Miracle-dz/UniFAR-FTUs huggingface dataset:
Lipoxin A4 Regulates Lipopolysaccharide-Induced BV2 Microglial Activation and Differentiation via the Notch Signaling Pathway

Inflammatory responses contribute to the pathogenesis of various neurological diseases, and microglia plays an important role in the process. Activated microglia can differentiate into the pro-inflammatory, tissue-damaging M1 phenotype or the anti-inflammatory, tissue-repairing M2 phenotype. Regulating microglia differentiation, hence limiting a harmful response, might help improve the prognosis of inflammation-related nervous system diseases. The present study aimed 1. to observe the anti-inflammatory effect of lipoxin A4 (LXA4) on the inflammatory response associated to lipopolysaccharide (LPS)-induced microglia activation, 2. to clarify that LXA4 modulates the activation and differentiation of microglia induced by LPS stimulation, 3. to determine whether LXA4 regulates the activation and differentiation of microglia through the Notch signaling pathway, 4. to provide a foundation for the use of LXA4 for the treatment of inflammatory related neurological diseases. To construct a model of cellular inflammation, immortalized murine BV2 microglia cells were provided 200 ng/ml LPS. To measure the mRNA and protein levels of inflammatory factors (interleukin [IL]-1β, IL-10, and tumor necrosis factor [TNF]-α) and M1 and M2 microglia markers (inducible nitric oxide synthase [iNOS], cluster of differentiation [CD]32, arginase [Arg]1, and CD206), we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA), immunofluorescence, or flow cytometry. To determine the mRNA and protein levels of Notch signaling components (Notch1, Hes1, and Hes5), we performed qRT-PCR and western blot. LXA4 inhibits the expression of Notch1 and Hes1 associated with M1 type microglial differentiation and decreases the M1 type microglia marker iNOS and related inflammatory factors IL-1β and TNF-α. Moreover, LXA4 upregulates the expression of the M2-associated Hes5, as well as the expression of the M2 microglia marker Arg1 and the associated inflammatory factor IL-10. These effects are blocked by the administration of the γ-secretase inhibitor DAPT, a specific blocker of the Notch signaling pathway. LXA4 inhibits the microglia activation induced by LPS and the differentiation into M1 type with pro-inflammatory effect, while promoting the differentiation to M2 type with anti-inflammatory effect. LXA4 downregulates the inflammatory mediators IL-1β, TNF-α, and iNOS, while upregulating the anti-inflammatory mediator IL-10, which acts through the Notch signaling pathway.
Revisiting Kalman and Aizerman Conjectures via a Graphical Interpretation

The paper revisits Aizerman and Kalman conjectures for absolute stability through the lens of a novel graphical interpretation. Even though these conjectures have been proven false, such a graphical interpretation suggests natural ways to introduce additional conditions in order to obtain new valid absolute stability criteria. As an illustration, the paper proves a new absolute stability criterion obtained by the iterative application of a variation of the circle criterion
Electrical and Thermo-Physical Properties of Ni-Alloy Reinforced Bi-2223 Conductors

The improved mechanical properties of the Ni-alloy reinforced Bi<sub>2-x</sub>Pb<sub>x</sub>Sr<sub>2</sub>Ca<sub>2</sub>Cu<sub>3</sub>O<sub>10-x</sub> (Bi-2223) - released on 2016 by the Sumitomo Electric Industries, <uri>Osaka, Japan</uri>, (HT-NX type) - renewed the interest on this material for high-field applications. Whether Bi-2223 can be preferred to other high-temperature superconductors for very-high-field magnets depends also on the solutions found to protect the coil in the case of quenches. A comprehensive study of the quench propagation requires information about the temperature and field dependence of the critical current <inline-formula><tex-math notation="LaTeX">$(I_{C})$</tex-math></inline-formula>, the thermal conductivity (<italic>κ</italic>) of the conductor, and the electrical resistivity of the normal matrix (<italic>ρ</italic><sub>norm</sub>). Most of these data are unavailable in the literature for the HT-NX tape. This paper intends to fill this gap, reporting the results of an extensive measurement campaign of its electrical and thermo-physical properties. We have found that <inline-formula><tex-math notation="LaTeX">$I_{C}$</tex-math></inline-formula> decreases linearly with <italic>T</italic> in the investigated range of fields 1–19 T. The well-known magnetic hysteresis of <inline-formula><tex-math notation="LaTeX">$I_{C}$</tex-math></inline-formula>(<italic>B</italic>) has been examined in detail. We have observed that the value of <inline-formula><tex-math notation="LaTeX">$I_{C}$</tex-math></inline-formula> at a given field varies upon repeated I–V measurements, with a consequent reduction of the hysteresis amplitude. The longitudinal <italic>κ</italic> has been investigated in magnetic fields up to 19 T. <italic>ρ</italic><sub>norm</sub> has been measured between 4.2 K and 300 K and a residual resistivity ratio of ∼40 has been found. The experimental study of the <inline-formula><tex-math notation="LaTeX">$I_{C}$</tex-math></inline-formula>(<italic>T</italic>,<italic>B</italic>) surface, of the thermal conduction properties <italic>κ</italic>(<italic>T</italic>,<italic>B</italic>), and of the matrix electrical resistivity <italic>ρ</italic><sub>norm</sub> provides fundamental elements for a quench propagation study, which is critical in view of high-field applications of this conductor.
SPERT: A Speed Limit Strategy for Recurrent Traffic Jams

This paper proposes and simulates a speed limit controller for recurrent traffic jams (SPERT). SPERT is a simple yet efficient variable speed limit (VSL) control strategy based on the behavior of the optimal controller without any need for online optimization. The online implementation of SPERT is a simple rule-based controller that activates and deactivates the corresponding variable speed limit when the densities of the dominant bottlenecks (which are found offline) reach predefined thresholds. These thresholds are defined in order to activate and deactivate the speed limits at the same bottleneck density at which they would be activated or deactivated in the nominal case. The simulation results show that SPERT is able to approach the optimal behavior while eliminating online computational cost, increasing robustness, and outperforming previously proposed easy-to-implement VSL control algorithms.
Stabilization of Minimum-Phase Linear Uncertain Systems With Quantized Measurement Output and Disturbances

In this paper, we study the robust stabilization problem of minimum-phase linear uncertain systems with the quantized measurement output and external disturbances. Although the stabilization problem of linear systems by quantized feedback control has been well addressed, the robust stabilization problem subject to external disturbances by quantized feedback control is still an open problem. It is quite challenging to tackle the system uncertainty and external disturbances by quantized output feedback. By Lyapunov analysis, we design a dynamic quantized output feedback control law to solve the problem. In contrast to existing results where the input-to-state stability with respect to external disturbances is attained for the linear system by quantized state feedback control, the asymptotic stability of the linear system is achieved in the presence of system uncertainties and disturbances by the dynamic quantized output feedback control law.
Digital Communication Systems Based on Three-Dimensional Chaotic Attractors

In this paper, we propose a methodology to design chaos-based communication systems, which exploits the topological structure of 3-D chaotic attractors. The first step consists in defining a proper partition of a Poincaré section of the attractor and the subsequent encoding of the chaotic trajectories. Then, the evolution mechanism of the chaotic attractor, according to the dynamical restrictions imposed by the chaotic flow, is represented by a state diagram, where each state represents a region of the Poincaré section or a branch in the template of the chaotic attractor. The state transitions are associated with segments of chaotic trajectories that connect the corresponding regions of the Poincaré section. The chaotic signals are transmitted over both additive white Gaussian noise and Rayleigh flat fading channels, and a trellis structure derived from the state diagram is used at the decoder to estimate the transmitted information sequence. Finally, the bit error rate performance of the system is analyzed.
Factors Affecting the Usage of Mobile Commerce using Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT)

Along with the development of the Internet, m-commerce become an alternative to shop online. This study aims to determine the factors that affect the user in using m-commerce to buy online using UTAUT (Unified Theory of Acceptance and Use of Technology) and TAM (Technology Acceptance Model). Constructs from the previous study are used such as performance expectancy, effort expectancy, social influence, and facilitating condition but also added perceived trust and perceived cost. Data obtained from 156 respondents were analyzed using PLS (Partial Least Squares). The analysis showed that performance expectancy, effort expectancy, social influence, and perceived trust significantly influences the use of m-commerce while facilitating condition and perceived cost have no significant effect on the use of m-commerce.
Enabling Field Force Operational Sustainability: A Big Bang-Big Crunch Type-2 Fuzzy Logic System for Goal-Driven Simulation

Business operational sustainability must allow creating economic value, building healthy ecosystems and developing strong communities. Hence, there is a need to develop solutions which can safeguard companies' business sustainability. Various solutions could have different costs and deliver different benefits. Therefore, there is a need to evaluate these solutions before being implemented. In reality, companies require achieving certain targets according to their plans and strategies. Goal-Driven Simulation (GDS) is an approach that allows evaluating solutions before implementing them in real-life while focusing on achieving desired targets. This paper presents a GDS based on interval type-2 Fuzzy Logic System (IT2FLS) optimized by the big bang-big crunch (BU-BC) algorithm with application to field force allocation within the telecommunications sector. The obtained results show the suitability of the proposed approach to model unexpected factors to protect the business sustainability in the telecommunications industry field force allocation domain.
Changes in Functional Connectivity Following Treatment With Emotion Regulation Therapy

Emotion regulation therapy (ERT) is an efficacious treatment for distress disorders (i.e., depression and anxiety), predicated on a conceptual model wherein difficult to treat distress arises from intense emotionality (e.g., neuroticism, dispositional negativity) and is prolonged by negative self-referentiality (e.g., worry, rumination). Individuals with distress disorders exhibit disruptions in two corresponding brain networks including the salience network (SN) reflecting emotion/motivation and the default mode network (DMN) reflecting self-referentiality. Using resting-state functional connectivity (rsFC) analyses, seeded with primary regions in each of these networks, we investigated whether ERT was associated with theoretically consistent changes across nodes of these networks and whether these changes related to improvements in clinical outcomes. This study examined 21 generalized anxiety disorder (GAD) patients [with and without major depressive disorder (MDD)] drawn from a larger intervention trial (Renna et al., 2018a), who completed resting state fMRI scans before and after receiving 16 sessions of ERT. We utilized seed-based connectivity analysis with seeds in the posterior cingulate cortex (PCC), right anterior insula, and right posterior insula, to investigate whether ERT was associated with changes in connectivity of nodes of the DMN and SN networks to regions across the brain. Findings revealed statistically significant treatment linked changes in both the DMN and SN network nodes, and these changes were associated with clinical improvement corresponding to medium effect sizes. The results are discussed in light of a nuanced understanding of the role of connectivity changes in GAD and MDD, and begin to provide neural network support for the hypothesized treatment model predicated by ERT.
Stock Price Manipulation Detection using Generative Adversarial Networks

We implemented Generative Adversarial Networks (GANs) for detecting abnormal trading behaviors caused by stock price manipulations. Long short-term memory (LSTM) was used as a base structure of our GANs, which learned normal market behaviors in an unsupervised way. After the training, the discriminator network of GANs was used as a detector to discriminate between normal and manipulative trading. Our work is different from the previous work in that we did not use manipulation cases to train the neural networks. Instead, we used normal data to train them, and simulated manipulation cases were only used for testing purposes. The detection system was tested with the trading data from the Stock Exchange of Thailand (SET). It can achieve 68.1% accuracy in detecting pump-and-dump manipulations in unseen market data.
Fuzzy Logic and Exponential Smoothing for Mapping Implementation of Dengue Haemorrhagic Fever in Surabaya

One of the infectious diseases found in Surabaya is Dengue Hemorrhagic Fever (DHF). Although prevention has been carried out by the government, there are still obstacles to determine the right handling because there is no map visualization for its distribution and the condition of the data itself is still not accurate. This study aims to provide information about dissemination analysis in the form of map visualization and forecasting that can help the government to provide appropriate handling of dengue cases was conducted. Fuzzy methods were applied in the Web Geographic Information System and Exponential Smoothing methods for forecasting. The data in this system is the number of patients with DHF, the amount of population density, the amount of rainfall, and the number of public facilities taken from 2012 to 2017. The results show that almost all sub-districts in Surabaya are DHF endemic areas, by being differentiated into these areas which are safe, alert, vulnerable, or need handling from the dangers of dengue fever. While the forecasting results using the exponential smoothing method, provide an accuracy of 55%.
A 20-kHz~16-MHz Programmable-Bandwidth 4th Order Active Filter Using Gain-Boosted Opamp With Negative Resistance in 65-nm CMOS

This brief presents a 4th order 20-kHz~16-MHz programmable-bandwidth active RC filter. For high linearity performance, the gain-boosted opamp in the biquad adopts negative resistance in auxiliary amplifiers to achieve high-gain without requiring extra common-mode feedback amplflifier, thereby minimizing total current and design complexity. To further enhance the filter linearity, a source follower using native nMOS transistors generates common-mode output without adding extra resistive loading to the opamp output. The prototype filter is fabricated in 65-nm CMOS and occupies 0.098 mm2. It features three programmable cutoff frequencies of 20 kHz, 2 MHz, and 16 MHz and achieves IIP3 of 22.1 dBm while consuming 19 mW under 1.8-V supply. The measured figure-of-merit considering the linearity, power, bandwidth, noise, and silicon area shows that the presented filter achives best-in-line compromise among conflicting design metrics.
Parallelization of Multi-label classification for large data sets

Over the last few years, multi-label learning has received a lot of attention in research and industries. Since a pattern can belong to more than one class at the same time, it is a very challenging task to classify a test pattern. Multi-label classification algorithms while inferring on large data sets take a long time to run. So, there is a growing demand of an effective and efficient method for multi-label classification problems, both in terms of accuracy and speed. We endeavour to improve the performance and accuracy of a multi-label classification algorithm which, given a pattern, can predict the set of labels it belongs to, for large data sets, using parallel computing in a distributed manner. We also reduced the dimensionality of large data sets with very large number of features by removing the redundant features using a feature selection method (Fscore) [1] to improve the accuracy and reduce the time taken for training phase of the multi-label classification algorithm.The result shows the benefits of using parallel processing over the traditional single-node execution, tested over five benchmark multi-label data sets, in terms of both accuracy and speedup of the process.
Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments

Rescue robots can be used in urban search and rescue (USAR) applications to perform the important task of exploring unknown cluttered environments. Due to the unpredictable nature of these environments, deep learning techniques can be used to perform these tasks. In this letter, we present the first use of deep learning to address the robot exploration task in USAR applications. In particular, we uniquely combine the traditional approach of frontier-based exploration with deep reinforcement learning to allow a robot to autonomously explore unknown cluttered environments. Experiments conducted with a mobile robot in unknown cluttered environments of varying sizes and layouts showed that the proposed exploration approach can effectively determine appropriate frontier locations to navigate to, while being robust to different environment layouts and sizes. Furthermore, a comparison study with other frontier exploration approaches showed that our learning-based frontier exploration technique was able to explore more of an environment earlier on, allowing for potential identification of a larger number of victims at the beginning of the time-critical exploration task.
Performance Improvement of Torque and Suspension Force for a Novel Five-Phase BFSPM Machine for Flywheel Energy Storage Systems

To improve the electromagnetic performance of the machine for a flywheel energy storage system, in this paper, a five-phase bearingless flux-switching permanent magnet machine with an E-core stator is presented. The topology and structure are introduced and the operation principle of the generation of torque and suspension force is also explained. Based on the simple variable method and overall trial and error method, the influence of the machine parameters on machine performance is investigated by using a finite element analysis. Then, the parameters are optimized. The results show the improvement of the torque and suspension force with increased amplitude and smaller fluctuation.
Hail Storms Recognition Based on Convolutional Neural Network*

Hail storm happens when hydrometeors are brought up above the frozen height level by strong updrafts. Before the time of hail fall, an abnormal 3D spatial structures of clouds could be captured by weather radar. If this pattern of 3D structure can be identified, hail storms are able to be forecasted. Based on these, an automatic hail recognition algorithm using deep learning method is proposed in this paper. The deep learning model we use is Convolutional Neural Network (CNN), whose inputs are three-dimensional storm cells including nine slices at different altitudes. The slices’ orientation is corrected according to the location of the bounded weak echo recognition (BWER), and the sizes of these slices are normalized using the cell cores as the reference points. Experiment on real weather cases shows that the method proposed in this paper has better performance than the traditional POSH method. It has a higher hit ratio and reduces the false alarm ratio significantly.
Intelligent Health Diagnosis Technique Exploiting Automatic Ontology Generation and Web-Based Personal Health Record Services

Growing interest in healthcare has promoted the use of symptom checkers, which are online health applications that provide diagnostic information on users’ health. However, their diagnostic accuracy remains low because the existing symptom checkers rely on manually constructed knowledge models through labor-intensive processes or perform diagnoses based on simple pairwise relationships between diseases and symptoms without considering personal health conditions. In this paper, we propose an intelligent health diagnosis technique that exploits automatically generated ontology and Web-based personal health record services. The proposed technique first automatically generates a human disease diagnosis ontology by exploiting two well-established ontologies for diseases and symptoms: a large-scale medical bibliographic database and an open biomedical repository. When a user enters the symptom-based queries, possible diagnoses are identified by analyzing the user’s queries and their health record data via semantic inferences of the automatically generated ontology. Subsequently, the ranked diagnostic results are provided to the user via ranking methods that consider the user’s symptoms, personal health attributes, and multi-level diagnosis. The proposed technique also provides the user’s diagnostic progress information, which can be used to track or monitor the progress of diseases by considering changes in symptoms over time. The proposed technique was evaluated through a comparison with the existing well-known symptom checkers and other related approaches. The evaluation results show that the proposed technique can feasibly help to improve diagnostic accuracy and deliver appropriate diagnostic information for healthcare action by users.
Differential Regulation of Human Treg and Th17 Cells by Fatty Acid Synthesis and Glycolysis

In this study we examined the metabolic requirements of human T helper cells and the effect of manipulating metabolic pathways in Th17 and Treg cells. The Th17:Treg cell axis is dysregulated in a number of autoimmune or inflammatory diseases and therefore it is of key importance to identify novel strategies to modulate this axis in favor of Treg cells. We investigated the role of carbohydrate and fatty acid metabolism in the regulation of human memory T helper cell subsets, in order to understand how T cells are regulated at the site of inflammation where essential nutrients including oxygen may be limiting. We found that Th17 lineage cells primarily utilize glycolysis, as glucose-deprivation and treatment with rapamycin resulted in a reduction in these cells. On the other hand, Treg cells exhibited increased glycolysis, mitochondrial respiration, and fatty acid oxidation, whereas Th17 cells demonstrated a reliance upon fatty acid synthesis. Treg cells were somewhat reliant on glycolysis, but to a lesser extent than Th17 cells. Here we expose a fundamental difference in the metabolic requirements of human Treg and Th17 cells and a possible mechanism for manipulating the Th17:Treg cell axis.