Miracle-dz/UniFAR-FTUs huggingface dataset:
Skill Improvement Through Learning in Therapy (SKILT): A Study Protocol for a Randomized Trial Testing the Direct Effects of Cognitive Behavioral Therapy Skill Acquisition and Role of Learning Capacity in Depression

Background To improve psychological treatments for major depressive disorder (MDD), a better understanding on how symptoms ameliorate during treatment is essential. In cognitive behavioral therapy (CBT), it is unclear whether procedures focused on the acquisition of CBT skills play a causal role in the improvement of CBT skills. In this randomized trial, we isolate a single CBT Skill Acquisition Procedure (CBTSAP) and test its direct effects on CBT skills and related therapy processes (i.e., change in (idiosyncratic) dysfunctional thinking and reward processing). We hypothesize that the CBTSAP causes improvements in CBT skills and related therapy processes compared to an active control condition. In addition, we hypothesize that individual differences in attentional bias and memory functioning (defined as learning capacity) moderate the effects of CBTSAP on outcomes and that using mental imagery as a cognitive support strategy to strengthen the effects of the CBTSAP will be most beneficial for patients with low learning capacity. Method 150 patients with MDD will be randomized to one of three conditions: 1. an active control condition, 2. CBTSAP, 2. CBTSAP plus mental imagery, all consisting of three sessions. Primary outcomes will be change in CBT skills, changes in (idiosyncratic) dysfunctional thoughts and behaviors, reward processing. Depressive symptoms are a secondary outcome. Measures of learning capacity will be conducted at baseline and tested as a potential moderator. Discussion Knowing whether and for whom the acquisition of CBT skills leads to change in therapy processes and a subsequent reduction of depressive symptoms will inform on how to personalize and optimize psychotherapy outcomes for depression. Trial registration The trial is registered at the German Clinical Trial Register (DKTR; registration number: DRKS00024116).
Automated Anomaly Detection for Surface Defects by Dual Generative Networks With Limited Training Data

The manufacturing process in the pharmaceutical industry requires for using vessels or tanks which inner surfaces can undergo fouling and/or may develop visible defects that have to be cleaned away. Surface contamination can present itself in different forms, and there is no available image database of stains. In this article, we propose to use an unsupervised anomaly detection approach, which trains models on one or just a few images of clean surfaces. First, we propose an inpainting GAN with global and local generators to make full use of every single image and to reduce the overfitting caused by a limited number of images in the training dataset. Furthermore, we propose a periodic noise injection technique to increase the number of images for training and to improve the detection performance of the network. The experimental results demonstrate that the proposed network and noise injection technique achieve significant performance gains in the challenging task of anomaly detection on the surfaces of pharmaceutical equipment and other real-world industrial datasets. We also consider possible generalizations of the proposed method.
Secrecy-Rate Optimization of Double RIS-Aided Space–Ground Networks

The physical-layer security (PLS) of a space–ground communication system is examined. To improve the security performance, a pair of reconfigurable intelligent surfaces (RISs) is integrated into the system and benchmarked against a scheme, where there is only a single RIS close to the ground station. As for the double-RIS scenario, we formulate a secrecy rate maximization problem, and then propose an alternating optimization (AO) algorithm for jointly optimizing three vectors, namely, the beamformer of the ground station and the reflecting vectors of two different RISs. Similarly, as for the single-RIS case, we also propose another AO algorithm for optimizing a pair of vectors, namely, the beamformer of the ground station and the reflecting vector of the single RIS. Both the double-RIS and the single-RIS AO algorithms are developed on the basis of the first-order Taylor expansion and Dinkelbach’s method, which allow us to approximate nonconvex optimization problems by convex ones. Our results demonstrate that the proposed double-RIS scheme outperforms the single-RIS benchmark scheme in terms of its security.
Reduction of effects of Fabry–Perot fringing in wavelength modulation experiments

One of the factors limiting the sensitivity of wavelength modulation experiments is the fringing that is created by inadvertent multiple reflections between parallel optical surfaces in the apparatus. It is shown that the effects of this “etaloning” can be countered by using detection harmonic orders greater than the second. A theoretical basis for the effect is presented and compared with experimental results obtained.
Scheduling of Vehicles from a Central Depot to a Number of Delivery Points

The optimum routing of a fleet of trucks of varying capacities from a central depot to a number of delivery points may require a selection from a very large number of possible routes, if the number of delivery points is also large. This paper, after considering certain theoretical aspects of the problem, develops an iterative procedure that enables the rapid selection of an optimum or near-optimum route. It has been programmed for a digital computer but is also suitable for hand computation.
The Study of Forensically Important Insects Recovered from Human Corpses in Taiwan

Simple Summary The identification of insect species, larval development and succession sequencing on remains can provide information for estimating the minimum post-mortem interval (min-PMI). Many forensic entomology experiments using animal carcasses to study the insect’s activities have been reported for the past decades from different areas. Entomological data from human cadavers have been actively reported since the year 2000 from several countries. In this study, we reported and summarized the data of forensically important species associated with human corpses by collecting insect specimens from 114 forensic cases during 2011–2018 in Taiwan. Twenty-two species representing nine families were identified. Locations (indoor vs. outdoor), environments (urban vs. suburban), and temperatures are the important factors affecting insect communities. The fundamental data of case frequency and abundance of forensically significant insects recovered from human cadavers in Taiwan would be beneficial for forensic practitioners and criminal investigations. Abstract A study of entomological specimens recovered from 117 human corpses in 114 forensic cases was conducted in Taiwan between 2011 and 2018. The comparisons and discussions of the entomological data were based on the locations (indoor vs. outdoor), environments (urban vs. suburban), season and decomposition stages of corpses. In the study, both morphology and DNA-based comparison methods were used to facilitate species identification. In total, nine families and twenty-two species were thus identified. The two most abundant fly species recovered from human corpses were Chrysomya megacephala (35.1%, 1735 out of 4949) and Chrysomya rufifacies (21.7%, 1072 out of 4949). As for case frequency, both the two were also the most common fly species (both 40%, 46 out of 114), particularly in outdoor cases (also both 74%, 25 out of 34). We found that Chrysomya pinguis and Lucilia porphyrina appeared in low temperature scenes in this study. Synthesiomyia nudiseta was the most common species detected on indoor (36%, 29 out of 80 cases) and urban (41%, 22 out of 54 cases) corpses. Sarcophagidae were strongly associated with urban environments (35%, 19 out of 54 cases), and Parasarcophaga (Liosarcophaga) dux, Liopygia ruficornis and Boettcherisca peregrina were the most frequent sarcophagid species collected from corpses. Hydrotaea spinigera was often found on corpses immersed in water (60%, three out of five cases) with advanced decay or remains stages. Megaselia scalaris was closely correlated with indoor cases (24%, 19 out of 80). In addition, Piophila megastigmata was collected from a corpse in the remains stage and the data represent the first report in Taiwan.
Research progress of PTV method in two-dimensional dilute phase particle velocity measurement

This review summarizes the research progress of particle tracking algorithm,and introduces the basic principles and main features of particle tracking velocimetry(PTV)with emphasis on current status and development of particle matching of PTV.The particle matching algorithms are classified,and two algorithms are especially introduced.In addition,the current problems and prospects of particle tracking algorithm are also discussed.It is proposed that the future study trends should be focused on particle recognition method,efficient matching algorithms and pseudo vector algorithms to improve the velocity measurement accuracy of PTV method.
Analysis and Design of a Gain-Enhanced 1–20-GHz LNA With Output-Stage Transformer Feedback

A gain-enhanced technique with output-stage transformer feedback (OSTF) for the low-noise amplifiers is proposed in this brief. The transformer is realized by a pair of magnetically coupled microstrip lines between the drain and source stages, one of which acts as a series-peaking inductor and the other as a source degenerate inductor. By adjusting the length and gap of the magnetically coupled microstrip lines, the tuning of the gain in high frequency is realized. To verify the proposed OSTF gain-enhanced scheme, two LNAs with the same structure are designed, with one having OSTF and the other not. The gain-enhanced wideband LNA with OSTF is fabricated in 0.15- $\mu \text{m}$ GaAs pseudomorphic high-electron mobility transistor process. The total chip area, including input and output testing pads, is 1.24 mm 2. The simulated results show excellent performance with a maximum gain improvement of 3-dB at 20 GHz. The measured results show that the gain-enhanced LNA has an average gain of 22.6 dB and a noise figure below 2 dB from 1 to 20 GHz, which are in good agreement with simulated ones.
Nonlinear effects associated with kinetic Alfvén wave

The nonlinear phenomena are of striking importance in understanding the particle acceleration, heating, and turbulence in the interplanetary space. Kinetic Alfvén wave (KAW) is one of the strong candidates responsible for accelerating the solar wind and powering the solar wind turbulence. Therefore, the nonlinear properties of KAW are attracting a good attention. In the present work, we have investigated the nonlinear effects associated with KAW in the solar wind plasma at around 1 A.U. The ponderomotive force of (relatively high frequency, high power) pump KAW may be used to excite the low‐frequency KAW (LKAW). For this purpose, we have derived the dynamical equations to analyze the nonlinear dynamics of relatively high frequency pump KAW in the presence of LKAW perturbation. The numerical solution has been carried out for the coupled system of equations by using the pseudospectral method for space integration and finite difference method along with the predictor corrector scheme for the evolution in time. The coupled system of nonlinear dynamical equations has been analyzed to study the nonlinear effects associated with pump KAW and the resulting turbulent spectra at 1 A.U.
Examining Toxic Leadership Nexus With Employee Cyberloafing Behavior via Mediating Role of Emotional Exhaustion

From the perspective of social exchange and the conservation of resources theories, the current study investigated the effect of toxic leadership on employee cyberloafing behaviour via the mediating role of emotional exhaustion and the moderating role of organisational commitment. SPSS models 1:moderation, 4: mediation, and 14: moderation mediation of PROCESS Hayes were used to evaluate the data from 357 employees of the Chinese corporate sector. The results show a positive relation between toxic leadership and cyberloafing, and emotional exhaustion mediates this relationship. Further, organisation commitment moderates the indirect, interactive effect of toxic leadership and emotional exhaustion on cyberloafing. The model was tested using 3-wave data; however, since the data were attained from a single source, the problem of common method bias cannot be ruled out. To avoid or lessen instances of cyberloafing behaviour and encourage good working attitudes, organisations must also take action and develop regulations governing internet use in the workplace.
Longitudinal Measurements of FGF23, Sarcopenia, Frailty and Fracture in Older Community Dwelling Women

FGF23 has been associated with frailty and functional performance in older individuals, but the association to sarcopenia is unknown. To investigate the association between FGF23, frailty, sarcopenia and fractures in older community dwelling women. Prospective longitudinal cohort study. Malmö, Sweden. 995 75-year-old women, followed prospectively for ten years, with re-investigations after five (n=667) and ten (n=324) years. C-terminal levels of FGF23 were measured and a frailty index of ‘deficits in health’ created. Sarcopenia was defined by low muscle mass and strength and “probable sarcopenia” by low muscle mass only. Incident fractures were continuously registered for 10-years. Based on tertiles of FGF23, odds ratio for frailty, sarcopenia and probable sarcopenia was investigated using logistic regression models adjusted for: eGFR, PTH, calcium, vitamin D and phosphate. Fracture-free survival during 10-year follow-up was depicted using Kaplan Meier curves. While fracture-free survival did not differ between tertiles, women in the highest tertile of FGF23 had lower muscle strength and gait speed, and higher proportion with impaired mobility at baseline. At age 75, these women had higher odds of also being frail (ORadj 1.6 (95% CI 1.1–2.4)) and suffering from probable sarcopenia (ORadj 1.8 (95% CI 1.1–3.1)), but not sarcopenia. At follow-up the association between FGF23 and probable sarcopenia was not evident. While the association with frailty was attenuated at age 80 after adjustment (ORadj 1.6 (95% CI 1.0–2.5)), women in the highest tertile had higher odds of being frail at age 85 (ORadj 3.4 (95% CI 1.7–6.6)). FGF23 may be a promising clinical marker for muscle strength, functional performance, and frailty in older women, but not for future fragility fractures. Whether FGF23 is also associated with sarcopenia requires further investigation.
The Effect of Encapsulating a Prebiotic-Based Biopolymer Delivery System for Enhanced Probiotic Survival

Orally delivered probiotics must survive transit through harsh environments during gastrointestinal (GI) digestion and be delivered and released into the target site. The aim of this work was to evaluate the survivability and delivery of gel-encapsulated Lactobacillus rhamnosus GG (LGG) to the colon. New hybrid symbiotic beads alginate/prebiotic pullulan/probiotic LGG were obtained by the extrusion method. The average size of the developed beads was 3401 µm (wet), 921 µm (dry) and the bacterial titer was 109 CFU/g. The morphology of the beads was studied by a scanning electron microscope, demonstrating the structure of the bacterial cellulose shell and loading with probiotics. For the first time, we propose adding an enzymatic extract of feces to an artificial colon fluid, which mimics the total hydrolytic activity of the intestinal microbiota. The beads can be digested by fecalase with cellulase activity, indicating intestinal release. The encapsulation of LGG significantly enhanced their viability under simulated GI conditions. However, the beads, in combination with the prebiotic, provided greater protection of bacteria, enhancing their survival and even increasing cell numbers in the capsules. These data suggest the promising prospects of coencapsulation as an innovative delivery method based on the inclusion of probiotic bacteria in a symbiotic matrix.
Physical Layer Authentication Based on Channel Polarization Response in Dual-Polarized Antenna Communication Systems

This study presents a novel approach for physical layer authentication based on channel polarization response (CPR). CPR is sensitive to variation in the physical properties of scatterers, and the CPR difference between various channels is higher than the channel frequency response (CFR) under rich scattering scenarios. Additionally, the estimation of CPR is continuous, the authentication interval can be adjusted according to the channel coherence time, then the proposed scheme can be applied to any rich scattering scenarios, including highly dynamic scenarios. Since the received polarization state is fixed during the channel coherence time, we can coherently stack the received polarization state to improve the signal to noise ratio (SNR) and the estimation accuracy of CPR, thereby achieving high authentication accuracy under ultra-low SNR. Moreover, since the transmitted polarization state of various transmitters is different, because of their unique hardware deficiencies, and since the CPR is dependent on the transmitted polarization state, the CPR of other transmitters is different, allowing the resolution of co- located attacks. We theoretically drive the false alarm probability, detection probability, optimal discriminant threshold, computational complexity, optimal stacking numbers, and optimal CPR points for authentication. Furthermore, extensive simulations and experiments are performed to verify the validity and effectiveness of the proposed scheme.
An Application of the Generalized Polynomial Expansion Method to Nonlinear Rotor Bearing Systems

The Generalized Polynomial Expansion Method (GPEM) is utilized to model a large-order flexible-rotor system with nonlinear supports. With the application of GPEM, a set of nonlinear ordinary differential equations are obtained. A hybrid method which combines the merits of the Harmonic Balance Method (HBM) and the Trigonometric Collocation Method (TCM) is used to solve for the nonlinear response of the system. This hybrid method together with reduction techniques can efficiently solve for the motion of the system. The overall algorithm presented provides a very efficient technique for investigating the periodic response of large-order nonlinear rotor systems. Two examples are used to illustrate the merits of the method.
Fully Cascade Consistency Learning for One-Stage Object Detection

Object detection is usually solved by deploying one single prediction head including classification and localization branches to obtain the final results. Recently proposed works utilize several prediction heads in a cascade learning manner to improve the detection performance. Despite achieving promising performance, existing cascade learning manner methods still meet with two inconsistency issues. Firstly, most of them refine the bounding boxes in different prediction heads only by depending on the localization accuracy (i.e., IoU), while ignoring the inconsistency between classification confidence and localization accuracy. Moreover, simply increasing the IoU threshold by experience to select positive samples makes the inconsistency issue even worse. Secondly, little consideration has been paid on the feature inconsistency between detection-specific features from different prediction heads and detection-generalized ones from backbone model. The extracted feature from backbone model contains the general representation for the whole images. While prediction heads need to be carefully designed to have specific ability which contains more discriminative expressions for the two sub-tasks classification and regression. The different contexture representations of the output features from these two parts lead to the feature inconsistency between backbone model and prediction head in cascade learning architecture. To solve these two inconsistency issues, this paper proposes a novel cascade consistency learning method for one-stage detector. Specifically, a feature adaptation module is firstly developed to calibrate features from different prediction heads and backbone model for solving the feature inconsistency. Then, we design an automatic positive sample threshold selection strategy for further solve the inconsistency between the classification and localization predictions. Moreover, the quality of bounding boxes in cascade learning manner are evaluated by taking both the classification confidence and localization accuracy into consideration. Experiments on MS COCO show that our proposed cascade consistency learning manner (dubbed $\text{C}^{2}\text{L}$ ) can achieve clear improvement over counterparts based on several different one-stage detectors, while performing favorably against state-of-the-arts.
Intrusion Detection in Vehicle Controller Area Network (CAN) Bus Using Machine Learning: A Comparative Performance Study

Electronic Control Units (ECUs) have been increasingly used in modern vehicles to control the operations of the vehicle, improve driving comfort, and safety. For the operation of the vehicle, these ECUs communicate using a Controller Area Network (CAN) protocol that has many security vulnerabilities. According to the report of Upstream 2022, more than 900 automotive cybersecurity incidents were reported in 2021 only. In addition to developing a more secure CAN protocol, intrusion detection can provide a path to mitigate cyberattacks on the vehicle. This paper proposes a machine learning-based intrusion detection system (IDS) using a Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN) and investigates the effectiveness of the IDS using multiple real-world datasets. The novelty of our developed IDS is that it has been trained and tested on multiple vehicular datasets (Kia Soul and a Chevrolet Spark) to detect and classify intrusion. Our IDS has achieved accuracy up to 99.9% with a high true positive and a low false negative rate. Finally, the comparison of our performance evaluation outcomes demonstrates that the proposed IDS outperforms the existing works in terms of its liability and efficiency to detect cyber-attacks with a minimal error rate.