Miracle-dz/UniFAR-FTUs huggingface dataset:
Impaired Kv7 channel function in cerebral arteries of a tauopathy mouse model (rTg4510)

In tauopathies, such as Alzheimer's disease with or without concomitant amyloid β plaques, cerebral arteries display pathological remodeling, leading to reduced brain tissue oxygenation and cognitive impairment. The precise mechanisms that underlie this vascular dysfunction remain unclear. Kv7 voltage‐dependent K+ channels contribute to the development of myogenic tone in rat cerebral arteries. Thus, we hypothesized that Kv7 channel function would be impaired in the cerebral arteries of a tauopathy mouse model (rTg4510), which might underlie cerebral hypoperfusion associated with the development of neurofibrillary tangles in tauopathies. To test our hypothesis we performed wire myography and quantitative PCR on cerebral arteries, mesenteric arteries and the inferior frontotemporal region of the brain surrounding the middle cerebral artery from tau transgenic mice (rTg4510) and aged‐matched controls. We also performed whole‐cell patch clamp experiments on HEK293 cells stably expressing Kv7.4. Here, we show that Kv7 channels are functionally impaired in the cerebral arteries of rTg4510 mice, but not in mesenteric arteries from the same mice. The quantitative PCR analysis of the cerebral arteries found no change in the expression of the genes encoding the Kv7 channel α‐subunits, however, we found reduced expression of the ancillary subunit, KCNE5 (also termed KCNE1L), in the cerebral arteries of rTg4510 mice. In the brain, rTg4510 mice showed reduced expression of Kv7.3, Kv7.5, and Kv2.1. Co‐expression of KCNE5 with Kv7.4 in HEK293 cells produced larger currents at voltages >0 mV and increased the deactivation time for the Kv7.4 channel. Thus, our results demonstrate that Kv7 channel function is attenuated in the cerebral arteries of Tg4510 mice, which may result from decreased KCNE5 expression. Reduced Kv7 channel function might contribute to cerebral hypoperfusion in tauopathies, such as Alzheimer's disease.
Loosely-Coupled INS/Vision based Absolute Navigation with Adaptive Kalman Filter

We represent adaptive Kalman filter which combines advantages of fast output inertial navigation and slow image based absolute navigation algorithm. In the feature matching process of the scene-matching algorithm, the outliers occasionally occur, resulting in the problem that the state variables of the Kalman filter diverge due to the mismatched improper navigation results. Since this can lead to a very dangerous situation in the navigation system, we propose an adaptive Kalman filter algorithm to solve this problem. The proposed algorithm uses the residual, which is the difference between the measured value and the model, as a judgment variable and does not use the measured value when the threshold value is exceeded. The performance of the proposed algorithm is verified by using flight data.
Neuronal variability and tuning are balanced to optimize naturalistic self-motion coding in primate vestibular pathways

It is commonly assumed that the brain’s neural coding strategies are adapted to the statistics of natural stimuli. Specifically, to maximize information transmission, a sensory neuron’s tuning function should effectively oppose the decaying stimulus spectral power, such that the neural response is temporally decorrelated (i.e. ‘whitened’). However, theory predicts that the structure of neuronal variability also plays an essential role in determining how coding is optimized. Here, we provide experimental evidence supporting this view by recording from neurons in early vestibular pathways during naturalistic self-motion. We found that central vestibular neurons displayed temporally whitened responses that could not be explained by their tuning alone. Rather, computational modeling and analysis revealed that neuronal variability and tuning were matched to effectively complement natural stimulus statistics, thereby achieving temporal decorrelation and optimizing information transmission. Taken together, our findings reveal a novel strategy by which neural variability contributes to optimized processing of naturalistic stimuli.
UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion

Privacy intrusion has become a major bottleneck for current trust-aware social sensing, since online social media allows anybody to largely disclose their personal information due to the proliferation of the Internet of Things (IoT). State-of-the-art social sensing still suffers from severe privacy threats since it collects users’ personal data and disclosure behaviors, which could raise user privacy concerns due to data integration for personalization. In this paper, we propose a trust-aware model, called the User and Item Similarity Model with Trust in Diverse Kinds (UISTD), to enhance the personalization of social sensing while reducing users’ privacy concerns. UISTD utilizes user-to-user similarities and item-to-item similarities to generate multiple kinds of personalized items with common tags. UISTD also applies a modified k-means clustering algorithm to select the core users among trust relationships, and the core users’ preferences and disclosure behaviors will be regarded as the predicted disclosure pattern. The experimental results on three real-world data sets demonstrate that target users are more likely to: (1) follow the core users’ interests on diverse kinds of items and disclosure behaviors, thereby outperforming the compared methods; and (2) disclose more information with lower intrusion awareness and privacy concern.
An Intelligent Scheme for the Identification of QoS Violations in Virtualized Environments

Current networking applications involve the definition and utilization of multiple virtualized resources on top of the available infrastructure. Software defined networks increase the performance compared with legacy systems as any functionality is managed through software. Securing the quality of service in such environments is significant to support novel applications that deliver their results in real time. In this paper, we propose a monitoring mechanism that observes the performance of the virtualized resources and identifies possible quality of service violations. Our model can be applied to any application domain, however, it is adapted to virtualized resources. We rely on a simple model that collects performance data, focuses on multiple parts of a virtualized functions chain and immediately concludes potential violations in real time. The proposed mechanism is incorporated in an SDN controller that is responsible to manage the virtualized resources. We provide an analytical description of the model and through a large set of simulations, we reveal its performance. Our results exhibit the timely identification of quality of service violations even in very dynamic environments where the performance of the network changes continuously.
Multilayered Echo-State Machine: A Novel Architecture for Efficient Intrusion Detection

Computers and other smart gadgets have become of a paramount importance in today’s transactions. Connected to the Internet, those devices offer the possibility to benefit from a myriad of electronic services, including social networking, banking, trade marketing, education and so on. Such activities are producing huge volume of information transiting with high velocity each day. Parallel to that, we have witnessed an epidemic increase in the number and the sophistication of cyberattacks, as they became more persistent and highly structured. In this context, modern intrusion detection systems are to be modeled so as to issue high detection rates in a tiny period of time in order to mitigate the risks. This paper is built on recurrent neural network with multilayered echo-state machine (ML-ESM) to model an intrusion detection. We assess our model on three publicly available data sets, namely, the DARPA KDD’99, NSL-KDD a reformed version of the latter, and UNSW NB 15. Performance metrics for both binary classification and multilabel classification are calculated and compared with those of some existing machine learning techniques and the recent state-of-the-art intrusion detection systems. Results indicate that the ML-ESM wins the challenge in both achieving a higher accuracy and considerably optimizing the processing time.
Forecast-based Optimization of Islanded Microgrids

In the context of the energy transition, the operational management of microgrids becomes an issue of increasing importance. In this paper, we compare two optimization models for planning generators and storage units in an islanded microgrid, while dealing with uncertainty in load- and PV-forecasts. These models are combined with a number of re-planning strategies, and evaluated through a case study of an islanded microgrid in the Netherlands. As a result, one of our algorithms is currently implemented in this microgrid. In our analysis of the case study, we identify features of the microgrid that play an important role in determining whether these approaches are useful and we show the effectiveness of using re- planning strategies.
A Study on the Effect of Sports on Academic Stress Using Wearable Galvanic Skin Response

Wearable sensors have opened a new door for research to understand different human emotions and psychological states. This is the first study to employ wearable devices to measure the Galvanic Skin Response (GSR) of students. A set of Microsoft Band 2 was used to collect data from nineteen engineering students while attending the same class. The data was collected throughout a whole semester. Students were later divided into categories according to their weekly activity level. This activity level was determined by an online survey that the students filled out. One-Way Analysis of Variance was performed to assess differences between groups. Correlation Analysis was also performed to evaluate the correlations between groups. Findings of this work include significant intra-group differences. Significant correlations were also found between GSR values and activity level. Significant correlations were also found within the GSR values between the groups themselves.
Cyber-Physical System Optimization Techniques in Mechatronic Redundant Accelerometer Array Devices (RAAD)

A modeler's approach to optimization greatly improved the calibration of redundant accelerometer array devices (RAADs) for batch estimates of stochastic parameters for zero bias (<inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula>) and sensitivity (<inline-formula><tex-math notation="LaTeX">$V/g$</tex-math></inline-formula>). Each device under test (DUT) has an orientation relative to a shared rigid-body frame. The strategy offers cyber-physical systems a reference-free approach for geometry. A function minimizes estimation errors of intrinsic parameters across a gamut of DUTs in array. The algorithmic goal reduces the population average standard deviation <inline-formula><tex-math notation="LaTeX">$(\overline{\sigma _g})$</tex-math></inline-formula> estimate of static gravity <inline-formula><tex-math notation="LaTeX">$(g)$</tex-math></inline-formula> measurements across more than or equal to three static world poses. RAAD achieves 72 sensor placements in three poses. Comparisons are made to root-mean-square-error (<inline-formula><tex-math notation="LaTeX">$\overline{\text{RMSE}}$</tex-math></inline-formula>) approaches in previous studies. The optimization model solves a nonlinear system with logical constraints using <sc>matlab</sc>'s <inline-formula><tex-math notation="LaTeX">$ {\rm fmincon}()$</tex-math></inline-formula> function. Precision is achieved for 48 design variables. The RAAD empirical value for <inline-formula><tex-math notation="LaTeX">$\overline{\sigma _g}$</tex-math></inline-formula>, initially <inline-formula><tex-math notation="LaTeX">$0.1042 g$</tex-math></inline-formula>, improves to <inline-formula><tex-math notation="LaTeX">$0.0027 g$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\overline{\text{RMSE}}$</tex-math></inline-formula> improves from <inline-formula><tex-math notation="LaTeX">$\text{0.0282}_{\text{RMSE}}$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$\text{0.0002}_{\text{RMSE}}$</tex-math></inline-formula>. The algorithm is tested in simulation with results yielding a high accuracy in estimates for zero bias, <inline-formula><tex-math notation="LaTeX">$\pm \text{0.35}\%,$</tex-math></inline-formula> and sensitivity in the range <inline-formula><tex-math notation="LaTeX">$-0.68\%$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$ \text{2.46}\%$</tex-math></inline-formula>. Simulated orientation results are accurate in the range <inline-formula><tex-math notation="LaTeX">$-\text{0.17}\%$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$\text{0.61}\%$</tex-math></inline-formula> across a deterministic grid. Method comparisons indicate that the <inline-formula><tex-math notation="LaTeX">$\overline{\sigma _g}$</tex-math></inline-formula> algorithm is superior to <inline-formula><tex-math notation="LaTeX">$\overline{\text{RMSE}}$</tex-math></inline-formula> for the calibration of intrinsic parameters.
Joint Offloading and Computing Design in Wireless Powered Mobile-Edge Computing Systems With Full-Duplex Relaying

Simultaneous wireless information and power transfer (SWIPT) and mobile-edge computing (MEC) are promising technologies in Internet of Things era, which can enhance the self-sustainability and computation capability of user equipment, respectively. In this paper, we propose a multiple-input multiple-output full-duplex (FD) relay-based SWIPT-MEC system. With MEC and SWIPT, UE can use its battery energy to execute a computation task locally or offload part or all of it to access point connected with an MEC server and refill battery while receiving the computation results. The FD mode allows relay to receive and transmit the offloaded bits simultaneously. Based on these, an energy-efficient problem is formulated to minimize the system energy consumption over a time block, while the latency constraints and the energy consumption constraints are guaranteed. Since the original problem is non-convex, we use an alternating optimization technique to convert the original problem into three subproblems. Some propositions and algorithms are proposed to solve these three subproblems. Numerical results demonstrate that the proposed optimization scheme achieves a significant performance gain by comparisons with three benchmark schemes.
Effect of Methotrexate in the Treatment of Distal Interphalangeal Joint Extensor Tendon Enthesopathy in Patients with Nail Psoriasis

To assess the effect of methotrexate on the development of distal interphalangeal joint extensor tendon enthesopathy in psoriasis, thirty-two people aged 34 to 57 years with nail psoriasis and distal interphalangeal joint extensor tendon enthesopathy (19 patients with Ps (psoriasis) and 13 with PsA (psoriatic arthritis) were started on methotrexate at 15 to 25 mg/week and the treatment was continued for 6 months). A total of 319 nails were examined. After six months of treatment, the thicknesses of the nail plate, nail bed and nail matrix were found to decrease in both groups of patients. Methotrexate treatment resulted in a decrease in the joint extensor tendon thickness only in patients with Ps (0.94 ± 0.05 vs. 0.96 ± 0.04, p < 0.001), where the tendon thickness after treatment correlated with the matrix thickness (r = 0.337, p = 0.018) and with the bed thickness (r = 0.299, p = 0.039). Methotrexate treatment resulted in a decrease in the extensor tendon thickness only in patients with Ps but not in PsA. The findings of this study may suggest the effectiveness of systemic treatment of nail psoriasis in patients without arthritis and the use of US nail examinations in Ps and PsA patients in morphological change assessment and response to treatment.
A Neighborhood-Based Value Iteration Algorithm for POMDP Problems

The excessive growth of the size of the search space has always been an obstacle to POMDP planning. Approximate approaches based on value functions such as GapMin breadth-first explore belief points only according to the difference between lower and upper bounds of the optimal value function, so the representativeness and effectiveness of the explored point set should be further improved. A Neighborhood-Based Value Iteration (NBVI) based on the distribution information of belief points is presented in this paper. NBVI uses the neighborhood relationship between the explored points to divide the explored point set into domains. The average gap of the belief points in the domain is considered as the exploration value of the domain, thereby determining the distribution of the points which is taken into consideration for further exploration. When exploring successor points, NBVI filters the successor point according to both the gap of the successor points and the distance from the successor points to domains. Experimental results show that NBVI is very competitive with GapMin in terms of both solution quality and convergence efficiency on large-scale problems.
A Novel Frequency Beam-Steering Antenna Array for Submillimeter-Wave Applications

This paper describes design and development of a novel frequency beam-scanning array antenna operating at Y-band (220–325 GHz). The proposed antenna is a traveling-wave antenna, with a meandered rectangular waveguide structure as the propagating medium. The array generates a very narrow beamwidth of about 2.5° with ±25° beam steering capability as the frequency sweeps from 230 to 245 GHz. The antenna beam in elevation is about 10°, which is generated with a slot-coupled cavity-backed patch array antenna along the elevation direction. The proposed antenna provides an overall gain of over 29 dBi and exhibits a radiation efficiency of more than 55% over the entire frequency range of operation. The antenna topology is designed to present a very low-profile structure to meet a very low volume and low mass requirements. The antenna consists of over 600 patch elements and is confined within a volume of 45 mm × 8.5 mm × 1.25 mm and weighs less than 4.5 g. A prototype of the antenna is fabricated using silicon micromachining with a high level of accuracy and is characterized using a special near-field measurement setup. The antenna exhibits a measured scanning range of over 48° with a gain of over 28.5 dBi, all of which are in close agreements with simulation results.
Characterization and Considerations for Upset in FPGA

The increase in performance and the relatively low cost have made the FPGA an attractive technology for use in various product areas. When used in safety-critical applications, the susceptibility against upsets due to cosmic radiation requires special considerations. In this paper, a number of mitigation techniques against upsets are discussed together with the use of a COTS IP. Furthermore, practical tests are performed to validate the upset rates and mitigation techniques. The tests are performed on a Xilinx UltraScale+MPSOC FPGA.
Path Generation with LSTM Recurrent Neural Networks in the Context of the Multi-Agent Patrolling

We propose a conceptually simple new decentralised and non-communicating strategy for the multi-agent patrolling based on the LSTM architecture. The recurrent neural networks and more specifically the LSTM architecture, as machines to learn temporal series, are well adapted to the multi-agent patrol problem to the extent that they can be viewed as a decision problem over the time. For a given scenario, a LSTM neural network is first trained from data generated in simulation for that configuration, then embedded in agents that shall use it to navigate through the area to patrol choosing the next place to visit by feeding it with their current node. Finally, this new LSTM-based strategy is evaluated in simulation and compared with two representative strategies, a cognitive and centralised one, and a reactive and decentralised one. Preliminary results indicate that the proposed strategy is globally not better than the representative strategies for the aggregating criterion of average idleness, but better than the decentralised representative for the evaluation criteria of mean interval and quadratic mean interval.
Voltage and Current Switching-Ripple Cancelation in the Double Dual Boost Converter

The classical interleaving buck converter is able to cancel out a percentage of the inductor current ripple driven to the output capacitor, in the same way, the classical interleaving boost converter is able to cancel out a percentage of the input current ripple drained from the source, depending on the number of interleaving stages and the operation point (duty cycle), the ripple can be actually zero. This paper study the potential of the double dual boost converter to achieve zero ripple at an arbitrary duty cycle by implementing a new design methodology and a special PWM scheme. This paper introduces also a new characteristic that is achievable by the double dual boost converter, the capability of achieving zero output voltage ripple. A detailed theoretical analysis as well as experimental results are included to demonstrate the principles of the proposition.
Electrochemical Enantiomer Recognition Based on sp3-to-sp2 Converted Regenerative Graphene/Diamond Electrode

It is of great significance to distinguish enantiomers due to their different, even completely opposite biological, physiological and pharmacological activities compared to those with different stereochemistry. A sp3-to-sp2 converted highly stable and regenerative graphene/diamond electrode (G/D) was proposed as an enantiomer recognition platform after a simple β-cyclodextrin (β-CD) drop casting process. The proposed enantiomer recognition sensor has been successfully used for d and l-phenylalanine recognition. In addition, the G/D electrode can be simply regenerated by half-minute sonication due to the strong interfacial bonding between graphene and diamond. Therefore, the proposed G/D electrode showed significant potential as a reusable sensing platform for enantiomer recognition.
Comparative Study of Character Recognition on Thai License Plate using DCT and FIR System

Thai characters have rather similar shapes, which they are not same as English characters that have distinct shapes. Thereby, performing of Thai character recognition is not simple. In this paper, the recognition of characters and numbers on Thai license plate using DCT and FIR system methods is proposed and compared base on recognition rate. There are two ways of studying in DCT method. First, compute the coefficients of 1D-DCT by separation in the horizontal and vertical complements of character and represent them as the features of character. Second, combine the coefficients of 1D-DCT at each component in form of power spectrum of DCT and use it as the character features. For FIR system method, the impulse responses of FIR system are used as the features of character. The 120 car images under variant illumination conditions are used to testing all three methods. Moreover, some images are incomplete conditions such peeling paint, stained with slush and so on. In the results, the success of recognition rate for separation of DCT coefficients, power spectrum of DCT and FIR system methods was 99.15%, 48.30% and 98.30%, respectively.