Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Reconstruction via Manifold Constrained Convolutional Sparse Coding for Image Sets

Convolution sparse coding (CSC) has attracted much attention recently due to its advantages in image reconstruction and enhancement. However, the coding process suffers from perturbations caused by variations of input samples, as the consistence of features from similar input samples are not well addressed in the existing literature. In this paper, we will tackle this feature consistence problem from a set of samples via a proposed manifold constrained convolutional sparse coding (MCSC) method. The core idea of MCSC is to use the intrinsic manifold (Laplacian) structure of the input data to regularize the traditional CSC such that the consistence between features extracted from input samples can be well preserved. To implement the proposed MCSC method efficiently, the alternating direction method of multipliers (ADMM) approach is employed, which can consistently integrate the underlying Laplacian constraints during the optimization process. With this regularized data structure constraint, the MCSC can achieve a much better solution which is robust to the variance of the input samples against overcomplete filters. We demonstrate the capacity of MCSC by providing the state-of-the-art results when applied it to the task of reconstructing light fields. Finally, we show that the proposed MCSC is a generic approach as it also achieves better results than the state-of-the-art approaches based on convolutional sparse coding in other image reconstruction tasks, such as face reconstruction, digit reconstruction, and image restoration.
The impact of Total Quality Management on organizational performance Case of Jordan Oil Petroleum Company

This study aimed to examine the impact of TQM implementation on organizational performance. The study was conducted in Jordan Petroleum Refinery Company (JPRC), the study sample size was (103) managers from different levels. The researcher depended on primary and secondary data. The results show that TQM has positive impact on organizational performance. In light of these findings, the study provided a set of recommendations.
Offering strategy for a photovoltaic power plant in electricity market

With the large-scale manufacturing and production, the per-unit cost of energy from photovoltaic modules have decreased, and the incentives given by the governments are slowly withdrawn. Consequently, the photovoltaic power plants are able to, and asked to, participate in the electricity markets to trade its power competing with conventional generators. This work contributes to the offering strategies for photovoltaic power plants in the day-ahead electricity market with balancing market. The uncertain variables considered are day-ahead electricity prices and actual solar power production for which scenarios are created from historical data. The photovoltaic power plant is modeled as a price taker and the offering problem is solved as a linear programming problem with stochastic optimization.
High Performance of v / f Method Based on PID Controller with Space Vector Modulation to Modified Permanent Magnet Synchronous Machine

This paper deals with the problem of v/f control of permanent magnet synchronous machine which is the open loop v/f method is inherently unstable. In order to overcome the v/f problems, the pi control is used to optimize the rotor speed and to achieve close loop for system as results to enhance the PMSM system. In addition, the space vector modulation is used to eliminate the pwm problems and to modified the efficiency of system. The simulation results of system with PID controller and SVM demonstrate that the system is more effective with high performance.
STRENGTHENING OF CONCRETE BEAMS WITH MECHANICALLY FASTENED FRP STRIPS

The current method of flexurally strengthening reinforced concrete members involves bonding fiber reinforced polymer (FRP) strips, which requires extensive time and skilled labor. An alternative method is being developed which uses powder actuated fastening systems to attach the FRP strips to the concrete surface. Powder actuated tools are inexpensive, readily available, and do not require sophisticated training to operate. The fasteners must be attached in a certain manner as not to destroy the concrete substrate, which reduces fastener strength and reduces the durability of the concrete member. This paper presents the initial experimental and analytical research from the investigation of this method on full-scale reinforced concrete T beams.
A pattern reconfigurable antenna system for automotive MIMO applications

This paper presents the design and evaluation of a multiple-input multiple-output (MIMO) antenna system consisting of pattern reconfigurable antennas. The reconfigurable antennas used are based on radiating elements that are fed in parallel and two possible phase differences between them can be switched with p-i-n diodes. Two parasitic elements in vicinity of one of the antennas enable the generation of further two directive patterns. The radiation patterns realized by this MIMO system are optimized for automotive urban scenarios based on the results of previous research. As a proof of concept the patterns realized by these antennas are evaluated in a channel simulation and different arrangements on a car's roof are investigated. The simulation results considering various scenarios show the improvement in channel capacity by as much as 4 bit/s/Hz and 3 bit/s/Hz compared to system using monopoles for rural and urban cases respectively.
Fe b 20 11 MCTP-10-21 UTTG-0510 TIFR / TH / 10-19 Toward NS 5 Branes on the Resolved Cone over Y p , q

Motivated by recent developments in the understanding of the connection between five branes on resolved geometries and the corresponding generalizations of complex deformations in the context of the warped resolved deformed conifold, we consider the construction of five branes solutions on the resolved cone over Y p,q spaces. We establish the existence of supersymmetric five branes solutions wrapped on two-cycles of the resolved cone over Y p,q in the probe limit. We then use calibration techniques to begin the construction of fully back-reacted five branes; we present an ansatz and the corresponding equations of motion. Our results establish a detailed framework to study back-reacted five branes wrapped on the resolved cone over Y p,q and as a first step we find explicit solutions and construct an asymptotic expansion with the expected properties.
Physical growth and nutritional status assessment of school children in Enugu, Nigeria

Background: Physical growth of a child is a reflection of its state of nutrition. In some developing countries such as Nigeria with changing economy and rapidly growing population, the nutritional status of the children is a reflection of the general well-being of the society. Materials and Methods: This was a descriptive cross-sectional study in which participants were selected using a multistage sampling method. Heights and weights of randomly selected school children aged 6–12 years were measured using standard protocols. Weight-for-age, height-for-age, and body mass index (BMI)-for-age expressed as Z-scores were used to characterize the nutritional status. Descriptive statistics was used to determine the frequency and standard deviations (SDs) of the anthropometric measurements. Age and gender differences in the mean body weight, height, and BMI were evaluated using an independent samples t-test. Significant levels were set at P< 0.05. Results: A total of 1305 males and 1311 females were enrolled in the study. The mean age was 8.9 ± 1.9 years. Their mean height, weight, and BMI were 136.6 ± 10.2 cm, 29.7 ± 7.7 kg, 15.7 ± 2.4 kg/m2, respectively. Their mean ± SD scores of the WAZ, HAZ, and BAZ were 0.33 ± 1.20, 0.78 ± 1.17, and − 0.51 ± 1.27, respectively. A majority (78.9%, 2090/2616) were in the normal growth category. Wasting, overweight, obesity, underweight, and stunting were noted in 9.3% (243/2616), 6.3% (166/2616), 4.4% (117/2616), 0.9% (26/2616), and 0.4% (13/2616) of the children, respectively. Wasting was more in males (P = 0.069), and overweight was more in females (P = 0.138). Conclusion: A majority of the children have normal growth with the remainder in both extremes of malnutrition. Institution of school-feeding programs in all Nigerian schools as well as nutrition education/campaign directed at parents and their children will help forestall the double burden of under- and over-nutrition among our children.
A neurobiologically inspired vowel recognizer using hough-transform - a novel approach to auditory image processing

Many pattern recognition problems can be solved by mapping the input data into an n-dimensional feature space in which a vector indicates a set of attributes. One powerful pattern recognition method is the Houghtransform, which is usually applied to detect specific curves or shapes in digital pictures. In this paper the Hough-transform is applied to the time series data of neurotransmitter vesicle releases of an auditory model. Practical vowel recognition of different speakers with the help of this transform is investigated and the findings are discussed.
Blood flow restriction attenuates eccentric exercise‐induced muscle damage without perceptual and cardiovascular overload

The aim of this study was to evaluate the acute effects of high‐intensity eccentric exercise (HI‐ECC) combined with blood flow restriction (BFR) on muscle damage markers, and perceptual and cardiovascular responses. Nine healthy men (26 ± 1 years, BMI 24 ± 1 kg m−²) underwent unilateral elbow extension in two conditions: without (HI‐ECC) and with BFR (HI‐ECC+BFR). The HI‐ECC protocol corresponded to three sets of 10 repetitions with 130% of maximal strength (1RM). The ratings of perceived exertion (RPE) and pain (RPP) were measured after each set. Muscle damage was evaluated by range of motion (ROM), upper arm circumference (CIR) and muscle soreness using a visual analogue scale at different moments (pre‐exercise, immediately after, 24 and 48 h postexercise). Systolic (SBP), diastolic (DBP), mean blood pressure (MBP) and heart rate (HR) were measured before exercise and after each set. RPP was higher in HI‐ECC+BFR than in HI‐ECC after each set. Range of motion decreased postexercise in both conditions; however, in HI‐ECC+BFR group, it returned to pre‐exercise condition earlier (post‐24 h) than HI‐ECC (post‐48 h). CIR increased only in HI‐ECC, while no difference was observed in HI‐ECC+BFR condition. Regarding cardiovascular responses, MBP and SBP did not change at any moment. HR showed similar increases in both conditions during exercise while DBP decreased only in HI‐ECC condition. Thus, BFR attenuated HI‐ECC‐induced muscle damage and there was no increase in cardiovascular responses.
Divergence of compost extract and bio-organic manure effects on lucerne plant and soil

Aim Application of organic materials into agricultural systems enhances plant growth and yields, and improves soil fertility and structure. This study aimed to examine the effects of “compost extract (CE)”, a soil conditioner, and bio-organic manure (BOM) on the growth of lucerne (Medicago sativa), and compare the efficiency between BOM (including numbers of microorganisms) and CE (including no added microorganisms). Method A greenhouse experiment was conducted with four soil amendment treatments (control, BOM, CE and CEBOM), and was arranged in a completely randomized design with 10 replicates for each treatment. Plant biomass, nutritive value and rhizobia efficacy as well as soil characteristics were monitored. Result CE rather than BOM application showed a positive effect on plant growth and soil properties when compared with the control. Lucerne nodulation responded equally to CE addition and rhizobium inoculation. CE alone and in combination with BOM significantly increased plant growth and soil microbial activities and improved soil structure. The synergistic effects of CE and BOM indicate that applying CE and BOM together could increase their efficiency, leading to higher economic returns and improved soil health. However, CE alone is more effective for legume growth since nodulation was suppressed by nitrogen input from BOM. CE had a higher efficiency than BOM for enriching soil indigenous microorganisms instead of adding microorganisms and favouring plant nodulation.
Deep Unsupervised Drum Transcription

We introduce DrummerNet, a drum transcription system that is trained in an unsupervised manner. DrummerNet does not require any ground-truth transcription and, with the data-scalability of deep neural networks, learns from a large unlabeled dataset. In DrummerNet, the target drum signal is first passed to a (trainable) transcriber, then reconstructed in a (fixed) synthesizer according to the transcription estimate. By training the system to minimize the distance between the input and the output audio signals, the transcriber learns to transcribe without ground truth transcription. Our experiment shows that DrummerNet performs favorably compared to many other recent drum transcription systems, both supervised and unsupervised.
Accuracy Quantification of RELAP 5 Predictions of NPP Events

The RELAP5/MOD3.3 has been released as last frozen version, before merging with another best-estimate thermal-hydraulic system code TRAC into an integrated code. It is thus of utmost importance to assess the accuracy of models built in frozen RELAP5 code against real plant transients. The purpose of the paper is to assess the code accuracy of RELAP5 predictions of nuclear power plant (NPP) events. Two events occurred in Krško NPP originating from sudden closure of Main Steam Isolation Valve (MSIV). The third event was the main circulation pump (MCP) trip test performed at nearly full power in Mochovce NPP. For quantitative assessment the improved fast Fourier transform based method (FFTBM) was used with the capability to calculate time dependent code accuracy. The results of quantitative analysis performed by improved FFTBM showed that the plant data were reproduced very well. Larger discrepancies were observed only for a few variables (e.g. steam flows or steam generator levels) but still acceptable. Very accurate were calculations of hot and cold leg temperatures, and primary and secondary pressures. The results suggest that latest versions of RELAP5 codes could reproduce the plant events very well.
Innovation Momentum in A Higher Education Ecosystem (A Study at The University of Indonesia)

This study aims to find a causal relationship on how the ecosystem affects individual esearchers when producing innovation in a higher education institution. It is filling the gap between existing psychological-based creativity theory and ecosystem-based organization management theory. It also answers the problem of low innovation productivity in higher education institutions by managing the innovation ecosystem. The study uses a qualitative method to find the causal relationships among variables in forming a system using causal loop diagram (CLD). It also uses a quantitative method to measure the effect of the relationships using the stock and flow diagram (SFD) of system dynamics. The post-positivism paradigm with a mixed methodology was used to explain a complex relationship among variables in the innovation ecosystem. The model was built based on theories and concepts available and supported by facts and information taken from previous unpublished research conducted by Thoha and Hutapea (2017) in 5 higher education institutions in Central Java and the hypothesis of the model will be tested and simulated at the University of Indonesia. Conflicting actions and policies to fulfill business goals and government regulation can create unfavorable conditionsfor innovation momentum. Pushing excessiveresearch and innovation targets without giving enough incentives creates a negative reaction from the researchers, fails to create a good work climate and producesa reversing effect on the productivity of innovation. Therefore management must balance their policy with emotional engagement of the researchers.
How Much Does Others' Protection Matter? Employment Protection, Future Labour Market Prospects and Well-Being

Employment protection legislation (EPL) is an important determinant of workers’ perceived labour market prospects and also their subjective well-being. Recent studies indicate that it is not only a worker’s own level of protection that matters for individual prospects and well-being, but also that of others. We examine how such cross-effects on well-being are mediated by workers’ perceived risk of job loss and future employability. We apply a structural model to data from the European Quality of Life Survey and the OECD Employment Protection Database. Our results indicate that both permanent and fixed-term workers’ perceived employability is affected by EPL, positively for fixed-term workers and negatively for permanent workers. Stricter protection for permanent workers is positively related to fixed-term workers’ perceived risk of job loss. EPL has significant indirect (cross-)effects on life satisfaction via the mediators. There are no indications for direct, non-mediated effects.
Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms

We present a new framework to analyze accelerated stochastic mirror descent through the lens of continuous-time stochastic dynamic systems. It enables us to design new algorithms, and perform a unified and simple analysis of the convergence rates of these algorithms. More specifically, under this framework, we provide a Lyapunov function based analysis for the continuous-time stochastic dynamics, as well as several new discrete-time algorithms derived from the continuous-time dynamics. We show that for general convex objective functions, the derived discrete-time algorithms attain the optimal convergence rate. Empirical experiments corroborate our theory.
Mouse GTSF1 is an essential factor for secondary piRNA biogenesis

The piRNA pathway is a piRNA‐guided retrotransposon silencing system which includes processing of retrotransposon transcripts by PIWI‐piRNAs in secondary piRNA biogenesis. Although several proteins participate in the piRNA pathway, the ones crucial for the cleavage of target RNAs by PIWI‐piRNAs have not been identified. Here, we show that GTSF1, an essential factor for retrotransposon silencing in male germ cells in mice, associates with both MILI and MIWI2, mouse PIWI proteins that function in prospermatogonia. GTSF1 deficiency leads to a severe defect in the production of secondary piRNAs, which are generated from target RNAs of PIWI‐piRNAs. Furthermore, in Gtsf1 mutants, a known target RNA of PIWI‐piRNAs is left unsliced at the cleavage site, and the generation of secondary piRNAs from this transcript is defective. Our findings indicate that GTSF1 is a crucial factor for the slicing of target RNAs by PIWI‐piRNAs and thus affects secondary piRNA biogenesis in prospermatogonia.
An Efficient Deep Convolutional Laplacian Pyramid Architecture for Cs Reconstruction At Low Sampling Ratios

The compressed sensing (CS) has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction models have been proposed and obtained superior performance. However, these methods suffer from blocking artifacts or ringing effects at low sampling ratios in most cases. To address this problem, we propose a deep convolutional Laplacian Pyramid Compressed Sensing Network (LapC-SNet) for CS, which consists of a sampling sub-network and a reconstruction sub-network. In the sampling sub-network, we utilize a convolutional layer to mimic the sampling operator. In contrast to the fixed sampling matrices used in traditional CS methods, the filters used in our convolutional layer are jointly optimized with the reconstruction sub-network. In the reconstruction sub-network, two branches are designed to reconstruct multi -scale residual images and muti -scale target images progressively using a Laplacian pyramid architecture. The proposed LapCSNet not only integrates multi-scale information to achieve better performance but also reduces computational cost dramatically. Experimental results on benchmark datasets demonstrate that the proposed method is capable of reconstructing more details and sharper edges against the state-of-the-arts methods.
Anomalous behavior detection of marine vessels based on Hidden Markov Model

In recent years, marine traffic has increased dramatically. Therefore, some vessels may show abnormal behaviors which may indicate different dangers or threats, and also decreases the marine security. These abnormal behaviors are called anomalies. Automatic detection of anomalies is one of the current issues in the maritime domain. Similar works have been proposed that often used parameters speed and location to detect anomalies, and also high false alarm rate is one of the main problems of those. In this paper, a novel approach to maritime vessels anomalous behavior detection based on Hidden Markov Model with the use of parameters speed, location and course have been proposed to increase the situational awareness. Implementation results of the proposed method represent low false alarm rate and more than 96% accuracy in the classification of normal behavior from anomalous behaviors.
Sound Event Detection and Time–Frequency Segmentation from Weakly Labelled Data

Sound event detection (SED) aims to detect when and recognize what sound events happen in an audio clip. Many supervised SED algorithms rely on strongly labelled data that contains the onset and offset annotations of sound events. However, many audio tagging datasets are weakly labelled, that is, only the presence of the sound events is known, without knowing their onset and offset annotations. In this paper, we propose a time–frequency (T–F) segmentation framework trained on weakly labelled data to tackle the sound event detection and separation problem. In training, a segmentation mapping is applied on a T–F representation, such as log mel spectrogram of an audio clip to obtain T–F segmentation masks of sound events. The T–F segmentation masks can be used for separating the sound events from the background scenes in the T–F domain. Then, a classification mapping is applied on the T–F segmentation masks to estimate the presence probabilities of the sound events. We model the segmentation mapping using a convolutional neural network and the classification mapping using a global weighted rank pooling. In SED, predicted onset and offset times can be obtained from the T–F segmentation masks. As a byproduct, separated waveforms of sound events can be obtained from the T–F segmentation masks. We remixed the DCASE 2018 Task 1 acoustic scene data with the DCASE 2018 Task 2 sound events data. When mixing under 0 dB, the proposed method achieved F1 scores of 0.534, 0.398, and 0.167 in audio tagging, frame-wise SED and event-wise SED, outperforming the fully connected deep neural network baseline of 0.331, 0.237, and 0.120, respectively. In T–F segmentation, we achieved an F1 score of 0.218, where previous methods were not able to do T–F segmentation.