Miracle-dz/UniFAR-FTUs huggingface dataset:
Flavonoids with Novel Nicotinic Activity as Potential Pharmacotherapies to Treat Ethanol- Induced Neurotoxicity

OF DISSERTATION FLAVONOIDS WITH NOVEL NICOTINIC ACTIVITY AS POTENTIAL PHARMACOTHERAPIES TO TREAT ETHANOL-INDUCED NEUROTOXICITY Ethanol causes neurotoxicity via several mechanisms at different points in the cycle of dependence, including neuroinflammation and oxidative stress during ethanol exposure as well as excitotoxicity during ethanol withdrawal. The primary therapeutic implication is that ethanol-induced neurotoxicity requires multifunctional pharmacotherapies which reduce all mechanisms. Using an innovative pharmacological high throughput screening method on a large plant extract library we discovered flavonoids with alpha7 nicotinic acetylcholine receptor (nAChR) activity. In addition to their well-known anti-inflammatory and antioxidant properties, this novel activity means they can potentially reduce excitotoxicity and therefore makes them ideal for inhibition of ethanol-induced neurotoxicity. Rhamnetin, the candidate compound, was first found to inhibit lipopolysaccharide induced inflammation in immortalized BV2 microglia, in part, via alpha7 nAChRs. We then established an in vitro model of ethanol inducedneurotoxicity using organotypic hippocampal slice cultures which incorporated both neuroinflammatory and excitotoxic components. Neuroinflammation enhanced excitotoxicity under control conditions but the reverse was observed during ethanol withdrawal. Both mechanisms are important but their interaction is not simple. Finally, rhamnetin was evaluated in this model and found to reduce neuroinflammation and excitotoxicity associated with ethanol withdrawal. In conclusion, the studies herein provide strong evidence for alpha7 nAChRs selective flavonoids as potential pharmacotherapies for the treatment of ethanol-induced neurotoxicity and further implicate neuroinflammation, excitotoxicity, and their interaction as critical mechanisms in this pathology.
Relay Control Systems with Discrete Time Equalizer

Different types of automatic systems are fundamentally different from each other by power actuators, which amplify the control actions formed by the regulators. Often, the control law implemented by regulators is determined by the type of power unit. In continuous automatic systems, electronic, magnetic, electric, hydraulic, pneumatic amplifiers are used as actuators. The control devices of these systems are also continuous. The creation of impulse systems is caused by the desire to reduce energy losses in actuators by fundamentally changing the method of forming a force effect on the control object. In these systems, power converters are made in the form of pulse modulators that implement switching of the executive circuits, which ensures that the average value of the power effect corresponds to the signal of a continuous regulator. Unlike continuous and pulsed systems, relay automatic systems are essentially nonlinear. The actuating device in them is a contact or non-contact power relay. From the point of view of control theory, sliding modes are of the greatest interest. Such modes are achieved with certain optimal control algorithms. In this regard, discrete regulators, called discrete time equalizers, can be successfully used to set the optimal or quasi-optimal laws of switching relay elements. The article proposes a relay automatic control system, the algorithm for switching non-linear elements in which is implemented on the basis of a discrete time equalizer.
Guided, Deep Testing of X.509 Certificate Validation via Coverage Transfer Graphs

SSL and TLS are two secure protocols for creating secure connections over the Internet. X.509 certificate validation is important for security and needs to be performed before an SSL/TLS connection is established. However, state-of-the-art testing techniques, such as frankencert and mucert, have revealed, through randomly mutating Internet accessible certificates, that there exist unexpected, sometimes critical, validation differences among different SSL/TLS implementations. Despite these strong efforts, certificate validation is still not thoroughly tested and more effective techniques are needed as this work shows.To this end, this paper introduces transcert, a novel approach for effectively guiding fuzzing to perform deep testing of X.509 certificate validation. The goal of transcert is to generate certificates that trigger diverse executions; it achieves this goal by introducing the concept of a coverage transfer graph to efficiently, precisely abstract program executions. In particular, it records the execution of how a given certificate is validated by a reference SSL/TLS implementation. It then constructs a coverage transfer graph to model the coverage transfer from a test certificate (seed) to its mutated certificates (mutants), and explores the coverage transfer graph by iteratively sampling and mutating certificates.We have implemented transcert and evaluated it against frankencert and mucert on four state-of-the-art SSL/TLS implementations. The evaluation results clearly show the strengths of transcert— during 10,000 iterations, transcert has revealed 3,469 validation differences, 8× as many as those revealed by frankencert and mucert. We have identified 11 root causes of validation differences, all of which have been confirmed and five have never been reported previously. We also found that the primary goal of certificate chain validation is stated ambiguously in the widely-adopted PKI standard RFC 5280.
Searching For Desired Person Doing Desired Action based on Visual and Audio Feature in Large Scale Video Database

How to find a person doing an action in a video database is a challenging problem because the result must be correct at an instance level with the specific person doing the appropriate action. Even though there have been many works about face recognition and action recognition, they often focus on only one separate task. In this paper, the problem could be formulated into an instance retrieval where input is a query consisting of examples of the target person and examples of the desired action, and the result is a list of ranked positive shots. In this work, we proposed a simple but efficient person-action retrieval system by combining multimodal features including visual feature and audio feature to deal with various types of instances by making use of available visual or audio cues. The evaluation results on a large-scale BBC Eastenders dataset with 3rd rank in a total of 6 teams in TRECVID INS 2019 has proved the effectiveness of the proposed method.
A New Compact CMOS Distributed Digital Attenuator

This article presents a new millimeter-wave (mm-wave) distributed digital attenuator with a compact size and high linearity. To overcome the large area consumption of conventional distributed attenuators, multiple unit attenuation cells are combined at a single node, forming a multistate cell. By distributing the multistate cells along transmission lines (T-lines), the number of T-lines is reduced, leading to a compact chip size at a given attenuation range and step. The linearity is improved by stacking multiple FET varistors in each unit attenuation cell. An analytical analysis confirms that the proposed distributed attenuator topology maintains a low phase error comparable to that of the conventional counterpart. To experimentally verify the proposed topology, two different mm-wave digital attenuators are designed and implemented using a 65-nm CMOS technology. The first attenuator (Att1) uses a regular nFET as varistor of the attenuation cell, whereas the other attenuator (Att2_TW) uses a triple-well nFET to reduce the insertion loss. The maximum attenuation range of both attenuators is 14 dB with a step of 1 dB. The measured insertion losses of Att1 and Att2_TW are 4.8 and 4.1 dB at 35 GHz, respectively. The insertion losses are no more than 6.2 dB over 10–50 GHz and 4.3 dB over 15–43 GHz, respectively. The input 1-dB compression powers are 15 and 14 dBm, respectively, at 35 GHz. The chip sizes, excluding probing pads, are as small as 0.19 and 0.29 mm2.
A revision of the theory of THz detection by MOSFET in the light of the self-mixing model

CMOS technology has been extensively used for the realization of image sensors at Terahertz frequencies. The explanation of its strong efficiency was usually given invoking a mechanism described by the plasma wave detection theory. This model predicts that, when a high frequency potential is applied between gate and source electrodes of a MOSFET, oscillations of the 2D electron gas, located in the inversion layer, converts THz radiation into a DC voltage. Recently, we developed a new model of the self-mixing rectification process occurring in the depleted portion of a semiconductors crossed by a radiofrequency electric field. We studied both the new double barrier structure and the extensively used depleted region in MOS. In this paper, on the light of these new results, we review the theory of the THz detection in a MOS-FET structure. For a comparison with the former approach, we notice that the volume of interaction between free carriers and the RF electric field considered in this model is much higher that the volume considered in the plasma wave model. Technology Computer-Aided Design software simulations, using the Harmonic Balance analysis, will be adopted as evaluation tool. This consideration suggests that self-mixing effect may be more relevant in determining the rectification process. In the authors opinion, this approach substantially improves understanding of the THz rectification in semiconductors and in particular in MOS-FET structures.
CMA Based Blind Equalization for GFDM Systems

Blind channel equalization for GFDM systems based on a constant modulus algorithm (CMA) is proposed in this paper. The system implements CMA together with a discrete Fourier transform (DFT) that enables the realization of blind equalizer which is comparable to pilot based system. Two performance parameters are used to evaluate the equalizer, first is the symbol error rate (SER) and second is the mean squared error (MSE). The obtained results from simulation show that the performance of the proposed system is better than that of pilot-based system. The result also indicates that its performance closely matches to the ideal estimation and equalization of the channel.
Fault Tolerant Control of Five-Level Inverter Based on Redundancy Space Vector Optimization and Topology Reconfigruation

Compared with two-level and three-level inverters, five-level inverters have more redundant space vectors to implement flexible control strategy. A fault tolerance method for open circuit fault of power tube in Neutral Point Clamped (NPC) five level inverter is proposed. The open circuit fault of inverter is divided into type I and type II in this paper. Redundant space vector optimization and topological reconfiguration fault tolerance strategies are used for type I and type II failures, respectively. The basic vector of the synthetic reference vector $\text{U}_{\mathrm {ref}}$ is reoptimize according to the redundant vector, and get a new control strategy after the decision. This method enables the inverter to achieve fault-tolerant operation without modifying hardware or adding bridge arms. The output performance of the inverter after fault is guaranteed and the cost of fault tolerance is greatly reduced. The feasibility and effectiveness of the proposed fault-tolerant method are verified through experiments.
A Hybrid Routing Protocol for Robust Wireless Sensor Networks

Deploying a large number of tiny sensor nodes in unattended locations leads to a wide variety of WSN applications in military, environment, health, and disaster management areas. As sensor nodes consist of limited energy resources and recharging them is not possible, it is necessary to design the energyefficient routing protocols. Most of the literature on routing protocols for WSNs can be broadly classified into centralized and distributed protocols. In centralized protocols such as low-energy adaptive clustering hierarchy (LEACH), nodes transmit the data directly to cluster heads. Cluster heads gather the information from all the sensor nodes in the cluster and forwards to the base station. Node failures is a common phenomenon in WSNs. Failure of cluster heads leads to loss of information in that cluster. Hence, centralized protocols are less robust to topology changes due to node failures. To overcome this problem, we can implement distributed algorithms such as average consensus algorithms, where nodes exchange information with only the direct neighbors and calculate the weighted average at every time instant. This process will continue until every node obtains the average of the initial parameter values. Although, this protocol is highly robust to topology changes, it consumes a lot of energy compared to the LEACH protocol. In this work, we compare the energy consumed in the LEACH protocol and the consensus routing protocol and propose an alternative algorithm which is more robust than LEACH and highly energy-efficient than consensus protocol.
Mobile Robot Path Planning Based on Improved DDPG Reinforcement Learning Algorithm

Mobile robotics has a wide range of applications and path planning is key to its realization. Mobile robots need to explore the environment autonomously to find their destinations. The Deep Deterministic Policy Gradient (DDPG) algorithm, a classical algorithm in deep reinforcement learning, has a large advantage in continuous control problems. However, the DDPG algorithm suffers from the problems of low training efficiency and slow convergence caused by the high proportion of illegal policies due to the lack of policy action filtering. In this paper, we propose a mobile robot path planning method based on an improved DDPG reinforcement learning algorithm, which uses a small amount of a priori knowledge to accelerate the training of deep reinforcement learning, reduce the number of trial and error, and adopt an adaptive exploration method based on the $\varepsilon$-greedy algorithm. Dynamically adjust the exploration factor to rationally allocate the probability of exploration and exploitation. The adaptive exploration method can improve the exploration efficiency, reduce the exploration duration and speed up the convergence of the algorithm. Simulation experiments are conducted in a grid environment, and the results show that the proposed algorithm can successfully find the optimal path. Moreover, the comparison experiments between Q-learning, SARSA and the proposed algorithm demonstrate that the proposed algorithm has better path planning performance, spends the least computation time and converges the fastest.
Weekly Storage Optimization by the Italian Transmission System Operator

The present paper deals with storage resources optimization strategies to be adopted in the context of the European Electricity Markets by the Italian Transmission System Operator (TSO), in order to exploit, as much as possible, existing and future storage resources. The state-of-the-art of grid-scale storage technologies deployment in Italy is provided. Then, a critical review of a large set of existing optimization algorithms proposed in literature is presented, with a main focus on pumping hydro optimization approaches, since this is the predominant storage technology available in Italy today. Having not found an approach that perfectly fits with the Italian Transmission System Operator, considering the structure of the electricity markets and the operational planning processes, a new approach is proposed to cope with these requirements.
Detection of stator turns short-circuit during sensorless operation by means of the Direct Flux Control technique

In the field of sensorless control techniques for Permanent Magnet Synchronous machines, many solutions have been proposed that are based on the exploitation of the machine anisotropies. Among them, the Direct Flux Control (DFC) technique allows sensorless operation by measuring the star-point voltage of the machine. Given the high signal to noise ratio of the DFC measurements, such technique can be potentially used not only for sensorless operation but also for condition monitoring and fault detection. In this work, after a brief introduction on the technique, an analysis of the effects of stator inter-turns short-circuit fault on the DFC measurements is presented in order to provide a better insight on the capability of DFC as fault detector. Simulation results are finally shown and discussed.
Optimización de temperatura de anillamiento de PCR multiplex para la detección de Listeria monocytogenes

Listeria monocytogenes es un patógeno intracelular oportunista que se ha convertido en una de las causas más importante de infecciones transmitidas por alimentos en todo el mundo. Debido a la importancia de L. monocytogenes en la salud pública se han descrito muchos protocolos para la identificación y tipificación de este organismo, pero en su mayoría son muy complejos y de larga duración. Por esta razón, se desarrolló un PCR multiplex, para la detección rápida y sensible de esta bacteria en diferentes tipos de muestras. El objetivo del presente estudio fue optimizar la técnica de PCR multiplex para la identificación de L. monocytogenes. Se probaron distintas temperaturas de anillamiento para determinar cuál presenta mayor especificidad, evitando las amplificaciones inespecíficas. Se obtuvo amplificaciones a 57°C y temperaturas menores. Se concluye que la mejor temperatura para tm es la de 57°C.
Improving Automated GUI Exploration of Android Apps via Static Dependency Analysis

Exploring GUIs of Android apps plays a key role in many important scenarios such as functional testing (e.g., finding crash errors), security analysis (e.g., identifying malicious behav-iors) and competitive analysis (e.g., storyboarding app features). To automate GUI exploration, existing techniques often try to visit as many GUI pages as possible via specific strategies, e.g., random (like Monkey) or heuristic (like Stoat, A3E). However, their effectiveness is still unclear and much under-explored. To this end, we conducted the first study in this paper to understand and characterize their limitations by carefully analyzing the coverage reports from a set of real-world, open-source apps. Through this study, we identified three key limitations due to the lack of dependency knowledge during exploration, i.e., widget-page dependency, widget-widget dependency and system-event dependency. To overcome them, we introduce dependency-informed exploration, an automated approach that leverages static dependency analysis to effectively improve GUI exploration performance. Given an app, our approach first constructs a GUI page transition model that captures the dependencies between GUI widgets, and then guides GUI exploration during a depth-first traversal. We realized our approach as a tool named Gesda, and evaluated it on 70 open-source Android apps. The results show Gesda outperforms existing state-of-the-art GUI exploration techniques, i.e., Monkey and Stoat. Additionally, Gesda uncovers 4 previously unknown crashes in 4 apps as a by-product of GUI exploration due to the benefit of dependency knowledge, while Monkey and Stoat have not discovered them.
A Small Sample DDoS Attack Detection Method Based on Deep Transfer Learning

When using deep learning for DDoS attack detection, there is a general degradation in detection performance due to small sample size. This paper proposes a small-sample DDoS attack detection method based on deep transfer learning. First, deep learning techniques are used to train several neural networks that can be used for transfer in DDoS attacks with sufficient samples. Then we design a transferability metric to compare the transfer performance of different networks. With this metric, the network with the best transfer performance can be selected among the four networks. Then for a small sample of DDoS attacks, this paper demonstrates that the deep learning detection technique brings deterioration in performance, with the detection performance dropping from 99.28% to 67%. Finally, we end up with a 20.8% improvement in detection performance by deep transfer of the 8LANN network in the target domain. The experiment shows that the detection method based on deep transfer learning proposed in this paper can well improve the performance deterioration of deep learning techniques for small sample DDoS attack detection.
An Empirical Study to Analyze the Impact of Instagram on Students' Academic Results

Instagram is very popular worldwide, however, a few studies have been conducted regarding the impact of Instagram on academic results. These studies are based on self-reported data which may provide inaccurate findings. Using actual usage data of 7 days, this paper presents a quantitative study [N=71] to examine the impact of Instagram on academic results. We also present how much Instagram is used in different time ranges (e.g. morning) and what is the impact of that on CGPA. We found that Instagram usage behaviour varies by time range. Students used it maximum in the evening and minimum in the morning. However, our correlational analysis shows that there is no correlation between Instagram usage data and CGPA. Supporting this, our comparative study also shows that high and low Instagram users have indifferent CGPA. These findings highlight the possibility that negative impact of social media can be mitigated through proper use.
Vehicle Thermal Modelling for Improved Drive Cycle Analysis of a Generic City Bus

In the UK local authorities are being incentivized to purchase low emission vehicles through schemes such as the Ultra Low Emissions Bus (ULEB) scheme. To qualify vehicles must prove they meet certain emissions limits over a pre-described drive cycle. One of the key factors that will impact vehicle performance is the operation of the Heating Ventilation and Air Conditioning (HVAC) system, a large consumer of energy in the system, thus it is vital to have an understanding of the thermal behavior of the cabin and HVAC loads required to maintain comfortable conditions. This paper outlines a lumped parameter model of a generic city bus for use in determining such loads and assisting in the creation of control strategies to minimize energy consumption. Using steady state analysis it is possible to determine the average power consumption required to maintain an internal cabin temperature over a single MLTB drive cycle. Dynamic analysis shows that by changing the HVAC strategy it is possible to reduce the energy consumption by removing the need to operate the system at full power for the entire duration of the cycle. By implementing a hysteresis based control strategy that over heats the environment then switches to a low power mode to allow the temperature to recover the model was able to demonstrate potential energy savings of up to 26.6% when compared to the baseline case operating at a constant steady power output.
Control of Incoming Drain Currents Drawn by Super-Junction MOSFETs in Voltage Source Bridge-Legs

To increase the efficiency of renewable energy power conversion systems, traditional silicon IGBTs can be replaced with silicon super-junction MOSFETs. However, the poor performance of the MOSFET’s intrinsic diode and the output capacitance present difficulties in voltage source converter bridge-legs. When a MOSFET in this circuit turns on, a charging current has to be sourced into the output capacitance of the complementary freewheeling MOSFET, even if the diode has been deactivated. The peak incoming drain current into the MOSFET turning on can be limited by using a large resistance in series with its gate. However, this increases MOSFET power dissipation. Also, the turn-on propagation delay time is increased. This paper presents a gate driver circuit for profiling the MOSFET’s incoming drain current to provide an improved trade-off between incoming peak current, turn-on power dissipation, and delay time.