Miracle-dz/UniFAR-FTUs huggingface dataset:
The Interplay among Empathy, Vicarious Trauma, and Burnout in Greek Mental Health Practitioners

Background. Mental health professionals are at risk of experiencing vicarious trauma and burnout as a consequence of the nature of their work. Studies and scholars so far have demonstrated that empathy interacts directly with burnout, and they imply an interaction with vicarious trauma. However, research has paid little attention to the interplay among vicarious trauma, empathy, and burnout in mental health professionals who practice psychotherapy. This study examines the interplay between mental health professionals’ (those practicing psychotherapy) vicarious trauma and empathy and investigates the ways they contribute to burnout. Methods. The sample consisted of 214 mental health professionals (32 males and 182 females), working in the public and private sectors. Specific instruments were administered online to the sample: (a) an improvised demographic questionnaire (age, gender, education, specialty, years of experience, years of supervision); (b) the Counselor Burnout Inventory, validated for the Greek population by Kounenou et al.; (c) the Vicarious Trauma Scale; and (d) the Jefferson Scale of Physician Empathy. Results. Correlation analysis showed that empathy and vicarious trauma were positively related to burnout. Moreover, multiple regression analysis revealed that supervision, empathy, and, to a greater degree, vicarious trauma have a significant impact on burnout. Conclusion. Unlike relative research on burnout, gender and work experience did not seem to play a significant role in the prediction of burnout in the present study. Several suggestions for future studies, as well as implications for mental health practitioners, are discussed.
UTD-PO Solution for E-Plane Radiation Pattern Calculation of Rectangular Horn Antennas With Rectangular-Shaped Corrugations

Proposing a novel hybrid uniform theory of diffraction-physical optics (UTD-PO) formulation, we develop a method to calculate the E-plane radiation pattern of corrugated E-plane rectangular horn antennas with rectangular corrugations. We derive the technique from the estimation of the total field that reaches the emitting source (a magnetic current line), thereby assuming the impingement of a plane wave above the horn (reciprocity is applied). To validate our method, we compare the performance of the UTD-PO analysis with that of the electric field integral equation (EFIE) solved using the method of moments (MoM). Our method shows improved computational efficiency over EFIE and other numerical techniques that fully discretize the geometry.
ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROTATING MACHINERY USING WAVELET TRANSFORMS AS A PREPROCESSOR

Abstract The purpose of condition monitoring and fault diagnostics are to detect and distinguish faults occurring in machinery, in order to provide a significant improvement in plant economy, reduce operational and maintenance costs and improve the level of safety. The condition of a model drive-line, consisting of various interconnected rotating parts, including an actual vehicle gearbox, two bearing housings, and an electric motor, all connected via flexible couplings and loaded by a disc brake, was investigated. This model drive-line was run in its normal condition, and then single and multiple faults were introduced intentionally to the gearbox, and to the one of the bearing housings. These single and multiple faults studied on the drive-line were typical bearing and gear faults which may develop during normal and continuous operation of this kind of rotating machinery. This paper presents the investigation carried out in order to study both bearing and gear faults introduced first separately as a single fault and then together as multiple faults to the drive-line. The real time domain vibration signals obtained for the drive-line were preprocessed by wavelet transforms for the neural network to perform fault detection and identify the exact kinds of fault occurring in the model drive-line. It is shown that by using multilayer artificial neural networks on the sets of preprocessed data by wavelet transforms, single and multiple faults were successfully detected and classified into distinct groups.
Monodromie des systèmes différentiels linéaires à pôles simples sur la sphère de Riemann

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Application of in-vivo laser scanning microscopy for evaluation of barrier creams

The occurrence of skin irritation and contact dermatitis is a common problem in various occupational groups. The use of barrier creams represents a frequently utilized prophylactic measure for the protection of the skin, even if their effectiveness is hotly debated. Up to now, a number of in-vivo and in-vitro methods are in existence for the evaluation of barrier creams (BC), which have shown different results regarding the effectiveness of BC. The aim of the present study was the application of the in-vivo laser scanning microscopy for the evaluation of barrier creams. Therefore, sodium fluorescein was applied topically to track its penetration. Three different barrier creams were investigated (Vaseline (B) and 2 different commercially available barrier creams (C and D)) and compared to a reference area (A). It was shown that Vaseline represents a 100% protection against sodium fluorescein as no fluorescence could be detected in the stratum corneum. For barrier cream C, only a weak fluorescent signal was detectable, while in the case of barrier cream D, the fluorescent signal was comparable to that of the reference skin area. The present study revealed that the laser scanning microscopy represents an adequate, non-invasive, quick in-vivo technique for the evaluation of BC that even tracks low differences of effectiveness, which enables the ranking of BC.
A robust approach to handle risk constraints in mid and long-term energy-planning of hydro-thermal systems

1. Abstract We consider the optimal management of a hydro-thermal power system in the mid and long-term. This is a large-scale multistage stochastic linear program, often solved by combining sampling with dynamic programming, for example by stochastic dual dynamic programming techniques. However, such methodologies are no longer applicable, at least not in a straightforward manner, when the model is set in a risk-averse formulation. We propose to take into account risk by using a chance-constrained formulation of the energy-planning problem. In particular, for a given confidence level, and having critical minimum reservoirs levels at each time stage, reservoirs can be kept above the critical volume in a probabilistic manner. When the uncertainty in the problem -typically, the natural inflows- is represented by a periodic autoregressive stochastic process, the corresponding probabilistic constraints can be expressed as Conditional Value-at-Risk constraints and the chance-constrained problem is a linear program. Such linear program can be thought of as a robustified deterministic variant of the original stochastic programming problem. Three robust adaptive management strategies are then proposed and the methodology is applied to the Brazilian system. 2. Keywords: long-term generation problem, chance constraints, Conditional Value at Risk, multistage stochastic linear programming, robust optimization.
Immunogenicity of CRISPR therapeutics—Critical considerations for clinical translation

CRISPR offers new hope for many patients and promises to transform the way we think of future therapies. Ensuring safety of CRISPR therapeutics is a top priority for clinical translation and specific recommendations have been recently released by the FDA. Rapid progress in the preclinical and clinical development of CRISPR therapeutics leverages years of experience with gene therapy successes and failures. Adverse events due to immunogenicity have been a major setback that has impacted the field of gene therapy. As several in vivo CRISPR clinical trials make progress, the challenge of immunogenicity remains a significant roadblock to the clinical availability and utility of CRISPR therapeutics. In this review, we examine what is currently known about the immunogenicity of CRISPR therapeutics and discuss several considerations to mitigate immunogenicity for the design of safe and clinically translatable CRISPR therapeutics.
Sensitivity of a Large Ensemble of Tropical Convective Systems to Changes in the Thermodynamic and Dynamic Forcings

Abstract A two-dimensional cloud-resolving model (CRM) is used to perform five sets of simulations of 68 deep convective cloud objects identified with Clouds and the Earth’s Radiant Energy System (CERES) data to examine their sensitivity to changes in thermodynamic and dynamic forcings. The control set of simulations uses observed sea surface temperatures (SSTs) and is forced by advective cooling and moistening tendencies derived from a large-scale model analysis matched to the time and location of each cloud object. Cloud properties, such as albedo, effective cloud height, cloud ice and snow path, and cloud radiative forcing (CRF), are analyzed in terms of their frequency distributions rather than their mean values. Two sets of simulations, F+50% and F−50%, use advective tendencies that are 50% greater and 50% smaller than the control tendencies, respectively. The increased cooling and moistening tendencies cause more widespread convection in the F+50% set of simulations, resulting in clouds that are opt.
Spherical harmonic stacking for the singlets of Earth's normal modes of free oscillation

We extend the spherical harmonic stacking (SHS) method of Buland et al. (1979) for the radial (vertical) component in the seismogram to the transverse (horizontal) components of the displacement field. Taking advantage of the orthogonality of the spherical harmonic functions (scalar and vectorial), SHS isolates and accentuates the signals of individual singlets of the Earth's normal modes of free oscillation. We apply the SHS on the broadband Incorporated Research Institutions for Seismology (IRIS) seismograms from up to 97 IRIS seismic stations for the 2004 Sumatra‐Andaman earthquake, in experiments targeted to spheroidal as well as toroidal modes—2S1, 0S3, 2S2, 3S1, 1S3, 0T2, and 0T3. We report the complete resolution of the singlet frequencies of these multiplets, some for the first time, and estimate the singlets' complex frequencies using the frequency domain autoregressive method of Chao and Gilbert (1980). The latter contain useful information to be used in inversions for the 3‐D structure of the Earth's interior.
Analysis and Design of a Dual-Mode VCO With Inherent Mode Compensation Enabling a 7.9–14.3-GHz 85-fs-rms Jitter PLL

This article presents a wideband, low-jitter frequency synthesizer utilizing a dual-mode voltage-controlled oscillator (VCO). Mode imbalance in the dual-mode VCO is analyzed theoretically and compensated through the proposed symmetric figure-8 transformer and capacitor arrays. The compact mode-switching circuitry fundamentally eliminates mode ambiguity in multi-mode autonomous circuits. A computer-aided algorithm based on sequential least-squares programming (SLSQP) and hierarchical optimization method is developed to automatically optimize the capacitor array in the wideband VCO. The implemented dual-mode VCO suppresses the phase noise (PN) difference across the operating frequency range, which further enables a sub-sampling phase-locked loop (SSPLL) to achieve near-minimum jitter across a wide frequency range without loop gain adaptation. Fabricated in a 40-nm CMOS process, the wideband SSPLL covers the frequency range of 7.9–14.3 GHz with 14.1–17.2-mW power consumption and occupies only 0.18-mm2 area. The SSPLL achieves better than −115-dBc/Hz in-band PN at a 10-GHz carrier. The rms jitter is less than 85 fs across the whole frequency range. The corresponding figure-of-merit tuning (FoM $_{T}$ ) is −247.1 to −248.1 dB.
On the effects of Ericksen and Deborah numbers on the flow in microfluidic capillaries

ABSTRACT The use of nematics in microfluidic devices offers methods of flow control that are inaccessible to isotropic fluids. While nematics have been used in channel flows found in lab on chip devices, their flow and microstructure formation still needs to be fully understood in geometries with a cylindrical cross-section. This paper investigates liquid crystal flows through capillaries via 1) the Beris-Edwards (BE) model and 2) the Leslie-Ericksen (LE) model, which emerges as a limiting case (uniaxial, constant order parameter) of the BE model. We show that the microstructure distribution is controlled by the Ericksen ( ) and Deborah ( ) numbers in the Beris-Edwards model. Defects at low Ericksen numbers arise when the elastic energy dominates the thermotropic energy. Defect destruction provides a new shear-thinning mechanism, which cannot be observed in the Leslie-Ericksen theory. GRAPHICAL ABSTRACT
Omnidirectional planar folded dipole antenna

An omnidirectional planar antenna comprising two back-to-back folded dipoles printed on a dielectric substrate is presented. A constructed prototype suitable for WLAN operation in the 5-GHz band, such as the 5.2-GHz (5150-5350 MHz) and 5.8-GHz (5725-5875 GHz) bands, is demonstrated. The constructed prototype shows a wide impedance bandwidth of larger than 1.1 GHz (about 20% centered at 5.5 GHz), and a stable antenna gain level of about 4.5 dBi, with small variations (< 2 dBi) for omnidirectional radiation, is obtained across the bandwidth. Details of the antenna design and the experimental results are presented.
Search space modulation in genetic algorithms: evolving the search space by sinusoidal transformations

An experimental form of Modulation (Reinterpretation) of the Search Space is presented. This modulation is developed as a mathematical method that can be implemented directly into existing evolutionary algorithms without writing special operators, changing the program loop etc. The main mathematical principle behind this method is the dynamic sinusoidal envelope of the search space. This method is presented in order to solve some theoretical and practical issues in evolutionary algorithms like numerical bounded variables, dynamic focalized search, dynamic control of diversity, feasible region analysis etc.
Fuzzy K-NN algorithm using modified K-selection

In this paper, a new K selection method in fuzzy K-NN (nearest-neighbor) algorithm, called the "updated fuzzy K-NN", is proposed. The main idea of this method is in the selection of K neighbors by considering the distance difference and the membership grade each neighbor has. The classification results of 32 classes of complex images are given. Compared to K-NN, and fuzzy K-NN algorithm, our method showed improved classification rate. <<ETX>>
KC-GCN: A Semi-Supervised Detection Model against Various Group Shilling Attacks in Recommender Systems

Various detection methods have been proposed for defense against group shilling attacks in recommender systems; however, these methods cannot effectively detect attack groups generated based on adversarial attacks (e.g., GOAT) or mixed attack groups. In this study, we propose a two-stage method, called KC-GCN, which is based on k -cliques and graph convolutional networks. First, we construct a user relationship graph, generate suspicious candidate groups, and extract influential users by calculating the user nearest-neighbor similarity. We construct the user relationship graph by calculating the edge weight between any two users through analyzing their similarity over suspicious time intervals on each item. Second, we combine the extracted user initial embeddings and the structural features hidden in the user relationship graph to detect attackers. On the Netflix and sampled Amazon datasets, the detection results of KC-GCN surpass those of the state-of-the-art methods under different types of group shilling attacks. The F1-measure of KC-GCN can reach above 93% and 87% on these two datasets, respectively.
Deep Learning Framework for Two-Way MISO Wireless-Powered Interference Channels

In this paper, we present a realistic and novel protocol for two-way communication in multiple-input-single-output (MISO) wireless-powered interference channels, namely, a simultaneous wireless information and power transfer (SWIPT) then wireless information transfer (WIT) protocol. In the protocol considered, transmitters first perform SWIPT in a forward link (FL) and receivers and then execute WIT using the harvested energy in a backward link (BL). Given that the operation of SWIPT in the FL affects the performance of WIT in the BL, our aim is to find a resource allocation strategy that maximizes the sum spectral efficiency (SE) of the FL and BL, which requires the joint optimization of the transmit beamforming vector, transmit power, and energy harvesting (EH) ratio in the FL and the receive beamforming vector in the BL. To deal with the non-convexity of the optimization problem, a deep learning (DL) framework is devised, in which the optimal resource allocation strategy is approximated by a well-designed deep neural network (DNN) model consisting of six independent DNN modules where the sigmoid and softmax functions are jointly utilized to properly model each control parameter. Furthermore, a two-stage training method is proposed where the DNN model is initialized using suboptimal solutions that are found using a low complexity algorithm in a supervised manner before it is fine-tuned using the main training based on unsupervised learning. Through intensive simulations performed in various environments, we confirm that the proposed method improves the training performance of the DNN model while reducing the training overhead. As a result, the proposed DL-based resource allocation achieves a near-optimal performance in terms of the sum SE with a low computation time.
Mediterranean Diet: The Role of Phenolic Compounds from Aromatic Plant Foods

Today’s global food system aggravates climate change while failing in meeting SDG2 and more. Yet, some sustainable food cultures, such as the Mediterranean Diet (MD), are simultaneously safe, healthy, and rooted in biodiversity. Their wide range of fruits, herbs, and vegetables convey many bioactive compounds, often associated with colour, texture, and aroma. Phenolic compounds are largely responsible for such features of MD’s foods. These plant secondary metabolites all share in vitro bioactivities (e.g., antioxidants), and some are evidenced in vivo (e.g., plant sterols lower cholesterol levels in blood). The present work examines the role of polyphenols in the MD, with respect to human and planetary health. Since the commercial interest in polyphenols is increasing, a strategy for the sustainable exploitation of Mediterranean plants is essential in preserving species at risk while valuing local cultivars (e.g., through the geographical indication mechanism). Finally, the linkage of food habits with cultural landscapes, a cornerstone of the MD, should enable awareness-raising about seasonality, endemism, and other natural constraints to ensure the sustainable exploitation of Mediterranean plants.
A TWO-DIMENSIONAL PLASMA ACTUATOR ANALYSIS CODE

Abstract : A two-dimensional plasma actuator analysis code has been developed. A time-accurate Navier-Stokes CFD code was coupled with a time-dependent, phenomenological model of an alternating current, single dielectric barrier discharge plasma actuator. The accuracy of the flow solver was demonstrated using a standard CFD validation case. The plasma actuator model's ability to affect fluid motion was tested by inducing motion in quiescent air over a flat plate using only the plasma actuator model. The resulting fluid motion was similar to the induced motion observed in plasma actuator experiments.
Generalized nested dissection

J. A. George has discovered a method, called nested dissection, for solving a system of linear equations defined on an n = k $\times$ k square grid in O(n log n) space and O($n{3/2}$) time. We generalize this method without degrading the time and space bounds so that it applies to any system of equations defined on a planar or almost-planar graph. Such systems arise in the solution of two-dimensional finite element problems. Our method uses the fact that planar graphs have good separators. More generally, we show that sparse Gaussian elimination is efficient for any class of graphs which have good separators, and conversely that graphs without good separators (including almost all sparse graphs) are not amenable to sparse Gaussian elimination.
APT Attack Detection Scheme Based on CK Sketch and DNS Traffic

In recent years, Advanced Persistent Threat (APT) attacks against sensors have emerged as a prominent security concern. Due to the low level of protection provided by sensors, APT attack organizations are able to develop intrusion schemes that allow them to infiltrate, attack, lurk, spread, and steal information from the target over an extended period of time. Through extensive research on the APT attack process and current defense mechanisms, it has been found that analyzing Domain Name Server (DNS) traffic in the communication control phase is an effective way of detecting APT attacks. However, analyzing APT attacks based on traffic usually involves the detection of a vast amount of DNS traffic, and current data preprocessing methods do not scale down data effectively, leading to low detection efficiency. In previous work, most efforts have been focused on calculating the features of request messages or corresponding messages without considering the association between request messages and corresponding messages. To address these issues, we propose a sketch-based APT attack traffic detection scheme. The scheme leverages the sketch structure to count and compress network traffic, improving the efficiency of APT detection. Our work also analyzes the limitations of traditional sketches in network traffic and proposes an improved sketch scheme. In addition, we propose several effective features for detecting APT attacks. We validate and evaluate our solution using 1,088,280 DNS traffic from a lab network and APT suspicious traffic from netresec and contagio, using eight machine learning models. The experimental results show that for the ExtraTrees model, our solution has a processing time of 0.0638 s and an accuracy of 0.97920, reducing the processing time by approximately 50 times and improving detection accuracy by a small margin compared to a dataset without sketch processing.
Prevalence of Research Design in Special Education: A Survey of Peer-Reviewed Journals

Special education policy and scholarship have increasingly emphasized the use of research that encompasses interdependent methodological approaches designed to address specific questions. Consequently, the knowledge available to practitioners, policymakers, and other consumers is a function of the research designs featured in the literature. This study describes the prevalence of methodological approaches appearing in special education journals (n = 33). An assessment of a sample of articles (n = 12,669) published from 1999 to 2019 found that much of the published research (n = 9,543) emphasizes nonexperimental, quantitative inquiry (57.86%). Experimental research is comparatively less common (25.51%) and encompasses both single-case (14.22%) and group experimental designs (11.29%). Qualitative studies (9.38%) appear far less frequently. Patterns of publication vary based on journal emphasis. A description of results is followed by a discussion of implications for the consumption and generation of evidence in special education.