Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of pressure drop through packed beds of granular materials: influence of moisture reduction and air velocity

Abstract The influence of the moisture content of the solids on the pressure drop through packed beds of soy, barley, lentils and oats was investigated. The properties of the grains and the packed beds were determined varying the moisture contents of the solids between the equilibrium moisture content and 0.24 g g-1 (mass of water per mass of dry solid). The pressure drops were measured as a function of the aeration velocity under different moisture contents of the solids. It was observed that a reduction in the moisture content caused a decrease in the particle dimensions but did not affect their shape. Due to the reduction in size caused by the moisture removal, the packed beds become denser and less permeable to the airflow, resulting in an increase in the pressure drop. In the moisture range investigated, the pressure drops increased by more than 39% for lentils, soy and barley, and by about 24% for oats, indicating that the energy consumption during aeration could rise significantly. The parameters of the Forchheimer’s equation were modified to take into account the influence of the moisture content of the solids on the pressure drops. For each particle, empirical equations were proposed and shown to be adequate to accurately predict the pressure drop of the packed beds as a function of the moisture content and aeration velocity.
An Enhanced TBAHIBE-LBKQS Techniques for Privacy Preservation in Wireless Network

In recent days, providing security to the data stored in wireless network is an important and challenging task. For this purpose, several existing privacy preservation and encryption algorithms are proposed in the existing works. But, it has some drawbacks such as, high cost, required more amount of time for execution and low level security. In order to overcome all these drawbacks, this paper proposes a novel technique such as, Tiered Blind and Anonymous Hierarchical Identity Based Encryption (TBAHIBE) and Location Based Keyword Query Search (LBKQS) for providing privacy preservation to the data stored in wireless network environment. In this work, the privacy is provided to the packet data stored in the Electronic Health Record (EHR). It includes two modules such as, secure data storage and location based keyword query search. In the first module, the packet data of the egg and, receptor, doctor and lab technician are stored in the encrypted format by using the proposed TBAHIBE technique. Here, the authenticated persons can view the packet data, for instance, the doctor can view the donor and receptor packet details. In the second module, the location based search is enabled based on the keyword and query. Here, the doctor, patient and other users can fetch the packet details in a filtered format. The main advantage of this paper is, it provides high privacy to the packet data in a secured way. The experimental results evaluate the performance of the proposed system in terms of computation cost, communication cost, query evaluation, encryption time, decryption time and key generation time.
ACTIVE FEL-KLYSTRONS AS FORMERS OF FEMTOSECOND CLUSTERS OF ELECTROMAGNETIC FIELD. DESCRIPTION OF MODELS ON THE BASIS OF SECTIONS OF ‘ORDINARY’ FEL

Physical processes of ultrashort electromagnetic cluster formation in multiharmonic parametrical free electron lasers are analyzed. The conditions, which are necessary for formation of such clusters, are found out. Two formation variants, which differ with input electromagnetic signal spectra and multiharmonic pump magnetic field spectra, are studied. Possibility of the ultrashort electromagnetic field cluster formation in the multiharmonic parametrical free electron lasers is shown.
The Interaction Effect of Financial Leverage on the Relationship Between Board Attributes and Firm Performance; Evidence of Non-financial Listed Companies of Pakistan

The eminence of corporate governance (CG) was grasped after the major blunders incorporate strategies and distinct corporate scandals around the world during the global financial crises. Advanced countries have passed numerous laws such as “Say on Pay” or the Sarbanes-Oxley Act to shield the shareholder’s wealth. However, evolving countries are still flourishing to gain recognition in corporate governance (CG) effectiveness. The intention of the study is to probe the link between the CG (board size, outside directors) and firm performance (Tobin’s Q). Leverage has been used as an interaction term in the current study. The data had been collected from 130 non-financial firms from the year 2012 to 2015 and Multiple Regression Techniques will be used as the instruments for data analysis. The results indicate that the board size and Tobin’s Q have a significant association and outside directors’ insignificant association with Tobin’s Q. The interaction effect of leverage found a significant connotation between board size, outside directors, and Tobin’s Q.
Distortion‐Free Displacement Mapping

Displacement mapping is routinely used to add geometric details in a fast and easy‐to‐control way, both in offline rendering as well as recently in interactive applications such as games. However, it went largely unnoticed (with the exception of McGuire and Whitson [MW08]) that, when applying displacement mapping to a surface with a low‐distortion parametrization, this parametrization is distorted as the geometry was changed by the displacement mapping. Typical resulting artifacts are “rubber band”‐like distortion patterns in areas of strong displacement change where a small isotropic area in texture space is mapped to a large anisotropic area in world space. We describe a fast, fully GPU‐based two‐step procedure to resolve this problem. First, a correction deformation is computed from the displacement map. Second, two variants to apply this correction when computing displacement mapping are proposed. The first variant is backward‐compatible and can resolve the artifact in any rendering pipeline without modifying it and without requiring additional computation at render time, but only works for bijective parametrizations. The second variant works for more general parametrizations, but requires to modify the rendering code and incurs a very small computational overhead.
THE ROLES OF ABSORPTION IN MUSIC THERAPY

Absorption refers to the phenomenon of total attentional engagement. The purpose of the current study was to gain a preliminary understanding of the roles that absorption may have in different music therapy approaches. The research questions were: a) How might music therapists know when their clients are in states of absorption? , b) How might absorption influence the ability of music therapy clients to progress towards therapeutic goals? , and c) What is the potential relationship between absorption and music? Three music therapists with self-identified expertise and experience in conceptualizing absorption in music therapy were interviewed about their observations and opinions of absorption in their music therapy practices. Analysis of the interview data revealed the following themes: 1. how music therapy experiences facilitate absorption for clients, 2. how absorption influences music therapy clients, and 3. transitioning to new experiential states. The results suggest that music therapists may facilitate states of absorption in clients by matching music to their kinesthetic and emotional experiences. Absorption states may help clients to access resources and to transition to new experiential states.
Local null controllability for a parabolic-elliptic system with local and nonlocal nonlinearities

This work deals with the null controllability of an initial boundary value problem for a parabolic-elliptic coupled system with nonlinear terms of local and nonlocal kinds. The control is distributed, locally in space and appears only in one PDE. We first prove that, if the initial data is sufficiently small and the linearized system at zero satisfies an appropriate condition, the equations can be driven to zero.
Determination of Boundary Shear In Smooth Closed Duct Using Momentum Balance Method ( MBM )

The importance of the boundary shear stress distribution is demonstrated by the use which is made of the local or mean value in many hydraulic problems. Currently, it is determined directly or indirectly from the measuring data in viscous sub-layer. However, accurate velocity data in the viscous sub-layer is very difficult to acquire relative to the data in the main flow region. An approach using Momentum Balance Method (MBM) to determine the boundary shear stress distribution along the wetted perimeter in three dimensional flows is proposed. This approach is based on a theoretical relationship between the boundary shear stress and parameters of main flow region within cross section. Such a novel method can be used to determine the boundary shear stress by the main flow data in order to avoid the viscous sub-layer measurements. The unique contribution of MBM is that, the application of this method to main flow data, thus developing a new method to measure local boundary shear stress accurately and quickly. Results of Laboratory experiments in closed ducts are applied in MBM. The comparisons with experimental data and other analytical methods show that the MBM estimates well the boundary shear stress distribution in rectangular closed duct. Hence, it reveals that MBM develops quite simple and useful engineering tools to practical research, and also easy to implement in numerical models. Key-Words: Boundary shear; Reynolds stress, Secondary flow; Turbulent flow; Velocity distribution.
From Implicit to Explicit Feedback: A deep neural network for modeling the sequential behavior of online users

We demonstrate the advantages of taking into account multiple types of behavior in recommendation systems. Intuitively, each user has to do some implicit actions (e.g., click) before making an explicit decision (e.g., purchase). Previous works showed that implicit and explicit feedback has distinct properties to make a useful recommendation. However, these works exploit implicit and explicit behavior separately and therefore ignore the semantic of interaction between users and items. In this paper, we propose a novel model namely Implicit to Explicit (ITE) which directly models the order of user actions. Furthermore, we present an extended version of ITE, namely Implicit to Explicit with Side information (ITE-Si), which incorporates side information to enrich the representations of users and items. The experimental results show that both ITE and ITE-Si outperform existing recommendation systems and also demonstrate the effectiveness of side information in two large scale datasets.
High-Intensity Interval Training, Solutions to the Programming Puzzle Part Anaerobic Energy, Neuromuscular Load and Practical Applications

High-intensity interval training (HIT) is a well-known, time-efﬁcient training method for improving cardiorespiratory and metabolic function and, in turn, physical performance in athletes. HIT involves repeated short ( \ 45 s) to long (2–4 min) bouts of rather high-intensity exercise interspersed with recovery periods (refer to the previously published ﬁrst part of this review). While athletes have used ‘classical’ HIT formats for nearly a century (e.g. repetitions of 30 s of exercise interspersed with 30 s of rest, or 2–4-min interval repetitions ran at high but still submaximal intensities), there is today a surge of research interest focused on examining the effects of short sprints and all-out efforts, both in the ﬁeld and in the laboratory. Prescription of HIT consists of the manipulation of at least nine variables (e.g. work interval intensity and duration, relief interval intensity and duration, exercise modality, number of repetitions, number of series, between-series recovery duration and intensity); any of which has a likely effect on the acute physiological response. Manipulating HIT appropriately is important, not only with respect to the expected middle- to long-term physiological and performance adaptations, but also to maximize daily and/or weekly training periodization. Cardiopulmonary responses are typically the ﬁrst variables to consider when programming HIT (refer to Part I). However, anaerobic glycolytic energy contribution and neuromuscular load should also be considered to maximize the training outcome. Contrasting HIT formats that elicit similar (and maximal) cardiorespiratory responses have been associated with distinctly different anaerobic energy contributions. The high locomotor speed/power requirements of HIT (i.e. C 95 % of the minimal velocity/power that elicits maximal oxygen uptake [v/p _ V O 2max ]
Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs

Machine translation of ancient languages faces a low-resource problem, caused by the limited amount of available textual source data and their translations. We present a multi-task modeling approach to translating Middle Egyptian that is inspired by recent successful approaches to multi-task learning in end-to-end speech translation. We leverage the phonographic aspect of the hieroglyphic writing system, and show that similar to multi-task learning of speech recognition and translation, joint learning and sharing of structural information between hieroglyph transcriptions, translations, and POS tagging can improve direct translation of hieroglyphs by several BLEU points, using a minimal amount of manual transcriptions.
Multi-Scale Visual Semantics Aggregation with Self-Attention for End-to-End Image-Text Matching

For both datasets, input images are resized to 224 × 224 with a random crop, and we use the 152-layer ResNet He et al. (2016) pre-trained on ImageNet Jia et al. (2009) to extract visual features. Different scales of feature maps (28 × 28, 14 × 14, 7 × 7) are captured by the last residual blocks in different stages (conv3_x, conv4_x, conv5_x) of ResNet. For the adaptive self-attention (ASA), we follow Wang et al. (2018) to initialize the weight layers. Note that BatchNorm Ioffe and Szegedy (2015) is crucial to the convergence of ASA. We set the dimensionality of instance candidates and instance-level features as C = 2048 and D = 1024 respectively. For sentences, the dimensionality of word embeddings is set as d = 300, and the pre-trained GloVe Jeffrey et al. (2014) is taken to initialize the word embedding matrix Wx. We set the number of hidden units in the bi-directional GRU and the dimensionality of joint embedding space to 1024. We follow [Lee et al. (2018); Faghri et al. (2018)] to set the margin m of triplet loss to 0.2 and threshold of maximum gradient norm to 2.0 for gradient clipping. The Adam optimizer Kingma and Ba (2015) is leveraged to train all models with a mini-batch size of 128. Besides, two training stages are applied to each model, where we freeze ResNet in the first 20 epochs with an initial learning rate of 5e−4, and the whole network is fine-tuned in the next 15 epochs with an initial learning rate of 2e−5. We follow Lee et al. (2018) to set the hyper-parameters.
A Near Zero-Energy Computation Using Quantum-dot Cellular Automata

Near zero-energy computing describes the concept of executing logic operations below the (kBT ln 2) energy limit. Landauer discussed that it is impossible to break this limit as long as the computations are performed in the conventional, non-reversible way. But even if reversible computations were performed, the basic energy needed for operating circuits realized in conventional technologies is still far above the (kBT ln 2) energy limit, i.e. the circuits do not operate physically reversible. In contrast, novel nanotechnologies like Quantum-dot Cellular Automata (QCA) allow for computations with very low energy dissipation and, hence, are promising candidates for breaking this limit. Accordingly, the design of reversible QCA circuits is an active field of research. But whether QCA in general (and the proposed circuits in particular) are indeed able to operate in a logically and physically reversible fashion is unknown thus far, because neither physical realizations nor appropriate simulation approaches were available yet. In this work, we address this gap by utilizing an established theoretical model that has been implemented in a physics simulator enabling a precise consideration of how energy is dissipated in QCA designs. Our results provide strong evidence that QCA is indeed a suitable technology for near zero-energy computing. Further, the first design of a logically and physically reversible adder circuit is presented which serves as proof-of-concept for future circuits with the ability of near zero-energy computing. CCS Concepts: rHardware → Reversible logic; Analysis and design of emerging devices and systems; Quantum dots and cellular automata;
Examining the Antecedents of Mobile Service Users’ Loyalty: An Empirical Study of Mobile Service Providers in Kuwait

Background and objective: Considering the increasing tough competitive environment, the role of user satisfaction and loyalty are the most important factors for the success and survival of any mobile service providers. Analysis of the determinant of user satisfaction can help mobile service providers to optimize their services to retain their user base. The competition among mobile service provider is very high. The research is aimed to empirically investigate the loyalty of online services of mobile operators in Kuwait using the American Satisfaction Index (ACSI) model. Materials and Methods: This exploratory study employed structured questionnaire and collected primary data from the users of the all mobile service providers in Kuwait. Valid responses collected from 312 mobile service users were tested with Structural Equation Modeling (SEM) technique. Results: Results of this research indicate that all hypothetical relationships demonstrate significant positive correlation and thus proposed research model is found generally plausible. Conclusions: Findings indicate that perceived quality of online mobile services providers is the most significant factor that affects mobile service user satisfaction and their loyalty. Significant Statement: The purpose this research is to examine the role of mobile service user satisfaction and their loyalty towards the online service provisioning by mobile service providers in Kuwait using a modified ACSI model. The results of this research offer many theoretical and managerial implications and contribution for researchers and mobile service providers offering online services in Kuwait
Effects Of Smoking On Cardiovascular Function During Maximal Treadmill Exercise

Background: Smoking is a major risk factor for cardiovascular morbidity and mortality. It deregulates cardiac autonomic function, blunts the chronotropic responses during exercise and lowers the maximum heart rate that can be achieved, substantially reducing the functional capacity and performance of the circulatory system. The aim of the study is to compare the cardiovascular functionamong smokers and nonsmokers during maximal exercise stress testing using TMT Methods:A case control study was carried out on 90 young asymptomatic male first degree relatives of myocardial infarction patients attending Medicine and Cardiology department of GRMC Gwalior comprising of 60 smokers as cases and 30 nonsmokers as controls. Chronotropic response by heart rate at rest, maximum heart rate attained, heart rate reserve, chronotropic index, heart rate recovery at 1 minute and exercise capacity by maximal exercise duration was compared between smokers and nonsmokers. The results were analysed with t test for continuous variables and χ2 test for categorical variables. Results: Smokers had a higher heart rate at rest than nonsmokers(p<.05) while there is a negative relationship to maximum heart rate attained (p<.05), heart rate reserve (p<.05) chronotropic index (p<.05) and exercise duration (p<.05) and with increasing burden of smoking this relationship becomes stronger. No significant difference in heart rate recovery was observed between smokers and nonsmokers. Conclusion: Cardiovascular prognosis as estimated by hemodynamic parameters like heart rate at rest, maximum heart rate attained, heart rate reserve, chronotropic index and exercise duration is poor in smokers compared to nonsmokers and the adverse effects become stronger with increase in severity of smoking.
MR image reconstruction using deep learning: evaluation of network structure and loss functions.

Background To review and evaluate approaches to convolutional neural network (CNN) reconstruction for accelerated cardiac MR imaging in the real clinical context. Methods Two CNN architectures, Unet and residual network (Resnet) were evaluated using quantitative and qualitative assessment by radiologist. Four different loss functions were also considered: pixel-wise (L1 and L2), patch-wise structural dissimilarity (Dssim) and feature-wise (perceptual loss). The networks were evaluated using retrospectively and prospectively under-sampled cardiac MR data. Results Based on our assessments, we find that Resnet and Unet achieve similar image quality but that former requires only 100,000 parameters compared to 1.3 million parameters for the latter. The perceptual loss function performed significantly better than L1, L2 or Dssim loss functions as determined by the radiologist scores. Conclusions CNN image reconstruction using Resnet yields comparable image quality to Unet with 10X the number of parameters. This has implications for training with significantly lower data requirements. Network training using the perceptual loss function was found to better agree with radiologist scoring compared to L1, L2 or Dssim loss functions.
“ Medical Image Labeling and Report Generation ”

In this thesis, we experiment with medical image tagging and text generation tasks, with the ultimate goal of helping physicians who need to generate reports from medical images. First, we address the task of medical image tagging, which aims to identify medical terms (tags) describing an image. Towards this end, we develop models employing deep learning and retrieval-based methods and apply them to two datasets that consist of X-ray exams. Second, we implement image captioning models in order to produce text that describes the findings present in an image and the final diagnosis. We also examine current datasets and methods and present results of our models evaluated with widely used measures.