Miracle-dz/UniFAR-FTUs huggingface dataset:
3-D-Printed Circularly Polarized Twisted-Ridge Horn Antenna

A novel wideband circularly polarized (CP) horn antenna with a pair of twisted ridges is investigated in this communication. The wide impedance bandwidth is obtained by adapting a pair of exponential-shaped ridges, whereas the wide axial ratio (AR) bandwidth is achieved by twisting the ridges. The impedance matching and AR can be tuned easily and independently, which is an attractive feature of the design. An $X$ -band right-hand circular polarization (RHCP) design was simulated with CST Microwave Studio. Its metal-printed prototype was fabricated to validate the simulation. The measured 15 dB impedance and 3 dB AR bandwidths are 53% (7.43–12.80 GHz) and 65% (7.63–15.00 GHz), respectively, with an overlapping bandwidth of 51% (7.63–12.80 GHz). The measured peak antenna gain is between 8.0 and 10.1 dBic in the operating band.
Characterization of Surface Waves in a Multilayer Coating—Application to Far-Field Control

Surface waves (SWs) may propagate in a material coating made up of homogeneous layers. They affect antenna performances or the far-field scattered from an object. Also, they must be accounted for in various numerical methods, such as impedance boundary conditions (IBCs), or transmission conditions used in a domain decomposition method. This work is focused on the control of the far-field radiated by SWs from a 3-D object. First, the standard problem of an infinite planar coating backed by an IBC is considered, the waves outside the coating satisfying the boundary condition at infinity. It is well known that the SWs wavenumbers are solutions of a transcendental equation that is obtained here via a very simple numerical method. Analytical results are presented for a lossless monolayer, some of which being, as far as we know, original. Then, from the known properties of the radiation operator, it is shown how these results can be used to control the far-field radiated by SWs on 3-D objects. Numerical experiments performed on a coated sphere and a cone–sphere demonstrate the pertinence of this analysis, even when small losses are present.
Comparação de Classificadores para a Estimação de Posicionamento de Cabeça

Resumo—Alvo de diversas pesquisas, a estimação de posicionamento de cabeça tem por objetivo determinar a posição de um indivíduo em uma determinada cena. Estratégias de análise de vídeo para estimação de posição de cabeça que funcionem com webcams convencionais têm um papel central em sistemas de rastreamento ocular de baixo-custo. Neste trabalho, os autores comparam três classificadores (baseados em kernel principal component analysis, KPCA; linear discriminant analysis, LDA; e support vector classification, SVC) para estimar a posição de cabeça em uma base de imagens contendo poses discretas. A extração de características de entrada dos classificadores utiliza transformada wavelet de Gabor, cujos aspectos conceituais básicos são apresentados no texto. Testes utilizando uma base de dados disponível publicamente na internet foram realizados. Os resultados mostram que os classificadores possuem acurácia superior a 70% para número total de classes não superior a 15. À medida em que o número de classes aumenta, o desempenho dos classificadores deteriora significativamente: para 93 classes, por exemplo, as acurácias medidas foram de 34%, 33% e 43% para os classificadores baseados em KPCA, LDA, e SVC, respectivamente. Palavras-chave—Estimação de pose de cabeça. Wavelet de Gabor. KPCA. LDA. SVC.
Broadly neutralizing antibodies against HCV use a CDRH3 disulfide motif to recognize an E2 glycoprotein site that can be targeted for vaccine design

Hepatitis C virus (HCV) vaccine efforts are hampered by extensive genetic diversity of HCV envelope glycoproteins E1 and E2. Structures of broadly neutralizing antibodies (bNAbs) (e.g., HEPC3, HEPC74) isolated from individuals who spontaneously cleared HCV infection facilitate immunogen design to elicit antibodies against multiple HCV variants. However, challenges in expressing HCV glycoproteins previously limited bNAb-HCV structures to complexes with truncated E2 cores. Here we describe crystal structures of full-length E2 ectodomain complexes
Arabic Sign Language Characters Recognition Based on Deep Learning Approach and a Simple Linear Classifier

One of the best ways of communication between deaf people and hearing people is based on sign language or so-called hand gestures. In the Arab society, only deaf people and specialists could deal with Arabic sign language, which makes the deaf community narrow and thus communicating with normal people difficult. In addition to that, studying the problem of Arabic sign language recognition (ArSLR) has been paid attention recently, which emphasizes the necessity of investigating other approaches for such a problem. This paper proposes a novel ArSLR scheme based on an unsupervised deep learning algorithm, a deep belief network (DBN) coupled with a direct use of tiny images, which has been used to recognize and classify Arabic alphabetical letters. The use of deep learning contributed to extracting the most important features that are sparsely represented and played an important role in simplifying the overall recognition task. In total, around 6,000 samples of the 28 Arabic alphabetic signs have been used after resizing and normalization for feature extraction. The classification process was investigated using a softmax regression and achieved an overall accuracy of 83.32%, showing high reliability of the DBN-based Arabic alphabetical character recognition model. This model also achieved a sensitivity and a specificity of 70.5% and 96.2%, respectively.
Baby on the Way, Sex Gone Away? A Dyadic Investigation of Sexual Satisfaction in Pregnancy

Baby on the Way, Sex Gone Away? A Dyadic Investigation of Sexual Satisfaction in Pregnancy David Brent Allsop School of Family Life, Brigham Young University Master of Science Research indicates that having high sexual satisfaction during pregnancy is difficult for couples. This negative trend is important considering that low sexual satisfaction can negatively affect relationship satisfaction, psychological health, and child outcomes. However, there is evidence this trend does not apply to all and different groups of couples exist in terms of their sexual satisfaction in pregnancy. The current study explored if different subsets of couples, composed of pregnant wives and husbands, exist in terms of their sexual satisfaction during pregnancy and what factors distinguish potential subsets. Using U.S. nationally representative data from 523 pregnant married couples and latent profile analysis adjusting for pregnancyrelated biological factors, two subsets of pregnant couples were identified—a larger subset of couples where wives and husbands were satisfied with sex overall (79%) and a smaller subset where wives and husbands were neutral about satisfaction with sex (21%). Having lower depressive symptoms among wives was associated with a greater likelihood of being in the more satisfied subset over the less satisfied subset—the only significant group membership predictor among a variety of biological, psychological, and relational factors, including sexual frequency. Implications include the notion that most U.S. couples do well navigating the sexual challenges in their control during pregnancy and the importance for medical professionals, practitioners, and educators to help women maintain good mental health during pregnancy to better sexual, relational, and psychological outcomes for expectant couples and improve child outcomes.
Inter-stride variability triggers gait transitions in mammals and birds

Speed-related gait transitions occur in many animals, but it remains unclear what factors trigger gait changes. While the most widely accepted function of gait transitions is that they reduce locomotor costs, there is no obvious metabolic trigger signalling animals when to switch gaits. An alternative approach suggests that gait transitions serve to reduce locomotor instability. While there is evidence supporting this in humans, similar research has not been conducted in other species. This study explores energetics and stride variability during the walk–run transition in mammals and birds. Across nine species, energy savings do not predict the occurrence of a gait transition. Instead, our findings suggest that animals trigger gait transitions to maintain high locomotor rhythmicity and reduce unstable states. Metabolic efficiency is an important benefit of gait transitions, but the reduction in dynamic instability may be the proximate trigger determining when those transitions occur.
Multivariate statistical evaluation of the chemical composition of fine particles: A case study

Monitoring and assessment of the physical and chemical constituents of airborne pollutants are now common features of the efforts of Queensland Environmental Protection Agency (EPA) to protect the public from the adverse effects of ambient air pollution. This paper describes the evaluation of the profiles of the 21 chemical constituents of 24-hourly integrated fine particle samples collected by EPA at Rocklea from 1995 to 2003. Because of the multivariate nature of the data, they were subjected to the multi-criteria decision making method, Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and Principal Component Analysis. The PROMETHEE results indicated that the quality of the fine particulate constituents at the site decreased in the order 1995 >1997 >1996 >1998 >2002 >2000 > 2003 > 2001 > 1999. The most important constituents influencing the ranking included the elemental carbon, sulfur, lead, bromine, vanadium, and chromium contents of the samples. Such multi-criteria ranking provides information that can assist the formulation and prioritization of mitigating measures. For example, the phasing out of leaded fuels contributed significantly to the lowering of Pb and Br in the samples. On the other hand, Principal Component Analysis showed that the major constituents clustered into groups with compositional profiles which suggested that the main sources of the particles were airborne soil, combustion activities (vehicular emission and vegetative burning), industrial emissions, secondary aerosols and sea salt. Correlation with other pollutants and meteorological data concurrently measured at the site provided further insights into the sources of the fine particles.
Extreme reduction in nutritional value of a key forage fish during the Pacific marine heatwave of 2014-2016

Pacific sand lance Ammodytes personatus are a key forage fish in the North Pacific for many species of salmon, groundfish, seabirds, and marine mammals and have historically been important to predators in relatively warm years. However, extreme declines in the nutritional value of sand lance in Prince William Sound, Alaska, USA, during 2012−2016 indicate that energy transfer from lower trophic levels to predators via sand lance may have been disrupted during the North Pacific marine heatwave in 2015 and 2016. Nutritional value (length, energy density, and whole-body energy) was measured in age-0 and age-1 sand lance collected during July in cool (2012−2013) and increasingly warm (2014−2016) years. The value of age-0 fish was relatively stable, with only minor differences among years for length and whole-body energy. By contrast, the value of age-1 fish significantly declined in 2015, and by 2016 they were 38% shorter and 13% lower in energy density compared to cooler years. This contributed to significant declines in whole-body energy of 44% in 2015 and 89% in 2016 compared to cooler years (2012−2014). The 2015 sand lance cohort experienced little growth or lipid accumulation from July 2015 at age-0 to July 2016 at age-1. This effective disruption of energy flow through pelagic food webs probably contributed to population declines and/or breeding failures observed among several predators in the Gulf of Alaska and suggests that tipping points were reached during the heatwave.
A Novel Strategy for Identifying Non-covalent KRas Inhibitors

By analyzing KRas and HRas X-ray structures, we developed a novel strategy that involves the design of a series of “synthetic” or “artificial” KRas mutations, namely T20I, D57E, D57F, T58A, T58V, and G60A, individually introduced into KRas with the G12C natural pathogenic mutation to create double mutants that we expect would enhance compound binding to the switch II pocket. The goal of using these mutants is to induce greater overall flexibility of the KRas structure to allow the switch II pocket (S-IIP) to open more frequently in the absence of a C12-covalently bound ligand. We developed sensitive assays for the Raf:KRas(GTP) interaction and SOS-driven GDP/GTP exchange to assess these KRas proteins, including the wild-type form, a mutant frequently found in human cancers (G12C), and the “artificial” G12C double mutants. By characterizing these KRas mutants, we hoped to identify at least one mutant that may provide enough flexibility for non-covalent binding to the switch II pocket, thus facilitating future non-covalent compound screening. The results of these assays provide preliminary support that some of the studied mutants demonstrate increased protein flexibility relative to that of KRas(G12C). This strategy of slightly increasing protein flexibility or destabilization through the introduction of selected mutations may be applied to other proteins for which low assay sensitivity is due to transient, high-energy, open-form binding sites.
Generalizing Transfer Bayesian Optimization to Source-Target Heterogeneity

Black-box optimization algorithms typically start a search from scratch, assuming little prior knowledge about the task at hand. In practice, this approach can be prohibitive for computationally expensive problems, as a large number of costly function evaluations are often needed before a suitable (near-optimal) solution is found. Under this observation, recent efforts have incorporated transfer learning capabilities into sequential model-based Bayesian optimization (BO) solvers, resulting in substantial performance speed-ups by leveraging information from related past problems. However, a common simplifying assumption in existing approaches is that the search spaces of a previously encountered source and the ongoing target task bear the same features and dimensionality, with the difference lying in their respective objective functions. In this article, we present a generalized transfer BO algorithm that relaxes the aforementioned assumption. Our method jointly transforms source features while training probabilistic transfer regression models for the target, thus applying to practical use-cases where (in addition to the difference in objective functions) the number of features could change across the source and target tasks; for example, features can be added and/or removed. The theoretical basis of our proposal is analyzed, and its empirical performance is demonstrated on synthetic benchmark functions as well as in realistic examples spanning engineering design and the automated configuration of a machine learning model. Note to Practitioners—Problems of industrial interest have a tendency of being repetitive in nature. For this reason, domain experts are always in high demand, as they are able to harness their experience of similar problems to come up with fast solutions in difficult situations. However, domain experts are not easy to find. Given this fact, the present paper puts forth a method for automating the process of knowledge extraction (through experiential learning) and transfer across problems in the domain of computationally expensive black-box optimization. The key novelty and motivation of this work lies in enabling the adaptive transfer of knowledge even when the number of features changes across the source and target problems. Our proposed approach is verified experimentally on a range of benchmarks as well as real-world problems of a computationally expensive nature, highlighting the utility of an optimization engine that is able to learn from experience without the need for constant human intervention.
Impact Assessment of Devolution Paradigm in Primary Education Sector: Policy Initiatives and Budgetary Allocations in Punjab Province

ARTICLE DETAILS ABSTRACT History: Accepted 18 October 2019 Available online 31 December 2019 The study aims to empirically testify the devolution intervention in the budgetary allocations of the primary education sector of the Punjab province. It addresses the question; whether devolution intervention has an impact on primary education policy and input indicators or not? This study is based on Content Analysis to derive a meaningful conclusion about policy interventions. The budgetary interventions are verified by Multivariate Analysis of Variance (MANOVA) and Descriptive Discriminant Analysis (DDA) to measure the impact of devolution intervention. The analysis shows that there is no mere shift in policy initiatives and budgetary allocations. However, the primary education sector is relatively better as compared to the pre-devolution period but still, a lot of interventions are required for further improvement.
Production Sourcing Strategy for an Apparel Original Brand Manufacturer in the Presence of Technology Spillover

Motivated by the practices of production sourcing in the apparel manufacturing industry, this article investigates an apparel manufacturing supply chain wherein an original brand manufacturer (OBM) may either outsource production to a competitive manufacturer (CM) or a non-CM (NCM) or set up factories to produce in-house, namely, Strategy C, Strategy NC, and Strategy N, respectively. If the OBM outsources production to the CM, technology spillover happens, and the CM is able to manufacture products with the same consumers’ preference level as the OBM does. We find that the OBM prefers Strategy N regardless of the manufacturing level and consumers’ product preference level when the investment cost is low. However, the OBM prefers Strategy C or NC when the investment cost is high. Specifically, when the manufacturing level is high, or the manufacturing level is medium, and the consumers’ product preference level is high, the OBM prefers Strategy NC; otherwise, the OBM prefers Strategy C. Furthermore, we demonstrate that employing the dual-sourcing strategy wherein the OBM outsources production to both CM and NCM simultaneously does not necessarily help OBM effectively mitigate the negative impact of technology spillover and delivery uncertainty, which depends on specific conditions.
An Integrated Modular Motor Drive With Shared Cooling for Axial Flux Motor Drives

In this article, a circumscribing polygon integrated modular motor drive topology with shared cooling for the power converter and the electrical machine is proposed and benchmarked with the nonintegrated version. The proposed topology is applied on an axial flux permanent magnet synchronous machine. The topology is capable of mechanically mounting the power converter, sufficiently cooling both the power converter and the electrical machine, and combining both of them into the same housing. The mechanical and the thermal design are done to ensure thermal decoupling between the power converter and the motor winding, and to provide a low thermal resistance from the power converter and the electrical machine to the ambient. Due to the limited space available for the power converter module, wide bandgap semiconductor technology is chosen for the implementation of the converter module, thanks to their small package size and low power losses. A highly modular, integrated, and compact drive is achieved compared to the nonintegrated one. A computational fluid dynamics (CFD) model is developed for one module of the proposed integrated drive to evaluate the maximum current that can be injected by one converter module without exceeding the junction temperature limit of the switches and the maximum winding temperature. An experimental setup is built to validate the results of the introduced multiphysics models.
Competing Indicators of Free Cash Flows and The Usefulness: Evidence from Palestine Exchange

This paper comes to examine the relative usefulness of two indicators of the free cash flows in explaining the value of corporations’ that listed on Palestine Exchange (PEX). The two competing indicators are Free Cash Flows to Firm (FCFF) and Free Cash Flows to Equity (FCFE). In addition to that, the paper aims at arranging the value relevance of the two competing variables of the free cash flows. In order to achieve the previous objectives, the study requires exploiting the accounting data of the listed corporations on the PEX during the period 2015 - 2019. Moreover, the study employs a variety of statistical procedures (descriptive statistics, Jarque-Bera test, correlation matrix, regression analysis, and Akaike info criterion for model selection). The sample of this paper consists of 18 industrial and service (9 industrial and 9 service) listed corporations on the PEX [90 firm-year]. The finding of this paper specifies that neither FCFF nor FCFE has a vital role in explaining company’s performance and its stock returns. Similarly, the findings are inconsistent with the theories of the free cash flows. This paper recommends the Palestine Exchange to regulate the obligatory disclosure about the indicators of the FCF in the annual report.
Reducing integrated circuit test cost through improvements in multisite testing and built-in self-test

The semiconductor industry is constantly growing and integrated electronics are increasingly assimilating into our daily lives. As these electronics grow in complexity, the cost of testing them has become a significant challenge to the industry. Two methods that are utilized by test engineers to reduce test costs are parallelism and Built-in Self-Test (BIST). Parallelism is implemented with MutliSite testing, but suffers from site-to-site variations. BIST reduces the tester’s string requirements but face challenges due to area limitations. In this thesis, two methods to identify site-to-site variations in MultiSite testing are developed, and an improved switch sizing for BIST solutions is presented. The two algorithms developed identify site-to-site variations in MultiSite testing by comparing each site’s distribution against an approximation of the parameter’s variation-free distribution. These algorithms produce scores which quantify how much site-to-site variation is present in each site’s data, a capability that traditional inferential statistic tests fail to provide. The two methods were compiled into a software packaged that was transferred to Texas Instruments. Further efforts were made to utilize the algorithm scores to pinpoint root causes of siteto-site variations. The tester board was simulated using two different simulations methods and various parameters were extracted. These parameters were correlated with scores from the two methods and the original volume probe data. Preliminary results were obtained that can serve as stepping stone to developing a method that automatically identifies issues with test board that cause site-to-site variations. Finally, an improved area allocation technique is presented that improves shift constancy for ADC BIST. The proposed sizing algorithm was developed after reviewing a previous ADC