Miracle-dz/UniFAR-FTUs huggingface dataset:
Modal strain-based damage identification of two prestressed concrete beams under different prestressing conditions

Vibration-based Structural Health Monitoring is used for monitoring civil infrastructure as it constitutes a non-destructive condition assessment method. In VBSHM the damage identification is based on identifying damage-related changes in the dynamic characteristics of a structure. However, the commonly employed dynamic characteristics, natural frequencies and mode shapes, are relatively insensitive to local damage of moderate severity. Furthermore, the sensitivity of natural frequencies to temperature fluctuations, may be sufficiently high to completely mask the effect of moderate damage. Modal strains and curvatures are more sensitive to local damage, but the direct monitoring of these quantities is challenging due to the very small strain levels occurring during ambient, or operational excitation. Additionally, the influence of the environmental factors on modal strains is not well-known. In this context, two prestressed concrete beams of different prestressing force are subjected to progressive damage tests to investigate the influence of damage on modal strains for different prestressing force. A methodology that allows for accurate dynamic strain measurements of the sub-microstrain level is employed. Dynamic tests are conducted at the beginning of each loading cycle. The response of the beam is recorded with four chains of multiplexed FBG strain sensors. Subsequently, the dynamic characteristics are obtained with subspace identification. The influence of damage on the modal strains of the two beams is investigated by comparing their values for different loading steps.
Locus of control as a predictor of academic attitudes among secondary school science teacher trainees at a public university in Uganda

(Authors are arranged in order of contribution to the paper.) A host of factors often determines academic attitudes among learners. This quantitative study using cross-sectional survey design was conducted to establish the prediction effect of locus of control on academic attitudes among secondary school science teacher trainees at a public university in Uganda. Data were collected from a census of 203 students using structured questionnaires comprising a biodata section, Locus of Control Scale and Academic Attitudes Scale. Analysis was done using SPSS, and information was presented in tables and later described. Results showed that the trainees used mainly external locus of control and had generally moderately negative academic attitudes. Locus of control significantly predicted academic attitudes (t = -2.595, p = .010). Further analysis revealed that there was a statistically significant difference in academic attitudes between students originating from rural areas and those from urban areas. It was concluded that the use of external locus of control shifted responsibility of academic issues from the students to other external causes which likely resulted in counterproductive view the science and hence negative academic attitudes among the students.It is recommended that trainees need to be educated before and during pre-service training to appreciate teaching as a noble profession, and that trainees with the right disposition should be selected at entry to preservice training so as to foster the needed changes in science and technology education for national socioeconomic transformation.
Estimates of heritability and correlations of morphometric traits in Clarkia (Onagraceae)

Interspecific heritability values were estimated using parent-offspring regression analyses for 11 morphological traits differentiating Clarkia nitens and C. speciosa subsp, polyantha. Estimates ranged from near 0 for anther color and germination percentage, to 0.8 for calyx length and petal tip color. Phenotypic, genetic, and environmental correlation matrices were computed to determine the extent of interspecific correlations of traits. Cluster analyses of the genetic and environmental correlation matrices each resulted in three clusters of correlated traits; however, the clusters derived from the two matrices were different. The clusters produced by analysis of the environmental correlation matrix were similar to the factors obtained from principal component analysis of the phenotypic correlation matrix. Genetic correlations may result from strong linkage due to interspecific chromosomal differences.
Anchor & Transform: Learning Sparse Representations of Discrete Objects

Learning continuous representations of discrete objects such as text, users, and URLs lies at the heart of many applications including language and user modeling. When using discrete objects as input to neural networks, we often ignore the underlying structures (e.g. natural groupings and similarities) and embed the objects independently into individual vectors. As a result, existing methods do not scale to large vocabulary sizes. In this paper, we design a Bayesian nonparametric prior for embeddings that encourages sparsity and leverages natural groupings among objects. We derive an approximate inference algorithm based on Small Variance Asymptotics which yields a simple and natural algorithm for learning a small set of anchor embeddings and a sparse transformation matrix. We call our method Anchor & Transform (ANT) as the embeddings of discrete objects are a sparse linear combination of the anchors, weighted according to the transformation matrix. ANT is scalable, flexible, end-to-end trainable, and allows the user to incorporate domain knowledge about object relationships. On text classification and language modeling benchmarks, ANT demonstrates stronger performance with fewer parameters as compared to existing compression baselines.
Micro Gas Cyclone Design and Performance Evaluation to Collect Dust Particles

The objective of this study is to develop a particle collector by using a micro gas cyclone with a body diameter of 10 mm. Design and performance of a cyclone separator is determined by collection efficiency and pressure drop. The behavior of a rectangular inlet-type cyclone for relatively high solid loading rates is reported in this study. For different solid loading flow rates varying from 11.7 to 16.7 liter per min, pressure drop and collection coefficient were evaluated through simulation and experimental work. The experimental part of this study was conducted using Snifter MA+ device. Since the Snifter MA+ is designed to handle flows which are approximated by laminar conditions and the flow regime within the cyclone is mostly turbulent, in order to achieve a suitable performance, the flow in the cyclone should be controlled to be close to laminar conditions. Given these circumstances, the highest cyclone efficiency was recorded for the lowest flow rate and in turbulent condition. The obtained efficiency shows that the cyclone has a reliable performance. As flow rate decreases, pressure drop reduces as well. In order to investigate the pressure drop behavior, velocity and flow trajectories for different flow rates, SOLIDWORKS software, which is applied for drawing and also for simulation was utilized to simulate the model. The simulation results show that by increasing the flow rate, pressure drop also increases.
Bounded Rationality in Service Networks with Open Routing

Self-interested customers populate various service systems. While self-interested, these customers may not be fully rational. We investigate the implications of boundedly rational customers on service networks where customers must visit multiple stations but can choose the order in which to visit the stations. We model customer strategy using anecdotal reasoning—that is, customers make decisions based on samples of system times experienced by customers who previously visited the system and followed each possible route. Using a fluid model, we fully characterize the evolution of customer routing decisions, with customers making decisions in each period based on anecdotal samples from the previous period. We completely specify the set of equilibrium routing profiles, where the fraction of customers choosing each route becomes stationary. In contrast with extant models of anecdotal reasoning, we find that anecdotally reasoning customers sometimes behave differently from fully rational customers, but not always. Equilibria can emerge under anecdotal reasoning that differ from fully rational equilibria, in which case, system performance suffers. Finally, we conduct extensive numerical tests, which demonstrate that our insights carry over to discrete systems that serve moderately many customers. If a service provider cannot control the service rates, then anecdotal reasoning can lead to waiting times that are much longer than necessary. However, although equilibria under customer anecdotal reasoning can differ significantly from those with fully rational customers, a service provider who controls the service rates would make the same decision under either form of customer reasoning, in both cases leading to a socially beneficial outcome.
A Lagrangian Method for Inverse Problems in Reinforcement Learning

We cast inverse problems in reinforcement learning as nonlinear equalityconstrained programs and propose a new game-theoretic solution method. Our approach is based on the saddle-point problem arising in the Lagrangian formulation and applies more broadly to other problems involving equilibrium constraints. As opposed to implicit differentiation, our Lagrangian method need not solve a fixed-point problem at every step. We demonstrate our approach in the context of imitation learning and in a new problem which we call the Optimal Model Design Problem: that of finding a Markov Decision Process model leading to policies which also perform well once evaluated under the true MDP. We show experiments in both discrete MDPs and under the continuous LQR setting.
Relating microstructural characteristics of fired clay to macroscopic material properties: a multiscale approach

Pore-forming additives and temper have been used to improve the mechanical and thermal properties of fired clay since the very beginning of its fabrication [9], depending on its further use in pottery or as a building material. Nowadays, such pore-forming additives are used in brick production to reduce the bulk density of fired clay, thereby reducing the effective thermal conductivity [5] in order to minimize corresponding heat losses through building façades. However, material models able to describe the effect of such additives on the overall material behaviour with respect to thermal conductivity, stiffness and strength have not yet been in the scientific focus. This motivated the development of physically sound material models to improve present optimization concepts, which essentially rely on phenomenological observations until now. Such material models, translating microstructural characteristics (such as porous space, morphology and the elastic and thermal properties of the constituent phases on a microscale) into corresponding material properties, are developed in the framework of continuum micromechanics or random homogenization theory [6, 7, 11], and were already successfully established for different materials, such as concrete [3, 10], wood [2, 8] or bone [1, 4]. A huge set of identification experiments to characterise the microstucture of the considered material is therefore needed, such as scanning electron microscopy coupled with energy dispersive X-rays and X-ray powder diffraction to identify the material phases and their morphology, μ-CT and mercury intrusion porosimetry to define the porous space, as well as nanoindentation and scanning thermal microscopy to determine elastic and thermal properties of the governing material phases. With the derived information on the microstructural characteristics, a multiscale material model was developed, and results from the model were consequently compared to macroscopic measurements on a set of 12 different fired clays, allowing for a validation of the proposed model. The validated model is assumed to contribute to a comprehensive understanding of how microstructural characteristics affect the overall material behaviour, representing the basis for complete new optimizations stragies for fired clay products.
BASAL ENERGY EXPENDITURE IN MEN LIVING IN GOIÂNIA

1. Nutrition – Goiânia, GO, Brazil. 2. Stricto Sensu Graduation Program in Health Sciences of the Federal University of Rio Grande do Norte – Natal, RN, Brazil. 3. State University of Rio de Janeiro – Rio de Janeiro, RJ, Brazil. 4. Stricto Sensu Graduation Program in Nursing and Biosciences of the Federal University of Rio de Janeiro State Rio de Janeiro, RJ, Brazil 5. Federal University of Rio de Janeiro – Rio de Janeiro, RJ, Brazil.
Crime Detection Using Text Recognition and Face Recognition

With rapid growth and development in big towns and cities, the crime is also increasing at an alarming rate. Various cases of theft, stealing crimes, burglary, kidnapping, human trafficking etc. are left unsolved because the vehicle involved could not be identified accurately as it is not feasible for human eye to verify characters from license plate of fast moving vehicles. Many a times there is no identification of the person who was involved in criminal activities. To avoid such situation, we have proposed an effective crime detection system using Text and Face recognition technique. Such systems will be very effective at toll tax collection, parking system, airports, and Border crossings. Text Recognition can be used to identify number plate and face recognition can be used for criminal identification. The efficiency of the system depends on the quality, illumination and view of the image captured. This paper aims to provide better result both in terms of speed and accuracy than conventional methods in use relating to the text and face recognition process for criminal identification.
Original Contribution VARIATION AS A MAIN FEATURE OF NORM

The past half-century has witnessed remarkable growth in the study of language variation, and it has now become a highly productive subfield of research in sociolinguistics. Variability is everywhere in language, from the unique details in each production of a sound or sign to the auditory or visual processing of the linguistic signal. All languages that we can observe today show variation; what is more, they vary in identical ways, namely geographically and socially. It’s no secret that languages like English are full of variation. So, the aim of the article is to detect the reasons of variation and to uncover rates of usage of different free variations for a given set of lexical items. The research work is carried out by using the descriptive, comparative methods by subjecting to analysis the specific language materials. The discovery of law of variation became a starting point for the evolution of linguistics. The problem of search of variation facts and its role in the functioning of language system concerns many specialists from the outset. The scope of the investigation was to set up a system out of chaos of phenomena. Currently, the fact of conditionality of variation by system relations existing in the language is considered to be established.
Bayesian Networks for the Analysis of Subjective Well-Being

We use Bayesian Networks to model the influence of diverse socio-economic factors on subjective well-being and their interrelations. The classical statistical analysis aims at finding significant explanatory variables, while Bayesian Networks can also help sociologists to explain and visualize the problem in its complexity. Using Bayesian Networks the sociologists may get a deeper insight into the interplay of all measured factors and their influence on the variable of a special interest. In the paper we present several Bayesian Network models – each being optimal from a different perspective. We show how important it is to pay a special attention to a local structure of conditional probability tables. Finally, we present results of an experimental evaluation of the suggested approaches based on real data from a large international survey. We believe that the suggested approach is well applicable to other sociological problems and that Bayesian Networks represent a new valuable tool for sociological research.
CORRELATION BETWEEN CORTISOL; GLUCOSE LEVELS, INSULIN RESISTANCE AND BODY MASS INDEX IN OVERT HYPOTHYROID NON-DIABETIC MEN PATIENTS

SUMMARY The present study was conducted on 24 hypothyroid non -diabetic men patients group aged 20-60 years and 24 control group aged 20-56 years in Baghdad province at the specialist center of Endocrine and Diabetic diseases to evaluate the relationship between cortisol, insulin, glucose, glycated haemoglobin, insulin resistance, body mass index (BMI) and their correlations among non–diabetic overt hypothyroid men patients. The results in hypothyroid patients group showed highly significant (P<0.01) increase in the level of TSH(43.74 ± 3.61mµ/L);while T4 level (48.59±0.98 nmol/L) decrease significantly(P<0.01). T3 nmol/l levels (1.31 ± 0.14) in hypothyroid group were not significant (p≥0.05) in comparsion with control group(1.52 ±0.083). Cortisol (274.08± 9.37ng/ml); insulin(15.13±1.14 µU/ml) increased highly significant in patients group. Fasting blood glucose (F.B.G) and HbA1C% had shown non differences. Insulin resistance and BMI increased highly significant in patients group. The results in patients group showed the following correlations; cortisol had positive correlation (0.65)with TSH and negative correlation(-0.32) with T4.Insulin resistance showed positive correlation with FBG(0.32) and BMI(0.33). BMI showed positive(0.39)correlation with increasing TSH level and negative correlation with T4(-0.36)and no correlation with T3. We concluded from our present study that such an increase in level of cortisol in the non-diabetic overt hypothyroid men patients with increasing TSH levels had contribute to elevate insulin level with increasing insulin resistance that Insulin resistance associated with distribution of body fat subsequently leading to an increase of body mass index(BMI) among hypothyroid patients through most the positive correlations.
miR-26a is Involved in Glycometabolism and Affects Boar Sperm Viability by Targeting PDHX

miR-26a is associated with sperm metabolism and can affect sperm motility and apoptosis. However, how miR-26a affects sperm motility remains largely unknown. Our previous study indicated that the PDHX gene is predicted to be a potential target of miR-26a, which is responsible for pyruvate oxidative decarboxylation which is considered as a key step for connecting glycolysis with oxidative phosphorylation. In this study, we first reported a potential relationship between miR-26a and PDHX and their expressions in fresh, frozen-thawed, and epididymal boar sperm. Then, sperm viability and survival were determined after transfection of miR-26a. mRNA and protein expression level of PDHX in the liquid-preserved boar sperm after transfection were also determined by RT-qPCR and Western Blot (WB). Our results showed that expression level of PDHX was significantly increased during sperm transit from epididymal caput to corpus and cauda. Similarly, expression of PDHX was significantly higher (P < 0.05) in fresh sperm as compared to epididymal cauda and frozen-thawed sperm. However, the expression of miR-26a in epididymal corpus sperm was significantly higher (P < 0.05) than that of caput and cauda sperm. Furthermore, after transfection of boar sperm with miR-26a mimic and inhibitor under liquid storage, the lowest and highest sperm viability was observed in miR-26a mimic and inhibitor treatment (P < 0.05), respectively. The protein levels of PDHX, after 24 and 48 h of transfection of miR-26a mimics and inhibitor, were notably decreased and increased (P < 0.05), respectively, as compared to negative control (NC) group. In conclusion, the novel and enticing findings of our study provide a reasonable evidence that miR-26a via PDHX, a link between glycolysis and oxidative phosphorylation, could regulate the glycometabolic pathway which eventually affect boar sperm viability and survival.
Transient Expression of Dengue Virus NS1 Antigen in Nicotiana benthamiana for Use as a Diagnostic Antigen

Dengue is a viral disease that represents a significant threat to global public health since billions of people are now at risk of infection by this mosquito-borne virus. The implementation of extensive screening tests is indispensable to control this disease, and the Dengue virus non-structural protein 1 (NS1) is a promising antigen for the serological diagnosis of dengue fever. Plant-based systems can be a safe and cost-effective alternative for the production of dengue virus antigens. In this work, two strategies to produce the dengue NS1 protein in Nicotiana benthamiana leaves were evaluated: Targeting NS1 to five different subcellular compartments to assess the best subcellular organelle for the expression and accumulation of NS1, and the addition of elastin-like polypeptide (ELP) or hydrophobin (HFBI) fusion tags to NS1. The transiently expressed proteins in N. benthamiana were quantified by Western blot analysis. The NS1 fused to ELP and targeted to the ER (NS1 ELP-ER) showed the highest yield (445 mg/kg), approximately a forty-fold increase in accumulation levels compared to the non-fused protein (NS1-ER), representing the first example of transient expression of DENV NS1 in plant. We also demonstrated that NS1 ELP-ER was successfully recognized by a monoclonal anti-dengue virus NS1 glycoprotein antibody, and by sera from dengue virus-infected patients. Interestingly, it was found that transient production of NS1-ER and NS1 ELP-ER using vacuum infiltration of whole plants, which is easier to scale up, rather than syringe infiltration of leaves, greatly improved the accumulation of NS1 proteins. The generated plant made NS1, even without extensive purification, showed potential to be used for the development of the NS1 diagnostic tests in resource-limited areas where dengue is endemic.
Identification of Loci Through Genome-Wide Association Studies to Improve Tolerance to Sulfur Deficiency in Rice

Sulfur (S) is an essential nutrient for plant growth and development; however, S supply for crop production is decreasing due to reduced inputs from atmospheric deposition and reduced application of S-containing fertilizers. Sulfur deficiency in soil is therefore becoming a widespread cause of reduced grain yield and quality in rice (Oryza sativa L). We therefore assessed the genotypic variation for tolerance to S deficiency in rice and identified loci associated with improved tolerance. Plants were grown in nutrient solution with either low (0.01 mM) or high (1.0 mM) supply of S. Plants grown under low-S treatment showed a reduction in total biomass, mainly due to a marked reduction in shoot biomass, while root biomass and root-to-shoot ratio increased, relative to plants under high-S treatment. Genome-wide association studies (GWAS) identified loci associated with root length (qSUE2-3, qSUE4, and qSUE9), and root (qSUE1, qSUE2-1, and qSUE3-1 and qSUE3-2) or total dry matter (qSUE2, qSUE3-1, and qSUE11). Candidate genes identified at associated loci coded for enzymes involved in secondary S metabolic pathways (sulfotransferases), wherein the sulfated compounds play several roles in plant responses to abiotic stress; cell wall metabolism including wall loosening and modification (carbohydrate hydrolases: beta-glucosidase and beta-gluconase) important for root growth; and cell detoxification (glutathione S-transferase). This study confirmed the existence of genetic variation conferring tolerance to S deficiency among traditional aus rice varieties. The advantageous haplotypes identified could be exploited through marker assisted breeding to improve tolerance to S-deficiency in modern cultivars in order to achieve sustainable crop production and food security.