Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Impact of Floating Car Data and Data Fusion on the Prediction of the Traffic Density, Flow and Speed Using an Error Recurrent Convolutional Neural Network

Traffic prediction helps mitigate the impact of traffic congestion. The accuracy of traffic predictions depends on the availability of the data used for the prediction as well as the prediction model. Data from fixed traffic detectors is only available at certain locations. On the other hand, connected vehicles can provide Floating Car Data (FCD) at any location and time. However, FCD may not be available at all vehicles, and this can impact predictions since the FCD may not reflect the state of all traffic. This impact is larger when predicting traffic density or flow, and existing studies generally use FCD to predict traffic speed or travel time only. This study proposes a traffic prediction model that can accurately predict the three fundamental traffic variables (traffic density, flow, and speed) using FCD and an error recurrent convolutional neural network that takes as input the three variables. These are estimated using FCD and data from induction loops. These estimates depend on the penetration rate of FCD, so we propose a method to locally and dynamically estimate this penetration rate. This method improves the estimation of the traffic variables, and hence their prediction. The proposed model is used to analyze the impact of the FCD penetration rate on the prediction of the traffic variables. We show how our proposal reduces the amount of FCD needed to improve the prediction obtained with data from traffic detectors. We show that our proposal only requires FCD from 4% of the vehicles to improve the prediction accuracy achieved with traffic detectors. Augmenting this percentage increases the accuracy of our model for the three traffic variables. However, we also show that our prediction model reduces the FCD sample size (or FCD penetration rate) needed to achieve prediction accuracy levels close to that obtained if all vehicles provided FCD.
Scan-Driven Fully-Automated Pipeline for a Personalized, 3D Printed Low-Cost Prosthetic Hand

The process of fitting a prosthetic hand that is comfortable, functional, easy to use, has an acceptable appearance and overall improves the amputees' quality of life is a complex, tedious and costly process. The very high price tag due to the time spent on manually fitting the device by a trained specialist makes these devices inaccessible to large portions of the population. We present a concept and preliminary results for a fully automated fitting and manufacturing pipeline for a personalized low-cost prosthetic hand. The hand is personalized in almost every aspect, from appearance to user interface, control and feedback. The pipeline only requires a 3D printer, RealSense cameras, a few basic mechanical components, and basic tools for the model assembly. The user scan-driven data and the user preferences initiate a fully-automated pipeline which culminates in a customized, easy-to-assemble PCB design and ready to print STL files, including the optimized orientation, support and layout, such that the final parts are only one click away. We believe that the proposed pipeline and design can highly impact the accessibility of prosthetic hands and could potentially be expanded to other medical applications.
“We Are All Trying to Find a Way to Help Ourselves”: A Look at Self-Help Strategies Among Psychotherapy Clients

Objective: This study examined self-help strategies engaged by psychotherapy clients and explored their views on such self-help approach. Methods: Secondary analysis of data from a qualitative research study was conducted. A total of 15 psychotherapy clients were recruited, and data were collected via semi-structured interviews. Thematic analysis of data was conducted using inductive approach to examine the content. Results: Three main themes revolving around self-help strategies were identified: (1) types of self-help strategies, (2) reasons for engaging in self-help activities, and (3) effectiveness of self-help strategies. Conclusion: The self-help approach to manage distress is common among psychotherapy clients. This study provided insights into understanding how and why clients use self-help strategies in their daily lives.
Fully Distributed Event-triggered Cooperative Output Regulation of Linear Parameter-varying Systems

The cooperative output regulation problem of linear parameter-varying multi-agent systems via fully distributed event-triggered adaptive control is studied in this paper. It is assumed that agents cannot communicate with each other continuously. To solve this problem, a fully distributed event-triggered adaptive controller is designed such that the problem studied in this paper can be solved properly.
Mapping, Structure and Modulation of PPI

Because of the key relevance of protein–protein interactions (PPI) in diseases, the modulation of protein-protein complexes is of relevant clinical significance. The successful design of binding compounds modulating PPI requires a detailed knowledge of the involved protein-protein system at molecular level, and investigation of the structural motifs that drive the association of the proteins at the recognition interface. These elements represent hot spots of the protein binding free energy, define the complex lifetime and possible modulation strategies. Here, we review the advanced technologies used to map the PPI involved in human diseases, to investigate the structure-function features of protein complexes, and to discover effective ligands that modulate the PPI for therapeutic intervention.
Neuromodulation With Thoracic Dorsal Root Ganglion Stimulation Reduces Ventricular Arrhythmogenicity

Introduction: Sympathetic hyperactivity is strongly associated with ventricular arrhythmias and sudden cardiac death. Neuromodulation provides therapeutic options for ventricular arrhythmias by modulating cardiospinal reflexes and reducing sympathetic output at the level of the spinal cord. Dorsal root ganglion stimulation (DRGS) is a recent neuromodulatory approach; however, its role in reducing ventricular arrhythmias has not been evaluated. The aim of this study was to determine if DRGS can reduce cardiac sympathoexcitation and the indices for ventricular arrhythmogenicity induced by programmed ventricular extrastimulation. We evaluated the efficacy of thoracic DRGS at both low (20 Hz) and high (1 kHz) stimulation frequencies. Methods: Cardiac sympathoexcitation was induced in Yorkshire pigs (n = 8) with ventricular extrastimulation (S1/S2 pacing), before and after DRGS. A DRG-stimulating catheter was placed at the left T2 spinal level, and animals were randomized to receive low-frequency (20 Hz and 0.4 ms) or high-frequency (1 kHz and 0.03 ms) DRGS for 30 min. High-fidelity cardiac electrophysiological recordings were performed with an epicardial electrode array measuring the indices of ventricular arrhythmogenicity—activation recovery intervals (ARIs), electrical restitution curve (Smax), and Tpeak–Tend interval (Tp-Te interval). Results: Dorsal root ganglion stimulation, at both 20 Hz and 1 kHz, decreased S1/S2 pacing-induced ARI shortening (20 Hz DRGS −21±7 ms, Control −50±9 ms, P = 0.007; 1 kHz DRGS −13 ± 2 ms, Control −46 ± 8 ms, P = 0.001). DRGS also reduced arrhythmogenicity as measured by a decrease in Smax (20 Hz DRGS 0.5 ± 0.07, Control 0.7 ± 0.04, P = 0.006; 1 kHz DRGS 0.5 ± 0.04, Control 0.7 ± 0.03, P = 0.007), and a decrease in Tp-Te interval/QTc (20 Hz DRGS 2.7 ± 0.13, Control 3.3 ± 0.12, P = 0.001; 1 kHz DRGS 2.8 ± 0.08, Control; 3.1 ± 0.03, P = 0.007). Conclusions: In a porcine model, we show that thoracic DRGS decreased cardiac sympathoexcitation and indices associated with ventricular arrhythmogenicity during programmed ventricular extrastimulation. In addition, we demonstrate that both low-frequency and high-frequency DRGS can be effective neuromodulatory approaches for reducing cardiac excitability during sympathetic hyperactivity.
Shelf Life and Simulated Gastrointestinal Tract Survival of Selected Commercial Probiotics During a Simulated Round-Trip Journey to Mars

To enhance the gastrointestinal health of astronauts, probiotic microorganisms are being considered for inclusion on long-duration human missions to the Moon and Mars. Here we tested three commercial probiotics—Bifidobacterium longum strain BB536, Lactobacillus acidophilus strain DDS-1, and spores of Bacillus subtilis strain HU58—for their survival to some of the conditions expected to be encountered during a 3-year, round trip voyage to Mars. All probiotics were supplied as freeze-dried cells in capsules at a titer of >109 colony forming units per capsule. Parameters tested were survival to: (i) long-term storage at ambient conditions, (ii) simulated Galactic Cosmic Radiation and Solar Particle Event radiation provided by the NASA Space Radiation Laboratory, (iii) exposure to simulated gastric fluid, and (iv) exposure to simulated intestinal fluid. We found that radiation exposure produced minimal effects on the probiotic strains. However, we found that that the shelf-lives of the three strains, and their survival during passage through simulations of the upper GI tract, differed dramatically. We observed that only spores of B. subtilis were capable of surviving all conditions and maintaining a titer of >109 spores per capsule. The results indicate that probiotics consisting of bacterial spores could be a viable option for long-duration human space travel.
Design of Upper Limb Rehabilitation Training System Combining BCI and AR Technology

This paper designed an upper limb rehabilitation training system combining AR technology and BCI technology for upper limb function of stroke patients. The system is divided into two phases :(1) AR-SSVEP phase, EEG signals of patients were collected in real-time by two different scintillation frequency squares, which adapt patients to the AR environment and focus; (2) AR-MI phase, the patient imagines the motion of the hand according to the instruction of the light, real-time acquisition of EEG signals and analysis of patient intentions, and given the feedback on AR technology, which let patients have a better visual experience so that can complete rehabilitation training better. This system uses 3dsMax to build AR scene and 3D characterÿs upper limb and its animations, then import into Unity software with the AR kit to build as AR interface, after packaging, finally build the program into the HoloLens device. The system has the characteristics of multilevel, portable, immersion, real-time and so on. It provides a new method for the rehabilitation of upper limb function in stroke patients, and portability of brain-computer interface and visual stimulation.
A Novel SOSM Control Method by Using Adaptive Fuzzy Technique

In practical application systems, the output constraints are widelspread. Violation of output constraints may cause system damage as well as performance degradation. In this paper, adaptive fuzzy second-order sliding mode (SOSM) controllers are designed which is studied for a class of non-linear systems with output constraints. The unknown bounds of uncertainties are approached dynamically by fuzzy logic systems. Through constructing a new barrier Lyapunov function, the output constraint problem has been well solved. Then, by combining adding a power integrator (API) technology and adaptive fuzzy control, a novel adaptive fuzzy SOSM controller is proposed. It is demonstrated that the proposed algorithm keeps the output variables within the specified constraint region. Moreover, the finite-time stability of the resulting closed-loop system under output constraints can be shown to be guaranteed by the Lyapunov method. Finally, an example is presented to demonstrate the validity of the proposed SOSM control strategy.
The Native Dietary Habits of the Two Sympatric Bee Species and Their Effects on Shaping Midgut Microorganisms

The intestinal microbial community composition of different bee species typically has host specificity, yet little is known about the underlying formation mechanism. There are signs that dietary habits vary in different bee species, suggesting that there may be close relationships between dietary habits and intestinal microorganisms. We explored this hypothesis by comparing the dietary habits and gut microbiota of two common bee species (Apis mellifera L. and Apis cerana cerana) in China. Bee bread and midgut samples from wild and laboratory-reared bees were collected, and the differences in intestinal microbial community composition and growth and development before and after the change in dietary habits of different bee species were compared. We found that the two sympatric species had different dietary specializations and similar metagenomic diversities. The microbiota composition differed between the two species. Moreover, we revealed that changes in native dietary habits destroyed the intestinal microbiota community composition, negatively affecting the growth and development of honeybees.
A Preprocessing and Feature Extraction Method of Ground-based Cloud Images for Photovoltaic Power Prediction

The photovoltaic system is affected by many factors, so its ultra-short-term prediction faces enormous challenges. Total Sky Imager (TSI) is used for sky monitoring, which is of great significance for photovoltaic power prediction, but its structural characteristics lead to distortion and loss of information. A method of cloud image preprocessing and feature extraction is proposed in this paper. Firstly, the template calibration method is proposed to establish the distortion model, and bilinear interpolation is used for gray reconstruction. Secondly, the method of combining Multi-Scale Retinex (MSR) method with grayscale transformation is proposed to compensate the illumination of cloud image. Then, digital image processing technology is used to extract spectral features, texture features, cloud features, shadow-band features, and other features that affect the photovoltaic power. Finally, the Gradient Boosting Decision Tree (GBDT) method is used to train and build the photovoltaic power regression model according to ground-based cloud images and photovoltaic power. Experiments show that the proposed preprocessing method of cloud image can restore the real information of the sky, and the photovoltaic power regression model constructed from the image features has high accuracy, and the R-Squared(R2) can reach 0.96. It provides an effective method for the ultra-short-term prediction of photovoltaic power.
A Novel Conflict Evidence Combination Method Based on Proof by Contradiction and Complete Frame of Discernment

The traditional Dempster-Shafer (D-S) evidence theory has not well addressed the conflict of evidence yet, and thus often lead to counter-intuitive result when combing highly conflicting evidence. Hence, a new method to combine the evidence based on the proof by contradiction and complete frame of discernment is proposed in this paper. First, assuming that each of the proposition on the complete frame of discernment is correct, and make inferences and calculations based on the hypothesis. Then, the support degree of each proposition can be obtained by the distance between the evidence and the vector corresponding to the expected result of each proposition. The proposition with the maximum belief value will be selected as the decision result of the target. We can calculate the distance between each evidence and the support degree by using the formula for calculating distance once again. At last, the final result can be obtained by the weighted method, which can solve the highly conflict issues effectively. Several comparative experimental simulations are used to illustrate the effectiveness of the proposed method.
Distribution, Feeding Habits, and Growth of Chub Mackerel, Scomber japonicus, Larvae During a High-Stock Period in the Northern Satsunan Area, Southern Japan

To evaluate the importance of the northern Satsunan area in southern Japan as a spawning and nursery ground for chub mackerel (Scomber japonicus), we investigated the early life history characteristics (e.g., larval distribution, feeding habits, and growth) of S. japonicus over five successive years. This area is considered the main habitat and spawning ground of the congeneric species, S. australasicus. Using polymerase chain reaction-restriction fragment length polymorphism analysis, we first confirmed that S. japonicus larvae were abundant in the northern Satsunan area, potentially representing a major spawning and nursery ground in the Japanese Pacific coastal area. The number of recorded larvae started to increase in 2016, corresponding to the population dynamics of the Pacific stock of the species, which has shown increasing trends in recent years. Morphological and DNA metabarcoding analyses of gut contents and stable isotope analysis showed that, in addition to copepods, the larvae fed substantially on appendicularians. The trophic pathway involving appendicularians might support the feeding habits of S. japonicus, promoting its coexistence with other dominant species. Both the instantaneous growth rate and daily specific growth rate were comparable to those in the southern East China Sea, which is the main spawning and nursery ground of the species. Our data strongly suggest that the northern Satsunan area has favorable conditions for sustaining high larval population densities, even during phases with high population numbers. Our results provide insights for the fisheries management for S. japonicus in the Japanese Pacific coastal area, especially during high-stock periods.
Endocrine Disrupting Chemicals and Reproductive Health in Boys and Men

Male reproductive health has declined as indicated by increasing rates of cryptorchidism, i.e., undescended testis, poor semen quality, low serum testosterone level, and testicular cancer. Exposure to endocrine disrupting chemicals (EDCs) has been proposed to have a role in this finding. In utero exposure to antiandrogenic EDCs, particularly at a sensitive period of fetal testicular development, the so-called ‘masculinization programming window (MPW)’, can disturb testicular development and function. Low androgen effect during the MPW can cause both short- and long-term reproductive disorders. A concurrent exposure to EDCs may also affect testicular function or damage testicular cells. Evidence from animal studies supports the role of endocrine disrupting chemicals in development of male reproductive disorders. However, evidence from epidemiological studies is relatively mixed. In this article, we review the current literature that evaluated relationship between prenatal EDC exposures and anogenital distance, cryptorchidism, and congenital penile abnormality called hypospadias. We review also studies on the association between early life and postnatal EDC exposure and semen quality, hypothalamic-pituitary-gonadal axis hormone levels and testicular cancer.
Stable Neural Adaptive Filters for Teleoperations With Uncertain Delays

Uncertainties in communication networks negatively affect the performance and usability of teleoperation systems, specially, in time-critical applications such as telesurgery. There already exist different methods to tackle this problem using filtering and learning approaches to smoothly estimate perturbed reference signals. Despite these efforts, the instability issue remains unsolved for such systems under random time-delay perturbations. This study employs and extends one of the best filtering techniques and proposes a new strategy based on learning capabilities of artificial neural networks to adaptively filter out delay-related disturbances and provides the most stable yet accurate estimations. To achieve this goal, an adaptive learning mechanism is proposed based on a Lyapunov-Krasovskii functional to not only analyse and guarantee the stability of the overall system, but also preventing the learning algorithm to get stuck in local optima. The proposed method is experimentally evaluated and compared with other closely similar methods in the recent literature. The results demonstrate the outstanding performance of the proposed solution in this study.
Role of Professional Societies on Increasing Indigenous Peoples’ Participation and Leadership in STEMM

Indigenous people are the most underrepresented racial/ethnic group in Science, Technology, Engineering, Mathematics, and Medicine (STEMM) in the United States. Most prior research suggests this trend is the result broadly of settler colonialism, and more specifically of cultural differences between students and school/university environments; poor academic preparation in K-12 schools; vague constructs of educational or vocational goals; insufficient financial aid; unwelcoming school and university environments; prejudice and racism; and social isolation. There is also a vast body of published work on the unique epistemologies and knowledge systems held by Indigenous peoples, which are only recently being acknowledged within mainstream STEMM communities. One potential reason for lower participation in STEMM programs and professions by Indigenous people that has generally gone unexplored relates to unique cultural and spiritual factors that could deter Indigenous people from STEMM fields. Our research investigates the range and variation of cultural/spiritual/ethical practical issues that may be affecting Indigenous people’s success in STEMM. Our research provides valuable insights for policy and practices within higher education institutions and industry to provide flexible pathways for Indigenous people to reduce or eliminate barriers related to culturally- and spiritually-informed issues. In this paper, we explore how our findings can be used by professional societies to provide leadership to higher education institutions and industry in the area of changing some standard practices to be more inclusive of Indigenous people. An important mode of systemic change in STEMM fields is through professional societies that guide future practices in various STEMM disciplines.
Rotor Active Magnetic Bearings System Control via a Tuned Nonlinear Saturation Oscillator

The proposed work focuses on improving the performance of the traditional nonlinear saturation controller (NSC) algorithm. In this work, a tuning mechanism can be implemented by providing the NSC control unit with the measured speed <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> of the rotor from a shaft encoder device in order to introduce a tuned NSC (TNSC). In other words, we are going to tune the NSC’s natural frequency <inline-formula> <tex-math notation="LaTeX">${\omega _{c}}$ </tex-math></inline-formula> at one half of <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> such that <inline-formula> <tex-math notation="LaTeX">$\Omega =2{\omega _{c}}$ </tex-math></inline-formula>. This TNSC is adopted to reduce the vibratory amplitudes of a 16-pole constant-stiffness rotor-Active magnetic bearings AMBs) system for a wide range of speeds and eccentricities. The whole controlled system is studied mathematically to seek its approximate solutions via the multiple scales technique. Different relations between the rotor’s amplitudes and its parameters are plotted so as to verify the proposed tuning mechanism and its crucial role in improving the NSC’s performance.