Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing 3D-printed LED optics for optimizing target plane application efficacy

At present, solid-state light sources are more efficacious than traditional lighting technologies. To provide benefits in the target applications, this efficacy advantage at the light source has to be supplemented by the optical system used in the lighting system. In general, optical systems can be broadly classified as refractive or reflective based on the optical elements used in the lighting system. Usually, these secondary optic elements are made using injection molding (lenses) or casting and subsequent machining and polishing (reflectors) in large-scale productions. This aspect tends to reduce the use of unique or custom optical solutions in practical applications. Additive manufacturing, or 3D printing, has been successfully used to manufacture small- to medium-scale production volumes of customized solutions in other industries. This technology provides an opportunity to manufacture optical components that maximize the efficacy of a target application by creating unique optical components that facilitate the distribution of light in desired directions. In this study, an optical system based on reflective principles was designed to provide a Type V distribution on the target plane. The designed reflector system was 3D-printed and laboratory tested for total light output, intensity distribution, light output distribution, and optical efficiency. The test results were compared with Monte Carlo ray-tracing simulation results.
Heteroscedastic regression in robust econometrics

We study the effect of heteroscedastic errors on different robust regression methods. Firstly we derive asymptotic heteroscedasticity tests for the least weighted squares regression, which is one of robust regression methods with a high breakdown point. These tests have the same form as standard heteroscedasticity tests for the least squares context. Secondly the idea of downweighting less reliable observations is used to define a robustification of the instrumental variables estimator, which is a popular method in econometrics. We derive asymptotic tests of heteroscedasticity of the errors. Finally it is examined how to use the heteroscedasticity of an uknown form to obtain a more efficient robust estimator in the linear regression model.
IN SILICO PROFILING OF PROLINE BIOSYNTHESIS AND DEGRADATION RELATED GENES DURING FRUIT DEVELOPMENT OF TOMATO

Advancements in DNA sequencing technologies with decreasing costs have sparked the generation of larger gene expression datasets generated at an accelerating rate. The study aimed to visualize the spatiotemporal profiles of the tomato (Solanum lycopersicum L.) genes involved in L-proline biosynthesis and to show their potential functions. Increasing L-proline accumulation, through upregulation and downregulation of genes responsible for L-proline biosynthesis and degradation, plays an essential role in tomato plants suffering abiotic and biotic stress. Understanding the possible mechanism of L-proline biosynthesis and degradation needs an urgent study of the expression pattern and function of genes involved in these physiological processes. The study identified the genes governing the L-proline biosynthesis and degradation pathways and their expression profiles in 10 stages of tomato fruit development using the Tomato Expression Atlas (TEA) bioinformatic tool. The analysis showed that L-proline biosynthesis resulted from three pathways governed by six genes, while its degradation occurred in two pathways managed by three genes. The bioinformatics analysis also showed the expression of proline synthesis/degradation-related genes in fruit parts at various developmental stages. However, proline degradation-related genes showed higher expression levels than biosynthesis-related genes. This study sheds light on a recent bioinformatics tool, which will pave the way to detect early plant performance by analyzing the expression profiles of genes
A Semi-Supervised Ladder Network-Based Indoor Localization Using Channel State Information

We propose a ladder network-based fingerprinting (LadderNetFi) method that combines unsupervised learning with supervised learning in the neural network (NN) model for channel state information (CSI)-based indoor localization. The unsupervised part of LadderNetFi, which serves as a denoising autoencoder with skip connections from its encoder to the decoder, focuses on detailed features related to supervised learning. By dealing with the measurement uncertainty in the architecture design, a better generalization of fingerprints from the preprocessing of CSI amplitude leads to performance improvement compared to other deep-learning-based methods. As semi-supervised learning, we verify that LadderNet is trained using only a small portion of labeled measurements with the unlabeled measurements, e.g., 2000 versus 20000, and only a part of reference points (RPs), e.g., 19 versus 31, provides better performance than state-of-the-art methods using all labeled measurements. By reducing human labor from labeling, the deployment for LadderNetFi is scalable.
Some results on star-line graphs

"Let $H$ be a connected graph with at least three vertices. The $H$-line graph, $HL(G)$, of a graph $G$ has all the edges of $G$ as its vertices, two vertices of $HL(G)$ are adjacent if the corresponding edges in $G$ are adjacent and belong to a common copy of $H$. In this paper we investigate some properties of the star-line graph $K_{1,n}L(G)$ of a graph $G$. We also obtain a Krausz type characterization for star-line graphs. Traversability of star-line graphs is also studied. "
Matters of time directionality in gravitation theory

Several issues related to time directionality are discussed which are all directly or indirectly relevant to gravitation theory. Important clarifications are achieved regarding in particular the concept of negative energy and the associated notion of negative mass. Several difficulties associated with the possibility for matter to occupy negative energy states are identified and solutions to the problems they represent are suggested. An alternative interpretation of negative energy states as voids in the positive energy portion of the vacuum is developed. Generalized gravitational field equations which allow to fulfill the requirements set by the axioms derived from this analysis are provided. A revised formulation of the discrete symmetry operations based on the insights gained while addressing the problem of negative energy is then proposed which gives rise to an improved notion of time reversal. Those results are then applied to provide significant new insights into many aspects of the semi-classical theory of black hole thermodynamics and regarding several long-standing problems in classical cosmology, including the problem of the small value of the cosmological constant, the problem of the nature of dark matter, and that of structure formation. Finally, it is shown that certain difficulties currently addressed through inflation theory, including the flatness problem, the problem of matter creation, and the problem of the origin of time asymmetry can be overcome more naturally by making use of the developments introduced while solving the problem of negative energy.
The effect of spin-lattice relaxation on multiple-quantum NMR coherences in solids

A phenomenological theory of spin-lattice relaxation of multiple-quantum coherences in systems of two dipolar coupled spins at low temperatures is developed. Intensities of multiple-quantum NMR coherences depending on the spin-lattice relaxation time are obtained. It is shown that the theory is also applicable to finite spin chains when the approximation of nearest neighbour interaction is used. An application of this theory to an estimation of the influence of decoherence processes on quantum entanglement and its fluctuations is briefly discussed.
A Deep Unfolded Prior-Aided RPCA Network for Cloud Removal

Clouds, together with their shadows, usually occlude ground-cover features in optical remote sensing images. This hinders the utilization of these images for a range of applications such as earth observation, land-cover classification and urban planning. In this work, we propose a deep unfolded and prior-aided robust principal component analysis (DUPA-RPCA) network for removing clouds and recovering ground-cover information in multi-temporal satellite images. We model these cloud-contaminated images as a sum of low rank and sparse elements and then unfold an iterative RPCA algorithm that has been designed for reweighted $\ell _{1}$ minimization. As a result, the activation function in DUPA-RPCA adapts for every input at each layer of the network. Our experimental results on both Landsat and Sentinel images indicate that our method gives better accuracy and efficiency when compared with existing state of the art methods.
Modeling and Testing of Fringe-Field Capacitive Moisture Sensor Under Certain Electrode Area

Soil moisture is closely related to the occurrence frequency and the time of landslides disasters, precisely in situ measurement for soil moisture becomes quite critical. The electrodes of conventional dielectric sensor for soil moisture are fragile, large, and unsuitable for integration. Interdigital (IDT) fringing-field capacitive sensors based on printed circuit board (PCB) technology can be used for noncontact measurement and small-scale packaging. In this article, to optimize the sensitivity and penetration depth of the IDT capacitive sensor with a fixed electrode contour area, the influences of IDT fingers shape and size on the sensitivity and penetration depth are discussed in a wide range by using equivalent capacitance theory. Then, the special working cases are intuitively and accurately analyzed by using the finite element analysis (FEA) method. The results show that the sensor with more electrode fingers and smaller finger gap has high sensitivity, yet has a thinner penetration depth, and it requires a tradeoff between sensitivity and penetration depth in practical applications. In addition, the performance of regular and circular IDT sensors with the same specifications is basically identical, and the circular IDT sensor has a slightly better accuracy. Finally, the proposed IDT sensors with different electrode specifications as well as the associated signal conditioning circuit are fabricated and calibrated to verify the theoretical and simulation results. The performance of the designed sensor was compared with other commercial sensors based on the thermogravimetric method. The experimental results meet the expected requirements well, with a repeatability correlation coefficient of $R^{2} \,\,=0.992$ and a mean square error of 0.01089.
Forecasting Tourism in the EU after the COVID-19 Crisis

The COVID-19 pandemic has restricted both business and social life over the last two years. Stop-and-go policies enacted as containment measures have further impacted the global economy, and tourism in particular. Tourism demand shows only weak signs of a sustainable recovery. The medium-term outlook remains highly uncertain, and yet few studies have addressed the development of the tourism and leisure industries in the years ahead. In this context, we forecast demand in selected EU countries in terms of total expenditure on outbound travel (tourism imports) using a panel pooled Fully Modified Ordinary Least Squares (FMOLS) approach. Baseline and downside scenarios are elaborated to project demand for foreign travel until 2025.
Silver Nanoparticles Stabilized by Poly (Vinyl Pyrrolidone) with Potential Anticancer Activity towards Prostate Cancer

Tumor necrosis factor (TNF-α) and inflammatory cytokine (IL-6) play a vital role in various cellular incidents such as the proliferation and death of cells during carcinogenesis. Hence, regulation of these biomarkers could be a promising tool for controlling tumor progression using nanoformulations. Silver nanoparticles-poly (vinyl pyrrolidone) (AgNPs-PVP) were prepared using the reduction of silver nitrate and stabilized with PVP. They are characterized through yield percentage, UV-VIS, FT-IR, size, charge, and morphology. The obtained AgNPs were tested for anticancer activity against prostate cancer (PC 3) and human skin fibroblast (HFS) cell lines. Moreover, biomarker-based confirmations like TNF-α and IL-6 were estimated. The synthesized AgNPs-PVP were stable, spherical in shape, with particle sizes of 122.33 ± 17.61 nm, a polydispersity index of 0.49 ± 0.07, and a negative surface charge of −19.23 ± 0.61 mV. In vitro cytotoxicity testing showed the AgNPs-PVP exhibited antiproliferation properties in PC3 in a dose-dependent manner. In addition, when compared to control cells, AgNPs-PVP has lower TNF-α with a significant value (∗p < 0.05); the value reached 16.84 ± 0.71 pg/ml versus 20.81 ± 0.44 pg/ml, respectively. In addition, HSF cells showed a high level of reduction (∗∗∗p < 0.001) in IL-6 production. This study suggested that AgNPs-PVP could be a possible therapeutic agent for human prostate cancer and anti-IL-6 in cancerous and noncancerous cells. Further studies will be performed to investigate the effect of AgNPs-PVP in different types of cancer.
Multimodal Affective Computing With Dense Fusion Transformer for Inter- and Intra-Modality Interactions

This paper proposes a dense fusion transformer (DFT) framework to integrate textual, acoustic, and visual information for multimodal affective computing. DFT exploits a modality-shared transformer (MT) module to extract the modality-shared features by modelling unimodal, bimodal, and trimodal interactions jointly. MT constructs a series of dense fusion blocks to fuse utterance-level sequential features of the multiple modalities from the perspectives of low-level and high-level semantics. In particular, MT adopts local and global transformers to learn modality-shared representations by modelling inter- and intra-modality interactions. Furthermore, we devise a modality-specific representation (MR) module with a soft orthogonality constraint to penalize the distance between modality-specific and modality-shared representations, which are fused by a transformer to make affective predictions. Extensive experiments conducted on five public benchmark datasets show that DFT outperforms the state-of-the-art baselines.
The shape of the CMB lensing bispectrum

Lensing of the CMB generates a significant bispectrum, which should be detected by the Planck satellite at the 5-sigma level and is potentially a non-negligible source of bias for fNL estimators of local non-Gaussianity. We extend current understanding of the lensing bispectrum in several directions: (1) we perform a non-perturbative calculation of the lensing bispectrum which is ~ 10% more accurate than previous, first-order calculations; (2) we demonstrate how to incorporate the signal variance of the lensing bispectrum into estimates of its amplitude, providing a good analytical explanation for previous Monte-Carlo results; and (3) we discover the existence of a significant lensing bispectrum in polarization, due to a previously-unnoticed correlation between the lensing potential and E-polarization as large as 30% at low multipoles. We use this improved understanding of the lensing bispectra to re-evaluate Fisher-matrix predictions, both for Planck and cosmic variance limited data. We confirm that the non-negligible lensing-induced bias for estimation of local non-Gaussianity should be robustly treatable, and will only inflate fNL error bars by a few percent over predictions where lensing effects are completely ignored (but note that lensing must still be accounted for to obtain unbiased constraints). We also show that the detection significance for the lensing bispectrum itself is ultimately limited to 9 sigma by cosmic variance. The tools that we develop for non-perturbative calculation of the lensing bispectrum are directly relevant to other calculations, and we give an explicit construction of a simple non-perturbative quadratic estimator for the lensing potential and relate its cross-correlation power spectrum to the bispectrum. Our numerical codes are publicly available as part of CAMB and LensPix.
Modeling and Control of Variable Speed Drive Based Loads for Grid Primary Frequency Support

The amount of electricity generation from renewable energy resources (RES) has been increasing significantly all over the globe. However, traditional power grid management is challenged when a large amount of intermittent and unpredictable RES-based generation units are integrated into the power network. This can lead to more severe grid frequency fluctuation events. In this paper, a variable speed drive (VSD) based motor load is utilized as a frequency responsive load to support grid frequency stability. A primary frequency control scheme is proposed and applied to the VSD-based motor load, which incorporates the sophisticated rotating speed feedback controller. Additionally, the proposed frequency responsive VSD-based load is modeled and simplified. As a result, a droop-like response can be achieved with multiple VSD load units. The effectiveness of the proposed model and control scheme is evaluated by experimental studies performed in a multi-converter-based hardware testbed (HTB).
On the interpretation of new late B- and early A-type periodic variable stars in NGC 3766

We investigate possible interpretations of the new periodic B- and A-type variable stars discovered in NGC 3766. They lie in the region of the Hertzsprung-Russell diagram between slowly pulsating B and delta Sct stars, a region where no pulsation is predicted by standard models of pulsating stars. We show that the two other possible causes of periodic light curve variations, rotational modulation and binarity, cannot provide a satisfactory explanation for all the properties observed in those stars either. The question of their origin is thus currently an open issue.
Universal behavior of two-dimensional bosonic gases at Berezinskii-Kosterlitz-Thouless transitions

We study the universal critical behavior of two-dimensional (2D) lattice bosonic gases at the Berezinskii-Kosterlitz-Thouless (BKT) transition, which separates the low-temperature superfluid phase from the high-temperature normal phase. For this purpose, we perform quantum Monte Carlo simulations of the hard-core Bose-Hubbard (BH) model at zero chemical potential. We determine the critical temperature by using a matching method that relates finite-size data for the BH model with corresponding data computed in the classical $XY$ model. In this approach, the neglected scaling corrections decay as inverse powers of the lattice size $L$, and not as powers of $1/\mathrm{ln}L$, as in more standard approaches, making the estimate of the critical temperature much more reliable. Then, we consider the BH model in the presence of a trapping harmonic potential, and we verify the universality of the trap-size dependence at the BKT critical point. This issue is relevant for experiments with quasi-2D trapped cold atoms.
Nonlinear theory of fractional microwave-induced magnetoresistance oscillations in a dc-driven two-dimensional electron system

Microwave-induced nonlinear magnetoresistance in a dc-driven two-dimensional electron system is examined using a multi-photon-assisted transport scheme directly controlled by the current. It is shown that near the second subharmonic of the cyclotron resonance, the frequency of the resistivity oscillation with the magnetic-field-normalized current density is double that at the cyclotron resonance and its harmonics, in excellent agreement with recent experimental findings by Hatke et al. [Phys. Rev. Lett. 101, 246811 (2008)]. The current-induced alternative emergence of resonant two-photon and single-photon processes is responsible for this frequency doubling. Near the third subharmonic of the cyclotron resonance, the current-induced consecutive appearance of resonant 0-/3-photon, two-photon, and single-photon processes may lead to the frequency tripling of the resistivity oscillation.
International Journal of Evaluation and Research in Education (IJERE)

Received Nov 7, 2021 Revised Aug 16, 2022 Accepted Sep 8, 2022 Smartphones have become an inextricable element of life in the twenty-first century. Since there is little research on the relationship between mobile phone use and academic behavior among students in local universities, hence, the current study sought to study the relationship between these two variables in the context of Malaysian higher education. The data from 435 students who answered an online Google Form of survey sent through a WhatsApp link was collected and analyzed using a descriptive correlational research approach. The results indicated that male students displayed a higher level of mobile phone use than female students in higher education. The study also found that negative phone use had a negative relationship with academic behavior, and positive phone use contributed to positive academic behavior. Regression analysis confirmed that excessive mobile phone use and mobile addiction had a higher impact on academic behavior, however, proper mobile use has a positive impact on academic behavior among the students in higher education. The study implies that all the universities need to have better guidelines to regulate the use of mobile phone in the classroom. Overall, the findings are useful in facilitating the monitoring of the proper use of mobile phone for enhancement of learning in tertiary institutions.
Optimal number of accrual groups and accrual group sizes in longitudinal trials with discrete‐time survival endpoints

In longitudinal trials, the number of accrual groups and their sizes should carefully be chosen to ensure a desired power to detect a specified treatment effect. Methods are proposed to obtain a cost‐effective combination of the number and size of accrual groups that provides high efficiency at minimal cost. We focus on trials where an event occurs at any point in time, but it is recorded on a discrete scale. The Weibull survival function is considered for modeling the underlying time to event. By using a cost function, it is shown that the ratio of the cost of recruiting and treating subjects to the cost of measuring them and also the survival pattern highly influence the optimal combination of the number and size of accrual groups. A maximin approach is further presented to obtain robust designs with respect to poor specification of these modeling parameters. We also show the application of the proposed optimal design methodology using real examples.
A review of job satisfaction surveys in health care

The healthcare sector is one of the sectors in the EU economy with significant employment potential, which is driven by the growing demand for healthcare. Job satisfaction contributes to the improvement of the quality of service, in every profession, and it is especially important in the field of work of professionals whose service should contribute to better and more efficient health care, and therefore it has been an important research topic for a number of years. The health care sector is facing many challenges. Therefore, providing a sufficient number of well-qualified and motivated staff will be a particular problem in countries facing a marked increase in the demand for health care. It is estimated that the shortage of health workforce will reach 12.9 million health workers by 2035. The health sector of the Republic of Serbia is facing similar challenges, but it also has its own specificities, conditioned by the historical, socio-economic, cultural, and political development of the country. This paper analyses the available literature on job satisfaction, factors influencing satisfaction, and challenges for improving job satisfaction in the health sector.