Miracle-dz/UniFAR-FTUs huggingface dataset:
THE INFLUENCE OF TRAIT ANXIETY ON CAREER SELF-MANAGEMENT AND THE MODERATOR EFFECT OF STATE ANXIETY ABOUT LAYOFFS DUE TO COVID 19 PANDEMIC AMONG WHITE-COLLAR EMPLOYEES

This study aims to contribute to the literature on sustainable careers by studying the effect of t-anxiety on the career self-management dimensions which are Personal Mastery (PM), Verbal Persuasion (VP), and Vicarious Learning (VL), and the moderator affect of s-anxiety about layoffs due to COVID 19 pandemic on these relationships. The causal comparison screening approach was adopted. Hypotheses were investigated by the SEM method. The research data were collected online from 365 white-collar employees between July 1, and August 30, 2021, using a simple random sampling approach. The purpose of collecting the data between these dates was that the ban on lying off the employees due to the COVID 19 pandemic has been lifted on June 30, 2021. Empirically, a negative and significant relationship was found between t-anxiety and PM. It was also found that positive and significant relationships exist between t-anxiety and VP and VL. A negative relationship between s-anxiety about layoffs due to COVID 19 pandemic and PM was determined. S-anxiety about layoffs due to COVID 19 pandemic has been found to have moderating negative effect on the relationships between t-anxiety and VP and VL. High levels of anxiety (both for t-anxiety and s-anxiety) affect PM negatively whereas low levels of anxiety strengthen it. PM gets weaker in anxious individuals, while VP and VL get stronger. Because of the s-anxiety about layoffs due to COVID 19 pandemic white-collar professionals are likely to face a career shock, resulting in unsustainable trajectories. ASEAN Journal of Psychiatry, Vol. 23(1) January, 2022; 1-17.
Professional qualification and childhood cancer in primary care

Jane Kelly Oliveira Friestino1,2 https://orcid.org/0000-0002-5432-9560 Carlos Roberto Silveira Corrêa1 https://orcid.org/0000-0002-9232-2263 Ana Izabel Jatobá de Souza3 https://orcid.org/0000-0003-3843-6144 Graciela Soares Fonsêca1 https://orcid.org/0000-0001-9506-0409 Ana Caroline Geremia1 https://orcid.org/0000-0002-0825-1344 Djalma de Carvalho Moreira Filho2 https://orcid.org/0000-0002-7943-0868
UNCERTAINTIES IN RISK ASSESSMENT OF HYDROGEN DISCHARGES FROM PRESSURIZED STORAGE VESSELS AT LOW TEMPERATURES

Evaluations of the uncertainties resulting from risk assessment tools to predict releases from the various hydrogen storage types are important to support risk informed safety management. The tools have to predict releases from a wide range of storage pressures (up to 80 MPa) and temperatures (at 20K) e.g. the cryogenic compressed gas storage covers pressures up to 35 MPa and temperatures between 33K and 338 K. Accurate calculations of high pressure releases require real gas EOS. This paper compares a number of EOS to predict hydrogen properties typical in different storage types. The vessel dynamics are modeled to evaluate the performance of various EOS to predict exit pressures and temperatures. The results are compared to experimental data and results from CFD calculations. 1.0 INTRODUCTION Hydrogen may be thermodynamically considered as an almost ideal gas over a very wide temperature and pressure range. Nevertheless, present technological developments stores hydrogen in the liquid state at about 20K under a low pressure of few bars and in the gaseous state at very high pressures up to 800 – 1000 bars at ambient temperatures. Recently, the operational regime of cryo compressed hydrogen (CcH2) storage was reported to cover pressures of up to 35 MPa and temperatures from +65 °C down to -240 °C [1,2]. Considering these wide ranges for temperature and pressures, the assumption of ideal gas behavior and application of the ideal gas equations of state (EOS) is not adequate for all situations. This has been recognized by the scientific community and different approaches describing high pressure gas releases at ambient conditions from storage tanks [3-7] and within vehicles [8] are described. This discrepancy of behavior between ideal gas and real gas is illustrated in Figure 1, where the state-of-the-art reference data provided by NIST [3] are compared against predictions using the EOS for ideal gas. It is shown that the ideal gas EOS accuracy in predicting the density pressure relationship is limited up to about 35MPa at 500K, and up to about 15MPa at 200K. From the risk assessment point of view a large number of release scenarios have to be analyzed, to give a comprehensive evaluation of the associated risks and to provide useful data for risk management purposes. In many scenarios at ambient conditions and moderate storage pressures the use of engineering equations based on the ideal gas EOS may give sufficiently accurate results to make proper decisions. For scenarios using very high pressure and cryogenic storage real gas behavior needs to be taken into account to reduce the level of uncertainty in the evaluations. For this purpose several EOS are being developed which may be classified into the cubic EOS type (equations by e.g. 1 Accesible from http://webbook.nist.gov/chemistry/fluid “hydrogen” and described in the PhD thesis by Leachman: http://www.boulder. nist.gov/div838/theory/refprop/leachman.pdf 2 http://en.wikipedia. org/wiki/Equation_of_state#Cubic_equations_of_state
Field Experiments and the Practice of Policy 1

i was not destined to be an economist. As the daughter of a mathematician, I was quite sure I would become an academic. My heroes were Gauss, the mathematical genius, and Emmanuel Le Roy Ladurie, the quantitative historian who found peasants interesting, rather than kings. But as the daughter of a physician who spent time trying to be helpful in countries where children were victims of war, I also aspired to be a change maker. I felt that the only repayment for the incredible luck I had in my life was to do whatever I could to try to improve the lives of the many people who were not that lucky. My heroes were Mother Teresa and Albert Schweitzer. Of course, I had no idea how to combine those two aspirations, but I hoped that one day I would find a way.
Virtual networks as enablers of knowledge absorptive capacity development

Organisations are finding it more difficult to keep abreast with the pace of change. The continuous rise of business opportunities and the increase in global competition demands a capability to acquire, assimilate, transform and apply external critical knowledge to renew and reconfigure existing capabilities and knowledge, and to innovate. Developing this dynamic capability requires, in turn, new proactive knowledge management tools, and new organisational forms. This chapter presents a framework in which virtual networks constitute more flexible new organisational structures to absorb and create knowledge. It also describes how embeddedness in such a network can affect most of the factors identified as antecedents of absorptive capacity. In addition, it evidences the important role of the firm�s relational capabilities in taking advantage of the relevant business information, knowledge, resources, technologies and capabilities circulating in the virtual networks.
Impact of Hair Color on Thermoregulation of Dairy Cows to Direct Sunlight

Summary:The effects of hair coat color on the thermoregulatory responses of heat-stressed cows exposed to direct solar radiation were observed. During August 2000, three white and three black lactating Holstein cows in Hawaii were exposed to 1.5 hr of sunlight for four trials in the morning and four trials in the afternoon with or without spray cooling. Solar radiation ranged between 495 and 971 watts/m 2 and THI ranged between 78 and 81 during the experimental trials. Cows with white hair coat absorb about 66% of the short wave radiation, while cows with black hair coat absorb about 89% of the short-wave radiation. Placing the cows under direct sunlight increased the surface temperature of the black cows by 4.8°C and the white cows by 0.7°C. Rectal temperatures of black cows increased 1.3°C/hr, while the rectal temperatures of white cows increased 0.8°C/hr. Increasing solar load on the skin surface increases the local rate of sweating. Spray wetting alone is not sufficient to cool cows exposed to high solar loads.
What Geographers Research: An Analysis of Geography Topics, Clusters, and Trends Using a Keyword Network Analysis Approach and the 2000-2019 AAG Conference Presentations

Recommended Citation Seong, Jeong C. ; Hwang, Chul Sue; Stanescu, Ana; Lee, Youngho; and Lee, Yubin (2022) "What Geographers Research: An Analysis of Geography Topics, Clusters, and Trends Using a Keyword Network Analysis Approach and the 2000-2019 AAG Conference Presentations," International Journal of Geospatial and Environmental Research: Vol. 9 : No. 1 , Article 1. Available at: https://dc.uwm. edu/ijger/vol9/iss1/1
Deep Structural Contrastive Subspace Clustering

Deep subspace clustering based on data self-expression is devoted to learning pairwise aﬃni-ties in the latent feature space. Existing methods tend to rely on an autoencoder framework to learn representations for an aﬃnity matrix. However, the representation learning driven largely by pixel-level data reconstruction is somewhat incompatible with the subspace clustering task. With the unavailability of ground truth, can structural representations, which is exactly what subspace clustering favors, be achieved by simply exploiting the supervision information in the data itself? In this paper, we formulate this intuition as a structural contrastive prediction task and propose an end-to-end trainable framework referred as Deep Structural Contrastive Subspace Clustering (DSCSC). Speciﬁcally, DSCSC makes use of data augmentation technique to mine positive pairs and constructs a data similarity graph in the embedding feature space to search negative pairs. A novel structural contrastive loss is proposed on the latent representations to achieve positive-concentrated and negative-separated property for subspace preserving. Extensive experiments on the benchmark datasets demonstrate that our method outperforms the state-of-the-art deep subspace clustering methods and imply the necessity of the proposed structural contrastive loss.
Retinol-binding protein 4 in twins – regulatory mechanisms and impact of circulating and tissue expression levels on insulin secretion and action

Objective: Retinol-binding protein 4 (RBP4) is an adipokine of which plasma levels are elevated in obesity and type 2 diabetes. The aims of the study were to identify determinants of plasma RBP4 and RBP4 mRNA expression in subcutaneous adipose tissue (SAT) and skeletal muscle, and to investigate the association between RBP4 and in vivo measures of glucose metabolism. Research Design and Methods: The study populations included 298 elderly twins (62-83 years) with glucose tolerance ranging from normal to overt type 2 diabetes, and 178 young (25-32 years) and elderly (58-66 years) non-diabetic twins. Peripheral and hepatic insulin sensitivity was assessed by a euglycemic, hyperinsulinemic clamp, and β -cell function was estimated from an intravenous glucose tolerance test. Results: The influence of environmental versus genetic factors in the regulation of plasma RBP4 increased with age. Plasma RBP4 was elevated in type 2 diabetes and increased with duration of disease. Plasma RBP4 correlated inversely with peripheral, but not hepatic, insulin sensitivity. However, the association disappeared after correction for covariates, including plasma adiponectin. Plasma retinol, and not RBP4, was inversely associated with insulin secretion. SAT RBP4 expression correlated positively with GLUT4 expression and inversely with glucose tolerance. Skeletal muscle RBP4 expression reflected intramuscular fat, and although suppressed by insulin, no association to insulin sensitivity was evident. RBP4 expression was not associated with circulatory RBP4. Conclusion: In conclusion, our data indicate that RBP4 levels in plasma, skeletal muscle and fat may be linked to insulin resistance and type 2 diabetes in a secondary and non-causal manner. Abbreviations: AU:
In-Process 3D Roll Pattern Measurement and Inspection by Fluorescence Strobo-Stereoscopy

This paper presents a novel method to in-process achieve 3D imaging of the roll pattern during the roll-toroll (R2R) process. Strobo-stereoscopy that enables 3D image reconstruction by synchronously controlling the light illumination system in accordance with the rotational position of the roll was employed to in-process inspect the engraved surfaces for cracks or wear damage as well as roll roundness and rotational error due to vibration or bearing damage. Fluorescent fluid with the characteristic of stoke shift and high speckle contrast enables this system to measure reflective surface with regular pattern. The proposed fluorescent strobostereoscopy consists of two cameras, bandpass filters and light emitting diode-based UV illumination system, reconstructs 3D images of the roll along the circumferential direction, and then provides panoramic full-view 3D images. The developed strobo-stereoscopic measurement system covered the scanning area 100 × 100 mm, and showed the axial accuracy is within 5% and the lateral accuracy is within 2%. The proposed roll inspection method can be integrated with R2R machine tools in an on-machine configuration for in-process, fast, convenient 3D roll inspection.
Perryite, (Ni,Fe)16PSi5, from the Mount Egerton aubrite: the first natural P-Si-ordered phosphide-silicide

Perryite, natural Ni-silicide, is a minor but regular constituent of the metal phase in enstatite chondrite (aubrite) and enstatite chondrite meteorites. Its synthetic analog was shown to have promising catalytic properties. The first-time solution of the crystal structure of natural perryite was completed on the material from the Mount Egerton aubrite. The mineral is trigonal, R3̄c, a = 6.6525(5), c = 37.998(5) Å, V = 1456.3(3) and Z = 6. The structure was refined to R1 = 0.0137 based on 457 independent observed reflections. The chemical formula obtained from the structure refinement, (Ni14.14Fe1.88)Σ16. 02PSi5, agrees with that derived from the electron microprobe data, (Ni13.39Fe2.65Co0.01)Σ16. 05P1.22Si4. 74. This research showed that P and Si in perryite are ordered, resulting in the simplified formula (Ni,Fe)16PSi8, in contrast to the currently accepted variant (Ni,Fe)8(Si,P)3. The detailed results of EBSD study reveal previously unknown relationships between perryite, associated α-(Fe,Ni) metal (also known as kamacite) and schreibersite, (Fe,Ni)3P. Since enstatitic meteorites represent the early stages of nebular accretion, our results demonstrate that the crystal-chemical factor could affect the differentiation of chemical elements upon the onset of the Solar System formation.
Increasing Inventories: e Role of Delivery Times

U.S. manufacturing inventories have been increasing since 2005, reversing a declining trend that lasted for decades. e rise is observed across U.S. manufacturing industries and types of inventories. While the long term decline is well-understood as a consequence of improvements in transportation and information technology, the reversal of the trend has not yet been studied. is paper explores the role of increasing delivery times due to the creation of global supply chains. As foreign inputs become cheaper, rms choose to source more inputs from abroad, and in particular inputs from China, which face long delivery times and frequent delays. is increases the rms’ exposure to volatility in demand leading to a greater incentive to hold inventories. I build a dynamic trade model that features stochastic delivery times for dierent inputs in the presence of idiosyncratic demand risk. In this framework, rms face a tradeo when sourcing inputs from dierent locations between their relative price and delivery times. I nd that the initial decrease in delivery times explains 61% of the decline in inventories from 1992 to 2004, and the increase in reliance on inputs from China, which face longer and more volatile delivery times, explains 34% of the increase in inventories from 2005 to 2018.
Quaternion error-based optimal control applied to pinpoint landing

Accurate control techniques for pinpoint planetary landing - i.e., the goal of achieving landing errors in the order of 100m for unmanned missions - is a complex problem that have been tackled in different ways in the available literature. Among other challenges, this kind of control is also affected by the well known trade-off in UAV control that for complex underlying models the control is sub-optimal, while optimal control is applied to simplifed models. The goal of this research has been the development new control algorithms that would be able to tackle these challenges and the result are two novel optimal control algorithms namely: OQTAL and HEX2OQTAL. These controllers share three key properties that are thoroughly proven and shown in this thesis; stability, accuracy and adaptability. Stability is rigorously demonstrated for both controllers. Accuracy is shown in results of comparing these novel controllers with other industry standard algorithms in several different scenarios: there is a gain in accuracy of at least 15% for each controller, and in many cases much more than that. A new tuning algorithm based on swarm heuristics optimisation was developed as well as part of this research in order to tune in an online manner the standard Proportional-Integral-Derivative (PID) controllers used for benchmarking. Finally, adaptability of these controllers can be seen as a combination of four elements: mathematical model extensibility, cost matrices tuning, reduced computation time required and finally no prior knowledge of the navigation or guidance strategies needed. Further simulations in real planetary landing trajectories has shown that these controllers have the capacity of achieving landing errors in the order of pinpoint landing requirements, making them not only very precise UAV controllers, but also potential candidates for pinpoint landing unmanned missions.
DR-DCGAN: A Deep Convolutional Generative Adversarial Network (DC-GAN) for Diabetic Retinopathy Image Synthesis

In image classification, one of the most significant elements is the broad range of data, particularly when supervised learning is used for the classification of images. Several reports are produced by experts who are experienced in their specialty. Even though a huge amount of medical data is complicated and has an expensive procedure which needs teamwork between the scientists and the clinics. The issue is mostly tried to be solved with the utilization of conventional techniques of data augmentation, creating a few adjustments to images of dataset for example rotating, zooming, cropping and size. In this proposed work, the modern technique of data augmentation is shown which is known as DC-GAN i.e., Deep Convolutional Generative Adversial Network. This is a procedure to produce artificial medical images. Moreover, for the improvement of DR, we will take the help of the classification model that is resnet50 for the eye related classification. The suggested technique is shown on the APTOS-Blindness dataset. First, the current online data augmentation methods are used and the production of artificial images of retina take place with the help of DCGAN. Now, we use the method of classification for both the techniques. In the end, after the training of method takes place by utilizing the original and artificial clinical images, the outcome shows-the suggested model identifies all the stages of DR unlike the present methods and achieve accuracy of 98.66.
Microbiological quality of pasteurized human milk from a Milk Bank of São Paulo Qualidade microbiológica do leite humano pasteurizado de um Banco de Leite Paulista Calidad microbiológica de la leche humana pasteurizada de un banco de leche paulista

Methods: This is a descriptive study conducted with 29 pasteurized expressed human milk (PEHM) samples obtained between July 2015 and March 2016 by assessing titratable acidity records as well as quantifying heterotrophic microbiota (mesophiles, psychrophiles, thermophiles), total and thermotolerant coliforms, filamentous and yeast-like fungi and Staphylococcus spp. The physical-chemical parameters were assessed via hydrogen-pH potential, K-energy content and Dornic-oD acidity. Descriptive and bivariate statistical analyzes were conducted.
DistilCSE: Effective Knowledge Distillation For Contrastive Sentence Embeddings

Large-scale contrastive learning models can learn very informative sentence embeddings, but are hard to serve online due to the huge model size. Therefore, they often play the role of"teacher", transferring abilities to small"student"models through knowledge distillation. However, knowledge distillation inevitably brings some drop in embedding effect. To tackle that, we propose an effective knowledge distillation framework for contrastive sentence embeddings, termed DistilCSE. It first applies knowledge distillation on a large amount of unlabeled data, and then fine-tunes student models through contrastive learning on limited labeled data. To achieve better distillation results, we further propose Contrastive Knowledge Distillation (CKD). CKD uses InfoNCE as the loss function in knowledge distillation, enhancing the objective consistency among teacher model training, knowledge distillation, and student model fine-tuning. Extensive experiments show that student models trained with the proposed DistilCSE and CKD suffer from little or even no performance decrease and consistently outperform the corresponding counterparts of the same parameter size. Impressively, our 110M student model outperforms the latest state-of-the-art model, i.e., Sentence-T5 (11B), with only 1% parameters and 0.25% unlabeled data.
Tropane alkaloids in mint teas at the Serbian market

The interest in tropane alkaloids as food contaminants has been increasing. The tropane alkaloids are plant toxins that mainly occur in Atropa, Datura and Hyoscyamus sp belonging to the Solanaceae family. A sensitive and selective LC-MS/MS method was applied for the analysis of atropine and scopolamine in the mint tea samples from the Serbian market. Tea, which has beneficial properties thanks to the phenolic compounds, can be accidentally contaminated by many weed seeds which contain the tropane alkaloids during the harvest. Only the tropane alkaloids present in the tea bags before the tea making were analysed. Atropine and scopolamine were detected in 30% of the analysed samples in the concentration above the limit of quantification.
Lexical Inferencing in L2 Japanese Reading: L2 Proficiency and L1 Reading as Predictors of Semantic Gap Filling (SGF) at Word Level

[Abstract] This exploratory study was conducted to seek the relationships of L2 proficiency and L1 reading to L2 Japanese Semantic Gap Filling (SGF) at word level, or lexical inferencing. Research questions were asked to investigate (1) which of the subcomponents of L2 linguistic knowledge correlates with L2 SGF, and (2) to what extent the variance of L2 SGF can be explained from L1 reading and L2 linguistic knowledge. Nineteen learners were tested for (1) L2 lexical inferencing in Japanese, (2) reading in English, and (3) L2 Japanese proficiency. While the participants had various native language backgrounds, they were assumed to be proficient enough to function academically in English because they were studying at institutions where English was the medium of instruction. Therefore, English was operationalized as their L1. Results of correlations and multiple regressions suggest that L2 proficiency, particularly knowledge of kanji meaning and semantic radicals account for variance in SGF while L1 reading appeared to be a predictor for SGF when participants were limited to those with (1) no prior exposure to kanji before studying Japanese and (2) ten years or longer of studying in English as a medium of instruction.