Miracle-dz/UniFAR-FTUs huggingface dataset:
Radial Velocity Profiles and Magnetic Field Probes for Hypervelocity Plasma Deflagration Jets

approved:. Janet Tate The fundamental physics of turbulence in plasma is not well understood. Recent studies of the plasma deflagration accelerator in the High Temperature Gasdynamics Lab at Stanford University have demonstrated the presence of small scale instabilities that limit the lifetime of the jet/Z pinch and are not reproduced by coaxial plasma accelerator simulations. To provide more data to inform these simulations, a radial profile of axial velocity was measured using spectrometry. Magnetic field probes utilizing chip inductors were designed to produce well-behaved results at high frequencies, enabling the use of techniques like wavelet decomposition to identify the fastest-growing instabilities in the flow. The velocity measurement produced a radial profile of axial velocity that was consistent with prior experimental data and reproduced key features of the simulations. The core of the jet was shown to travel uniformly at 92 ±5 km/s out to a 5 mm radius, and the shear flow around the jet rose to ∼120 km/s at 10 mm. An impurity emission line (Fe I) was used to generate the velocity profile and thus the experiment may have failed to capture features tied specifically to the physical characteristics of the plasma species. Doppler and Stark broadening measurements revealed the presence of artificial broadening which increased the uncertainty. The magnetic field probe project produced prototypes that measure axial, azimuthal, and radial field variation. A calibration procedure for the probes was designed and demonstrated. The calibration curve shows the onset of resonances at ∼10 MHz, calling for improvements to achieve the desired maximum operating frequency of 100 MHz. The curve also suggests that the probe will produce a signal of order ∼1 V during operation, showing that the circuit components are sufficient for detection of the fine structure of the magnetic field.
LEGAL ASPECTS AND EFFECT WORK FAMILY CONFLICT, JOB INSECURITY, AND TRANSFORMATIONAL LEADERSHIP STYLE ON TURNOVER INTENTION

Purpose: The objective of this study is to analyze the relationship between legal aspects, work-family conflict, job insecurity, transformational leadership style, and turnover intention. Theoretical framework: The role theory proposed by Khan et al. (1964) emphasizes the individual's social role and how it effects his conduct in the workplace and society. Design/methodology/approach: This investigation is quantitative in nature. The subjects of this study were 200 SME employees in Palopo City who were selected using a procedure of random sampling. Through social media, online questionnaires with a Likert scale ranging from 1 to 5 were distributed to collect data. The study employed Structural Equation Modeling (SEM), whereas SmartPLS 3.0 was used for data processing. Findings: Legal aspects play a crucial role in determining the level of work-family conflict, job insecurity, transformational leadership style, and turnover intention in an organization. For instance, laws and regulations pertaining to employment contracts, compensation, benefits, and job security can directly impact an employee's level of job satisfaction and intention to leave their job. Additionally, legal requirements such as mandatory leave and flexible working arrangements can reduce work-family conflict and enhance employee well-being. Moreover, legal frameworks for leadership development and training can foster transformational leadership styles that have been shown to decrease turnover intention among employees. Therefore, understanding the legal aspects that impact the workforce can help organizations develop effective policies and practices that address work-family conflict, job insecurity, and leadership style, and ultimately reduce turnover intention among their employees. Research, Practical & Social implications: Legal aspects are crucial for the development and success of small and medium-sized enterprises (SMEs) in Indonesia. Compliance with employment laws and regulations, such as those related to labor rights, employee benefits, and job security, can help SMEs to attract and retain skilled and talented employees. Moreover, transformational leadership styles, which have been shown to enhance employee engagement and job satisfaction, can be developed and nurtured through legal frameworks such as leadership training programs. By understanding and complying with legal aspects and providing effective leadership training, SMEs in Indonesia can improve their employee productivity, foster innovation and growth, and ultimately contribute to the economic development of both the organization and the country as a whole. Originality/value: This study explores the relationship between family conflict, Job Insecurity, and Transformational Leadership Style on turnover intention, and psychological attachment that has not been explored theoretically and tested empirically in the Indonesian context.
Analysis of Severe Injury Accident Rates on Interstate Highways Using a Random Parameter Tobit Model

In this study, a random parameter Tobit regression model approach was used to account for the distinct censoring problem and unobserved heterogeneity in accident data. We used accident rate data (continuous data) instead of accident frequency data (discrete count data) to address the zero cell problems from data where roadway segments do not have any recorded accidents over the observed time period. The unobserved heterogeneity problem is also considered by using random parameters, which are parameter estimates that vary across observations instead of fixed parameters, which are parameter estimates that are fixed/constant over observations. Nine years (1999–2007) of panel data related to severe injury accidents in Washington State, USA, were used to develop the random parameter Tobit model. The results showed that the Tobit regression model with random parameters is a better approach to explore factors influencing severe injury accident rates on roadway segments under consideration of unobserved heterogeneity problems.
From quantum to classical dynamics: Dynamic crossover in the relativistic $O(N)$ model

D. Mesterházy, ∗ J. H. Stockemer, † and Y. Tanizaki 4, ‡ Department of Physics, University of Illinois at Chicago, Chicago, IL 60607, USA Institut für Theoretische Physik, Universität Heidelberg, Philosophenweg 16, 69120 Heidelberg, Germany Department of Physics, The University of Tokyo, Tokyo 113-0033, Japan Theoretical Research Division, Nishina Center, RIKEN, Wako, Saitama 351-0198, Japan (Dated: April 29, 2015)
Proton pump inhibitors – their possible influence on the development of small intestine bacterial overgrowth in children

Proton pump inhibitors (PPIs) are safe drugs that have been commonly used for many years by both general practitioners and specialists. They are widely used in the paediatric population, mainly in the treatment of gas-troesophageal reflux disease, but also for oesophagitis, gastritis and/or duodenitis. Proton pump inhibitors use increases the pH of the stomach, which disrupts the physiological defensive barrier and may lead to the development of an abnormal microbiome in the further parts of the digestive tract, resulting in small intestinal bacterial overgrowth (SIBO) and symptoms such as diarrhoea, production of gas, nonspecific abdominal pain, flatulence, lack of growth or weight loss, protein or energy malnutrition, and vitamin A, D, K and B12 deficiency. The study analyses the influence of combining PPIs with probiotics or prokinetics on the chance of SIBO developing in children. There are currently no diagnostic standards or therapeutic guidelines for children diagnosed with SIBO.
Contribution to 2D/3D face recognition/authentification

3D face analysis including 3D face recognition and 3D Facial expression recognition has become a very active area of research in recent years. Various methods using 2D image analysis have been presented to tackle these problems. 2D image-based methods are inherently limited by variability in imaging factors such as illumination and pose. The recent development of 3D acquisition sensors has made 3D data more and more available. Such data is relatively invariant to illumination and pose, but it is still sensitive to expression variation. The principal objective of this thesis is to propose efficient methods for 3D face recognition/verification and 3D facial expression recognition. First, a new covariance based method for 3D face recognition is presented. Our method includes the following steps : first 3D facial surface is preprocessed and aligned. A uniform sampling is then applied to localize a set of feature points, around each point, we extract a matrix as local region descriptor. Two matching strategies are then proposed, and various distances (geodesic and non-geodesic) are applied to compare faces. The proposed method is assessed on three datasetsincluding GAVAB, FRGCv2 and BU-3DFE. A hierarchical description using three levels of covariances is then proposed and validated. In the second part of this thesis, we present an efficient approach for 3D facial expression recognition using kernel methods with covariance matrices. In this contribution, we propose to use Gaussian kernel which maps covariance matrices into a high dimensional Hilbert space. This enables to use conventional algorithms developed for Euclidean valued data such as SVM on such non-linear valued data. The proposed method have been assessed on two known datasets including BU-3DFE and Bosphorus datasets to recognize the six prototypical expressions.
Empowering In-Network Classification in Programmable Switches by Binary Decision Tree and Knowledge Distillation

Given the high packet processing efficiency of programmable switches (e.g., P4 switches of Tbps), several works are proposed to offload the decision tree (DT) to P4 switches for in-network classification. Although the DT is suitable for the match-action paradigm in P4 switches, the range match rules used in the DT may not be supported across devices of different P4 standards. Additionally, emerging models including neural networks (NNs) and ensemble models, have shown their superior performance in networking tasks. But their sophisticated operations pose new challenges to the deployment of these models in switches. In this paper, we propose Mousikav2 to address these drawbacks successfully. First, we design a new tree model, i.e., the binary decision tree (BDT). Unlike the DT, our BDT consists of classification rules in the form of bits, which is a good fit for the standard ternary match supported by different hardware/software switches. Second, we introduce a teacher-student knowledge distillation architecture in Mousikav2, which enables the general transfer from other sophisticated models to the BDT. Through this transfer, sophisticated models are indirectly deployed in switches to avoid switch constraints. Finally, a lightweight P4 program is developed to perform classification tasks in switches with the BDT after knowledge distillation. Experiments on three networking tasks and three commodity switches show that Mousikav2 not only improves the classification accuracy by 3.27%, but also reduces the switch stage and memory usage by $2.00\times $ and 28.67%, respectively. Code is available at https://github.com/xgr19/Mousika.
Cause of death in fatal missing person cases in England and Wales

Typically, fewer than 0.5% of missing incidents result in an individual being found deceased. Whereas previous research has examined the features of missing–homicide, missing–suicide and some aspects of missing–accident cases, this study sought to provide the first reliable estimates of the number and proportion of fatalities by all causes of death, specifically: suicide, accident, homicide and natural causes. Using data obtained on 615 fatal disappearances via Freedom of Information returns from 22 English and Welsh police forces covering a two-year period, this article presents data on the relative probability of fatality by cause of death for adults and children and for males and females. Overall, fatal outcomes are, thankfully, rare, occurring once in every 358 missing incidents. Some 98% of fatalities involved adults; just over 80% involved males. The findings affirm suicide as the largest single cause of death accounting for three-quarters of female fatalities and four in every five male fatalities. Although homicide was the least probable cause of death among male missing persons, females faced a roughly similar chance of dying as a result of an accident or homicide. The research indicates that estimates of probability can be used to inform investigative decision-making and scenario development, offering important context to the overall likelihood of an individual facing a fatal outcome. Further research is needed to replicate these findings.
Impact of ambient temperature on adverse pregnancy outcomes: a birth cohort study in Fuzhou, China

Background Previous studies have identified a series of specific adverse pregnancy outcomes (APOs) linked with temperature extremes. Most of them focus on preterm birth, low birth weight, and stillbirth. Other possible adverse outcomes were under-researched. This study aimed to investigate the impact of ambient temperature on maternal complications, white blood cell count (WBC), newborn hearing, and neonatal jaundice. Methods A total of 418 participants were recruited from Fuzhou Maternity & Child Healthcare Hospital in 2016. Participants were invited to fill out a structured questionnaire. The gridded near-surface air temperatures at a resolution of 0.1°* 0.1° for Fuzhou were extracted from a published dataset. Meteorological data and PM2.5 were extracted based on participants’ residential addresses using R packages “ncdf4” and “raster. ” Multivariate logistic regression models were used to quantify the effects of ambient temperature on APOs after controlling for confounders. Results Overall, there were 107 APOs, accounting for 25.6% of all participants. Every 1°C increase in mean temperature was associated with a 10.0% increase in APOs (aOR = 1.100, 95%CI 1.006–1.203) during the period of early pregnancy. However, negative associations were observed in the middle pregnancy period, and a 1°C increase in mean temperature was associated 8.8% decrease in APOs (aOR = 0.912, 95%CI 0.846–0.982). Diurnal temperature variation had a significant impact on APOs in the third trimester. Infant jaundice was negatively associated with temperature exposure in the middle and late pregnancy periods. The risk of neonatal jaundice increased at lag weeks 2–9 in the first trimester, with the greatest lagged effect (aOR = 1.201, 95%CI 1.020–1.413) observed at lag week 3. A 1°C increase in mean temperature led to a 29.6% (aOR = 1.296, 95%CI 1.019–1.649) increase in high WBC. A 1°C increase in temperature variation was associated with more than two times (aOR = 2.469, 95%CI 1.001–6.089) increase of high WBC in the first trimester and about five times (aOR = 4.724, 95%CI 1.548–14.409) increase in the third trimester. Conclusion Ambient temperature affects neonatal jaundice, newborn hearing loss, and infections during pregnancy. In addition to the identified epidemiologic link and susceptible exposure windows, there is a need to understand the underlying biological mechanisms for better recommendations for climate change adaptation policies.
The Effect of Public Contract Winning Announcements on Share Prices: An Event-Based Study on the Pharmaceutical Industry

The purpose of this paper is to examine and elucidate the effect of the announcement of winning a US federal contract by pharmaceutical companies, traded on the NASDAQ stock exchange on the share prices. The analysis is based on a sample of 327 event days from 12 pharmaceutical companies listed on the NASDAQ market for a given period (2010 – 2021). The sample is analysed by using the event study methodology and Capital Asset Pricing Model (CAPM) in order to examine the abnormal returns and cumulative abnormal returns during an event window of 10 days before the event and 10 days after the event. While prior researchers have identified significant positive abnormal returns following different types of contract announcements, this study aims to contribute to the existing literature by exploring the specific impact of the public contract in the pharmaceutical market on market shares. By investigating this unique aspect, the research fills a gap in previous studies and enhances our understanding of the relationship between public contract announcements and market performance. The data collection utilises secondary data from the official open data source of US federal spending information and daily stock prices. The collected data is analysed quantitatively using event study methodology, allowing for statistical examination of abnormal returns, such as ANOVA analysis, t-statistic, beta index, time series analysis and linear regression. The research findings indicate a significant impact of public contract-winning announcements on cumulative abnormal returns, suggesting a positive reaction from the market. However, average abnormal returns within the event window were positive but not statistically significant. These results imply that winning a government contract has a limited impact on the stock market. The implications of this research highlight the importance of public contract announcements in the pharmaceutical industry and their influence on share prices. The findings provide valuable insights for investors and stakeholders, aiding decision-making processes related to investment opportunities and market performance.
Water fragmentation by bare and dressed light ions with MeV energies : Fragment-ion-energy and time-of-flight distributions

The energy and time-of-flight distributions of water ionic fragments produced by impact of fast atoms and bare and dressed ions; namely, ${\mathrm{H}}^{+}, {\mathrm{Li}}^{0\char21{}3+}, {\mathrm{C}}^{1+}$, and ${\mathrm{C}}^{2+}$ are reported in this work. Fragment species as a function of emission energy and time-of-flight were recorded by using an electrostatic spectrometer and a time-of-flight mass spectrometer, respectively. An improved Coulomb explosion model coupled to a classical trajectory Monte Carlo (CTMC) simulation gave the energy centroids of the fragments for the dissociation channels resulting from the removal of two to five electrons from the water molecule. For the energy distribution ranging up to 50 eV, both the experiment and model reveal an isotropic production of multiple charged oxygen ions, as well as hydrogen ions. From the ion energy distribution, relative yields of the dissociation resulting from multiple ionization were obtained as a function of the charge state, as well as for several projectile energies. Multiple-ionization yields with charge state up to 4+, were extracted from the measurements of the time-of-flight spectra, focused on the production of single and multiple charged oxygen ions. The measurements were compared to ion-water collision experiments investigated at the keV energy range available in the literature, revealing differences and similarities in the fragment-ion energy distribution.
Force and Moment Measurements Applicable to a Flexible Weapons System

Military operations are dynamic in nature, as time-dependent requirements or adversary actions can contribute to differing levels of mission performance among systems. Future military operations commonly use multi-criteria decision analysis techniques that rely on value-focused thinking (VFT) to analyze and ultimately rank alternatives during the Analysis of Alternatives phase of the acquisition process. Traditional VFT approaches are not typically employed with the intention of analyzing time-variant performance of alternatives. In this research, a holistic approach towards integrating fundamental practices such as VFT, systems architecture, and modeling and simulation is used to analyze time-dependent data outputs of an alternative’s performance within an operational environment. Incorporating this approach prior to Milestone A of the acquisition process allows for the identification of time-based capability gaps and additional dynamic analysis of possible alternatives that can be implemented as a flexible means of assessment. As part of this research, the pre-acquisition methodology is implemented with a hypothetical multi-domain Intelligence, Surveillance, and Reconnaissance mission in order to exemplify multiple time-dependent analysis possibilities.
The Correlation Between Night Shift Work Schedules, Sleep Quality, and Depression Symptoms

Purpose Poor sleep and depressive symptoms are two negative effects of night shift work on physical and mental health. This study evaluated the correlation between sleep quality and depression symptoms among nurses in Saudi Arabia. Specifically, we assessed depressive symptoms and sleep quality observed among nurses who worked night shifts and compared the outcomes with those who worked exclusively day shifts in hospital settings. Patients and Methods A total of 191 participants (55.5% men, 44.5% women) participated. The response rate was 63.6%. The hospital anxiety and depression scale (HADS) and Pittsburgh Sleep Quality Index (PSQI) were used to assess depression score and sleep quality, respectively. Results Nurses who worked night shifts had substantially higher PSQI ratings (p<0.05) than those who worked day shifts. In addition, there was a clear relationship between the HADS and PSQI scores. According to the binary logistic regression, longer working hours and inadequate sleep were both independently linked to depressive symptoms among nurses. Conclusion Poor sleep quality brought on by night shifts may explain why Saudi nurses who work night shifts experience higher rates of depression than those who work day shifts only.
Tundra conservation challenged by forest expansion in a complex mountainous treeline ecotone as revealed by spatially explicit tree aboveground biomass modeling

ABSTRACT The subarctic forest tundra transition zone is one of the most vulnerable ecological regions worldwide and susceptible to climate change. Forest changes could lead to biodiversity losses when tundra areas become colonized. However, the impact of complex landscapes with barriers and channels for seed dispersal is highly understudied. Hence, we investigated potential tree aboveground biomass (AGB) change in mountainous central Chukotka (Siberia) with the individual-based spatially explicit vegetation model Larix vegetation simulator (LAVESI). In a climate sensitivity study, we simulate forest dynamics until 3000 CE for Representative Concentration Pathways (RCPs) with and without hypothetical cooling after 2300 CE to twentieth-century levels. The current state and spatiotemporal dynamics of tree AGB are validated against field and satellite-derived data. Our results suggest densification of existing tree stands and a lagged forest expansion depending on the distance to the current tree line (~39 percent of the total study area, RCP 8.5) under all considered climate scenarios. In scenarios with cooling after 2300 CE, forests stopped expanding and then gradually retreated to their pre-twenty-first-century position (~10 percent, RCP 8.5). However, forest remnants remain in the colonized area, leaving an imprint of forests in former tundra areas, which will likely have an adverse impact on tundra biodiversity.