Obsessive-compulsive disorder and comorbid depression: the role of OCD-related and non-specific factors

Although comorbid depression is a predictor of poor treatment response in obsessive-compulsive disorder (OCD), there is limited understanding of factors that contribute to depression severity in OCD. The current study examines the influence of OCD-related factors (autogenous obsessions and obsessional beliefs) and non-specific factors (avoidance and anxiety) on depression severity in a sample of OCD patients. There were 56 participants with only OCD and 46 with OCD and comorbid depression. Self-report questionnaires measuring depression, OCD-related factors, and non-specific factors were completed. Although there were no significant differences between the two groups on these variables, depression severity was positively correlated with anxiety, avoidance, obsessional beliefs, and autogenous obsessions in the whole sample. When entered into a multiple regression model to predict depression severity, these factors accounted for 51% of the variance. While OCD-related factors remained significant predictors after controlling for non-specific factors, the non-specific factors made the most significant contributions to the model. Our findings suggest that in addition to dealing with autogenous obsessions, addressing anxiety and avoidance might lead to improvements in the treatment of OCD with comorbid depression

Product Recommendation System (Targeted Recommendation using Deep Learning in Computer Vision)

Traditional product displays in shopping malls often fail to effectively engage customers due to their generic nature. This report presents a project on developing a product recommendation system that utilizes facial recognition technology to predict a user's age and gender. The system leverages the Multi-task Cascaded Convolutional Networks (MTCNN) algorithm, along with a classifier and the OpenCV library, to accurately recognize faces and extract age and gender information. The primary objective of this project is to create a personalized recommendation system that suggests products tailored to an individual's age group and gender. By employing facial recognition techniques, the system is capable of identifying a user's face in real-time and making predictions regarding their age and gender. The project workflow involves several key steps. First, the MTCNN algorithm is utilized to detect and extract facial features from images or video streams. Once the face is successfully recognized, a classifier model is employed to predict the user's age and gender based on the extracted features. The OpenCV library provides the necessary tools for implementing these functionalities. The recommendations are then generated by mapping the predicted age and gender to specific age groups and gender categories. Each age group and gender category are associated with a set of products suitable for the corresponding demographic. These recommendations aim to enhance the user experience by providing relevant and personalized suggestions that align with their specific needs and preferences. To evaluate the effectiveness of the system, extensive testing and validation are conducted using various datasets. The performance metrics considered include accuracy, precision and Mean Absolute Error (MAE). The results of the project demonstrate the potential of utilizing facial recognition technology to develop accurate and efficient product recommendation systems. The system's ability to accurately predict a user's age and gender contributes to a more personalized and tailored user experience. The project's findings open up possibilities for further research and development in the field of recommendation systems, paving the way for improved user engagement and customer satisfaction in various industries

An Enhanced Entropy Approach to Detect and Prevent DDoS in Cloud Environment

Distributed Denial of Service (DDoS) attack launched in Cloud computing environment resulted in loss of sensitive information, Data corruption and even rarely lead to service shutdown. Entropy based DDoS mitigation approach analyzes the heuristic data and acts dynamically according to the traffic behavior to effectively segregate the characteristics of incoming traffic. Heuristic data helps in detecting the traffic condition to mitigate the flooding attack. Then, the traffic data is analyzed to distinguish legitimate and attack characteristics. An additional Trust mechanism has been deployed to differentiate legitimate and aggressive legitimate users. Hence, Goodput of Datacenter has been improved by detecting and mitigating the incoming traffic threats at each stage. Simulation results proved that the Enhanced Entropy approach behaves better at DDoS attack prone zones. Profit analysis also proved that the proposed mechanism is deployable at Datacenter for attack mitigation and resource protection which eventually results in beneficial service at slenderized revenu

Callisto's Atmosphere and Its Space Environment: Prospects for the Particle Environment Package on Board JUICE

The JUpiter ICy moons Explorer (JUICE) of the European Space Agency will investigate Jupiter and its icy moons Europa, Ganymede, and Callisto, with the aim to better understand the origin and evolution of our Solar System and the emergence of habitable worlds around gas giants. The Particle Environment Package (PEP) on board JUICE is designed to measure neutrals and ions and electrons at thermal, suprathermal, and radiation belt energies (eV to MeV). In the vicinity of Callisto, PEP will characterize the plasma environment, the outer parts of Callisto's atmosphere and ionosphere and their interaction with Jupiter's dynamic magnetosphere. Roughly 20 Callisto flybys with closest approaches between 200 and 5,000 km altitude are planned over the course of the JUICE mission. In this article, we review the state of the art regarding Callisto's ambient environment and magnetospheric interaction with recent modeling efforts for Callisto's atmosphere and ionosphere. Based on this review, we identify science opportunities for the PEP observations to optimize scientific insight gained from the foreseen JUICE flybys. These considerations will inform both science operation planning of PEP and JUICE and they will guide future model development for Callisto's atmosphere, ionosphere, and their interaction with the plasma environment

Design and construction of the MicroBooNE Cosmic Ray Tagger system

The MicroBooNE detector utilizes a liquid argon time projection chamber (LArTPC) with an 85 t active mass to study neutrino interactions along the Booster Neutrino Beam (BNB) at Fermilab. With a deployment location near ground level, the detector records many cosmic muon tracks in each beam-related detector trigger that can be misidentified as signals of interest. To reduce these cosmogenic backgrounds, we have designed and constructed a TPC-external Cosmic Ray Tagger (CRT). This sub-system was developed by the Laboratory for High Energy Physics (LHEP), Albert Einstein center for fundamental physics, University of Bern. The system utilizes plastic scintillation modules to provide precise time and position information for TPC-traversing particles. Successful matching of TPC tracks and CRT data will allow us to reduce cosmogenic background and better characterize the light collection system and LArTPC data using cosmic muons. In this paper we describe the design and installation of the MicroBooNE CRT system and provide an overview of a series of tests done to verify the proper operation of the system and its components during installation, commissioning, and physics data-taking

Ionization Electron Signal Processing in Single Phase LArTPCs II. Data/Simulation Comparison and Performance in MicroBooNE

The single-phase liquid argon time projection chamber (LArTPC) provides a large amount of detailed information in the form of fine-grained drifted ionization charge from particle traces. To fully utilize this information, the deposited charge must be accurately extracted from the raw digitized waveforms via a robust signal processing chain. Enabled by the ultra-low noise levels associated with cryogenic electronics in the MicroBooNE detector, the precise extraction of ionization charge from the induction wire planes in a single-phase LArTPC is qualitatively demonstrated on MicroBooNE data with event display images, and quantitatively demonstrated via waveform-level and track-level metrics. Improved performance of induction plane calorimetry is demonstrated through the agreement of extracted ionization charge measurements across different wire planes for various event topologies. In addition to the comprehensive waveform-level comparison of data and simulation, a calibration of the cryogenic electronics response is presented and solutions to various MicroBooNE-specific TPC issues are discussed. This work presents an important improvement in LArTPC signal processing, the foundation of reconstruction and therefore physics analyses in MicroBooNE.Comment: 54 pages, 36 figures; the first part of this work can be found at arXiv:1802.0870

Ionization Electron Signal Processing in Single Phase LArTPCs I. Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation

We describe the concept and procedure of drifted-charge extraction developed in the MicroBooNE experiment, a single-phase liquid argon time projection chamber (LArTPC). This technique converts the raw digitized TPC waveform to the number of ionization electrons passing through a wire plane at a given time. A robust recovery of the number of ionization electrons from both induction and collection anode wire planes will augment the 3D reconstruction, and is particularly important for tomographic reconstruction algorithms. A number of building blocks of the overall procedure are described. The performance of the signal processing is quantitatively evaluated by comparing extracted charge with the true charge through a detailed TPC detector simulation taking into account position-dependent induced current inside a single wire region and across multiple wires. Some areas for further improvement of the performance of the charge extraction procedure are also discussed.Comment: 60 pages, 36 figures. The second part of this work can be found at arXiv:1804.0258

A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

We have developed a convolutional neural network (CNN) that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a $\nu_\mu$ charged current neutral pion data samples

Synchrony and Physiological Arousal Increase Cohesion and Cooperation in Large Naturalistic Groups

Separate research streams have identified synchrony and arousal as two factors that might contribute to the effects of human rituals on social cohesion and cooperation. But no research has manipulated these variables in the field to investigate their causal – and potentially interactive – effects on prosocial behaviour. Across four experimental sessions involving large samples of strangers, we manipulated the synchronous and physiologically arousing affordances of a group marching task within a sports stadium. We observed participants’ subsequent movement, grouping, and cooperation via a camera hidden in the stadium’s roof. Synchrony and arousal both showed main effects, predicting larger groups, tighter clustering, and more cooperative behaviour in a free-rider dilemma. However, synchrony and arousal interacted on measures of clustering and cooperation: such that synchrony only encouraged closer clustering — and encouraged greater cooperation—when paired with physiological arousal. The research has implications for understanding the nature and co-occurrence of synchrony and physiological arousal in rituals around the world. It also represents the first use of real-time spatial tracking as a precise and naturalistic method of simulating collective rituals