Miracle-dz/UniFAR-FTUs huggingface dataset:
Responding to the Covid-19 pandemic at a university counseling center: administrative actions, client retention, and psychotherapy outcome

ABSTRACT In this article, we examine how Counseling and Psychological Services (CAPS) at a large private university in the western United States addressed the abrupt suspension of in-person mental health services and switch to telebehavioral health due to the Covid-19 pandemic. We used descriptive data, binary logistic regression, and hierarchical linear modeling to investigate changes in number of sessions and clients, use of online mental health platforms, and changes in clients’ psychological functioning. Overall, we found that CAPS had a decrease in scheduled appointments (including intake) and no change in attendance rates. We estimate that premature termination impacted about 32–34% of individual therapy clients and 29–37% of group clients. The number of new users for our online therapeutic platforms did not appear to be affected. Clients in 2020 were not significantly more distressed than clients in the previous 3 years and we found no significant differences in outcome between 2020 and previous years, indicating that levels of distress and treatment trajectories remained similar across years. Future research should focus on longer-term client outcomes as well as increasing accessibility to therapy in pandemic conditions.
Causality Between Money and Prices: Evidence from Pakistan

This paper re-examines the causal relationship between money and prices in Pakistan using recent data on money and prices and taking care of time series properties. The results of the paper suggest a unidirectional causality running from money to prices. Further, it seems that the monetary expansion has a greater impact on wholesale sale prices compared to CPI.
Fluctuation of Depositional Environment in the Bang Mark Coal Deposit, Krabi Mine, Southern Thailand: Stable Isotope Implication

The isotopic composition of organic sulfate-sulfur and pyrite in coal from the Bang Mark coal deposit in the Krabi mine, southern Thailand, was analyzed to determine the depositional environment of the coal. High-sulfur content in coal has δ 34 S values that range from +1.6 to +9.8 ‰. Organic sulfur in coal has δ 34 S values that vary narrowly from -1.4 to +4.7 ‰. The δ 34 S values of pyrite range from -3.0 to +1.4 ‰. The δ 34 S values indicate that most of the sulfur in pyrite and coal was derived from a magmatic or hydrothermal source. This sulfur was associated with organic matter during or after deposition. This also suggested that the source of sulfur could have come from hydrothermal mineralization surrounding the area. Twenty-four fossil shell specimens of Viviparus sp. , Melanoides sp. , and Mya arenaria were analyzed for their content of isotopic δ 13 C and δ 18 O. The δ 13 C in these shells have negative values that range from -5.9 to -0.1 ‰ and average - 2.9 ± 2.8‰. The δ 18 O in these shells has a wider range of negative values, ranging from -11.7 to -2.9‰. It averages -8.1 ± 5.2‰. The δ 13 C and δ 18 O values indicate freshwater shells in the lower sequence and brackish water shells in the upper sequence. Therefore, the depositional environment of the Bang Mark coal deposit based on stable isotopic study was dominated by freshwater in the beginning and later became brackish water as a result of marine invasion under warmer conditions in a tropical region. 34 S, δ 13 C, δ 18 O.
UNIVERSIDADE FEDERAL DE SERGIPE CENTRO DE CIÊNCIAS EXATAS E TECNOLÓGICAS PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO PERFORMANCE ANALYSIS OF VIRTUALIZATION TECHNOLOGIES IN HIGH PERFORMANCE COMPUTING ENVIRONMENTS

High Performance Computing (HPC) aggregates computing power in order to solve large and complex problems in different knowledge areas, such as science and engineering, ranging from 3D real-time medical images to simulation of the universe. Nowadays, HPC users can utilize virtualized Cloud infrastructures as a low-cost alternative to deploy their applications. Despite of Cloud infrastructures can be used as HPC platforms, many issues from virtualization overhead have kept them almost unrelated. In this work, we analyze the performance of some virtualization solutions Linux Containers (LXC), Docker, VirtualBox and KVM under HPC activities. For our experiments, we consider CPU, (physical network and internal buses) communication and disk I/O performance. Results show that different virtualization technologies can impact distinctly in performance according to hardware resource type used by HPC application and resource sharing conditions adopted.
Political Connections and Corporate Social Responsibility: Political Incentives in China

To explore the motivations underpinning corporate social responsibility (CSR) decisions in China, a country characterized by extensive government intervention, this paper investigates whether building a good relationship with the government is a political incentive that is driving firms to conduct CSR by examining the effects of political connections on the latter. Our results indicate that politically connected firms exhibit better CSR. However, the effect is considerably more significant for firms with existing political relationships. Additionally, findings show that the effect is more prominent in firms for which political connections are more valuable, namely, non&#8208;state&#8208;owned enterprises, small firms, and firms operating in less market&#8208;oriented cities, indicating that CSR can serve as a differentiation strategy to compete against other bidders. Dividing CSR activities into economic, environmental and social aspects, we find that the social&#8208;based activities are more likely to be driven by political motivations. By categorizing CSR and political connections, this paper not only expands the scope of political CSR and renders the generated results that have been mixed together more distinguishable, but also provides a more precise understanding of the fundamental drivers of CSR in China from the perspective of resource exchange.
Direct thermo-optical tuning of silicon microresonators for the mid-infrared

We use light from a visible laser diode to directly tune silicon-on-chip microresonators by thermo-optical effect. We show that this direct tuning is local, non invasive and has a much smaller time constant than global temperature tuning methods. Such an approach could prove to be highly effective for Kerr comb generation in microresonators pumped by quantum cascade lasers, which cannot be easily tuned to achieve comb generation and soliton-modelocked states. © 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Vadose Zone Lag Time Effect on Groundwater Drought in a Temperate Climate

An essential factor in the propagation of drought, from meteorological drought to groundwater drought, is the delay between a precipitation event and the groundwater recharge reaching the groundwater table. This delay, which mainly occurs in the vadose zone of the hydrological cycle, is often poorly studied. Therefore, this paper proposes a method for estimating the spatially distributed delay in the vadose zone using the kinematic wave approximation of Richards’ equation combined with the van Genuchten–Burdine and Brooks–Corey parametric model. The modeling was approached (1) using a detailed parametrization of soil and geological layers and (2) using lumped hydraulic and physical properties of geological layers. The results of both approaches were compared against the physically based flow model Hydrus-1D. This analysis shows that using a detailed parametrization of soil and geological layers results in good comparison, with a Nash–Sutcliffe efficiency of 0.89 for Brooks–Corey and 0.80 for van Genuchten–Burdine. The delay result of the Brooks–Corey model was incorporated into the groundwater recharge time series from 1980 to 2013 to analyze the effect of this delay on groundwater drought. The results show that the delay in the vadose zone influences groundwater drought characterization features such as the number, duration, and intensity of drought events.
Infrequent discourse relation identification using data programming

Discourse parsing is an important task in natural language processing as it supports a wide range of downstream NLP tasks [2, 5, 14, 15, 21, 33]. While the overall performance of discourse parsing has been recently improved considerably [32, 44], the performance on identifying relatively infrequent discourse relations is still rather low (∼ 20 in terms of F1 score). To resolve the gap between the performance of infrequent and frequent relations, we propose a novel method for discourse relation identification that is centered around “a paradigm for the programmatic creation of training datasets,” called Data Programming (DP) [35]. The main idea in our approach is to overcome the issue of limited labeled data for infrequent relations by leveraging unlabeled data in addition to labeled data. Our experiments show that our method improves the performance on most of the infrequent relations with minimal negative effect on frequent relations.
Design and Control of Modular Multilevel Converter in an Active Front End Application Master of Science Thesis in Electric Power Engineering

In CERN, the European Organization for Nuclear Research, the particles’ accelerators demand controlled magnetic fields for the particles move. The necessary magnetic fields are achieved by supplying electromagnets with current precisely controlled by power electronics. The magnets are supplied with dc current and a diode rectifier circuit or a more advanced Active Front End converter is used for the connection with the grid. The Active Front End converters offer the possibility to control both sides of the circuit, the ac and the dc side. In this way, the ac side current can be controlled to approach the sinusoidal waveform and to avoid the reactive power flow. At the same time the dc-link voltage can be regulated. A proposed topology to operate as an Active Front End is the Modular Multilevel Converter. The Modular Multilevel Converter is a new solution in the field of medium and high power electronics. The converter’s operation is based on the modular approach. It consists of modules, each one being a half-bridge connected in parallel to a capacitor. The special characteristic of this idea is that it is possible to build the sinusoidal waveform of the voltage by adding several modules in series in each phase-leg of the converter. An arm inductance is connected in series with the modules of every arm. In contrast to the two level voltage source converter, where the output phase voltage can be either plus or minus the half of the dc-link voltage, the MMC can change its output with steps equal to each module capacitor’s voltage level. Some features of the converter that are interesting especially for this application are: i. the redundancy in operation due to the several modules used in each arm that is very important for experiments running for long periods of time and being difficult to interrupt in case of failure.
Empowering Education: Exploring Teachers’ Classroom Practices in Vocational Schools

Teaching is indeed a complex provision, which requires teachers being competent in many respects. They should be very knowledgeable in subjects they teach; they should also acquire skills in transferring their subject matters; they need to be skillful in classroom management, and are capable of designing and developing teaching materials. For that reasons, teachers should always engage in professional development to guarantee their best performance. Understanding teachers’ ways of classroom practices is the first step for teachers’ professional development. Therefore, it is timely to conduct a classroom research to explore teachers’ performance, and thus this research explored teachers’ classroom practices in six vocational schools in Aceh. The data was collected mainly through observation of the instructional process in the classroom and in the workshop. The research found several effective attributes of teachers, such as creative, interactive, democratic, intelligent, smart, and well-prepared teachers. The study also identified ‘negative’ teachers’ attributes, such as authoritative, dominated, and withdrawn teachers.
Hypomethylation of 111 Probes Predicts Poor Prognosis for Glioblastoma

Glioblastoma (GBM) is a complicated brain tumor with heterogeneous outcome. Identification of effective biomarkers is an urgent need for the treatment decision-making and precise evaluation of prognosis. Based on a relatively large dataset of genome-wide methylation (138 glioblastoma patients), a joint-score of 111 methyl-probes was found to be of statistical significance for prognostic evaluation. Low joint-score were significantly associated with adverse outcomes (OS: P < 0.001, PFS: P = 0.03). Multivariable analyses adjusted for known risk factors confirmed the low joint-score of 111 methyl-probes as a high risk factor. The prognostic value of the methylated joint-score was further validated in another dataset of glioblastoma patients (OS: P = 0.006). Additionally, variance analysis revealed that aberrant genetic and epigenetic alterations were significantly associated with the joint-score of those methyl-probes. In conclusion, our results supported the joint-score of 111 methyl-probes as a potential prognosticator for the precision treatment of glioblastoma.
Tilburg University Common Stochastic Trends in the Current Account

Solow residuals are used as proxies for productivity shocks in many empirical studies. Considering the shortcomings of this approach this paper proposes the common trends approach as an alternative. The common trends econometric technique is utilized here in an attempt to identify and analyze the long run effects of country-specific and global productivity shocks on fluctuations in investment and the current account. The theoretical framework utilized provides long run restrictions relevant for identifying global and country-specific productivity shocks. Our estimations yield the following stylized facts. Generally, consistent with theoretical predictions, the long run effects of positive idiosyncratic (country-specific) productivity shocks on the current account are significantly negative. Further, permanent global shocks are impotent (by theoretical restriction) in explaining fluctuations in the current account though very significant in explaining investment fluctuations.
What Drives Labour Productivity in the Ageing Agriculture of Thailand

Thailand has particularly low labour productivity in agriculture as compared toÂ industry and services. The situation is worrisome as the country population isÂ increasingly ageing amidst the slow pace of structural transformation and theÂ confronting middle-income trap. It is thus the purpose of this paper to investigateÂ factors affecting labour productivity in the agricultural sector of Thailand takingÂ into account the role of population ageing. The error correction modelingÂ technique and time series data during 1970-2014 are employed to examine sourcesÂ of the agricultural labour productivity. The results show that major factorsÂ positively influencing the agricultural labour productivity are the capital-labourÂ ratio, land-labour ratio, research budget-labour ratio, and education level. Â However, there is no statistical evidence that the population ageing variable has aÂ significant impact on the productivity. The results highlight the importance ofÂ physical capital accumulation, farm size, agricultural research, and human capitalÂ investment.JEL classification numbers: J24, O13Keywords: Labour Productivity, Population Ageing, Thai Agriculture
Integrating machine learning and decision support in tactical decision-making in rugby union

Abstract Rugby union, like many sports, is based around sequences of play, yet this sequential nature is often overlooked, for example in analyses that aggregate performance measures over a fixed time interval. We use recent developments in convolutional and recurrent neural networks to predict the outcomes of sequences of play, based on the ordered sequence of actions they contain and where on the field these actions occur. The outcomes considered are gaining territory, retaining possession, scoring a try, and being awarded or conceding a penalty. We consider several artificial neural network architectures and compare their performance against baseline models. Accounting for sequential data and using field location improved classification accuracy over the baseline for some outcomes. We then investigate how these prediction models can provide tactical decision support to coaches. We demonstrate that tactical insight can be gained by conducting scenario analyses with data visualisations to investigate which strategies yield the highest probability of achieving the desired outcome.
Composition of the Cuticular Waxes of Picea abies and P. sitchensis

The chemical compositions of the cuticular lipids of the needles of Norway spruce (Picea abies (L.) Karst. ) and Sitka spruce (P. sitchensis) (Bong.) Carr. ) were investigated by gas chromatography coupled with mass spectrometry. Fractionation of the wax extracts was carried out using column chromatography. Long chain secondary alcohols, diols and free fatty acids were found as major classes in both species: n-alkanes, n-alkenes, primary alcohols, a,w-diols, ketones and w-hydroxyacids constituted the minor wax classes. The presence of large quantities of estolides (high molecular weight biopolymers) was tentatively confirmed by saponification of the crude extracts which led to the recovery of large quantities of w-hydroxyacids, a,w-diols and free fatty acids. Qualitative differences between the two examined species were observed for minor constituents such as long chain aldehydes, methyl- and ethyl-esters and some terpenes. Quantitatively the two wax extracts differed mainly in their proportions of diols and fatty acids.
Counterfactual Treatment Effects: Estimation and Inference

Abstract This article proposes statistical methods to evaluate the quantile counterfactual treatment effect (QCTE) if one were to change the composition of the population targeted by a status quo program. QCTE enables a researcher to carry out an ex-ante assessment of the distributional impact of certain policy interventions or to investigate the possible explanations for treatment effect heterogeneity. Assuming unconfoundedness and invariance of the conditional distributions of the potential outcomes, QCTE is identified and can be nonparametrically estimated by a kernel-based method. Viewed as a random function over the continuum of quantile indices, the estimator converges weakly to a zero mean Gaussian process at the parametric rate. We propose a multiplier bootstrap procedure to construct uniform confidence bands, and provide similar results for average effects and for the counterfactually treated subpopulation. We also present Monte Carlo simulations and two counterfactual exercises that provide insight into the heterogeneous earnings effects of the Job Corps training program in the United States.
Bipolar fully recurrent deep structured neural learning based attack detection for securing industrial sensor networks

Attack detection is a significant problem to be resolved to attain security in industrial sensor network. Few research works have been designed for performing attack discovery process with the help of different classification algorithms. However, classification performance was lowered to accurately find attack nodes and thereby getting higher security level (SL). In order to overcome such limitations, bipolar fully recurrent deep structured neural learning based attack detection (BFRDSNL‐AD) technique is proposed in this article. The BFRDSNL‐AD technique comprises of three main layers, namely, input, hidden, and output layer to accurately carry‐out attack detection process. The input layer in BFRDSNL‐AD Technique takes a number of sensor nodes as input. The designed BFRDSNL‐AD technique utilized numbers of hidden layers in order to deeply examine each sensor node in industrial sensor network. The result of the hidden layer is fed‐back into the network along with the inputs in order to discover the temporal dynamic behavior of sensor nodes. In BFRDSNL‐AD technique, output layer applies bipolar activation function to determine the association between identified node features to give attack detection result. If the output layer result is 1, then sensor node is considered as normal. Otherwise, sensor node is considered as malicious attack in industrial sensor network. Thus, the BFRDSNL‐AD technique increases the attack detection performance with higher accuracy and minimal time. From that, BFRDSNL‐AD technique gets enhanced SL in industrial sensor network. The simulation of BFRDSNL‐AD technique is conducted using metrics such as security, data delivery rate, data loss rate, and delay with respect to a different number of sensor nodes and data packets.
Effects of amount of excess solid, the type of stirring and sedimentation time on solubility of sodium phenytoin and lamotrigine

Solubility is the maximum quantity of a drug dissolved in a given volume of solvent at a specific temperature. Several factors affect equilibrium solubility. Therefore, different solubility data have been reported for a solute in a certain solvent and temperature in the literature. These variations in solubility are one of the possible reasons for unsuccessful attempts of medicinal chemists for developing models as well as deviation of experimental works for solubility prediction in aqueous, non-aqueous and solvent mixtures. The present research aim is to investigate the effect of the amount of excess solid and the type of stirring on the solubility of drugs. The solubility of two antiepileptic drugs, namely sodium phenytoin and lamotrigine was determined in water, ethanol, and HCl 0.1 M at 37 °C. Different excess amounts of drugs were added to the constant volume of solvent. Additionally, different stirring methods such as magnetic stirrer and shake-flask and sedimentation time were investigated on the solubility values. Saturation solubility of drugs after dilution with water was measured using a spectrophotometer, and the concentration was calculated according to the calibration curve. Amount of excess solid, especially when the drug is in ionized form, and sedimentation time after 24 h have a significant effect on solubility values.
Measuring the satisfaction gap in Management Education: A roadmap for achieving excellence

This study aims to develop a deeper understanding of the management education in India and to uncover the constructs/factors that underlie the desired expectations to reveal the underlying satisfaction and the benefits that students look for. This paper focuses on identifying and evaluating techniques used to take on the challenges of quality improvement in management education. A questionnaire consisting of 43 items was developed to measure the constructs and its dimensions. The purpose of this work is to examine the performance of eight alternative measures of service quality of management education in India. The dimensions are as follows: Tangible aspects (5 items), Reliability (5 items), Competence (5 items), Industry Institute interaction (6 items), Course structure (6 items), Internship output (6 items), Employability (5 items) and Inculcation of Entrepreneurial spirit (5 items). The first draft of the questionnaire was subject to a pilot testing through a focus group and an expert evaluation. Data were gathered from 425 MBA students' effective sample from various institutes (mix of public, private and autonomous offering PGDM) located in Lucknow, North India. Structural equation modeling was used to test the management education service quality. Confirmatory factor analysis with partial disaggregation was performed on the eight dimensions of management education quality.