Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical Investigations of a Tip Turbine Aerodynamic Design in a Propulsion System for VTOL Vehicles

High thrust and low specific fuel consumption (SFC) are important for vertical takeoff and landing (VTOL) vehicles. An effective way to decrease the SFC is to increase the bypass ratio (BPR) of the propulsion system. The air-driven fan (or fan-in-wing) has a very high bypass ratio and has proved to be successful in VTOL aircrafts. However, the tip turbine that extracts energy for the air-driven fan faces the low-solidity problem and performs inadequately. In this study, we developed a high-reaction method for the aerodynamic design of a tip turbine to solve the low-solidity problem. A typical tip turbine was selected and designed by both conventional and high-reaction methods. Three-dimensional flow fields were numerically simulated through a Reynolds-averaged Navier-Stokes (RANS)-based computational fluid dynamics (CFD) method. The energy extraction rate was proposed to evaluate and display the energy extraction capability of the turbine. The results showed that the high-reaction turbine could solve the low-solidity problem and significantly increase the isentropic efficiency from approximately 80.0% to 85.6% and improve the isentropic work by 71.9% compared with the conventional method (from 10.28 kW/kg to 17.67 kW/kg).
Automatic Detection and Recognition of Text-Based Traffic Signs from images

Detection and recognition of texts in traffic signs has been widely studied and with the advance in image capture technology has helped to improve or to create new methods to achieve this issue. In this work, we presented a method for detection, segmentation and recognition of text-based traffic signs from images analyzing and processing techniques. The results show that the computational cost and accuracy rate considering the proposed approach are acceptable to real time applications, with an execution time under 0.5 seconds, with a hit rate of 94.38% in the plate detection, 83.42% in the character segmentation and 89.23 in the digit classification.
A Digital Platform Designed for Youth Mental Health Services to Deliver Personalized and Measurement-Based Care

Mental disorders that commonly emerge during adolescence and young adulthood are associated with substantial immediate burden and risks, as well as potentially imparting lifetime morbidity and premature mortality. While the development of health services that are youth focused and prioritize early intervention has been a critical step forward, an ongoing challenge is the heterogeneous nature of symptom profiles and illness trajectories. Consequently, it is often difficult to provide quality mental health care, at scale, that addresses the broad range of health, social, and functional needs of young people. Here, we describe a new digital platform designed to deliver personalized and measurement-based care. It provides health services and clinicians with the tools to directly address the multidimensional needs of young people. The term “personalized” describes the notion that the assessment of, and the sequence of interventions for, mental disorders are tailored to the young person—and their changing needs over time, while “measurement-based” describes the use of systematic and continuing assessment of a young person’s outcomes over the entire course of clinical care. Together, these concepts support a framework for care that transcends a narrow focus on symptom reduction or risk reduction. Instead, it prioritizes a broader focus on enhancing social, health, and physical outcomes for young people and a commitment to tracking these outcomes throughout this key developmental period. Now, with twenty-first century technologies, it is possible to provide health services with the tools needed to deliver quality mental health care.
Low-RCS, Circular Polarization, and High-Gain Broadband Antenna Based on Mirror Polarization Conversion Metasurfaces

In this paper, a novel slot antenna array that is based on mirror polarization conversion metasurfaces (MPCM) is proposed. It achieves circular polarization (CP) and effectively reduces the radar cross section (RCS) and increases gain in the entire x-band. This design uses the mirrored composition of the polarization conversion metasurfaces (PCM) on the top surface of the substrate. The MPCM covers a 2 × 2 slot antenna array that is fed with by a sequentially rotating network. The CP radiation is realized by the polarization conversion characteristics of the PCM. At the same time, the reduction of RCS is achieved by 180° (±30°) reflection phase difference between two adjacent PCMs. The improvement in gain is achieved by using a Fabry–Perot cavity, which is constituted by the ground of the antenna and the PCM. Simulated and measured results show that approximately 46.4% of the operating bandwidth is in the range of 7.5–12 GHz (AR < 3 dB) and the gain of the antenna with MPCM is at least 5 dB higher than the reference antenna. Moreover, the monostatic RCS is reduced from 8 to 20 GHz.
Characteristic Analysis and Risk Assessment for Voltage–Frequency Coupled Transient Instability of Large-Scale Grid-Connected Renewable Energy Plants During LVRT

The voltage–frequency coupled transient stability and risk assessment issues of weak grid-connected large-scale renewable energy plants (REPs) during low voltage ride through (LVRT) have received little attention to date. The shape and mechanism of voltage–frequency coupled transient instability during LVRT are investigated in detail by the voltage-vector-triangle graphic (VTG) method utilized in this article, which avoids complex mathematical calculations. In addition, the margin of the transmission line's voltage angle (MTVA) is defined in this article and is then used to quantitatively assess the transient instability risk of the large-scale REPs during LVRT. Furthermore, the expressions of the MTVA are deduced under different conditions and the major influencing factors, which resulted in instability risk, are determined correspondingly. On this basis, the transient instability risk assessment standard of the large-scale REPs is built and the instability risk minimized scheduling strategy is proposed in this article, which does not change the internal control structure and parameters of the REPs during LVRT. Finally, both the correctness of the VTG method and the risk assessment standard, and the effectiveness of the scheduling strategy proposed in this article are validated by experiments and simulations.
New Approaches for Online Control of Urban Traffic Signal Systems

Adaptive Traffic Control Systems (ATCS) control a set of traffic signals at connected intersections in a network. They continuously adapt the signalization in real-time to the current traffic demand. In this thesis a new ATCS prototype has been developed and evaluated. A comprehensive overview of the state-of-the-art of traffic signal control is given, followed by an overview of the conceptual design of the ATCS prototype. Every quarter of an hour, signal timings of all signalized intersections are optimized on a central computer and sent to the local controllers where they are executed. The first task is to estimate the traffic demand of the next optimization interval. Based on detector counts of previous time intervals, a forecasting module estimates detector counts of the next interval. These counts are used as constraints for the estimation of Origin-Destination flows, traffic volumes on different routes and on all links of the network. The next module makes use of classic formulas for the calculation of fixed time signal plans in order to adjust a network-wide common cycle length and individual phase durations. The subsequent model-based offset optimization aims at establishing a good coordination of adjacent intersections. A macroscopic traffic flow model is used to evaluate the effects of different offset combinations. Different optimization algorithms have been implemented, thereof two based on Genetic Algorithms. A third, deterministic algorithm has been developed as well. At the beginning of each time interval, the new signal timings have to be implemented at each intersection. Based on the state-of-the-art and on a simulation study a smooth transition technique has been identified and implemented. Finally, the ATCS prototype has been evaluated by means of a comprehensive microsimulation study. It has some potential to improve travel times compared to an optimized fixed time signal control. The degree of this improvement depends on the network.
Identification of crystallization kinetics parameters by genetic algorithm in non‐isothermal conditions

Purpose – This paper sets out to show the feasibility of the genetic algorithm inverse method for the determination of the parameters of crystallization kinetics laws in isothermal and non‐isothermal conditions, using multiple experiments.Design/methodology/approach – The mathematical model for crystallization kinetics determination and the numerical methods of its resolution are introduced. Crystallization kinetic parameters determined by approximate physical analysis and the inverse genetic algorithm method are presented. Injection molding simulations taking into account crystallization are performed using the finite element method.Findings – It is necessary to perform the optimization on two parameters, transformed volume fraction and number of spherulites to obtain correct results. It is possible to use results from different samples, in spite of the dispersion of some values.Research limitations/implications – Experimental data for isothermal and non‐isothermal conditions were used and obtained good .
Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning

Train station parking (TSP) accuracy is important to enhance the efficiency of train operation and the safety of passengers for urban rail transit. However, TSP is always subject to a series of uncertain factors such as extreme weather and uncertain conditions of rail track resistances. To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. Three algorithms were developed, involving a stochastic optimal selection algorithm (SOSA), a Q-learning algorithm (QLA), and a fuzzy function based Q-learning algorithm (FQLA) in order to reduce the parking error in urban rail transit. Meanwhile, five braking rates are adopted as the action vector of the three algorithms and some statistical indices are developed to evaluate parking errors. Simulation results based on real-world data show that the parking errors of the three algorithms are all within the ±30cm, which meet the requirement of urban rail transit.
Validity of subjective scale for shoe weight discrimination in short distance run

Less BF pre-activation associated with an increased RE corroborated previous results stating that less muscle tuning was beneficial for RE (Nigg et al., 2003). That might benefit runners with lower shank length by decreasing eccentric action needed to decelerate the forward swing of the shank before landing. Runners benefiting from high energy return had more economical BF activation during push-off potentially due to the greater BF braking activation that might enhance joint energy transfer. Even if significant, it should be noted that only half of the RE variations were explained by this combination of variables (R 1⁄4 0.52). Participant-specific RE responses to LBS depended on the runners’ ability to adopt more proximal (VM) instead of distal (TA, GM) muscular activations during braking. These changes were associated with greater leg stiffness and ankle dorsiflexion that may enhance energy storage in elastic components, which might be returned economically during push-off with less ankle plantarflexion before toe-off (Madden et al., 2016). This result suggested that minimizing muscle contributions around the ankle may be determinant to improve RE under high LBS. Participant-specific RE responses to shoe mechanical features were mainly explained by muscle activation responses in recreational runners. Future studies should identify if runners adopting the biomechanical adaptations highlighted in this study have specific profiles to recommend a given shoe feature to a given group of runners by avoiding prior experiments.
Sponges and Their Microbiomes Show Similar Community Metrics Across Impacted and Well-Preserved Reefs

Sponge diversity has been reported to decrease from well-preserved to polluted environments, but whether diversity and intra-species variation of their associated microbiomes also change as function of environmental quality remains unknown. Our study aimed to assess whether microbiome composition and structure are related to the proliferation of some sponges and not others under degraded conditions. We characterized the most frequent sponges and their associated bacteria in two close areas (impacted and well-preserved) of Nha Trang Bay (Indo-Pacific). Sponge assemblages were richer and more diverse in the well-preserved reefs, but more abundant (individuals/m. transect) in the impacted environments, where two species (Clathria reinwardti and Amphimedon paraviridis) dominated. Sponge microbiomes from the polluted zones had, in general, lower bacterial diversity and core size and consequently, higher intra-species dispersion than microbiomes of sponges from the well-preserved environments. Microbial communities reflect the reduction of diversity and richness shown by their host sponges. In this sense, sponges with less complex and more variable microbiomes proliferate under degraded environmental conditions, following the ecological paradigm that negatively correlates community diversity and environmental degradation. Thereby, the diversity and structure of sponge microbiomes might indirectly determine the presence and proliferation of sponge species in certain habitats.
Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

The increase of the technology related to radio localization and the exponential rise in the data traffic demanded requires a large number of base stations to be installed. This increase in the base stations density also causes a sharp rise in energy consumption of cellular networks. Consequently, energy saving and cost reduction is a significant factor for network operators in the development of future localization networks. In this paper, a localization method based on ray-tracing and fingerprinting techniques is presented. Simulation tools based on high frequencies are used to characterize the channel propagation and to obtain the ray-tracing data. Moreover, the fingerprinting technique requires a costly initial learning phase for cell fingerprint generation (radio-map). To estimate the localization of mobile stations, this paper compares power levels and delay between rays as cost function with different distance metrics. The experimental results show that greater accuracy can be obtained in the location process using the delay between rays as a cost function and the Mahalanobis distance as a metric instead of traditional methods based on power levels and the Euclidean distance. The proposed method appears well suited for localization systems applied to indoor and outdoor scenarios and avoids large and costly measurement campaigns.
Virtual values for taint and information flow analysis

Security controls such as taint analysis and information flow analysis can be powerful tools to protect against many common attacks. However, incorporating these controls into a language such as JavaScript is challenging. Native implementations require the support of all JavaScript VMs. Code rewriting requires developers to reason about the entire abstract syntax of JavaScript. In this paper, we demonstrate how virtual values may be used to more easily integrate these security controls. Virtual values provide hooks to alter the behavior of primitive operations, allowing programmers to create the desired security controls in a more declarative fashion, facilitating more rapid prototyping. We demonstrate how virtual values may be encoded in JavaScript using a combination of JavaScript object proxies and the Sweet.js macro library, and use that implementation to build taint and information flow controls into JavaScript. Finally, we show some benchmark results to demonstrate the overhead of this approach.
RF-Driven Crowd-Size Classification via Machine Learning

In this letter, we propose a machine learning solution for crowd-size classification in an indoor environment. Narrow-band radio frequency signals are used to identify a pattern according to the number of people. Experimental data collected by a low-cost software-defined radio platform are postprocessed by applying a feature mapping along with the random forest technique for classifying the crowd-size scenarios. The proposed solution has significant accuracy in classification performance.
Distributed Meta-Brokering P2P Overlay for Scheduling in Cloud Federation

The interconnected cloud (Intercloud) federation is an emerging paradigm that revolutionizes the scalable service provision of geographically distributed resources. Large-scale distributed resources require well-coordinated and automated frameworks to facilitate service provision in a seamless and systematic manner. Unquestionably, standalone service providers must communicate and federate their cloud sites with other vendors to enable the infinite pooling of resources. The pooling of these resources provides uninterpretable services to increasingly growing cloud users more efficiently, and ensures an improved Service Level Agreement (SLA). However, the research of Intercloud resource management is in its infancy. Therefore, standard interfaces, protocols, and uniform architectural components need to be developed for seamless interaction among federated clouds. In this study, we propose a distributed meta-brokering-enabled scheduling framework for provision of user application services in the federated cloud environment. Modularized architecture of the proposed system with uniform configuration in participating resource sites orchestrate the critical operations of resource management effectively, and form the federation schema. Overlaid meta-brokering instances are implemented on the top of local resource brokers to keep the global functionality isolated. These instances in overlay topology communicate in a P2P manner to maintain decentralization, high scalability, and load manageability. The proposed framework has been implemented and evaluated by extending the Java-based CloudSim 3.0. 3 simulation application programming interfaces (APIs). The presented results validate the proposed model and its efficiency to facilitate user application execution with the desired QoS parameters.
S-Shaped Nonlinearity in Electrical Resistance of Electroactive Supercoiled Polymer Artificial Muscle

S-shaped nonlinearity is found in the electrical resistance– length relationship in an electroactive supercoiled polymer artificial muscle. The modulation of the electrical resistance is mainly caused by the change in the contact condition of coils in the artificial muscle upon deformation. A mathematical model based on logistic function fairly reproduces the experimental data of electrical resistance–length relationship. key words: actuator, supercoiled polymer artificial muscle, twisted and coiled polymer actuator, electrical resistance, mathematical model
Modest dose anti-thymocyte globulin administered intraoperatively is safe and effective in kidney transplantations: a retrospective study

Background Anti-thymocyte globulin (ATG) as induction therapy in renal transplantation is facing the dilemma of reducing the incidence of acute rejection (AR) and delayed graft function (DGF) or increasing risks of infection and malignancy. The purpose of this study was to delineate the safety and efficiency of the optimal ATG dosage. Methods We retrospectively evaluated 91 deceased donor kidney transplant recipients (KTRs) in our institution between March 2011 and January 2019. The patients were classified into three groups based on induction therapy: (1) Group 1: modest-dose ATG (three mg/kg) intraoperatively (N = 21); (2) Group 2: low-dose ATG (1–1.5 mg/kg) intraoperatively (N = 23); (3) Group 3: basiliximab 20 mg both on day 0 and 4 (N = 47). In Groups 1 and 2, all patients received a daily low-dose program (1–1.5 mg/kg each day) with target dosage of six mg/kg. Induction therapy was combined with standard immunosuppressive regimen consisting of calcineurin inhibitors, mycophenolate/the mammalian target of rapamycin inhibitors and corticosteroids. Results There was no significant difference in patient characteristics among groups. The outcomes of infection rate, biopsy-proven acute rejection, post-transplant diabetes mellitus, graft survival, and patient survival were similar among groups. Compared to the daily low-dose ATG regimen, the intraoperative modest-dose regimen did not cause more dose interruption and hence was more likely to reach the target ATG dosage. The intraoperative modest-dose regimen also seemed to reduce the rate of DGF. Discussion In recent years, a trend of using a “lower” dose of ATG has seemed to emerge. Our results suggest intraoperative modest-dose ATG followed by daily low-dose ATG regimen was safe and effective in cadaveric renal transplantations for preventing DGF, AR, and graft loss.
Placement of distributed crack sensor on I‐shaped steel girders of medium‐span bridges, using available field data

It is critical to detect cracks in steel girders of bridges before they have the potential to compromise the integrity of the structure. Both distributed binary sensors and distributed fiber optic sensors are capable of detecting cracks that are wider than 0.2 mm in steel girders. The objective of this paper is to report the optimum placement of these sensors on the girder to detect smallest possible length of the crack. In this work, the optimized placement of crack sensors was studied using FEM of two typical medium‐span simply supported steel girder bridges (Girder A, 30‐m–long span, and Girder B, 22‐m–long span). Using loads estimated from field monitoring data and FEM, a map of crack opening along the length of the crack was calculated for stable crack lengths. Using these maps and given the detectable crack opening of 0.2 mm, the optimum place to position a distributed crack sensor to detect the smallest crack length was determined. For Girder A, the sensor should be placed at 150 to 250 mm above flange at midspan and at one third from the support, and for the rest of the length of the girder, it should be placed at 200–300 mm above the bottom flange. For Girder B, the optimum placement for installation of binary sensor is estimated to be at 150 to 220 mm above the tension flange. The proposed method of calculation of placement can be used for installation of distributed sensors on other types of bridges.