Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Hole Transport in Ni/Y₂O₃/n-4H-SiC MOS for Self-Biased Radiation Detection

We report the fabrication of novel self-biased high resolution radiation detectors achieved in n-type 4H-SiC metal-oxide-semiconductor (MOS) devices. Vertical MOS structure has been realized by pulsed laser deposition of 40 nm Y<sub>2</sub>O<sub>3</sub> layer on 20 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> n-type 4H-SiC epilayer followed by sputter coating a nickel gate, which revealed a record-high hole diffusion length. The MOS device exhibited a remarkable radiation detection response to 5486 keV alpha particles with a charge collection efficiency of 82% and an energy resolution of 72 keV full width at half maximum (FWHM) at zero applied bias. The hole diffusion length has been calculated to be 56 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> using a drift-diffusion model. Such long hole diffusion length and a flat-band potential of 2.1 V, enabled to attain high efficiency and resolution in the self-biased mode. Band energy calculations indicated that the presence of Y<sub>2</sub>O<sub>3</sub> layer may have neutralized the hole traps usually present in a metal-4H-SiC interface thereby substantially improving the hole transport. Such high performing self-biased radiation detectors fabricated on 4H-SiC are intended for applications in harsh environment space missions, wherein carrying detector power supplies as a payload becomes a critical logistic issue. The present study opens the high potential of other wide bandgap semiconductors as self-biased MOS devices as well.
Wireless Powered Opportunistic Cooperative Backscatter Communications: To Relay or Not?

In this article, we propose a wireless powered opportunistic cooperative backscatter communication network, where an Internet of Things (IoT) node conveys information to its associated receiver via backscatter communications with the help of a hybrid access point (HAP) in each transmission block. The HAP provides energy signals for the IoT node in the first half transmission block, and continues to do or relays the IoT node’s signal to the receiver via decode-and-forward protocol in the second half transmission block. We investigate under which condition the HAP serves as the relay node in the second half transmission block in terms of the achievable throughput. To this end, a mixed integer non-convex optimization is formulated to maximize the throughput of the IoT node by optimizing the power reflection coefficient (PRC) of the IoT node and the operation mode of the HAP during the second half transmission block, while meeting the energy-causality constraint of the IoT node. We derive the closed-form expressions for the optimal PRC and operation mode, based on which a scheme is proposed to determine the optimal operation mode of the HAP. Simulations validate the derived results and study the impacts of various parameters on the optimal operation mode and throughput.
Applications: Equivalence Tests Can Enhance Environmental Science and Management

Tests of point hypotheses are common in observational environmental studies, but there is concern about their appropriateness. Exhortations to restrict sample sizes on the basis of a power analysis for such tests may fail to satisfy the environmental professional, because sample sizes are not always easily controlled. Testing of interval hypotheses via equiva‐lence test procedures offers a way of testing meaningful hypotheses and of giving effect to the precautionary principle. An example is given which concerns mining impacts on stream benthic invertebrate communities, and includes calculation of Bayesian posterior probabilities.
A Data-Driven Multi-Height Empirical LoS Probability Model for Urban A2G Channels

Line-of-sight (LoS) probability modeling plays an essential role in the reliability determination of millimeter wave (mmWave) communication systems. However, since most LoS probability models do not consider altitude information of communication terminals, they cannot be directly applied to air-to-ground (A2G) communication scenarios. In this paper, we propose a new multi-height LoS probability model for mmWave unmanned aerial vehicle (UAV) communication scenarios. A machine learning (ML)-based parameter estimation method is also developed, which trains the data from the constructed virtual urban scenes. We first propose a LoS/none-LoS (NLoS) identification method to recognize the LoS path and calculate the LoS probability. Then, we construct a two-layer single-input multiple-output back propagation neural network (BPNN) which trains the relationship between the model parameters and the altitude of UAVs. Simulation results show that the proposed LoS probability model has a good consistency with the ray tracing (RT) simulation data and the currently existing models when altitudes of UAVs are low. As the altitude increases, our model is still applicable and can achieve an excellent agreement with the RT data.
Double-directional Multipath Data at 140 GHz Derived from Measurement-based Ray-launcher

The double-directional model of the radio channel is an indispensable tool in the design and evaluation of wireless systems. However, double-directional channel sounding is increasingly more difficult as the carrier frequency increases due to the need to steer narrower antenna beams across azimuth and elevation angles both at transmit (Tx) and receive (Rx) ends, resulting in a huge time duration required for measuring a single Tx-Rx link. To tackle the problem, we utilized a measurement-based ray-launcher (MBRL) to estimate the double-directional path data from single-directional radio channel sounding. The latter performs beam scanning only at one of Tx or Rx ends, allowing us to perform many more Tx-Rx link measurements than double-directional sounding. The MBRL maps multipaths measured from the single-directional channel sounding on a geometry of the measurement site, allowing us to identify the reflection points and angular information of the multipaths that we were not able to measure. Double-directional multipath data covering 12 line-of-sight and 56 non-line-of-sight indoor links at 140 GHz band are the main result of this paper. The data will serve for the design and evaluation of communications links, localization and sensing at 140 GHz.
Beamforming, Antenna Selection, and Power Allocation Factor Design for Downlink Two-User MISO-NOMA Systems

In this paper, we consider the joint antenna selection (AS), beamforming (BF), and power allocation factor (PAF) design problem for a two-user downlink power-domain non-orthogonal multiple access (PD-NOMA) systems. Different from conventional sum-rate-oriented problems, the proposed problem is concerned about the fairness between users, which is improved by maximizing the achievable rate of the weaker user with hardware constraints such as the total power constraint (TPC), per-antenna power constraint (PAPC), and the number of activated antennas, to prevent the extremely low communication quality. To solve the problem, we divide it into two stages due to its complexity. Simulation results demonstrate that with such procedures, the proposed approach achieves better fairness performance than most sum-rate-oriented counterparts and it can be bettered if we perform the two-stage design.
COMPETITION BETWEEN TWO WEEDS

Every gardener knows that plants compete with ach other? e pecial? ly if one of the plants is a weed. Al? though gardeners help their plants win competitive interactions by removing or poisoning weeds, plants in nature must fight on their own? finding ways to de? feat rival plants. The question is: What causes competing plants to win or lose? Competition between plants repre? sents one of the best-documented inter? actions between biological species. Hundreds of examples show that plant species affect one another through the use of shared resources, including light, nutrients and water. A tomato seedling in your garden, for instance, may grow poorly in the shade cast by a mature oak tree. Another tomato plant may sur? vive better in the shade, because some individual plants have different com? petitive abilities. The outcome of com? petition between plants may also de? pend on the composition of the local community and debris or chemicals left by plants from earlier generations. Although botanists have assembled a virtual catalogue of examples of com? petition, we know very little about the mechanisms that govern differences in competitive ability. I shall describe a se? ries of experiments that reveal an intri? cate^? and somewhat surprising? web
A Novel Negative Link Prediction Algorithm for Social Networks

In recent years, the use of social media networks has increased. With the rise in social network usage, social media businesses have begun to place a greater emphasis on large-scale social network analysis. As a result, several components of the signed network have been studied and analysed. Link prediction is a crucial aspect of this signed network analysis. The majority of previous study focused on forecasting the network’s good aspects, such as favourable user connections, but the negative component(i.e., Negative Link) is just as significant as the positive (i.e., positive link). It enhances the performance of several current positive link analysis programmes. As a result, we concentrate on predicting negative ties from a social network that contains both positive and negative interactions in this research. To improve prediction precision, we use social theories such as balancing and status theory. We utilise the XGBoost classifier to determine if a particular missing link is positive or negative. Finally, we use accuracy and F1 measure measures to assess our proposed model.
NUMERICAL INVESTIGATION OF THE GIBSON'S METHOD - EFFECTS OF CONNECTING TUBING -

In order to calculate the efficiency of a hydro power plant some quantities needs to be measured. One of them is the flow rate, which can be difficult to measure with good accuracy. There are several methods for that task. Gibson’s method is one of them and has the advantage to be economical and easily installed at the site. In order to achieve accurate results with this method, some criterions must be fulfilled. Two of these criterions are the measuring length must be larger than 10 m and the mean initial velocity times the measuring length should be larger than 50 m 2 /s. These criterions are rarely fulfilled in low head hydro power plants. This paper presents a numerical analysis of the effects of the connecting tubing between the pressure taps at the conduit and the differential pressure sensor. A one dimensional code programmed in Matlab is used for the numerical simulations where the governing equations, continuity and equation of motion, are discretizised with a second order Godunov-type scheme. The simulations are made for different initial flow rates and different valve closures. The result shows that the tubing has an overall small influence on the calculated flow rate error. A positive bias error appears in all tested cases and has a maximum (1.2 to 1.3 % depending on the test case) at the smallest Reynolds number.
Harmonic Mitigation in a Grid Interfacing PV Assisted Three-Phase Multilevel Switched Capacitor Inverter

The Power electronic devices (PEDs) play a vital role in connecting renewable energy sources to the distribution system, but they also have the potential to cause a range of power quality (PQ) issues over time. In this work, a new three-phase Multilevel Switched-Capacitor Inverter (MLSCI) is exhibited that can operate up to a higher voltage on Switched-Capacitor (SC) technology. A boost converter is utilised in this instance. A five-level output may be achieved using the three phases of MLSCI proposed here without the requirement for additional balancing control, which is a desirable feature. Using MATLAB/Simulink, the proposed study makes a significant contribution by comparing grid voltage and current of Total Harmonic Distortion (THD) with and without compensation using hysteresis Current Controller techniques, which is accomplished with the help of hysteresis Current Controller techniques.
Unpaired Motion Style Transfer with Motion-Oriented Projection Flow Network

Existing motion style transfer methods trained with unpaired samples tend to generate motions with inconsistent content or inconsistent number of frames when compared with the source motion. Moreover, due to the limited training samples, these methods perform worse in unseen style. In this paper, we propose a novel unpaired motion style transfer framework that generates complete stylized motions with consistent content. We introduce a motion-oriented projection flow network (M-PFN) designed for temporal motion data, which encodes the content and style motions into latent codes and decodes the stylized features produced by adaptive instance normalization (AdaIN) into stylized motions. The M-PFN contains dedicated operations and modules, e.g., Transformer, to process the temporal information of motions, which help to improve the continuity of the generated motions. Comparisons with the state-of-the-art methods show that our method effectively transfers the style of the motions while retaining the complete content and has stronger generalization ability in unseen style features.
Causal Intervention for Generalizable Face Anti-Spoofing

Generalizable face anti-spoofing (FAS) has drawn growing attention due to its robustness to unseen real scenarios. Existing domain generalization methods leverage adversarial learning or meta-learning to mitigate the domain bias and improve generalizability. However, these methods are heuristic and suffer from complicated min-max problems or cumbersome meta-updates. In this paper, we propose a simple yet effective Causal Intervention method for generalizable Face Anti-Spoofing, namely CIFAS. Firstly, we figure out the generalizability is undermined by a domain-aware confounder based on the structural causal model. Instantiating the confounder as the domain-specific factor, a domain embedding module is employed with Dirichlet mixup to obtain representative domain features. Consequently, we propose a novel backdoor adjustment model for causal intervention to capture the true causality and learn a robust FAS model. Our CIFAS is the first attempt to introduce causal learning into FAS. Extensive experiments on seven cross-dataset tests demonstrate that CIFAS outperforms the state-of-the-art methods.
Predicting and Characterizing Legal Claims of Hospitals with Computational Intelligence: the Legal and Ethical Implications

In this paper we propose a fuzzy logic-based approach to analyze UK National Health Service (NHS) public administrative data related to pre- and post-pandemic claims filed by patients, analyzing the legal and ethical issues connected to the use of Artificial Intelligence systems, including our own, to take critical decisions having a significant impact on patients, such as employing computational intelligence to justify the management choices related to Intensive Care Unit (ICU) bed allocation. Differently from previous papers, in this work we follow an unsupervised approach and, specifically, we perform an analysis of UK hospitals by means of a computational intelligence algorithm integrating Fuzzy C- Means and swarm intelligence. The dataset that we analyse allows us to compare pre- and post-pandemic data, to analyze the ethical and legal challenges of the use of computational intelligence for critical decision-making in the health care field.
Exact 2‐D scattering from a slot in a ground plane backed by a semielliptical cavity and covered with a multilayer isorefractive diaphragm

A semielliptical channel flush‐mounted under a metal plane and slotted along the interfocal distance of its cross section is separated from the half‐space above by a multilayer diaphragm. The cavity, the diaphragm, and the half‐space are all isorefractive to each other. Both the cavity and the multilayer diaphragm are filled with materials isorefractive to the medium in the half‐space above. This is a two‐dimensional geometry where the source is invariant with respect to the axial variable. The resulting electromagnetic boundary‐value problem is solved exactly in terms of Mathieu functions, when the excitation source is either a plane wave or a line source. For plane wave incidence, the polarization is with either the electric or the magnetic field parallel to the axis of the structure and the direction of incidence is arbitrary in a plane perpendicular to the axis. For line source excitation, the polarization is with either the electric or the magnetic field parallel to the axis of the structure and the source is arbitrarily located. Numerical results are also provided.
Template-Based Fabrication of Vertically Aligned Carbon Nanotube Arrays

—Vertical arrays of nanowires and nanotubes have recently been used in an increasing number of applications. These applications almost invariably rely on the physical features of these nanoelements, necessitating a better control over their geometrical properties to improve their performance. Here, we report the manufacturing process of vertically aligned, hollow carbon nanotube (CNT) arrays of different tube diameters, spacing, and length as needed for their intended application. A template-based manufacturing method was employed to form CNTs inside the pores of commercially available anodized aluminum oxide membranes using chemical vapor deposition. After removing the carbon layer from the surface of the membrane with oxygen plasma, the membrane surface was selectively removed by either reactive ion etching, wet chemical etching, or a combination of ion milling and wet etching to expose the CNTs embedded in the membrane. The results show that to reach longer individually addressable exposed CNTs, dry etching processes like RIE are more promising. The work provides a scalable and reliable manufacturing recipe for a CNT-based array device that can be used for various sensing and also biomedical applications.
Financial Disruptions and the Organization of Innovation: Evidence from the Great Depression

We examine innovation following the Great Depression using data on a century's worth of U.S. patents and a difference-in-differences design that exploits regional variation in the crisis severity. Harder-hit areas experienced large and persistent declines in independent patenting, mostly reflecting the disruption in access to finance during the crisis. This decline was larger for young and inexperienced inventors and lower-quality patents. In contrast, innovation by large firms increased, especially among young and inexperienced inventors. Overall, the Great Depression contributed to the decline in technological entrepreneurship and accelerated the shift of innovation into larger firms.
ADHD and Psychopathy in Adolescent Asian-Americans: A Cross-Sectional Analysis

Background The positive association of attention-deficit/hyperactivity disorder (ADHD) on the development of psychopathic traits in White-American juveniles has long been demonstrated in various studies. Less is known, however, about the role of specific attention-deficit/hyperactivity disorder symptomatology and how they interact with each dimension of the three-factor model of psychopathy in Asian juveniles. Objective The purpose of this paper is to explore the relationship between Asian-American juveniles with ADHD as evaluated by the ADHD Rating Scale-5: For Children and Adolescents (ADHD-RS-IV), and the exhibition of psychopathic traits as measured by the Youth Psychopathic Traits Inventory (YPI; Andershed et al. 2002). Previous studies have examined the prevalence of attention deficit and hyperactivity disorder (ADHD) in White-American prison inmates and in forensic patients with psychopathic traits. However, it is not clear whether ADHD and psychopathy scores also correlate in Asian, teenage non-incarcerated samples. This is a quantitative survey investigating the relationship between 1000 Asian teenagers exhibiting characteristics of ADHD as the For and Adolescents (ADHD-RS-IV) and the exhibition of psychopathic traits as measured by the
Machine Learning with Electronic Patient Records for Diagnosis Prediction in General Practice

General practitioners have to diagnose patients that visit for a multitude of reasons. Diverse diagnoses may be possible for the same complaints expressed by different patients. The general practitioner has to make a well-informed decision based on contextual factors, prior knowledge and experiences. This study aims to improve the diagnostic process by supporting general practitioners with AI to predict probable diagnoses. Machine learning is used with data from electronic patient records (EPR) to predict probabilities for diagnoses of common reasons to visit the general practitioner. A patient’s medical history is extracted from the EPR to serve as contextual information. The patient’s medical history is represented as a sequence of episodes, where episodes group related patient visits. The relevance of irregular time intervals between the episodes is investigated as well. A recurrent neural network (RNN) encodes the patient’s medical history into a latent patient representation, which is used for prediction by a classification network. The RNN-based model improves probabilistic performance and achieves significantly higher accuracy at 3 and 10 than the conditional probability baseline, which is what general practitioners currently use occasionally. Explicitly handling the irregular time intervals between episodes in the patient’s medical history does not seem to be beneficial for the predictive performance.
Choice-Based Network Revenue Management under Online Reviews

This paper proposes a choice-based network revenue management model that integrates the eﬀect of reviews. The dependency between reviews and revenue is two-fold: customers write reviews based on their price/quality perception, and reviews impact sales. A complicating factor in this model is that the eﬀects of reviews are delayed, e