Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact Solutions of the Nonlocal Nonlinear Schrödinger Equation with a Perturbation Term

Abstract Analytical solutions of both the nonlinear Schrödinger equation (NLSE) and NLSE with a perturbation term have been attained. Besides, analytical solutions of nonlocal NLSE have also been obtained. In this paper, the nonlocal NLSE with a perturbation term is discussed. By virtue of the dependent variable substitution, trilinear forms of this equation is attained. Lax pairs and Darboux transformation of this equation are obtained. Via the Darboux transformation, two kinds solutions of this equation with the different seed solutions are attained.
The use of silver conductive paint for crack propagation sensor customization

The paper presents experimental verification of customized resistive crack propagation sensors as an alternative for common SHM techniques. To manufacture new sensors, electrically conductive paint with silver microparticles is utilized to prepare tailored measuring grid on epoxy or polyurethane coating as a driving/insulating layer. That method is designed to enhance functionality and usability compared to commercially available crack gauges. By using paint with conductive metal particles, the shape of sensor measuring grid can be more easily adapted to the structure, while in the previous approach only few grid-fixed sensors are available. Fatigue tests on coupon CT specimen as well as thin aluminum plate are presented and discussed to evaluate the ability of the developed sensors to detect and monitor fatigue cracks. Two types of data acquisition system with various sensing layer interconnection is proposed, in which a series of conductive stands are connected to Wheatstone bridge or become a part of RLC band pass filter.
Critically Compressed Quantized Convolution Neural Network based High Frame Rate and Ultra-Low Delay Fruit External Defects Detection

High frame rate and ultra-low delay fruit external defects detection plays a key role in high-efficiency and high-quality oriented fruit products manufacture. However, current traditional computer vision based commercial solutions still lack capability of detecting most types of deceptive external defects. Although recent researches have discovered deep learning 's great potential towards defects detection, solutions with large general CNNs are too slow to adapt to high-speed factory pipelines. This paper proposes a critically compressed separable convolution network, and bit depth degression quantization to further transform the network for FPGA acceleration, which makes the implementation of CNN on High Frame Rate and Ultra-Low Delay Vision System possible. With minimal searched specialized structure, the critically compressed separable convolution network is able to handle external quality classification task with a minuscule number of parameters. By assigning degressive bit depth to different layers according to degressive bit depth importance, the customized quantization is able to compress our network more efficiently than traditional method. The proposed network consists 0.1% weight size of MobileNet (alpha = 0.25), while only a 1.54% drop of overall accuracy on validation set is observed. The hardware estimation shows the network classification unit is able to work at 0.672 ms delay with the resolution of 100*100 and up to 6 classification units parallelly.
An Ecological Study of a Universal Employee Depression Awareness and Stigma Reduction Intervention: “Right Direction”

Objective: Right Direction (RD) was a component of a universal employee wellness program implemented in 2014 at Kent State University (KSU) to increase employees' awareness of depression, reduce mental health stigma, and encourage help-seeking behaviors to promote mental health. We explored changes in mental health care utilization before and after implementation of RD. Methods: KSU Human Resources census and service use data were used to identify the study cohort and examine the study objectives. A pre-post design was used to explore changes in mental health utilization among KSU employees before and after RD. Three post-intervention periods were examined. A generalized linear mixed model approach was used for logistic regression analysis between each outcome of interest and intervention period, adjusted by age and sex. Logit differences were calculated for post-intervention periods compared to the pre-intervention period. Results: Compared to the pre-intervention period, the predicted proportion of employees seeking treatment for depression and anxiety increased in the first post-intervention period (OR = 2.14, 95% Confidence Interval [CI] = 1.37–3.34), then declined. Outpatient psychiatric treatment utilization increased significantly in the first two post-intervention periods (OR =1.89, 95% CI = 1.23–2.89; OR = 1.75, 95% CI = 1.11–2.76). No difference was noted in inpatient psychiatric treatment utilization across post-intervention periods. Unlike prescription for anxiolytic prescriptions, receipt of antidepressant prescriptions increased in the second (OR = 2.25, 95% CI = 1.56–3.27) and third (OR = 2.16, 95% CI = 1.46–3.20) post-intervention periods. Conclusions: Effects of RD may be realized over the long-term with follow-up enhancements such as workshops/informational sessions on mindfulness, stress management, resiliency training, and self-acceptance.
Learning-Vector-Quantization-Based Topology Sustainability for Clustered-AANETs

Aeronautical ad hoc networks (AANETs) dramatically increase the Internet access rates of aircraft by widening the coverage area thanks to the air-to-air links established. However, the mobility and atmospheric effects on AANETs increase air-to-air link breakages, leading to frequent aircraft replacement and reducing link quality. These broken air-to-air links should be transferred to other aircraft to enable the sustainability of the AANET topology. At that point, the wrong and late transfer decisions of broken links make topology sustainability challenging by reducing packet transfer success and increasing end-to-end latency, respectively. Despite these challenges, to the best of our knowledge, air-to-air link transfers between aircraft in AANETs have not been investigated by any study to enable the topology sustainability. This article proposes to utilize learning vector quantization in three phases - winning cluster selection, intra-clus-ter link determination, and attribute update - to enable the sustainability of AANET topology, which is in the form of aircraft clusters. In winning cluster selection, we consider each cluster in the topology as a pattern. Then we aim to find the best matching cluster of an aircraft observing air-to-air link breakage through pattern classification. Then we take airplanes in a cluster pattern as weight vectors with location and queuing delay attributes to determine the intra-cluster links of newly assigned aircraft. Here, the aircraft is modeled according to a G/G/1 queuing system for delay attribute calculations. Finally, according to the freshly established intra-cluster links, we update weight vectors' attributes using the throughput rate as the learning rate. We simulate the proposed system by utilizing OMNET++ and Weka tools with realistic air traffic data obtained from flight radar databases. In these simulations, we can reduce the end-to-end latency of AANET topology by 25 percent and increase the packet transfer rate by 31 percent compared to the methodologies in the literature.
Real-World Treatment Patterns and Outcomes of Growth Hormone Treatment Among Children in Israel Over the Past Decade (2004–2015)

Objective: To assess a decade of growth hormone (GH) treatment patterns and outcomes in a real-world setting in Israel using a state-of-the-art computerized database. Methods: This large retrospective database study included 2,379 children initiating GH treatment in Maccabi Healthcare Services (between January 2004 and December 2014). Good adherence with therapy (proportion of days covered >80%) was assessed during follow-up. Results: At GH treatment initiation: 62.1% were boys; height standard deviation score (SDS) was −2.36 ± 0.65 (mean ± SD); age was 9.8 ± 3.1 years; and time from short stature diagnosis to first GH purchase was 4.8 ± 3.3 years. Mean treatment period was 3.5 ± 0.95 years; 79.4% of children were treated for more than 3 years. The two main indications for GH therapy were idiopathic short stature (ISS) (n = 1,615, 67.9%) and GH deficiency (GHD) (n = 611, 25.7%). Children in the highest socio-economic-status (SES) tertile comprised 61.3% of ISS and 59.7% of GHD. After 3 years, mean height gain SDS was 1.09 ± 0.91 for GHD and 0.96 ± 0.57 for ISS (p = 0.0004). Adult height (age 15 for girls and 17 for boys) was recorded for 624 patients (26.2%) with better outcomes for GHD than ISS (−1.0±0.82 vs. −1.28±0.93, respectively; p = 0.0002). Good adherence was achieved in 78.2% of the cohort during the first year and declined thereafter to 68.1% during the third year of the treatment. Conclusions: Children who initiate GH therapy are predominantly male, belong mainly to the upper SES, commence treatment a long period after initial recognition of short stature, and have suboptimal adherence. Appropriate referral, diagnosis, and follow-up care may result in better treatment outcomes with GH therapy.
Vanadate post-treatments of anodised aluminium and AA 2024 T3 alloy for corrosion protection

ABSTRACT Porous anodic films formed on aluminium in sulphuric acid were post-treated using various double-dip processes that incorporate a sparingly-soluble, vanadate, corrosion inhibitor. The processes were optimised by investigating the effects of the double-dip order, the rinsing step, the immersion time and the double-dip method. The optimised processes were applied to AA 2024-T3 alloy, which was then exposed to salt spray. V-Ce(III) and V-Zn processes provided corrosion protection similar to that of sealed, sulphuric acid-formed films. A V-Ni process provided increased protection, similar to that of hot water sealed films formed in chromic acid.
Neural Network-Based Intuitive Physics for Non-Inertial Reference Frames

Classical mechanics offers us reliable means to predict various physical quantities, but it is difficult to derive the precise dynamic equations underlying most phenomena and obtain physical quantities in real-world situations. Intuitive physics, the ability to intuitively understand and predict physical phenomena, prevents this complication. However, its applications are confined to the inertial frame of reference. Here, we explored the potentials of neural network-based intuitive physics for solving non-inertial reference frames. We designed three experiments, each of which represents different types of real-world challenges. The task required predicting the speed of an object while the observer accelerates. We demonstrated that multilayer perceptron, invariant methods, and long-term memory networks successfully learn underlying dynamics from observations. This implies that neural network-based intuitive physics provides alternative means to predict various quantities in real-world applications that are unsolvable by classical physics methods.
How Beamforming Can Solve The Brain Source Imaging Problem?

Localizing and reconstructing the time course of spatially distributed sources from electroencephalographic recordings is a challenging task in beamforming. Indeed, the location of the dipoles constituting a given spatially distributed source must be known in advance to create a filter that will not cancel them out. In this paper, we present a new scheme named SABLE which uses Sparsity And Beamforming for brain source Localisation and Estimation without requiring such a prior. SABLE shows how the two beamforming steps (i.e. filtering and localization) can be merged and extended to deal with spatially distributed sources by modeling the hyper synchronous nature of their activity. Numerical experiments show the good behaviour of the SABLE technique in the context of epilepsy.
Performance Improvement of MoS₂ Gas Sensor at Room Temperature

The poor response time and incomplete recovery of MoS2 sensor at room temperature (RT) have been a serious issue to obstruct its widely sensing applications. In present work, we comprehensively studied three methods: rising temperature, building field-effect transistors (FETs), and adding light illumination to improve the performance of the device. The related sensing mechanism of the three processes was also analyzed. The high response rate up to 2510%, low theoretical concentration detection limit down to 693 ppb, and excellent recovery at RT to NO2 were achieved for the MoS2 FET sensor with the aid of light illumination. This significant improvement in sensing performances is mainly attributed to the activated sensing layer by phonon energy transfer and enhanced population of the charge carrier raised from optical energy. Schottky barrier modulation was also an important factor to impact sensing performance. Furthermore, the MoS2 nanosheets exhibit good photosensing characteristics such as rapid photon response, high light-induced photocurrent, and so on. It has been demonstrated that the light illuminated FET configuration can effectively accelerate response time and promote recovery rate of MoS2-based sensor operating at RT, which provides a strategy to design an optoelectronic device with optimal sensing properties.
RPNDroid: Android Malware Detection using Ranked Permissions and Network Traffic

The number of malware attacks on the Android platform has escalated over the past few years. The seriousness of these attacks can be depicted from the fact that around 25 million Android smartphones were infected with malware within the first half of 2019. Keeping these threats in mind, we aim to develop a hybrid Android malware detector based on ranked permissions and network traffic features. In this work, first, we find the permissions that are frequently present in normal and malicious apps and rank the permissions based upon their frequency in normal and malware dataset. Additionally, we applied different support thresholds to remove the unnecessary and redundant permissions from the rankings. Further, we merge the ranked permissions with the network traffic features to form a hybrid vector for all the apps in the dataset. Finally, we propose a novel algorithm that applies machine learning algorithms on the hybrid vectors consisting of permissions and traffic features to detect Android malware. The experimental results demonstrate that by using the hybrid approach, we could achieve 95.96% detection accuracy with the proposed algorithm.
Deciphering asymmetric charge transfer at transition metal dichalcogenide–graphene interface by helicity-resolved ultrafast spectroscopy

Helicity-resolved ultrafast spectroscopy reveals complex charge/spin dynamics in TMD/graphene heterostructures. Transition metal dichalcogenide (TMD)/graphene (Gr) heterostructures constitute a key component for two-dimensional devices. The operation of TMD/Gr devices relies on interfacial charge/energy transfer processes, which remains unclear and challenging to unravel. Fortunately, the coupled spin and valley index in TMDs adds a new degree of freedom to the charges and, thus, another dimension to spectroscopy. Here, by helicity-resolved ultrafast spectroscopy, we find that photoexcitation in TMDs transfers to graphene by asynchronous charge transfer, with one type of charge transferring in the order of femtoseconds and the other in picoseconds. The rate correlates well with energy offset between TMD and graphene, regardless of compositions and charge species. Spin-polarized hole injection or long-lived polarized hole can be achieved with deliberately designed heterostructures. This study shows helicity-resolved ultrafast spectroscopy as a powerful and facile approach to reveal the fundamental and complex charge/spin dynamics in TMD-based heterostructures, paving the way toward valleytronic and optoelectronic applications.
Adoption of Management Accounting Tools in Egypt

This research tries to discover the status of management accounting practice in the Egyptian business environment, through exploring the usage of the traditional and contemporary management accounting tools in the manufacturing companies of the public business sector. Findings show that traditional management accounting tools are still dominant in the Egyptian firms, although the adoption of contemporary management accounting tools is considered acceptable compared with other studies conducted in some developing countries. صلختسم : فاشتكا ثحبلا اذه لواحي عضو سرامم تا نم ،ةيرصملا لامعلأا ةئيب يف ةيرادلإا ةبساحملا مادختسا فاشكتسا للاخ لأا تاود ل ةيديلقتلا ةيرادلإا ةبساحمل تاودلأاو ةرصاعملا يف كلذو تاكرشلا ةعباتلا ةيعانصلا . ماعلا لامعلأا عاطقل تاكرشلا يف ةدئاس لا زت لا ةيديلقتلا ةيرادلإا ةبساحملا تاودأ نأ جئاتنلا رهظت ،ةيرصملا ىلع يتلا ىرخلأا تاسا ردلاب ةنراقم لًاوبقم ربتعي ةرصاعملا ةيرادلإا ةبساحملا تاودأ دامتعا نأ نم مغرلا . ةيمانلا نادلبلا ضعب يف تيرجأ Hesham Ahmed Mahmoud Yousef Ph.D. Student at Cairo University Assistant Lecturer at Sohag University Hesham.yousef@commerce.sohag. edu.eg Prof. Dr. Hanafi Zaki Professor of Cost and Management Accounting Cairo University, Egypt Adoption of Management Accounting Tools in Egypt Hesham Yousef 61 ةيراجتلاو ةيلاملا تاسا ردلا ةلجم ا ددعلا لولأ 2021
Grounded Theory Analysis of Work-based TVET and Intersectional Challenges Between Construction Workers

In this study, I examine training in the work-based part of Norwegian technical vocational education and training (TVET). The TVET model includes two years in school followed by two years of apprenticeship at an authorized training enterprise. The empirical findings are based on one year of fieldwork combined with interviews, while following communities of plumbers and apprentices on construction sites and at a training agency. The article describes how work tasks and training on the construction site are continued and elaborated at the training agency, where technical theory and drawing are intertwined in practical plumbing. Over the past two decades, Norway has welcomed a significant number of eastern European migrant workers. Construction is among the industries most affected. This study explores how plumbers negotiate work and training at the intersection with dominant groups of foreign construction workers.
AUDIT COMMITTEE ATTRIBUTES AND REAL EARNINGS MANAGEMENT IN NIGERIA

This study extends existing research on managers manipulative behaviour of the accounting earnings through real earnings management by examining the impacts of audit committee attributes in Nigeria. Quantitative analyses were implemented involving a sample of 72 non-financial firms with 360 firm-year observations for a five-year period (2014-2018). Data was obtained from the annual reports of these companies as well as from Thompson Reuters and Bloomberg databases. The Panel Corrected Standard Error was used to test the model studied. The finding shows that audit committee size prevents managers’ activities in earnings manipulations. Also, the result establishes that the audit committee independence presence on the audit committee control managers’ opportunistic behaviour while audit committee financial expertise was monitors in curtailing earnings manipulation practice. The findings shall give insight to financial analysts, investors, and regulators on the importance of AC in enhancing the quality of the financial report, also show the role of the audit committee characteristics to deter real earnings manipulations.
Microbiological and genetic characteristics of Bacillus velezensis bacillibactinproducing strains and their effect on the sulfate-reducing bacteria biofilms on the poly(ethylene terephthalate) surface

It was evaluated the antibiofilm-forming properties of NUChC C1 and NUChC C2b isolates (from the collection of the Department of Biology of the T.H. Shevchenko National University “Chernihiv Colehium”) against the sulfate-reducing bacteria biofilms on the poly(ethylene terephthalate) surface. NUChC C1 and NUChC C2b isolates were isolated by classical microbiology methods on Postgate’s “B” medium and their cultural-morphological, some physiological-biochemical properties and molecular-genetic characteristics were investigated. To identify bacteria the sequencing and analysis of the 16S rRNA gene were carried out. The bacteria were identified as Bacillus velezensis. Based on PCR-ISSR analysis, it was found that the studied bacteria belong to different strains. The 16S rRNA gene sequences were submitted in GenBank as MN508954.1 (NUChC C1), MN749356.1 and MN749357.1 (NUChC C2b). In the genome of B. velezensis the presence and transcriptional activity of the genes for the synthesis of bacillibactin (dhbC, dhbF), fengycin (fenA) and polyglutamic acid (epsK) were studied. Among these only genes belonging to bacillibactin synthesis operon were detected and only they demonstrated activity. The observed mode of dhbC and dhbF genes expression during 144 hours of cultivation differed between two B. velezensis strains: gradually increasing in NUChC C1 and sharply increased after 24 hours with decreasing on 144th hour in NUChC C2b. Antagonistic properties of the studied strains of B. velezensis against sulfate-reducing bacteria Desulfovibrio oryzae NUChC SRB1 and NUChC SRB2 were not observed. Siderophore-producing strains of Bacillus velezensis inhibit the formation of bacterial biofilms on the polymeric material poly(ethylene terephthalate) during its long-term exposure (50 days) in a culture of sulfate-reducing bacteria under conditions of sufficient iron supply. Bacillibactin-producing strains prevent the development of bacterial biofilms on the poly(ethylene terephthalate) surface. This is one of the reasons for the prolongation of the process of poly(ethylene terephthalate) biodegradation in natural ecosystems.
edmcr - Euclidean Distance Matrix Completion in R

Through motivating examples, we introduce the Euclidean distance matrix completion problem, and explore its use in various ﬁelds in the literature. We then introduce edmc , a function available in the R package edmcr , which solves the Euclidean distance matrix completion problem, and the related sensor network localization problem, using a variety of algorithms. Workingthrough examples using both real and simulated data, we show how each of the algorithms implemented in edmc can be used to eﬀectively complete a Euclidean distance matrix, the pros and cons of each algorithm, and their use in various types of completion problems. We end with a brief introduction to the theoretical background of the algorithms, outlining some of the more technical details implemented in each.
Effect of Social Media on Knowledge Work

 Abstract — This paper examines the impact of social media on knowledge work. It discloses and highlights which specific aspects, areas and tasks of knowledge work can be improved by the use of social media. Moreover, the study includes a survey about higher education students’ viewpoints in regard to the use of social media as a means to enhance knowledge work and knowledge sharing. The analysis has been conducted based both on empirical data and on discussions about the sources dealing with knowledge work and how it can be enhanced by using social media. The results show that social media can improve knowledge work, knowledge building and maintenance tasks in which communication, information sharing and collaboration play a vital role. Additionally, by using social media, personal, collaborative and supplementary work activities can be enhanced. Based on the results of the study, we suggest how knowledge work can be enhanced when using the contemporary information and communications technologies (ICTs) of the 21 st century and recommend future directions towards improving knowledge work.