Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly efficient few-cycle laser wakefield electron accelerator

A significant part of the laser wakefield acceleration (LWFA) research effort focuses on studying high-energy, quasi-monoenergetic electron beams. For other applications, such as the production and application of intense betatron x-ray radiation, Bremsstrahlung γ-rays and positron beams, the beam’s spectral quality is secondary to the number of electrons produced. This work discusses 3D particle-in-cell simulations of a highly efficient LWFA acceleration process, generating a broad spectrum of electrons, driven by a 12 TW few-cycle laser on high-density gas targets. In some cases, laser absorption in plasma exceeds 80%, and up to 27% of the driving laser energy is transferred to electrons over 20 MeV leaving the plasma. We also observe a deceleration of the accelerated beam at the plasma downramp and plasma exit, which arises from transitioning from laser-driven to beam-dominated wake, and also from the induced axial electric field. This effect is similar to magnetic vortex acceleration, where the induced axial electric field, instead of accelerating plasma ions, would slow down the opposite-charged electron beam and also a strong return current and backward electron beam.
Do party supporters accept policy compromises in coalition governments?

Government formation in multi-party systems often requires coalition negotiations and finding common ground among coalition partners. Supporters of parties involved in the government formation process face a tradeoff when evaluating such bargaining processes: on the one hand, voters usually prefer seeing their party being in government rather than in opposition; on the other hand, negotiations require coalition compromises that they might dislike. In this paper, we study voters’ willingness to accept policy compromises during government formation processes. We argue that voters’ acceptance of policy compromises depends on both the strength of their party attachment and the importance they assign to the issue at stake during the coalition negotiations. Not giving in on important issues is key, especially for supporters of challenger parties, who hold strong policy preferences on a selected number of issues. To test these expectations, we collected original survey data immediately after the Spanish general election in November 2019. The results show support for the hypothesized effects, shed light on the pressure potential coalition partners face during government formation and help explain the failures of government formation attempts in increasingly polarized societies.
A Microbial Arsenic Cycle in Sediments of an Acidic Mine Impoundment: Herman Pit, Clear Lake, California

ABSTRACT The involvement of prokaryotes in the redox reactions of arsenic occurring between its +5 [arsenate; As(V)] and +3 [arsenite; As(III)] oxidation states has been well established. Most research to date has focused upon circum-neutral pH environments (e.g., freshwater or estuarine sediments) or arsenic-rich “extreme” environments like hot springs and soda lakes. In contrast, relatively little work has been conducted in acidic environments. With this in mind we conducted experiments with sediments taken from the Herman Pit, an acid mine drainage impoundment of a former mercury (cinnabar) mine. Due to the large adsorptive capacity of the abundant Fe(III)-rich minerals, we were unable to initially detect in solution either As(V) or As(III) added to the aqueous phase of live sediment slurries or autoclaved controls, although the former consumed added electron donors (i.e., lactate, acetate, hydrogen), while the latter did not. This prompted us to conduct further experiments with diluted slurries using the live materials from the first incubation as inoculum. In these experiments we observed reduction of As(V) to As(III) under anoxic conditions and reduction rates were enhanced by addition of electron donors. We also observed oxidation of As(III) to As(V) in oxic slurries as well as in anoxic slurries amended with nitrate. We noted an acid-tolerant trend for sediment slurries in the cases of As(III) oxidation (aerobic and anaerobic) as well as for anaerobic As(V) reduction. These observations indicate the presence of a viable microbial arsenic redox cycle in the sediments of this extreme environment, a result reinforced by the successful amplification of arsenic functional genes (aioA, and arrA) from these materials.
Intelligent system for depression scale estimation with facial expressions and case study in industrial intelligence

As a mental disorder, depression has affected people's lives, works, and so on. Researchers have proposed various industrial intelligent systems in the pattern recognition field for audiovisual depression detection. This paper presents an end‐to‐end trainable intelligent system to generate high‐level representations over the entire video clip. Specifically, a three‐dimensional (3D) convolutional neural network equipped with a module spatiotemporal feature aggregation module (STFAM) is trained from scratch on audio/visual emotion challenge (AVEC)2013 and AVEC2014 data, which can model the discriminative patterns closely related to depression. In the STFAM, channel and spatial attention mechanism and an aggregation method, namely 3D DEP‐NetVLAD, are integrated to learn the compact characteristic based on the feature maps. Extensive experiments on the two databases (i.e., AVEC2013 and AVEC2014) are illustrated that the proposed intelligent system can efficiently model the underlying depression patterns and obtain better performances over the most video‐based depression recognition approaches. Case studies are presented to describes the applicability of the proposed intelligent system for industrial intelligence.
All Your Fake Detector are Belong to Us: Evaluating Adversarial Robustness of Fake-News Detectors Under Black-Box Settings

With the hyperconnectivity and ubiquity of the Internet, the fake news problem now presents a greater threat than ever before. One promising solution for countering this threat is to leverage deep learning (DL)-based text classification methods for fake-news detection. However, since such methods have been shown to be vulnerable to adversarial attacks, the integrity and security of DL-based fake news classifiers are under question. Although many works study text classification under the adversarial threat, to the best of our knowledge, we do not find any work in literature that specifically analyzes the performance of DL-based fake-news detectors under adversarial settings. We bridge this gap by evaluating the performance of fake-news detectors under various configurations under black-box settings. In particular, we investigate the robustness of four different DL architectural choices—multilayer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN) and a recently proposed Hybrid CNN-RNN trained on three different state-of-the-art datasets—under different adversarial attacks (Text Bugger, Text Fooler, PWWS, and Deep Word Bug) implemented using the state-of-the-art NLP attack library, Text-Attack. Additionally, we explore how changing the detector complexity, the input sequence length, and the training loss affect the robustness of the learned model. Our experiments suggest that RNNs are robust as compared to other architectures. Further, we show that increasing the input sequence length generally increases the detector’s robustness. Our evaluations provide key insights to robustify fake-news detectors against adversarial attacks.
Seed dormancy varies widely among Arabidopsis thaliana populations both between and within Fennoscandia and Italy

Abstract The timing of germination is a key life‐history trait in plants, which is strongly affected by the strength of seed dormancy. Continental‐wide genetic variation in seed dormancy has been related to differences in climate and the timing of conditions suitable for seedling establishment. However, for predictions of adaptive potential and consequences of climatic change, information is needed regarding the extent to which seed dormancy varies within climatic regions and the factors driving such variation. We quantified dormancy of seeds produced by 17 Italian and 28 Fennoscandian populations of Arabidopsis thaliana when grown in the greenhouse and at two field sites in Italy and Sweden. To identify possible drivers of among‐population variation in seed dormancy, we examined the relationship between seed dormancy and climate at the site of population origin, and between seed dormancy and flowering time. Seed dormancy was on average stronger in the Italian compared to the Fennoscandian populations, but also varied widely within both regions. Estimates of seed dormancy in the three maternal environments were positively correlated. Among Fennoscandian populations, seed dormancy tended to increase with increasing summer temperature and decreasing precipitation at the site of population origin. In the smaller sample of Italian populations, no significant association was detected between mean seed dormancy and climate at the site of origin. The correlation between population mean seed dormancy and flowering time was weak and not statistically significant within regions. The correlation between seed dormancy and climatic factors in Fennoscandia suggests that at least some of the among‐population variation is adaptive and that climate change will affect selection on this trait. The weak correlation between population mean seed dormancy and flowering time indicates that the two traits can evolve independently.
Computational Fluid Dynamic Analysis of Flow around Mangrove roots.

The objective of this paper is to study the flow patterns around mangrove roots to understand its significance in coastal risk reduction. A computational model of two-dimensional Pneumatophores mangrove roots is developed, and flow is simulated using ANSYS software with a predefined mathematical model and boundary conditions. Computational fluid dynamic analysis of a two-dimensional, viscous, incompressible, turbulent flow under steady and unsteady boundary condition is performed using the Finite volume method. Detailed velocity profiles, residual plots, pressure distributions, vectors of the velocity magnitude and pressure and velocity at different positions around the mangrove roots are presented to visualize the importance of mangroves in reducing the wave velocity. The simulation outcome confirms that the mangrove roots of mangrove species can reduce the initial velocity of water flow. The flow patterns and flow structures around the root in the mangrove forest establishes precious data in increasing the effectiveness of current breakwater model.
The impact of clonal hematopoiesis on outcomes in patients with aplastic anemia

Over the years, not only have the T-cell mediated immune mechanisms of aplastic anemia (AA) involved in AA development started to become better understood, but there is now also a better understanding of the roles played by somatic mutations, cytogenetic abnormalities and defective telomerase functions and other genetically-related factors. Somatic gene mutations suggestive of clonal hematopoiesis are detected in approximately one third of patients with AA. Recent studies have suggested that some of these may predict a better response to immunosuppressive therapy, whereas others indicate poorer outcomes with higher risks of clonal evolution to myelodysplastic syndrome or acute myeloid leukemia, and that therefore better results may be obtained based on allogeneic stem cell transplantation. Furthermore, recent advances in molecular techniques may be useful in differentiating aplastic anemia from hypocellular myelodysplastic syndrome and other clonal hematopoiesises of indeterminate potential. All of these are summarized in this review which includes further insights into treatment personalization based on the molecular pathogenesis of AA.
Teacher’s Attitude towards Information Technology In Relation To Their Gender, Locality and Type of School

The purpose of conducting this study was to find out the teacher’s attitude towards Information Technology (IT) in relation to their Gender, Locality and Type of School. A sample of 100 secondary school teachers working in government and private schools of district Jalandhar was selected through Stratified Random Sampling technique. Attitude Scale towards Information Technology for Teachers (ASTITT-NI) by Nasrin and Islahi (2012) was used for data collection. Mean, SD and t-test were used to compare the attitude towards information technology of male and female, rural and urban, government and private secondary school teachers. The findings of the study revealed that i) there is no significant difference in the attitude of male and female secondary school teachers towards Information Technology ii) Significant difference was found in the attitude of secondary school teachers from rural and urban areas towards information technology. iii) Significant difference was found in the attitude of private and government secondary school teachers towards Information Technology. Private school teachers were found to possess higher level of favourable attitude towards Information technology as compared to government senior secondary school teachers. Keyword: Attitude, Effective teachers, Information Technology (IT), Secondary School teachers
Examining the contribution of near real-time data for rapid seismic loss assessment of structures

This study proposes a probabilistic framework for near real-time seismic damage assessment that exploits heterogeneous sources of information about the seismic input and the structural response to the earthquake. A Bayesian network is built to describe the relationship between the various random variables that play a role in the seismic damage assessment, ranging from those describing the seismic source (magnitude and location) to those describing the structural performance (drifts and accelerations) as well as relevant damage and loss measures. The a priori estimate of the damage, based on information about the seismic source, is updated by performing Bayesian inference using the information from multiple data sources such as free-field seismic stations, global positioning system receivers and structure-mounted accelerometers. A bridge model is considered to illustrate the application of the framework, and the uncertainty reduction stemming from sensor data is demonstrated by comparing prior and posterior statistical distributions. Two measures are used to quantify the added value of information from the observations, based on the concepts of pre-posterior variance and relative entropy reduction. The results shed light on the effectiveness of the various sources of information for the evaluation of the response, damage and losses of the considered bridge and on the benefit of data fusion from all considered sources.
Distributed Intersection Conflict Resolution for Multiple Vehicles Considering Longitudinal-Lateral Dynamics

Multiple vehicles control at an intersection is one of the most challenging scenarios in the vehicle control field due to the high number of constraints. However, regarding the control strategies of an isolated intersection, few studies have considered the longitudinal-lateral dynamics comprehensively. In the approaches that treat the turning curve as a straight line, a longitudinal dynamic model is adopted, while the lateral execution ability is ignored. The ideal decisions that ignore vehicle dynamics are not accurately executed by actual vehicles, as doing so would be futile. In addition, due to the strong coupling and nonlinearity of the longitudinal-lateral dynamics model, the optimization problem is difficult to solve. Therefore, in this paper, a reasonable model, the simplified vehicle dynamics model coupled with longitudinal-lateral dynamics is used to describe longitudinal, lateral, and yaw motions comprehensively. Optimization of the longitudinal and lateral control of each vehicle is realized simultaneously. The multi-objective optimization problem is decomposed and solved by each vehicle utilizing model predictive control (MPC) in a distributed manner. A novel safety constraint interpretive method (SCIM) is proposed to reduce the number of constraints and facilitate the solution by converting the safety constraint. The asymptotic stability of a local closed-loop system is guaranteed by a terminal constraint, and the global feasibility is proven. Finally, a demanding intersection scenario, with comparison between the Proportional-Integral-Derivative (PID) method and Adaptive Cruise Control (ACC) method, is carried out in a Processor-in-the-Loop (PiL) test. The results demonstrate that intersection safety is guaranteed with smoother control inputs obtained by the designed distributed method. The control inputs are more suitable for vehicle execution, especially in turning maneuvers.
Neonatal Bacterial Meningitis: Single Reference Center Experience and Review of Current Literature: A Retrospective Cohort Study

OBJECTIVE: Despite improvements in neonatal care, neonatal bacterial meningitis is still an emerging problem worldwide with high rates of mortality. The present study evaluates data on suspected- and culture-proven neonatal bacterial meningitis in the light of a single tertiary reference center experience in Turkey in comparison with the globe. STUDY DESIGN: In this retrospective cohort study newborns admitted to Hacettepe University Ihsan Dogramaci Children’s Hospital Neonatal Intensive Care Unit during a 5-year-period between April 2014-May 2019 and who underwent atraumatic lumbar puncture were included. RESULTS: Two hundred sixty-four patients fulfilled the inclusion criteria. Most common symptoms in all patients raised suspicion in favor of NBM and resulted in lumbar puncture were fever (34.5%, n=91), respiratory distress (31.1%, n=82), lethargy (31.1%, n=82), and apnea (26.1%, n=69). The incidence of culture-proven NBM among suspected patients was 5.7% (n=15/264); while the incidence is 3.1 per 1000 (15/4574) at all Neonatal Intensive Care Unit admissions. Respiratory distress (60.0%, n=9/15) and apnea (40.0%, n=6/15) were the most common symptoms in patients with NBM; which may be due to the predominance of premature newborns in the NBM group. The most common microorganisms in CSF cultures were coagulase-negative Staphylococci with Methicillin-resistant Staphylococcus epidermidis being most common among all. CONCLUSIONS: The present study underlines high rates of culture-proven neonatal bacterial meningitis among suspected newborns despite improvements in modern health care, which raises attention to careful evaluation of these patients and early administration of properly-selected antibiotics. Our incidence rates are in keeping with studies from the developed world.
The Analysis of Surgery Patients Anxiety Reviewed from Pre Operative Services at Dr. R. M Djoelham Binjai Public Hospital

Surgery is a form of therapy that can threaten the integrity of a person's body and soul. The psychological response that usually occurs in preoperative patients is anxiety. Anxiety is an emotional reaction to subjective individual judgment, which is influenced by the subconscious and the cause is not specifically known. Type of research is quantitative and qualitative research (method mixed). This research was conducted at DR. R.M Djoelham general hospital Binjai. Qualitative population of surgeons, anesthetists and nurses at RSUD DR. RM Djoelham Binjai, samples obtained by Purposive Sampling as many as 33 people. Data were analyzed by univariate, bivariate (Chi Square), Multivariate (logistic regression). Based on these results it is concluded that there is a relationship between providing information, consultation and education on patient anxiety. Based on the results of the bivariate analysis, it is found that there is a relationship in the dimension of Information Giver has a value of p (sig) 0.037, consultation (sig) 0.005, education p (sig) 0,002. The concluded that there is a relationship between providing information, consultation and education on patient anxiety. Based on the results of interviews from informants, it was found that the provision of information, consultation and education before or after the operation had been implemented, both DPJP doctors, anesthetists and nursing staff.
Spatial clustering of willingness to pay for ecosystem services

This is an open access article under the terms of the Creative Commons AttributionNonCommercialNoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is noncommercial and no modifications or adaptations are made. © 2021 The Authors. Journal of Agricultural Economics published by John Wiley & Sons Ltd on behalf of Agricultural Economics Society. Applied Choice Research Group, University of Stirling, Management School, Stirling, UK Department of Geography, University of Western Ontario, London, ON, Canada Department of Business and Economics, Berlin School of Economics and Law, Berlin, Germany Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)—including machine learning (ML) and Big Data analytics—as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.