to devote more time and financial resources to training to compete in the era of globalization. Practical implications – It is implied from the findings that a concerted effort by training professionals in Indian and manufacturing companies is required to improve the content as well as image of the training function to get the due attention and resources within the organization. Originality/value – The paper presents an original perspective on situation of training function in Indian organizations and will be of importance to training professionals and other stakeholders.

We show here novel cantilever designs that express torsional and lateral modes exhibit excellent mass-change sensitivity to molecular self-assembly on gold (75-135 fg/Hz) which is superior to that of widely investigated bending modes. Lead zirconate titanate (PZT) millimeter-sized cantilevers were designed with two types of anchor asymmetry that induced expression of either torsional or lateral modes in the 0-80 kHz frequency range. Experiments and supporting calculations show that anchor asymmetry enables resonant mode impedance-coupling. The sensitive torsional and lateral modes enabled measurement of self-assembled monolayer formation rate at picomolar levels. The anchor design principle was extended to microcantilevers via finite element simulations, which caused both 97% sensitivity improvement relative to conventional designs, as well as new nonclassical resonant mode shapes.

Every day, humans interact with text spanning from different sources such as news, literature, education, and even social media. While reading, humans process text word by word, accessing the meaning of a particular word from the lexicon, and when needed, changing its meaning to match the context of the text (Harley, 2014). The process of reading can induce a range of emotions, such as engagement, confusion, frustration, surprise or happiness. For example, when readers come across unfamiliar jargon, this may confuse them, as they try to understand the text.In the past, scientists have addressed the emotion in text from a writer's perspective. For example the field of Sentiment Analysis, aims to detect the emotional charge of words, to infer the intentions of the writer. However, here we propose the reverse approach: detect emotions produced on readers while processing text.Detecting which emotions are induced by reading a piece of text can give us insights about the nature of the text itself. A word-emotion detector can be used to assign specific emotions experienced by readers to specific words or passages of text. This area of research has never been explored before.There are many potential applications to a word-emotion detector. For example, a word-emotion detector can be used to analyze how passages in books, news or social media are perceived by readers. This can guide stylistic choices to cater for a particular audience. In a learning environment, it can be used to detect the affective states and emotions of students, so as to infer their level of understanding which can be used to provide assistance to students over difficult passages. In a commercial environment, it can be used to detect reactions to wording in advertizements. In the remainder of this report, we detail the first steps we followed to build a word-emotion detector. Moreover, we present the details of our system developed during QCRI's 2015 Hot Summer Cool Research internship program, as well as the initial experiments. In particular, we describe our experimental setup in which viewers watch a foreign language video with modified subtitles containing deliberate emotion inducing changes. We analyze the results and provide discussion about the future work.The Pokerface SystemA Pokerface is an inscrutable face that reveals no hint of a person's thoughts or feelings. The goal of the ‘Pokerface’ project is to build a word-emotion detector that works even if no facial movements are present. To do so, the Pokerface system uses a unique symbiose of the latest consumer-level technologies such as: eye-tracking to detect words that are being read; electroencephalography (EEG) to detect brain activity of the reader; and facial-expression recognition (FER) to detect movement in a reader's face. We then classify the brain activity and facial movements detected into emotions using Neural Networks.In this report, we present the details of our Pokerface system, as well as the initial experiments done during QCRI's 2015 Hot Summer Cool Research internship program. In particular, we describe the setup in which viewers watch a foreign language video with subtitles containing deliberate emotion inducing changes.MethodologyTo detect emotions experienced by readers as they read text, we used different technologies. FER and EEG are used to detect emotional reactions through changes in facial expressions and brainwaves, while eye-tracking is used to identify the stimulus (text) to the reaction detected. A video interface was created to run the experiments. Below we describe each of them independently, and how we used them in the project.EEGEEG is the recording of electrical activity along the scalp (Niedermeyer and Lopes da Silva, 2005). EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain. EEG is one of the few non-intrusive techniques available that provides a window on physiological brain activity. EEG averages the response from many neurons as they communicate, measuring the electrical activity by surface electrodes. We can then use the brain activity of a user to detect their emotional status.Data GatheringIn our experiments, we used the Emotiv | EEG EPOC neuroheadset (2013), which has 14 EEG channels plus two references, inertial sensors, and two gyroscopes. The raw data from the neuroheadset was parsed with the timestamps for each sample.Data Cleaning and Artifact RemovalAfter retrieving the data from the EEG, we need to remove “artifacts” which are changes in the signals that do not originate from neurons (Vidal, 1977), such as ocular movements, muscular movements, as well as technical noise. To do so, we used the open source toolbox EEGlab (Delorme & Makeig, 2004) to remove artifacts and filtering (removing the 4–45 Hz line noise).ERP CollectionWe decided to consider remaining artifacts as random noise and move forward with extracting Event Related Potentials (ERPs), since all of other options we had found required some level of manual intervention. ERPs are the relevant sections of our EEG data with regards to stimuli and the subjects' reaction time. To account for random effects form the artifacts, we averaged the ERPs over different users and events. To do so, we used EEGlab's plugin ERPlab, to add events codes to our continuous EEG data based on stimulus time.EventsOur events were defined as textual modifications in subtitles, designed to induce emotions of confusion, frustration or surprise. The time at which the subject looks at a word was marked to be the stimulus time (st) for that word, and the reaction time was marked to be st+800 ms, because we rarely see a reaction to a stimulus 800 ms after its appearance (Fischler and Bradley, 2006).The ERPs were obtained as the average of different events corresponding to the same condition (control or experimental).Eye-TrackingAn eye-tracker is an instrument to detect the movements of the eye. Based on the nature of the eye and human vision, the eye-tracker identifies where a user is looking by shining a light that will be reflected into the eye, such that the reflection will be captured by image sensors. The eye-tracker will then measure the angle between the cornea and pupil reflections to calculate a vector and identify the direction of the gaze.In this project, we used the EyeTribe eye-tracker to identify the words a reader looked at while reading. It was set up in a Windows machine. Before an experiment, the user needs to calibrate the eye-tracker. Recalibration is necessary every time the user changes their sitting position. While the eye-tracker is running, Javascript and NodeJS were used to create a function that extracts and parses the data from the machine and prints it into a text file. This data includes the screen's x and y coordinates of the gaze; the timestamp; and an indicator of whether the gaze point is a fixation or not. The data is received at a rate of 60fps. The gaze points are used to determine which words are looked at at any specific time.Video InterfaceIn our experiments, each user was presented with a video with subtitles. To create the experimental interface, we made different design choices based on previous empirical research. Therefore, we used Helvetica font, given its consistency across all platforms (Falconer, 2011), and used the font size 26 given that it improves the readability of subtitles on large desktops (Franz, 2014). We used Javascript to detect the location of each word that was displayed on the screen.After gathering the data the experiment, we used an off-line process to detect the “collisions” between the eye-tracker gaze points and the words displayed to the user. To do so, we used both time information and coordinate information. The result was a series of words annotated with the specific time-spans in which they were looked at.FERFacial Expression Recognition (FER) is the process of detecting an individual's emotion by accessing their facial expressions in an image or video. In the past, FER has been used for various purposes, including psychological studies, tiredness detection, facial animation and robotics, etc.Data GatheringWe used the Microsoft Kinect with the Kinect SDK 2.0 for capturing the individual's face. The data extracted from the Kinect provided us with color and infrared images, as well as depth data. However, for this project we only worked with the color data. The data from the Kinect was saved as a sequence of color images, recorded at a rate of 30 frames per second (fps). The code made use of the process of multithreading to ensure high fps, and low memory usage. Each image frame was assigned a timestamp in milliseconds, which was saved in a text file.Feature ExtractionAfter having extracted the data from the Kinect, the images were processed so as to locate the facial landmarks. We tested the images with Face++ which is a free API for face detection, recognition and analysis. Using Face++, we were able to locate 83 facial landmarks on the images. The data obtained from the API was the name of the landmark along with it's x and y-coordinates.The next step involved obtaining Action Units (AUs) by using the facial landmarks located through Face++. Action Units are the actions of individual muscles or groups of muscles, such as, raising the outer eyebrow, or stretching of lips etc (Cohn et al. 2001). To determine which AUs to use for FER, as well as how to calculate them, Tekalp and Ostermann's (2000) was taken as a reference.ClassificationThe final step of the process was classifying the image frames into one of the eight different emotions - happiness, sadness, fear, anger, disgust, surprise, neutral and confused. We used the MATLAB Neural Network toolkit (MathWorks, Inc., 2015) and designed a simple feed-forward neural network with backpropagation.Pilot resultsEEGIn our pilot classification study, we used we experimented with the Alcoholism data used in Kuncheva and Rodriguez (2012) paper, from the UC Irvine (UCI) machine learning repository (2010) which contains ERP raw data for 10 alcoholic subjects and 10 sober subjects. We extracted the features using the interval feature extraction. Total of 96K Features were extracted from each subject's data. We Achieved around 98% accuracy with the training data.FERWe experimented on three different individuals as well as several images from the Cohn-Kanade facial expressions database. It was found that the application had roughly 75–80% accuracy, and this accuracy could further be improved by adding more data into the training set. It was also observed that the classifier was more accurate when classifying certain emotions than some others. For example, the images depicting happiness were classified accurately more than images that depicted any other emotion. The classifier had difficulty distinguishing between fear, anger and sadness.ConclusionIn this paper, we presented Pokerface, a word-emotion detector that can detect the emotion of users as they read text. We built a video interface that displays subtitled videos and used the EyeTribe eye-tracker to identify the word a user is looking at a certain time in the subtitles. We used the Emotiv Epoc headset to obtain EEG brainwaves from the user, and the Microsoft Kinect to obtain their facial expressions, and extracted features from both. We used Neural Networks to classify both the facial expressions and the EEG brainwaves into emotions. In the future.Future directions of work include to improve the accuracy of the FER and EEG emotion classification components. Furthermore, the EEG results can be improved by exploring additional artifact detection and removal techniques. Furthermore, we want to integrate the whole pipeline in a seamless application that allows effortless experimentation.Once the setup is streamlined, Pokerface can be used to explore many different applications to optimize users' experiences in education, news, advertizing, etc. For example, the word-emotion detector can be utilized in Computer-assisted learning to provide students with virtual affective support, such as detecting confusion and providing clarifications.

Late preterm infants, born at 34–36 (+6) weeks gestation, are physiologically more immature than term infants. As a consequence, they have an increased risk of morbidity and mortality. Late preterm were compared with term babies. This retrospective observational study found that the late preterm group was associated with assisted reproduction, twin pregnancy, cesarean delivery and pre-eclampsia during pregnancy. The risk of hospitalization was 6 times greater in these infants and neonatal intensive care admissions were twice as common as that in the term babies. Neonatal respiratory morbidity and jaundice were greater in the preterm group. The use of surfactant, oxygen and respiratory support (continuous positive airway pressure and cytomegalovirus [CMV]) was also higher. There were no significant differences in hypoglycemia and neonatal mortality between both groups. COMMENT

With exploration in harsh environments and consequent high pressure and temperature conditions, the calculation of reservoir properties has become complex and thus the changes in pressure transient response need to be understood and appreciated by taking appropriate challenging measures. The paper deals with the various challenges arising when dealing with the drilling and testing of HPHT gas wells with Hydrogen sulfide and Carbon dioxide, located in Krishna Godavari (KG) Basin and the difficulties faced while executing it. The paper focuses on the experience while drilling the reservoir with a different mud program and mechanical failure caused by HPHT conditions & highly corrosive environment. The paper also highlights the preference of SOBM over WBM while drilling the reservoir section. It also describes the learning process as the exploratory well campaign progresses from one well to other. It briefs about the challenges while performing MDT as per the program in these high temperature environment. The paper briefs about the decision involved in selection of proper grade tubing, elastomer, packer, flowhead equipments, DST tools & explosives in this HPHT environment along with Hydrogen sulfide & Carbon Dioxide. In any gas well testing, exhaustive amount of data over the requisite period of time are necessary; data redundancy necessitates redressing of equipments. The biggest challenges faced by industry are high temperature rather than high pressures, so making metallurgy an important basis of consideration. It also highlights about the method followed during correlation of prospective zones using different logs. The paper discusses the unexpected results and observations obtained during execution of our program and the lessons learnt from it.

Companies with successful environmental and sustainability programs recognize the need for these programs to be enterprise-wide. Ad-hoc efforts are difficult to scale, manage, repeat, or improve upon. Just like any enterprise-wide program, the issues and requirements of a sustainability initiative are complex and multidimensional. Processes, applications, infrastructure and operations must be aligned with the business goals and requirements. Underlying all of this is the fact that both greenness and sustainability require a robust and adaptable IT infrastructure. This chapter applies the lessons learned from effective use of enterprise architecture (EA) to sustainability initiatives. In particular, it focuses on facilitating the alignment of business visions encompassing financial, environmental, and social responsibility with processes and operational capabilities. Using an architectural approach leverages the key practices that are already in place in successful organizations to drive enterprise-wide sustainability efforts.

Infant massage was first introduced in China in 2nd century BC. Massaging the newborn has been a tradition in India and other Asian countries since time immemorial. Various oil-based preparations have been used depending on the regional availability. There has been a recent surge in this ancient art particularly as a therapy among parents and professionals in the Western world. Evidence exists supporting the benefits of touch and massage therapy. We reviewed the literature to look at the various techniques of providing massage, its benefits, possible mechanism of action and adverse effects. The review suggests that massage has several positive effects in terms of weight gain, better sleep-wake pattern, enhanced neuromotor development, better emotional bonding, reduced rates of nosocomial infection and thereby, reduced mortality in the hospitalized patients. Many studies have described the technique and frequency of this procedure. Massage was found to be more useful when some kind of lubricant oil was used. Harmful effects like physical injury and increased risk of infection were encountered when performed inappropriately. The review also discusses the different hypotheses put forward regarding the mechanism of action. As of now there are very few studies describing the long term impact of neonatal massage.

It is widely recognized that training has to come out of its traditional dormant state to the main stream of corporate functioning and contribute proactively towards attainment of organizational objectives. However, this long overdue importance will be short-lived if training professionals do not show commitment towards enhancing value of training. The process of training delivery is mediated by pre and post delivery factors which have an influence in constructing favorable or unfavorable training climate. This paper addresses the all important issue of training climate and the role played by various stakeholders in developing this. The theoretical model and assumptions are substantiated through case examples. Keywords: Training Climate, Strategy, Evaluation

Objectives: To determine incidence, related factors, outcome, bacterial organisms and their sensitivity patterns with regard to ventilator-associated pneumonia (VAP) in children. Setting: Level III PICU of a tertiary care center. Design: Prospective cohort study. Methods: Children in the age group of 1 month to 15 years, admitted to the pediatric intensive care unit requiring ventilatory support (V.I.P.BIRD infant-Pediatric ventilator) for at least 48 h. Clinical criteria used to define VAPs were the same as used by and Elward et al. and Salata et al. Results: Forty patients met the inclusion criteria and 8 (20%) had VAP. The risk factor significantly related with development of VAP was the use of H 2 blockers (Ranitidine) for >2 days. All other related factors were not significantly related to occurrence of VAP. Conclusion: Use of H 2 blockers (Ranitidne) is associated with higher incidence of VAP in children.

A study was carried out to assess the occurrence of Staphylococcus aureus and the methicillin resistant Staphylococcus aureus (MRSA) in the dairy farm environment of Jammu district and the clinical setting in the SKUAST- J, R. S. Pura vis-a-vis their phenotypic and genotypic characterization for the antimicrobial pattern and the mecA gene producing ability. A total of 60 samples of environment comprising of dust and clinical setting swabs were collected and analysed using standard bacteriological methods. The S. aureus isolates were further processed to identify and characterize MRSA, with an aim to ascertain the characteristics that might be associated with the virulence and resistance of MRSA, making use of conventional and molecular methods. Polymerase chain reaction was employed to identify 533bp mecA gene from MRSA isolates. The results revealed a prevalence of S. aureus 14/30 (46.7%) and 18/30 (60%) in dairy farm environment and clinical settings, respectively. From 32 positive S. aureus dust swabs, 6 (18.75%) were found to be MRSA. This study revealed resistance of S. aureus to Penicillin G, Ampicillin and Streptomycin. However, sensitivity to Cephalothin, Amikacin and Gentamicin was found. The present study revealed that Staphylococcus aureus and MRSA transmission can occur from infected farms, dairy and clinical environments to animals. However, future studies are required to elucidate the transmission potential and magnitude of the problem.

The need to familiarise and sensitise employees towards organisation culture and their job context is addressed by HR professionals through various approaches. Among these, mentoring intervention has acquired a prominent place. However, in instituting mentoring programmes, organisations often neglect broader cultural issues both national and organisational. This is all the more true in Indian organisations wherein lesser emphasis is given on local social milieu and its impact on employees in terms of their attitude towards mentoring. Case study approach has been adopted to substantiate the conceptual tenets of the proposed model. It is argued that mentoring is a culturally sensitive process and organisations have to be sensitive to the social context while implementing any mentoring initiative. This paper provides an original perspective on factors responsible for success of mentoring initiative.