Miracle-dz/UniFAR-FTUs huggingface dataset:
Running head: IMPACTS OF STRESS ON TRACKING BEHAVIOR 1 Impacts of Stress During Early Puberty on Tracking Behavior in Hamsters

In classical conditioning paradigms, sign-tracking (ST) behavior is classified by incentive salience attributed to a reward-associated cue instead of the reward itself. Goal-tracking (GT) behavior on the other hand, is when incentive salience is attributed to the reward instead of the reward-associated cue. The study investigated any possible effect of chronic stress during puberty on tracking behavior during adulthood. Experimental male golden hamsters (n=10) were exposed to social stress during early puberty by placing them in cages of adult male hamsters once a day from Postnatal Day 28 to Postnatal Day 42, while those in the control group (n=10) were placed in new clean empty cages during the process of subjugation. During adulthood, hamsters were placed in chambers where they learned to associate the onset of two LED light cues to the release of a food reward at the food cup on the other side of the chamber. Contrary to what was hypothesized, stress during early puberty did not impact tracking behavior in adulthood, however it did seem to negatively impact learning behavior.
An Experimental Study of Plasma Fluctuations in the TCV and TEXTOR Tokamaks

Keywords: tokamak ; plasma ; phase-contrast imaging ; reflectometry ; zonal flow ; edge-localised modes These Ecole polytechnique federale de Lausanne EPFL, n° 5610 (2013)Programme doctoral PhysiqueFaculte des sciences de baseCRPP - Physique du Tokamak TCVJury: O. Schneider (president), S. Brunner, E. Gusakov, P. Hennequin Public defense: 2013-1-11 Reference doi:10.5075/epfl-thesis-5610Print copy in library catalog Record created on 2013-01-07, modified on 2017-05-12
Using Biological Knowledge for Layout Optimization of Construction Site Temporary Facilities: A Case Study

In recent years, a number of studies have successfully transformed various models for biological collective behavior into intelligent optimization algorithms. These bio-inspired optimization techniques have been developed to provide better solutions than traditional methods to a variety of engineering problems. This paper attempts to apply and compare recent bio-inspired algorithms for determining the best layout of construction temporary facilities. To validate the performance of the proposed techniques, an actual building construction project was used as a test problem. Based on the obtained results, the performance of each bio-inspired algorithm is highlighted and discussed. This paper presents beneficial insights to decision-makers in the construction industry that are involved in handling optimization problems.
BUSINESS EXCELLENCE INITIATIVES: A STUDY OF DUBAI QUALITY AWARD-WINNING ORGANIZATIONS IN UAE

This paper presents the central results of a survey analysis carried out in the UAE with respect to business excellence initiatives. The main purpose of this research is to investigate the use of business excellence initiatives in Dubai quality award-wining organizations. The study used a self-administered questionnaire to confirm the adaptability of business excellence initiatives by Dubai quality award-wining organizations. Several statistical tests including descriptive statistics, internal consistency reliability and content validity were employed using the Statistical Package for the Social Sciences (SPSS). The result of the study shows that various business excellence initiatives already implemented by Dubai quality award-wining organizations for their efforts towards business excellence. The award-wining organizations believe that business excellence initiatives are important in helping them reach their organizational goals.
A Survey on Mental State Analysis using Natural Language Processing

: Human beings are full of emotions & the mental state of human beings is varied depending on the surrounding and situation. As the situation changes so as the mood of the person also changes. Being a human we are also able to recognize someone’s emotion by their facial expressions and their wordings. But when try to recognize someone’s emotion or sentiments in the written material, than it is a tedious job. At the same time if we want to simulate the same task by the machine then it is a difficult one. In this research proposal the emotion detection in text based documents are discussed. The phenomenon uses the concept of Natural Language Processing (NLP). In literature several kinds of emotion/sentiment identification has been proposed (like sentiment analysis in movies, news articles, social network etc). In this paper section 1 gives the brief introduction, section 2 describes the literature of the relevant work, section 3 gives the comparative analysis of the various methods
Derivation and validation of a type 2 diabetes treatment selection algorithm for SGLT2-inhibitor and DPP4-inhibitor therapies based on glucose-lowering efficacy: cohort study using trial and routine clinical data

Objective: To establish whether clinical patient characteristics routinely measured in primary care can identify people with differing short-term benefits and risks for SGLT2-inhibitor and DPP4-inhibitor therapies, and to derive and validate a treatment selection algorithm to identify the likely optimal therapy for individual patients. Design: Prospective cohort study. Setting: Routine clinical data from United Kingdom general practice (Clinical Practice Research Datalink [CPRD]), and individual-level clinical trial data from 14 multi-country trials of SGLT2-inhibitor and DPP4-inhibitor therapies. Participants: 26,877 new users of SGLT2-inhibitor and DPP4-inhibitor therapy in CPRD over 2013-2019, and 10,414 participants randomised to SGLT2-inhibitor or DPP4-inhibitor therapy in 14 clinical trials, including 3 head-to-head trials of the two therapies (n=2,499). Main outcome measures: The primary outcome was achieved HbA1c 6 months after initiating therapy. Clinical features associated with differential HbA1c outcomes with SGLT2-inhibitor and DPP4-inhibitor therapies were identified in routine clinical data, with associations then tested in trial data. A multivariable treatment selection algorithm to predict differential HbA1c outcomes was developed in a CPRD derivation cohort (n=14,069), with validation in a CPRD validation cohort (n=9,376) and the head-to-head trials. In CPRD, we further explored the relationship between model predictions and secondary outcomes of weight loss and treatment discontinuation. Results: The final treatment selection algorithm included HbA1c, eGFR, ALT, age, and BMI, which were identified as predictors of differential HbA1c outcomes with SGLT2-inhibitor and DPP4-inhibitor therapies using both routine and trial data. In validation cohorts, patient strata predicted to have a [≥]5 mmol/mol HbA1c reduction with SGLT2-inhibitor therapy compared with DPP4-inhibitor therapy (38.8% of CPRD validation sample) had an observed greater reduction of 8.8 mmol/mol [95%CI 7.8-9.8] in the CPRD validation sample, a 5.8 mmol/mol (95%CI 3.9-7.7) greater reduction in the Cantata D/D2 trials, and a 6.6 mmol/mol [95%CI 2.2-11.0]) greater reduction in the BI1245.20 trial. In CPRD, there was a greater weight reduction with SGLT2-inhibitor therapy regardless of predicted glycaemic benefit. Strata predicted to have greater reduction in HbA1c on SGLT2-inhibitor therapy had a similar risk of discontinuation as on DPP4-inhibitor therapy. In contrast, strata predicted to have greater reduction in HbA1c with DPP4-inhibitor therapy were half as likely to discontinue DPP4-inhibitor therapy than SGLT2-inhibitor therapy. Conclusions: Routinely measured clinical features are robustly associated with differential glycaemic responses to SGLT2-inhibitor and DPP4-inhibitor therapies. Combining features into a treatment selection algorithm can inform clinical decisions concerning optimal type 2 diabetes treatment choices.
Application of Signal Advance Technology to Electrophysiology

Medical instrumentation used in diagnosis and treatment relies on the accurate detection and processing of various physiological events and signals. While signal detection technology has improved greatly in recent years, there remain inherent delays in signal detection/processing. These delays may have significant negative clinical consequences during various patho-physiological events. Reducing or eliminating such delays would increase the ability to provide successful early intervention in certain disorders thereby increasing the efficacy of treatment. In recent years, a physical phenomenon referred to as Negative Group Delay (NGD), demonstrated in simple electronic circuits, has been shown to temporally advance the detection of analog waveforms. Specifically, the output is temporally advanced relative to the input, as the time delay through the circuit is negative. The circuit output precedes the complete detection of the input signal. This process is referred to as signal advance (SA) detection. An SA circuit model incorporating NGD was designed, developed and tested. It imparts a constant temporal signal advance over a pre-specified spectral range in which the output is almost identical to the input signal (i.e., it has minimal distortion). Certain human patho-electrophysiological events are good candidates for the application of temporally-advanced waveform detection. SA technology has potential in early arrhythmia and epileptic seizure detection and intervention. Demonstrating reliable and consistent temporally advanced detection of electrophysiological waveforms may enable intervention with a pathological event (much) earlier than previously possible. SA detection could also be used to improve the performance of neural computer interfaces, neurotherapy applications, radiation therapy and imaging. In this study, the performance of a single-stage SA circuit model on a variety of constructed input signals, and human ECGs is investigated. The data obtained is used to quantify and characterize the temporal advances and circuit gain, as well as distortions in the output waveforms relative to their inputs. This project combines elements of physics, engineering, signal processing, statistics and electrophysiology. Its success has important consequences for the development of novel interventional methodologies in cardiology and neurophysiology as well as significant potential in a broader range of both biomedical and non-biomedical areas of application.
Supplementary Materials for: Motion Guided Region Message Passing for Video Captioning

As shown in Table 1, we supplement more results to the Table 5 in the main paper: #4 and #5: We apply ATGD to more model variants. Comparing #4 and #1, #5 and #0, we can conclude that ATGD is a generally effective caption decoder, and can be applied on top of different video feature encoders to obtain better captioning performance than two-layer LSTMs (which is used in most methods). #obj: We use detected object features in the baseline model, but the features are spatially mean-pooled as in #1. As can be seen, the performances of #obj is worse than #0 (grid) and #1 (region). We conjecture the reason is that the object features can ignore some background regions because of extracting information only from object bounding box areas, while grid features (#0) contains all the information of a feature map and region features (#1) can capture the background information by the soft attention maps. This information loss can be compensated by the MGCMP, that is why the performances of our model with object and region features are close in the Table 4 of the main paper.
The method of lines for the analysis of dielectric waveguide bends

The analysis of a wide class of waveguide bends using the method of lines is suggested. The discretization is performed in radial direction. The cross section of the waveguide may consist of many inhomogeneous layers. Loss can be taken into account by using complex permittivities. The radiation loss is calculated by the use of absorbing boundary conditions. The presented algorithm is verified by the analysis of a rib waveguide bend. The results are in good agreement with those published in the literature.
WATERMARKER USING DISCRETE MULTIWAVELET

: In this paper, a multibit, multiplicative, watermarking using the texture area transform is presented. Performance improvement is obtained by means of a new Just Perceptual Weighting (JPW) model than existing one. The new model incorporates various masking effects of human visual perception by taking into account the eye’s sensitivity to noise changes depending on spatial frequency and texture of all the image area. The area with dense high brightness and dense low brightness is taken for watermarking. The quality of the watermarked image and robustness of the watermark are improved by this proposed system compared with a variety existing algorithms. The application for implementing the algorithm is developed using MATLAB 7.6 IndexTerms
Implementation of Face Recognition System Using LBP

-In this research, we implemented face recognition algorithmic approach which consist of both frame and texture information to represent face images. The whole area of face is partitioned into small blocks from which Local Binary Pattern (LBP) histograms of size (8x8) are extracted and concatenated into a single image histogram. The extracted enhanced feature histograms represents the given face image. On the basis of extracted features of face, classification is performed by using nearest neighbor techniques. Analysis of results seemingly shows the perfection of the implemented scheme over all considered methods (PCA and Local Binary Pattern) on ORL database which include testing the efficiency of the implemented approach against lighting, aging and different facial expressions of the faces. In addition to its simplicity and efficiency, the scheme requires less than 50% time for feature extraction.
CLASSIFICATION OF HYPERSPECTRAL IMAGE USING CNN

: There are many methods that are so far developed to classify hyperspectral images. Different from traditional spectral-spatial classification frameworks where the spectral feature extraction (FE), spatial FE, and classifier training are separated, these processes are integrated into a unified network in our model. In this project we are proposing a model using stationary wavelet transform (SWT), principal component analysis (PCA) and convolutional neural networks (CNN) for HSI classification. SWT is used to extract the meaningful features from the hyperspectral Data. PCA selects a subset of transformed coefficients for classification by reducing redundancy which can be also called as dimension reduction (DR) and reshapes the image data to into 2D from 3D. A CNN will be designed to classify the selected transformed coefficients and predict the accurate output. In this project, we are planning to use popular online hyperspectral image dataset to show better accuracy when compared with other frame works which are already in implemented.
Prediction-Driven Surge Planning with Application in the Emergency Department

Determining emergency department (ED) nurse staffing decisions to balance the quality of service and staffing cost can be extremely challenging, especially when there is a high level of uncertainty in patient-demand. Increasing data availability and continuing advancements in predictive analytics provide an opportunity to mitigate demand uncertainty by utilizing demand forecasts. In this work, we study a two-stage predictiondriven staffing framework where the prediction models are integrated with the base (made weeks in advance) and surge (made nearly real-time) staffing decisions in the ED. We quantify the benefit of having the ability to use the more expensive surge staffing and identify the importance of balancing demand uncertainty versus demand stochasticity. We also propose a near-optimal two-stage staffing policy that is straightforward to interpret and implement. Lastly, we develop a unified framework that combines parameter estimation, realtime demand forecasts, and capacity sizing in the ED. High-fidelity simulation experiments for the ED demonstrate that the proposed framework can reduce annual staffing costs by 11%–16% ($2 M–$3 M) while guaranteeing timely access to care.
EEG-Based Brain-Computer Interfaces

Communicating with the mind seems something futuristic and taken from a science-ﬁction novel. However, machines which are able to decode brain signals and assign them to speciﬁc outputs, such as moving a robotic arm, or typing words on a screen, already exist. Such machines are called Brain-Computer Interfaces. In this work, we aim to ﬁnd measurable diﬀerences between two simple forms of communication: conveying yes or no , in a direct-recognition approach, i.e., by recognizing a signature of the actual answer. 22 subjects (ages between 23 and 60) underwent a visual task where they answered to yes and no questions, as their EEG recordings were gathered. The recordings were processed using, among others, an automatic artifact removal algorithm (Artifact Subspace Reconstruction). The Common Spatial Patterns method was then employed to design spatial ﬁlters (by maximally diﬀerentiating the variance of each class) and to build the input feature vectors. These input vectors were classiﬁed using SVMs whose parameters were tuned through the gridsearch method, and the results were plotted in a 2D graphical representation which displayed the classiﬁcation accuracy by frequency and time of the classiﬁed trials. Finally, the features which presented the highest classiﬁcation accuracy on the training sets, were used to evaluate the performance of the classiﬁer on new data. The classiﬁcation accuracy of the test sets failed to reach the theoretical chance level (65%), even when the classiﬁcation accuracy on the training sets was over 86% for all analysis. Our analysis, following this methodology, failed to ﬁnd detectable diﬀerences between these two answers.
Development of methods for material properties identification in composite structures

This work proposes a non-destructive method for the identification of material properties of composite materials. This method takes advantage of the simulation capabilities of ANSYS software, using ANSYS Parametric Design Language (APDL) to perform modal analyses and extract natural frequencies of the specimens. ANSYS and MATLAB are integrated to solve several optimisation problems. The proposed optimisation problems have for design variables the material elastic constants and make use of Nature-inspired metaheuristic optimisation algorithms to evaluate an objective function using a derivative-free method. These objective functions relate experimental natural frequencies, which are extracted from numerous studies carried out in the past by other authors, with computationally obtained ones. The Nature-inspired metaheuristic optimisation algorithms tested are the Genetic algorithm, the Particle Swarm Optimisation algorithm, the Grey Wolf Optimisation algorithm, the Firefly Algorithm and Cuckoo Search algorithm. The different search agents population generated by the algorithms search the available space looking for the global minimum of the objective function, independently of the initial population position in the search space. The proposed method is applied to several specimens of different materials either they are constructed with synthetic fibres, such as glass fibres, or natural fibres, such as wood fibres and plywood. This method proved to obtain the material elastic constants within an acceptable range compared to other methods, provided that enough natural frequencies are accurately measured and provided.
A STUDY ON STUDENT PERCEPTION OF SERVICE QUALITY IN HIGHER EDUCATION

Liberalization of the higher education sector has increased the access of students to institutions of higher learning. Students of today are far more informed and have more choices in terms of institutions to pursue their higher studies. In the age of competition, the institutions of higher learning need to understand the customers' (students') perceptions of service quality and identify the gap between their expectations and these perceptions. The paper studies the students' perceptions of service quality in the present educational scenario. This study attempts to examine the relationship between service quality dimensions and overall service quality and students satisfaction. This research is gathered through the use of focus groups as the primary data collection method. This study provides management with a ‘snapshot’ of the current provision of service quality at the higher education institutes. It also offers suggestions that could be implemented to improve service quality, given the limited resources available to management.
Regulation of Inflammasomes by Application of Omega-3 Polyunsaturated Fatty Acids in a Spinal Cord Injury Model

Omega-3 polyunsaturated fatty acids (PUFA n3) ameliorate inflammation in different diseases and potentially improve neurological function after neuronal injury. Following spinal cord injury (SCI), inflammatory events result in caspase-1 mediated activation of interleukin-1 beta (IL-1b) and 18. We aim to evaluate the neuroprotective potency of PUFA n3 in suppressing the formation and activation of inflammasomes following SCI. Male Wistar rats were divided into four groups: control, SCI, SCI+PUFA n3, and SCI+Lipofundin MCT (medium-chain triglyceride; vehicle). PUFA n3 or vehicle was intravenously administered immediately after SCI and every 24 h for the next three days. We analyzed the expression of NLRP3, NLRP1, ASC, caspase-1, IL-1b, and 18 in the spinal cord. The distribution of microglia, oligodendrocytes, and astrocytes was assessed by immunohistochemistry analysis. Behavioral testing showed significantly improved locomotor recovery in PUFA n3-treated animals and the SCI-induced upregulation of inflammasome components was reduced. Histopathological evaluation confirmed the suppression of microgliosis, increased numbers of oligodendrocytes, and the prevention of demyelination by PUFA n3. Our data support the neuroprotective role of PUFA n3 by targeting the NLRP3 inflammasome. These findings provide evidence that PUFA n3 has therapeutic effects which potentially attenuate neuronal damage in SCI and possibly also in other neuronal injuries.
Comparative study of Speaker Recognition using GMM and RBF

: There are so many techniques for text-independent speaker recognition. However this text-independent speaker recognition is very difficult because the recognition is performed irrespective of what one he is saying. In this work to extract features of speech signal, Linear Prediction Coefficients (LPC) and Mel-Frequency Cepstral Coefficients (MFCC) are used. The extracted features are matched using GMM using Expectation Maximization (EM) algorithm and RBF using Gradient Descent Algorithm. The two systems are compared with the performance and execution time
The Probability of Injury to Car Occupants in Frontal and Side Impacts

An in-depth crash injury investigation was undertaken to provide data to relate the probability of injury to impact severity, in frontal and side impacts. Estimates for the probability of injury for unbelted and belted occupants were established using Probit Analysis. This has quantified the reductions in the probability of injury due to seat belt use, and thus provides additional information supporting the effectiveness of seat belts for different impact severities. The results obtained are suitable for matching with experimental test data to establish improved estimates of human tolerance to impact. Language: en