Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual reality and augmented reality displays: advances and future perspectives

Virtual reality (VR) and augmented reality (AR) are revolutionizing the ways we perceive and interact with various types of digital information. These near-eye displays have attracted significant attention and efforts due to their ability to reconstruct the interactions between computer-generated images and the real world. With rapid advances in optical elements, display technologies, and digital processing, some VR and AR products are emerging. In this review paper, we start with a brief development history and then define the system requirements based on visual and wearable comfort. Afterward, various VR and AR display architectures are analyzed and evaluated case by case, including some of the latest research progress and future perspectives.
A comparative study of tense and aspect categories in Arabic and English

Abstract Based on the definition of “tense” and “aspect”, this review paper intends to compare and analyze the similarities and differences between Arabic and English tense and aspect categories in terms of their forms, functions and usage. Moreover, in the current review paper, I propose comparison and analysis that account for tense and aspect categories in Arabic and English. The comparison and analysis are based on the qualitative method which depends solely on the recent and previous research works. In addition, the comparison and analysis assume that there is a great deal of differences in tense and aspect categories in Arabic and English in terms of their forms, functions and usage. Furthermore, Arabic views temporality through aspectual distinctions of perfective category versus imperfective category. On the other hand, English expresses temporality through various verbal tense and aspect categories.
An efficient chemical reaction algorithm for multi-objective combinatorial bi-level optimization

ABSTRACT The Bi-Level Optimization Problem (BLOP) is defined as a mathematical program with two nested optimization tasks. Although many applications fit the bi-level framework, however, existing resolution methods were most proposed to solve single-objective bi-level problems. Regarding Multi-objective BLOPs (MBLOPs), there do not exist too many previous studies because of the difficulties associated with solving these complex problems. Additionally, a recently proposed metaheuristic, called Non-dominated sorting Chemical Reaction Optimization (NCRO), has been successfully applied to solve single-level Multi-Objective Problems (MOPs). NCRO applies a quick-non-dominated sorting technique that makes it one of the most powerful search algorithms in solving MOPs. Based on these observations, a new Bi-level Multi-objective CRO method, called BMCRO, is proposed in this article for solving MBLOPs. The main idea behind BMCRO is to come up with good solutions in an acceptable execution time within the bi-level framework. Experimental results on well-established benchmarks reveal the outperformance of the proposed algorithm against a bi-level variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) which is developed for this purpose.
Highly Training Algorithm for Enhancement of Speech Signal Data (HTA-ESSD)

The enhancement of speech aims to maximize the quality of speech by utilizing HTA (Highly Training Algorithm). The main aim of enhancement is to maximize the intelligibility or perceptual quality of the speech signal data. We represent HTA, aimed at fast removal and very effective of background noise from the signal-channel of speech signal data based on analytically determined output-weights and randomly selected-hidden units. The feature learning with HTA may not be effective for the natural signals, even with the larger number of the hidden nodes, C-HTA (Classified Highly Training Algorithm) are employed by leveraging the sparse auto-encoders. This work is mainly to apply C-HTA and HTA to enhance the speech-signal data. The proposed HTA is evaluated on Aurora database at three SNRs. We also compare our introduced algorithm with many state-artmethods.
IN SITU Deposition of Fe-TiC Nanocomposite on Steel by Laser Cladding

The possibility of deposition of Fe–TiC nanocomposite on the surface of carbon steel substrate with the laser coating method had been investigated. Mechanical milling was used for the preparation of raw materials. The mixture of milled powders was used as a coating material on the substrate steel surface and a CO2 laser was used in continuous mode for coating. Microstructural studies were performed by scanning electron microscopy. Determinations of produced phases, crystallite size and mean strain have been done by X-ray diffraction. The hardness and wear resistance of coated samples were measured. The results showed that the in situ formation of Fe–TiC nanocomposite coating using laser method is possible. This coating has been successfully used to improve the hardness and wear resistance of the substrate so that the hardness increased by about six times. Coated iron and titanium carbide crystallite sizes were in the nanometer scale.
Novel Suspended-Line Gap Waveguide Packaged With Stacked-Mushroom EBG Structures

A novel low-loss suspended-line gap waveguide (SLGW) supporting transverse electromagnetic (TEM) propagation is presented for millimeter-wave applications for the first time. The suspended line is composed of two parallel strips connected by metal via holes. Periodic stacked-mushroom electromagnetic band gap (SM-EBG) structures are used to package the suspended line by creating a stopband for all parallel-plate modes. According to the different packaging requirements of the circuit, the SM-EBG unit cell can be composed of several stacked mushrooms and metal covers. The air gaps’ thickness between face-to-face patches of the adjacent mushrooms is near zero, confining more power in the air-filled guiding region of the SLGW. Furthermore, the SM-EBG structures with near-zero thick air gaps can provide more stable mechanical support for the multilayer structure. Therefore, SLGW has a lower loss and more robust gap stability compared with the conventional air-filled printed versions of GWs, such as printed ridge gap waveguide (PRGW) and inverted microstrip gap waveguide (IMGW). On the other hand, compared with the self-packaged double-sided interconnected strip line (DSISL), the proposed SLGW packaged with SM-EBG structures has the advantage of no electrical contact, avoiding the power leakages in the gap between plates at discontinuities and simplifying the assembly. The design and analysis of the SM-EBG structure, the design of SLGW, and the microstrip-to-SLGW transition design are given in this article. A prototype of the proposed SLGW with back-to-back transition is fabricated packaged with SM-EBG structures to prove the concept.
Realizing the Power of Text Mining and Natural Language Processing for Analyzing Patient Safety Event Narratives: The Challenges and Path Forward

Abstract Patient safety event (PSE) reports are a useful lens to understand hazards and patient safety risks in healthcare systems. However, patient safety officers and analysts in healthcare systems and safety organizations are challenged to make sense of the ever-increasing volume of PSE reports, including the free-text narratives. As a result, there is a growing emphasis on applying text mining and natural language processing (NLP) approaches to assist in the processing and understanding of these narratives. Although text mining and NLP in healthcare have advanced significantly over the past decades, the utility of the resulting models, ontologies, and algorithms to analyze PSE narratives are limited given the unique difference and challenges in content and language between PSE narratives and clinical documentation. To promote the application of text mining and NLP for PSE narratives, these unique challenges must be addressed. Improving data access, developing NLP resources to practically use contributing factor taxonomies, and developing and adopting shared specifications for interoperability will help create an infrastructure and environment that unlocks the collaborative potential between patient safety, research, and machine learning communities, in the development of reproducible and generalizable methods and models to better understand and improve patient safety and patient care.
Abrupt change in Vietnam coastal upwelling as a response to global warming

Coastal upwelling is of great social and economic significance to the human community. The Vietnam coastal upwelling is a fundamental part of upwelling systems in the South China Sea, western Pacific Ocean. However, its sedimentary response to global warming has been largely unknown. Here we reconstruct a 3200‐year sedimentary record based on high‐resolution geochemical analyses of a marine core from off the Vietnamese coast. The results reveal an abrupt sedimentary change since approximately 1950 ce. The abrupt change is explained by concurrent rapid intensification of the coastal upwelling. This can be ascribed to significant global change since the beginning of the 20th century. Global warming could enhance wind speed during the summer and strengthen the coastal upwelling. The forced upwelling results in an increase in nutrient availability and marine primary production; however, ongoing global warming may also increase the risk of more frequent algal blooms in this area.
Integrated Salmonella control in Turkey Production

In recent years, an increase of fattening turkey flocks positive for Salmonella spp. was noted in the European Union. Considering these latest trends, there is a high need to increase knowledge on specific risk factors for fattening turkey flocks in conjunction with successful control measures to combat Salmonella infections on farm. Here, we report a commercial turkey fattening farm that was found positive for Salmonella enterica serovar Typhimurium for two consecutive batches. By means of the Food Safety Program, a risk assessment developed by Elanco (Antwerpen, Belgium) and a farm walk, a Food Safety Index was generated, highlighting specific risk factors. Consequently, an action plan was set up, leading to a favorable increase in the Food Safety Index from 45% to 67%. Among others, vaccinating fattening turkeys was an important control measure. Monitoring of the Salmonella status of the batches was performed by sampling paper coming from the transport boxes at day of arrival and boot sock samples at the age of 14 weeks. Finally neck skin samples were taken at the slaughterhouse. Uptake of the vaccine was evaluated by sampling of the caeca and taking cloacal swabs 24 hours after first and second vaccination, respectively. Results coming from the analysis of the caeca indicate that vaccination at day one was efficient. Analysis of the cloacal swabs taken after second vaccination was inconclusive. Cooperatively, these actions improved the overall biosecurity of the farm as shown by the negative Salmonella status of the boot sock and neck skin samples in the third batch.
An Efficient Explicit Decoupled Group Method for Solving Two–Dimensional Fractional Burgers’ Equation and Its Convergence Analysis

In this paper, the Crank–Nicolson (CN) and rotated four-point fractional explicit decoupled group (EDG) methods are introduced to solve the two-dimensional time–fractional Burgers’ equation. The EDG method is derived by the Taylor expansion and 45° rotation of the Crank–Nicolson method around the x and y axes. The local truncation error of CN and EDG is presented. Also, the stability and convergence of the proposed methods are proved. Some numerical experiments are performed to show the efficiency of the presented methods in terms of accuracy and CPU time.
A high gain V-band power amplifier for 5G applications

This paper presents a high gain millimeter-wave (mmW) power amplifier (PA) fabricated in a 55 nm CMOS technology for V-band 5G wireless communication applications. An accurate magnetically-coupledresonator (MCR) analysis and evaluation method is proposed to capture the MCR characteristics and optimize PA gain and bandwidth. A low-lossmatching-resonator (LLMR) technology is developed to enhance power gain and efficiency. The PA achieves a peak gain of 21.1 dB at 67 GHz with a 3 dB bandwidth (BW−3dB ) of 10 GHz. At 67 GHz, the measured saturated output power (Psat ), the output 1 dB compression point (OP1dB ) and the peak PAE are 13.9 dBm, 9.7 dBm and 11.8%, respectively.
Determination of optical properties in tissue: modeling and simulation

In this paper, a Monte Carlo (MC) based model of diffuse reflectance is introduced to retrieval the light absorption and scattering coefficients of multi-layered tissue. The absorption and transport scattering coefficients of biological tissues determine the radial dependence of the diffuse reflectance that is due to a point source. The MC model of diffuse reflectance consists of a forward part and an inverse one. The forward refers to a multi-layered MC simulation at a certain source detector separation, which results a time-resolved profile. The inverse one is a fitting procedure based on Levenberg-Marquardt algorithm using forward model in iteration, and the normalized intensity in each time bin is fitted. Tests with independent MC simulations show the errors in the deduced absorption and reduced scattering coefficients. Although a high amount of computation time is needed, compared with investigation methods using diffuse approximation to the transport equation, this model is valid for a wider range of optical properties and is easily adaptable to exiting probe geometries.
Ultrasonic Characterization of Nanoparticle-Based Ceramics Fabricated by Spark-Plasma Sintering

Resonant ultrasound spectroscopy was used to determine elastic constants and internal friction parameters of bulk nanoparticle-based ceramic materials compacted by spark plasma sintering. Boron nitride-based and boron carbon nitride-based materials were studied, and the results were compared with similar bulk materials prepared from graphene nanoplatelets. The results showed that such nanoparticle-based materials can be strongly anisotropic, and can have very different elastic constants depending on the nanoparticles used. From the temperature dependence of the internal friction parameters, the activation energy for sliding of the individual monolayers along each other was determined for each material. Very similar values of the activation energy were obtained for boron nitride, boron carbon nitride, and graphene, ranging from 15 to 17 kJ/mol.
Model-Based Design of Experiments for High-Dimensional Inputs Supported by Machine-Learning Methods

Algorithms that compute locally optimal continuous designs often rely on a finite design space or on the repeated solution of difficult non-linear programs. Both approaches require extensive evaluations of the Jacobian Df of the underlying model. These evaluations are a heavy computational burden. Based on the Kiefer-Wolfowitz Equivalence Theorem, we present a novel design of experiments algorithm that computes optimal designs in a continuous design space. For this iterative algorithm, we combine an adaptive Bayes-like sampling scheme with Gaussian process regression to approximate the directional derivative of the design criterion. The approximation allows us to adaptively select new design points on which to evaluate the model. The adaptive selection of the algorithm requires significantly less evaluations of Df and reduces the runtime of the computations. We show the viability of the new algorithm on two examples from chemical engineering.
Climatology and vulnerability to climate change in the “Altos de Jalisco” region, Mexico

The State of Jalisco has high levels of vulnerability to climate change, so it is necessary to identify the areas that present the highest risk. At present, it is important to have information to design and implement measures that reduce the effects of climate change on water resources. In Jalisco, the “Altos de Jalisco” and North regions are very vulnerable as they show a current and future water deficit. The objective of this study is to expose the conditions of vulnerability of water resources in the “Altos de Jalisco”, an area of great economic importance for Jalisco and Mexico. The intention is to show the climatic conditions, the changes experienced by climate change through R-Climdex indicators and projections of future scenarios of temperature, humidity and precipitation using PRECIS, to finally expose the vulnerability of the water resource in the region. The results show conditions that coincide with the IPCC reports regarding global estimates of water availability; by the middle of this century, a 10-30% decrease in fresh water is estimated in the dry tropical areas (Mexico and Jalisco) that are already suffering from water stress. It is likely that as the century progresses, an increase in temperature and decrease in relative humidity and intensity of rainfall, and the consequent increase in the extent of areas affected by drought. Likewise, a decrease in water reserves is expected, which would reduce the availability of water in the region as we approach the end of the century.
Futuristic Trends and Innovations for Examining the Performance of Course Learning Outcomes Using the Rasch Analytical Model

The literature on engineering education research highlights the relevance of evaluating course learning outcomes (CLOs). However, generic and reliable mechanisms for evaluating CLOs remain challenges. The purpose of this project was to accurately assess the efficacy of the learning and teaching techniques through analysing the CLOs’ performance by using an advanced analytical model (i.e., the Rasch model) in the context of engineering and business education. This model produced an association pattern between the students and the overall achieved CLO performance. The sample in this project comprised students who are enrolled in some nominated engineering and business courses over one academic year at Prince Sultan University, Saudi Arabia. This sample considered several types of assessment, such as direct assessments (e.g., quizzes, assignments, projects, and examination) and indirect assessments (e.g., surveys). The current research illustrates that the Rasch model for measurement can categorise grades according to course expectations and standards in a more accurate manner, thus differentiating students by their extent of educational knowledge. The results from this project will guide the educator to track and monitor the CLOs’ performance, which is identified in every course to estimate the students’ knowledge, skills, and competence levels, which will be collected from the predefined sample by the end of each semester. The Rasch measurement model’s proposed approach can adequately assess the learning outcomes.
An Investigation of Impact of Debt Structure on the market value of Jordanian Transportation Companies

The objective of this paper is to investigate the impact of debt structure on the market value of transportationcompanies,debt structure was measured by both debt ratio and short term debt, while market value was measured by market share price. The paper conducted on the data of all the 12 transportation companies listed in Amman Stock Exchange. The study relied on multiple regression analysis to examine the impact of debt ratios on firm market value.The analysis result indicates thatmarket value is effected by debt structure significantly, both debt ratio andshort term debthad a negative impact on market value, and suggests that more debt is not always useful for market value.
COVID-19 incidence and mortality in non-dialysis chronic kidney disease patients

Many studies reported a higher risk of COVID-19 disease among patients on dialysis or with kidney transplantation, and the poor outcome of COVID-19 in these patients. Patients in conservative management for chronic kidney disease (CKD) have received attention only recently, therefore less is known about how COVID-19 affects this population. The aim of this study was to provide evidence on COVID-19 incidence and mortality in CKD patients followed up in an integrated healthcare program and in the population living in the same catchment area. The study population included CKD patients recruited in the Emilia-Romagna Prevention of Progressive Renal Insufficiency (PIRP) project, followed up in the 4 nephrology units (Ravenna, Forlì, Cesena and Rimini) of the Romagna Local Health Authority (Italy) and alive at 1.01. 2020. We estimated the incidence of COVID-19, its related mortality and the excess mortality within this PIRP cohort as of 31.07. 2020. COVID-19 incidence in CKD patients was 4.09% (193/4,716 patients), while in the general population it was 0.46% (5,195/1,125,574). The crude mortality rate among CKD patients with COVID-19 was 44.6% (86/193), compared to 4.7% (215/4,523) in CKD patients without COVID-19. The excess mortality of March-April 2020 was +69.8% than the average mortality of March-April 2015–19 in the PIRP cohort. In a cohort mostly including regularly followed up CKD patients, the incidence of COVID-19 among CKD patients was strongly related to the spread of the infection in the community, while its lethality is associated with the underlying kidney condition and comorbidities. COVID-19 related mortality was about ten times higher than that of CKD patients without COVID. For this reason, it is urgent to offer a direct protection to CKD patients by prioritizing their vaccination.
Diiodomethane as a halogen bond donor toward metal-bound halides

A 1,3,5,7,9-pentaazanona-1,3,6,8-tetraenate (PANT) chloride platinum(II) complex (1) was obtained via the metal-mediated double coupling of 2,3-diphenylmaleimidine with both nitrile ligands in trans-[PtCl2(NCtBu)2]. Compound 1 was then co-crystallized with diiodomethane forming solvate 1·½CH2I2. The XRD experiment reveals that this solvate displays the halogen bonds H2C(I)–I⋯Cl–Pt and hydrogen bonds I2C(H)–H⋯Cl–Pt, which join two complex and one CH2I2 molecules in a heterotrimeric supramolecular cluster. Inspection of the CCDC database reveals only one example of the halogen bond H2C(I)–I⋯I–Pt between the CH2I2 molecule and metal-coordinated halide in the structure of VEMWOA. In VEMWOA, CH2I2 serves solely as a halogen bond donor with no hydrogen bond contribution. Results of the Hirshfeld surface analysis and DFT calculations (M06/DZP-DKH level of theory) followed by topological analysis of the electron density distribution within the formalism of Bader's theory (QTAIM method) for both 1·½CH2I2 and VEMWOA confirmed the formation of these weak interactions. The evaluated energies of halogen bonds involving CH2I2 are in the 2.2–2.8 kcal mol−1 range.