Miracle-dz/UniFAR-FTUs huggingface dataset:
Immune Checkpoints as Therapeutic Targets in Autoimmunity

Antibodies that block the immune checkpoint receptors PD1 and CTLA4 have revolutionized the treatment of melanoma and several other cancers, but in the process, a new class of drug side effect has emerged—immune related adverse events. The observation that therapeutic blockade of these inhibitory receptors is sufficient to break self-tolerance, highlights their crucial role in the physiological modulation of immune responses. Here, we discuss the rationale for targeting immune checkpoint receptors with agonistic agents in autoimmunity, to restore tolerance when it is lost. We review progress that has been made to date, using Fc-fusion proteins, monoclonal antibodies or other novel constructs to induce immunosuppressive signaling through these pathways. Finally, we explore potential mechanisms by which these receptors trigger and modulate immune cell function, and how understanding these processes might shape the design of more effective therapeutic agents in future.
A Design of Granular Takagi–Sugeno Fuzzy Model Through the Synergy of Fuzzy Subspace Clustering and Optimal Allocation of Information Granularity

Fuzzy models have been commonly used in system modeling and model-based control. Among various fuzzy models, Takagi–Sugeno (TS) fuzzy models form one of the intensively studied and applied categories of models. In this study, we are concerned with a development of a granular TS fuzzy model realized on a basis of numerical evidence and completed through a combination of fuzzy subspace clustering and the principle of optimal allocation of information granularity. The TS fuzzy models are built with the use of the fuzzy subspace clustering algorithm. Information granularity is regarded as a crucial design asset whose optimal allocation gives rise to granular fuzzy models and makes the constructed models to become better in rapport with experimental data. In comparison with fuzzy models, granular fuzzy models produce results (outputs) that are information granules rather than numeric entities being encountered in fuzzy models. In contrast with the commonly used optimization criteria, which emphasize the highest accuracy encountered at the numeric level, the performance of the granular TS fuzzy model is quantified in terms of the coverage and specificity criteria where such criteria are of interest in the evaluation of quality of information granules vis-à-vis experimental (numeric) data. Experimental results are reported for both synthetic datasets and publicly available data sets coming from the UCI machine learning repository.
Feature Selected Cancer Data Classification with Genetic Programming

Classification is used to distribute data to classes defined on the dataset. Classification algorithms determine the classes in which the data in the test set is to be included by learning the distribution of classes in the training set. It is directly dependent on the choice of which properties to use in the classification. The most prominent features of cancer data in this work are selection and classification using genetic programming method. It has been seen that very successful classification results are obtained with Genetic Programming.
A Multilevel Pulse-Modulated Polar Transmitter Based on a Doherty Power Amplifier and Memoryless Digital Predistortion

This letter presents an aliasingfree multilevel pulse modulation (MLPM) architecture to implement a linear Doherty power amplifier (PA). This letter is believed to be the first reported results of this type of Doherty architecture to achieve linear amplification rather than the usual situation of a continuous amplitude modulated carrier and continuous load modulation. Our results show that both high efficiency and good linearization can be achieved with simple memoryless digital predistortion. Doherty operation with MLPM is believed to have benefits in linearization due to the MLPM discrete drive as less power is dissipated by the PA, leading to less thermal-related memory effects. A prototype transmitter system is constructed using a commercially available Doherty PA for validation. Measurements are performed using a 64-QAM long-term evolution (LTE) downlink signal at 2140 MHz with 20-MHz bandwidth. The overall system achieved an output power level of 40.1 dBm with a power-added efficiency of 39.5% while passing the −45-dBc downlink spectral requirements of the LTE standard.
An Efficient Differential Evolution Algorithm for Solving 0–1 Knapsack Problems

The traditional differential evolution algorithm was originally, and still is mainly, used to solve continuous optimization problems. As a result, it has not commonly been considered as applicable for several real-world problems in the permutation-based domain. In this paper, a novel differential evolution algorithm, which incorporates several effective components, is introduced. These components increase search effectiveness by providing a good balance between exploration (discovering new solutions) and exploitation (further exploring current solutions) processes. Moreover, a dual representation of solutions, which has the capability to allow normal continuous handling of variables by differential evolution operators, and at same time provide binary variables for fitness measurement, is employed. To judge the performance of the proposed algorithm, 14 instances of 0–1 knapsack problems have been solved and the results have been compared with those obtained from 11 state-of-the-art algorithms. Results show that the proposed algorithm was able to outperform other algorithms in solving small and medium sized knapsack problems and is competitive in large-sized problems.
On Competing Mechanisms Under Exclusive Competition

We study games in which several principals design incentive schemes in the presence of privately informed agents. Competition is exclusive: each agent can participate with at most one principal, and principal-agents corporations are isolated. We analyze the role of standard incentive compatible mechanisms in these contexts. First, we provide a clarifying example showing how incentive compatible mechanisms fail to completely characterize equilibrium outcomes even if we restrict to pure strategy equilibria. Second, we show that truth-telling equilibria are robust against unilateral deviations toward arbitrary mechanisms. We then consider the single agent case and exhibit sufficient conditions for the validity of the revelation principle.
Dynamic Intermittent Suboptimal Control: Performance Quantification and Comparisons

This paper presents a novel intermittent suboptimal event-triggered controller design for continuous-time nonlinear systems. The stability of the equilibrium point of the closed-loop system, and the performances are analyzed and quantified theoretically. It is proven that the static and the dynamic event-triggered suboptimal controllers have a known degree of suboptimality compared to the conventional optimal control policy. In order to generate dynamic event-triggering framework, we introduce an internal dynamical system. Moreover, the Zeno behavior is excluded. Finally, a simulation example is conducted to show the effectiveness of the proposed intermittent mechanisms.
Analysis and Fractional PD Control Bifurcation of a Fractional-Order Genetic Regulatory Networks with Delays

In this paper, a fractional-order gene regulatory network model with two delays is investigated. The existence condition of Hopf bifurcation is deduced by analyzing its characteristic equation, where the total delay is taken as a bifurcation parameter. These bifurcation behaviors may cause the network system heavy oscillation and instability. An fractional PD control method is proposed to control bifurcations in the system. Finally, numerical simulations are provided to verify the theoretical analysis. Simulation results show that the nonlinear behavior of the system can be controlled by this method.
A Cascade Coupled Convolutional Neural Network Guided Visual Attention Method for Ship Detection From SAR Images

Convolutional neural networks (CNNs) have found applications in ship detection from synthetic aperture radar (SAR) images. However, there are some challenges hamper their advance. First, the detected bounding boxes are not very compact. Second, there are quite a few missing detections for small and densely clustered ships. Third, objects with analogical scatterings on land are detected as ships by making mistake. This is due to: 1) the CNN-based SAR ship detectors cannot utilize the spatial information very sufficiently; 2) features learned from CNNs only describe SAR images in space domain while neglecting the information hidden in frequency domain; and 3) information contained in the meta-data file, which may link to other sources, is not taken into account. To overcome these problems, in this paper, a cascade coupled CNN-guided (3C2N-guided) visual attention method for SAR ship detection is proposed. This method considers the newly presented 3C2N model as a qualified ship proposal generator because the images’ spatial information is utilized more sufficiently. The 3C2N model, with coupled CNN as the baseline, consists of a sequence of cascade detectors for training. Complementally, a pulse cosine transformation-based visual attention model in frequency domain is operated on the adaptive regions for ship discrimination. This could further refine the proposals’ locations and could significantly reduce the missing detections and false alarms. In addition, the digital elevation model data are adopted to remove ship-like targets on land. Experimental evaluations on 25 Sentinel-1 images demonstrate that the proposed method is superior to the previous state-of-the-art methods.
The Iris Feature Point Averaging Method in Student Eye Gaze Tracking

In order to solve the problems that the infrared camera must be used to illuminate the human eye to obtain the iris image, and because the eyelid covers, the iris image can not be extracted completely, which cause the failure of tracking the iris accurately. In this paper, the Average Feature Point (AFP) is proposed to locate iris center. This method uses the camera of notebook to take human photos, recognizes face by Haar features, and further intercepts the human eye region; and the First Valley Method (FVM) is used to select the appropriate iris binarization threshold by analyzing the eye gray histogram; after that, use the feature of the maximum connected domain to eliminate the noise of the image; finally AFP is used to locate the center of the iris. A large number of experiments have been conducted on different states of human images, the results showed that AFP improved the accuracy of locating iris center compared with the traditional Hough method.
Negev Bedouin Teachers’ Attitudes Toward ADHD and Its Pharmacological Treatment

Little is known about the attitudes of Negev Bedouin toward attention-deficit/hyperactivity disorder (ADHD) and its pharmacological treatment. This study examines the perspectives of Negev Bedouin teachers on pharmacological treatment. Thirty-six teachers are asked to consider how their views influence the way they relate to pupils’ parents. A grounded-theory analysis of semistructured interviews illuminates ambivalence in teachers’ attitudes. Teachers, like the rest of their community, when asked about the implications of an ADHD evaluation for their children, respond that ADHD and its pharmacological treatment cause dishonor. When asked what ADHD means when it is their pupils who are diagnosed and treated, however, teachers, like the education establishment, accept the need for medication. However, they fail to communicate this need to parents because their attempts to do so show parents that they consider their children “flawed”—causing parents to oppose treatment even more lest they succumb to social stigma.
Self-reported alcohol-associated symptoms and drinking behavior in three ALDH2 genotypes among Japanese university students.

BACKGROUND Alcohol abuse is one of the most serious health problems among young adults. Nearly half of the Japanese population is sensitive to alcohol due to a genetic polymorphism in low K(m) aldehyde dehydrogenase (ALDH2). In the present study, we investigated the effects of the ALDH2 genotype on both self-reported alcohol-associated symptoms and alcohol drinking behavior among Japanese university students. METHODS The study subjects were 423 (389 males and 34 females) university students in a medical university. The subjects completed a questionnaire regarding self-reported alcohol-associated symptoms and alcohol drinking behavior. The ALDH2 genotype was determined through digestion of polymerase chain reaction (PCR) products by a restriction enzyme Ksp632I. The frequency of alcohol-associated symptoms generally increased in the order ALDH2*1/*1, ALDH2*1/*2, ALDH2*2/*2 among males. The frequency of those who drink > or = 5 days/week was less than 10% in all genotype groups. However, the frequency of those who drink 1-4 days/week was significantly higher in ALDH2*1/*1 than that in ALDH2*1/*2 and in ALDH2*2/*2. A similar tendency also was observed in females. Mean amounts of alcohol consumption per occasion in the three ALDH2 genotypes stratified by drinking frequency generally increased significantly in the order ALDH2*2/*2, ALDH2*1/*2, ALDH2*1/*1 in both sexes. The proportion of binge drinkers defined by those who drink ethanol of > or = 75 ml per occasion on average also increased in the order ALDH2*2/*2 (0.0%), ALDH2*1/*2 (9.8%), ALDH2*1/*1 (22.1%) among male drinkers (> or = 1 day/month). CONCLUSIONS We for the first time demonstrated clear associations between the ALDH2 genotype, self-reported alcohol-associated symptoms, and alcohol drinking behavior among Japanese university students.
Coordinated Control of multi-UUVs with Double Independent Markovian Switching Topologies and Time-varying Delays

In this paper a new method that applied the consensus algorithm to solve the coordinate control problems of leaderless multi-UUVs formation with double independent Markovian switching communication topologies and same time-varying delays among the sensors is investigated. In order to reduce the capacity per data package transforming between the multi-UUVs formation, the communication topology is divided into two different switching parts which are used to transform the position and velocity information respectively. Then, the state feedback linearization is used to simplify and rewrite the nonlinear and coupled math model of the UUV into a double-integrator model. With the simplified model, coordinate control of the multi-UUVs can be regarded as approximating consensus problem with time-varying delays. With the help of Lyapunov-Krasovskii theorem, sufficient conditions of consensus control which can guarantee stability of the multi-UUVs formation are proposed and proven. Finally, the theoretical results are validated by simulation results.
Constrained Receding Horizon Contouring Error Control for a Feed Drive System

I0635. The tracking error model is formed based on the desired curve and the orthogonal error is regarded as an approximation of real contouring error. The control policy is derived from the optimization of a quadratic cost function, which penalizes future errors and control variables in each sampling time over a finite horizon. The performance of the proposed algorithm is verified via the computer simulations with a predefined trajectory. The result shows the improvement of tracking accuracy by comparing with unconstrained method.
Study on performance of silica nanoparticle dispersed synthetic ester oil under AC and DC voltages

Synthetic ester oil (SEO) is becoming a replacement for mineral oil in transformers due to its biodegradability and for its better insulation properties. Silica nanoparticles dispersed in SEO can increase its corona inception voltage (CIV) and breakdown voltage compared to pure SEO. Physico-chemical studies were carried out to understand the characteristics of nanoparticles and nanofluids. Methodical experimental studies were carried out to understand the corona discharge activity in SEO based nanofluid. A ultra-high frequency (UHF) sensor was used to identify corona activity and it is concluded that the UHF signal radiated from corona activity in SEO and nanoparticle dispersed SEO have a bandwidth in the range of 700 MHz to 2 GHz. A comparative study on the CIV of SEO and nanofluid under harmonic AC voltages has been carried out, which indicates a reduction in the CIV as the total harmonic distortion of the supply voltage increases. Phase resolved partial discharge analysis indicates that the magnitude of discharges formed in nanofluid is much lower than in the SEO. Comparative statistical analysis are made with the breakdown voltages of the synthetic ester oil and nanofluid. A comparative analysis on the energy of the UHF signal due to the corona discharges is performed.
A Novel Robust Perfect Tracking Control Method for Nonlinear Servo Systems

In this paper, we extend perfect tracking control to nonlinear systems, and propose a novel robust perfect tracking control (RPTC) strategy for nonlinear servo tracking systems. The overall control system consists of four parts: a model-based friction compensator, a feedforward perfect tracking controller, an improved internal model controller as the feedback controller, and a disturbance observer for position feedback. First, the friction compensator is introduced to compensate the nonlinear dynamic friction, and the feedforward perfect tracking controller is applied to widen the frequency band. Then, the internal model controller with a differentiator is adopted to yield improved tracking accuracy. Moreover, by utilizing the disturbance observer, the robustness against external disturbances and plant uncertainties is ensured. Finally, high accuracy tracking and ideal robustness are achieved by the RPTC scheme. The stability of the closed loop system is analyzed. Simulation results demonstrate that the proposed RPTC strategy significantly improves the tracking accuracy and enhances the robustness.
Multi-Sensor Integrated Navigation Algorithm Using Adaptive Federated Kalman Filter for MAVs

In this paper, a novel adaptive information-distributing algorithm is proposed for the federated Kalman filter (FKF) with application to the multi-sensor integrated navigation system of commercial micro aerial vehicles (MAVs). A new matrix-form information distribution coefficient is constructed based on two new vector-form indicators, i.e., bias and amplitude. The bias indicator evaluates the deviation of the local filters from the global optimal estimations. The amplitude indicator is closely related with the oscillation magnitude of local filters' state estimations, which is caused by either the external noise or the sensor's faults. Based on the two vector-form indicators, the information distribution coefficients are adjusted dynamically according to the different characteristics of the separate state estimation in each local filter. Then an adaptive federated Kalman filter (AFKF) model integrating the inertial navigation system (INS) and other sensors is established. Simulation results demonstrate that the proposed method improves the precision and disturbance rejection performance of the FKF effectively.
Design of a Circularly Polarized Parasitic Array With Slot-Coupled DRA With Improved Gain for the 5G Mobile System

A high-gain circularly polarized dielectric resonator antenna is proposed for the fifth generation of a mobile network. The parasitic elements are used to increase the antenna gain. By choosing suitable spacings among the elements, a significant increase in the gain of antenna can be obtained with no degradation in pattern characteristics. The dielectric constants of all elements are identical. The excited element is fed through an X-shaped slot coupled to a 50 Ω microstrip line. The proposed structure is designed, simulated, fabricated, and measured. The results show that the proposed antenna has about 5 GHz (17.8%) bandwidth, about 2.2 GHz axial ratio bandwidth, and also its gain is around 10.5 dB, which meet the requirements of the fifth generation mobile network.
A Low Cost Polarized Navigation Sensor Design

In this paper, a low-cost bionic polarized light collection sensor is designed based on the sand ant navigation mechanism. The paper presents the basic theory and hardware implementation plan. At the same time, the algorithm and principle of polarization angle acquisition are introduced, which describe the calculation method in detail. At the end of the paper, the causes of the polarization angle error are analyzed and the error compensation method is given. The result of experiment shows that the sensor can collect the information of polarized light and the azimuth angle can be gotten effectively. Its error is about 2° and cost is low.
A Scalable FPGA Architecture for Randomly Connected Networks of Hodgkin-Huxley Neurons

Human intelligence relies on the vast number of neurons and their interconnections that form a parallel computing engine. If we tend to design a brain-like machine, we will have no choice but to employ many spiking neurons, each one has a large number of synapses. Such a neuronal network is not only compute-intensive but also memory-intensive. The performance and the configurability of the modern FPGAs make them suitable hardware solutions to deal with these challenges. This paper presents a scalable architecture to simulate a randomly connected network of Hodgkin-Huxley neurons. To demonstrate that our architecture eliminates the need to use a high-end device, we employ the XC7A200T, a member of the mid-range Xilinx Artix®-7 family, as our target device. A set of techniques are proposed to reduce the memory usage and computational requirements. Here we introduce a multi-core architecture in which each core can update the states of a group of neurons stored in its corresponding memory bank. The proposed system uses a novel method to generate the connectivity vectors on the fly instead of storing them in a huge memory. This technique is based on a cyclic permutation of a single prestored connectivity vector per core. Moreover, to reduce both the resource usage and the computational latency even more, a novel approximate two-level counter is introduced to count the number of the spikes at the synapse for the sparse network. The first level is a low cost saturated counter implemented on FPGA lookup tables that reduces the number of inputs to the second level exact adder tree. It, therefore, results in much lower hardware cost for the counter circuit. These techniques along with pipelining make it possible to have a high-performance, scalable architecture, which could be configured for either a real-time simulation of up to 5120 neurons or a large-scale simulation of up to 65536 neurons in an appropriate execution time on a cost-optimized FPGA.