Miracle-dz/UniFAR-FTUs huggingface dataset:
Protective Mechanism of Humanin Against Oxidative Stress in Aging-Related Cardiovascular Diseases

Physiological reactive oxygen species (ROS) are important regulators of intercellular signal transduction. Oxidative and antioxidation systems maintain a dynamic balance under physiological conditions. Increases in ROS levels destroy the dynamic balance, leading to oxidative stress damage. Oxidative stress is involved in the pathogenesis of aging-related cardiovascular diseases (ACVD), such as atherosclerosis, myocardial infarction, and heart failure, by contributing to apoptosis, hypertrophy, and fibrosis. Oxidative phosphorylation in mitochondria is the main source of ROS. Increasing evidence demonstrates the relationship between ACVD and humanin (HN), an endogenous peptide encoded by mitochondrial DNA. HN protects cardiomyocytes, endothelial cells, and fibroblasts from oxidative stress, highlighting its protective role in atherosclerosis, ischemia–reperfusion injury, and heart failure. Herein, we reviewed the signaling pathways associated with the HN effects on redox signals, including Kelch-like ECH-associated protein 1 (Keap1)/nuclear factor erythroid 2-related factor 2 (Nrf2), chaperone-mediated autophagy (CMA), c-jun NH2 terminal kinase (JNK)/p38 mitogen-activated protein kinase (p38 MAPK), adenosine monophosphate-activated protein kinase (AMPK), and phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt)-Janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3). Furthermore, we discussed the relationship among HN, redox signaling pathways, and ACVD. Finally, we propose that HN may be a candidate drug for ACVD.
No Difference in Arousal or Cognitive Demands Between Manual and Partially Automated Driving: A Multi-Method On-Road Study

Introduction Partial driving automation is not always reliable and requires that drivers maintain readiness to take over control and manually operate the vehicle. Little is known about differences in drivers’ arousal and cognitive demands under partial automation and how it may make it difficult for drivers to transition from automated to manual modes. This research examined whether there are differences in drivers’ arousal and cognitive demands during manual versus partial automation driving. Method We compared arousal (using heart rate) and cognitive demands (using the root mean square of successive differences in normal heartbeats; RMSSD, and Detection Response Task; DRT) while 39 younger (M = 28.82 years) and 32 late-middle-aged (M = 52.72 years) participants drove four partially automated vehicles (Cadillac, Nissan Rogue, Tesla, and Volvo) on interstate highways. If compared to manual driving, drivers’ arousal and cognitive demands were different under partial automation, then corresponding differences in heart rate, RMSSD, and DRT would be expected. Alternatively, if drivers’ arousal and cognitive demands were similar in manual and partially automated driving, no difference in the two driving modes would be expected. Results Results suggest no significant differences in heart rate, RMSSD, or DRT reaction time performance between manual and partially automated modes of driving for either younger or late-middle-aged adults across the four test vehicles. A Bayes Factor analysis suggested that heart rate, RMSSD, and DRT data showed extreme evidence in favor of the null hypothesis. Conclusion This novel study conducted on real roads with a representative sample provides important evidence of no difference in arousal and cognitive demands. Younger and late-middle-aged motorists who are new to partial automation are able to maintain arousal and cognitive demands comparable to manual driving while using the partially automated technology. Drivers who are more experienced with partially automated technology may respond differently than those with limited prior experience.
Disruptions of Circadian Rhythms and Thrombolytic Therapy During Ischemic Stroke Intervention

Several endogenous and exogenous factors interact to influence stroke occurrence, in turn contributing to discernable daily distribution patterns in the frequency and severity of cerebrovascular events. Specifically, strokes that occur during the morning tend to be more severe and are associated with elevated diastolic blood pressure, increased hospital stay, and worse outcomes, including mortality, compared to strokes that occur later in the day. Furthermore, disrupted circadian rhythms are linked to higher risk for stroke and play a role in stroke outcome. In this review, we discuss the interrelation among core clock genes and several factors contributing to ischemic outcomes, sources of disrupted circadian rhythms, the implications of disrupted circadian rhythms in foundational stroke scientific literature, followed by a review of clinical implications. In addition to highlighting the distinct daily pattern of onset, several aspects of physiology including immune response, endothelial/vascular and blood brain barrier function, and fibrinolysis are under circadian clock regulation; disrupted core clock gene expression patterns can adversely affect these physiological processes, leading to a prothrombotic state. Lastly, we discuss how the timing of ischemic onset increases morning resistance to thrombolytic therapy and the risk of hemorrhagic transformation.
Analyzing Software Engineering Processes with Provenance-based Knowledge Graphs

Insights and assessments about the quality, reliability, or trustworthiness of software systems is important for many software applications. Especially for large or mission-critical software systems, reliable measures and assertions are crucial. Since software repositories contain information about source code, software development processes, and team interactions, we extract the provenance of software artifacts from those repositories and store the provenance according to a provenance model defined using W3C PROV data model. We use the recorded provenance to discover insights about the software and its development process, which we apply and evaluate for a large aerospace software system.
Efficient Plant Diseases Recognition based on Modified Residual Neural Network and Transfer Learning

Efficient and accurate recognition of plant diseases based on leaf images is a hot research topic. The plant diseased leaf images are complex and diverse. It is generally difficult to extract reliable features. In this paper, a new plant disease recognition method is proposed, based on a Modified Residual Neural Network (MRNN) and transfer learning. Compared with the classical residual neural network ResNet-50, the residual block structure in MRNN is modified. The experiment results on the AI Challenger dataset show MRNN can achieve 91.4% recognition accuracy which is higher than other classic CNN models. Combined with the Kaggle Cassava dataset, the MRNN is trained with transfer learning, which improves the accuracy, robustness and generalization ability. The experiments results show that the proposed method not only has an advantage in accuracy, but also has a significant improvement in training speed, which validates the efficiency and effectiveness of the proposed approach.
VR-Interactions for Planning Planetary Swarm Exploration Missions in VaMEx-Vtb

Ahstract-Virtual testbeds (VTBs) are essential for researchers and engineers during the planning, decision making, and testing phases of space missions because they are much faster and cost-effective than physical models or tests. Moreover, they allow to simulate the target conditions that are not available on earth for real-world tests, and it is possible to change or adjust mission parameters or target conditions on-the-fly. However, such highly specialized and flexible tools are often only available as desktop tools with limited visual feedback and a lack of usability. On the other hand, VR is predestinated for easy, natural interaction even in complex decision making and training scenarios, while simultaneously offering high fidelity visual feedback and immersion. We present a novel tool that combines the flexibility of virtual testbeds with an easy-to-use VR interface. To do that, we have extended a VTB for planetary exploration missions, the VaMEx-VTB (Valles Marineris Exploration-VTB), to support sophisticated virtual reality (VR) interactions. The VTB is based on the modern game engine ‘Unreal Engine 4’, which qualifies it for state-of-the-art rendering. Additionally, our system supports a wide variety of different hardware devices, including head-mounted displays (HMDs) and large projection powerwalls with different tracking and input methods. Our VR-VTB enables the users to investigate simulated sensor output and other mission parameters like lines-of-sight or ground formations for a swarm of different spacecraft, including autonomous ground vehicles, flying drones, a humanoid robot, and supporting orbiters. Moreover, the users can directly interact with the virtual environment to distract the swarm units or change environment parameters, like adding boulders or invoking sand storms. Until now, we have used our system for three different scenarios: a swarm-based exploration of the Valles Marineris on planet Mars, a test scenario of the same swarm units on the Canary Islands, and the autonomous building of a moon base. An expert review shows the general usability of our VR-VTB.
A Hybrid Vehicle Hardware-in-the-Loop System With Integrated Connectivity for Ehorizon Functions Validation

Urbanization led to an increasing number of vehicles on the roads, resulting in more polluted air and more congested urban centers. This is being mitigated by the Hybrid Electric Vehicles equipped with telecommunication devices, which allow the implementation of predictive control strategies. This research is focused on the setup of an innovative and universal simulation environment for the development and the validation of predictive control strategies supported by Vehicle-to-Everything (V2x) connectivity. This helps the testing and validation of predictive control strategies, granting safety, reliability, and reproducibility. The simulation environment consists of a connected Hardware-in-the-Loop (HiL) system to test a supervisory controller (Hybrid Control Unit) where the predictive functions will be implemented. In addition to all the advantages of a conventional HiL layout, it can exchange real data from cloud service providers and nearby devices. The over-the-air interfaces between the powertrain controllers, the cellular network, and the Intelligent Transportation Systems (ITS-G5) are handled using a custom connectivity control unit with proprietary functionalities. Finally, this work presents the testing of the end-to-end communication for both the short- and long-range data exchange between real controllers.
UHF DTV Antenna System diagnostics by radiation pattern measuring

The article deals with the new Large Antenna System (AS) diagnostics method at VHF / UHF range in the air during scattering from the AS. This method is based on comparing the calculated radiation pattern of AS, as an array of unified segments, in the fully functional state with the calculated radiation pattern of AS generated in a state of malfunction caused by faulty segments. Based on results, it is possible to determine a faulty segment by doing a single measurement of the horizontal radiation pattern of AS.
INVESTIGATION OF CATALYTIC CONVERSION OF VEGETABLE OIL/GAS OIL MIXTURES

The catalytic conversion of vegetable oil-containing gas oils is one option of the production of diesel fuels with a bio blending component with reduced aromatic content and, with very little sulfur content. The aim of the experimental work was the investigation to prepare high-quality diesel fuel by the conversion of mixtures of vegetable oil and crude gas oil distillate with different vegetable oil contents (0-25%) on a supported metal catalyst with strong hydrogenation activity. The influence of the process parameters (T=300-380°C, P=50- 80 bar, LHSV=0.75-3.0 h -1 , hydrogen/feedstock volume ratio=600 Nm 3 /m 3 ) on the yield and quality of the products was studied. We concluded, that under favourable reaction conditions (T=360-380°C, P=80 bar, LHSV=0.75 h -1 , hydrogen/feedstock volume ratio=600 Nm 3 /m 3 ) and 0-15% vegetable oil content of the feedstock, the target fractions of the products had less than 10 mg/kg sulfur and nitrogen content, and a total aromatic content less than 20% (poly- aromatic content is not shown). In case of using feedstock with high vegetable oil content (15%) the products had significantly higher cetane number than the provided values of the standard for diesel gas oils (EN 590:2008).
Human activity recognition based on triaxial accelerometer using multi-feature weighted ensemble

Human activity recognition (HAR) has been widely used in some areas such as smart home, health care and so on. However, there are still some low recognition accuracy cases in actual scenarios. In order to improve the accuracy of recognition, we propose a multi-feature weighted ensemble classification method on triaxial accelerometer sensor data. We perform weighted integration on five base classifiers to obtain the final prediction classification label. Among these five base classifiers, three are K-nearest neighbor (KNN) classifiers with different features respectively using three traditional feature extraction methods from original data. Another two are currently popular deep learning models—Attention Mechanisms on Long Short-Term Memory Network (Attention-LSTM) and Convolutional Neural Network (CNN), which can automatically extract features and classify. We demonstrated the feasibility of this ensemble method on a dataset containing eight human daily activities. Comparing experimental results, our method achieved the best recognition effect, with an accuracy of 95.58%.
Space Vector Modulation for Three-Phase Multilevel Switched-Capacitor Inverter

Multilevel inverters have received great attention from industry and academy in the last decades. This fact is due to their interesting advantages when compared to two-level topologies such as reduced output voltage distortion and reduced voltage over the power devices. The analysis and proposition of modulation strategies are very important for the proper operation of these converters. This paper proposes a Space Vector (SV) modulation strategy for a new three-phase multilevel switched capacitor inverter. The three nearest vectors are selected from a proposed SV diagram in order to minimize the output voltage distortion. Furthermore, the dwell times are defined and the selected vectors are disposed in suitable switching sequences. Finally, simulation and experimental results are obtained to demonstrate the good performance and applicability of the proposed technique.
Fastcat: An Open-Source Library for Composable EtherCAT Control Systems

Robotic testbeds used for flight and research technology development are highly complicated electromechanical systems demanding large engineering effort to configure and manage safety-critical hardware. The software that interoperates with robotic testbeds needs to be robust yet flexible to accommodate rapidly changing testbed needs and other operational demands in a cost-efficient manner. The Jet Propulsion Laboratory (JPL) presents a new open-source C++ library called Fastcat for powerful and flexible EtherCAT device configuration for testbed and robotic applications. Fastcat implements a set of C drivers called ‘Just SOEM Drivers’ built on the popular Simple Open EtherCAT Master library for a small, but highly tested set of supported EtherCAT devices. Fastcat offers a rich set of capabilities such as supporting an arbitrary number of EtherCAT Masters, a YAML input configuration file, and a visualization tool ‘Fcviz’ to graph the bus topology using the open-source Graphiz library. Furthermore, a novel feature called ‘Fastcat Signals’ can efficiently route data and commands between devices to compose self-contained closed loop control systems such as heaters or joint-torque controllers with zero added latency. Abstract device types like signal generators, functions, and PID controllers and offline versions of device hardware drivers allow for powerful composability, offline-only testing, and flexible hybrid partial online – offline bus topologies. The Fastcat core library is middleware agnostic, but ROS2 wrappers are developed for ease-of-use. This paper will describe design tradeoffs, an overview of the implementation of these capabilities, and early results from the first JPL Robotic testbed use.
Determination of epinephrine and dopamine by capillary electrophoresis with optical fiber light-emitting diode induced fluorescence detection

A new analytical method based on capillary electrophoretic separation and optical fiber LED induced fluorescence detection has been developed for the determination of epinephrine and dopamine.Using micellar electrokinetic capillary chromatography,the applied voltage,micellar concentration,pH and the concentration of the buffer were optimized.Under the optimal conditions,the calibration curves of epinephrine and dopamine ranging from 2.2×10-9 to 1.1×10-7 mol/L and from 2.6×10-8 to 1.2×10-6 mol/L were shown to be linear,respectively.The limit of detections(S/N=3) for epinephrine and dopamine were 2.6×10-8 mol/L and 2.3×10-8 mol/L,respectively.The method was applied to the determination of epinephrine and dopamine in human plasma with satisfactory results.
Multi-View Facial Expression Recognition based o n Multitask Learning and Generative Adversarial Network

Facial expressions contain rich emotional information, which is an important method in human communication. At present, most of the researches on facial expression recognition is conducted on the frontal faces. However, in real life, the captu re device may capture facial expression data from various poses. Different from the existing techniques, in this paper, We propose a multitask deep learning method that uses the links between var ious poses and expressions to improve the accuracy of expression recognition. We use the adversarial network to supplement the e xpression information of the face for the head poses with a sever e lack of expression information. There are some advantages: Firstly, we use vgg16 to train the expression images of each deflecti on angle separately and find that the expression recognition acc uracy rate for small offset angle (−30 °, −15 °, 15°, 30 °) is larger t han 0 ° angle when the face angle is greater than 45 °, the accur acy of recognition decreases sharply. So we use multitask learnin g to jointly train these small offsets angle images and frontal ima ges, the multitask learning can learn the emotion-preserving repr esentations at various poses to predict the expression class label f rom the input face, and bring again to its recognition accuracy r ate. Secondly, for the poses of a severe lack of expression inform ation, we use TP-GAN to convert a large deflection pose image i nto a frontal face and supplement its expression information. Th e experimental results show that our proposed algorithm has a g ood recognition effect on facial expressions for all poses. Compa red with the most advanced expression recognition methods, this paper has also achieved the state-of-the-art recognition results.
Design of Embedded Real-time Video Fire AI Recognition System

In order to solve the problem of the current video flame detection algorithm, such as low accuracy, high false alarm rate and high computing power requirements of the hardware system, a cost-effective embedded fire AI recognition system was proposed by referring to the idea of deep neural network. The system transplanted the lightweight algorithm MobileNet-SSD, and combined the multi-feature fusion technology to improve the small target detection ability of the algorithm. The experimental results show that this system overcomes the shortcomings of traditional fire detection technology, not only improves the accuracy of flame detection, but also reduces hardware costs, so it has greater practical value and market prospects.
Incorporating Hierarchical Information for UAV based Semantic Mapping

Power, size and weight are major constraining factors for UAVs, which have caused researchers to investigate alternative information representations, such as semantics, to reduce the computational load onboard UAVs. However, the connection between spatial and semantic information has not been fully explored and the literature on including semantic information for UAV navigation is limited. This paper provides a new taxonomy on spatial and semantic relations through consideration of a selected subset of the literature. Concepts of semantic information are related with spatial maps and two hypotheses for improving algorithms based on the literature are generated. The algorithms are tested in numerical simulations on the task of semantic mapping and the effects of influential parameters are examined. Results indicate that a defined hierarchical structure of semantic information enables improved map reproduction. The proposed algorithm exhibits the potential to improve the use of semantic information in UAV deployment scenarios, such as agriculture, search and rescue and industrial inspections.
Glaucoma detection through digital processing from fundus images using MATLAB

A considerable proportion of the global population has lost vision due to the late diagnosis of glaucoma, causing ocular pressure on the optic nerve, and blindness to become irreversible. A number of 60.50 million people suffer open angle (OAG) and angle closure glaucoma (ACG) in 2010, increasing to 79.60 million by 2020, and of these, 74% will have OAG. For that reason, the objective of this project was to implement a computational technique of early diagnosis of glaucoma using imaging processing in MATLAB, with the aim of preventing future more severe damages. Together with this, it also sought to improve the precision of the technique to complement the work of the specialist, and providing more accessibility to the patient. The entire ACRIMA database was used for the diagnosis and then analyzed and processed by MATLAB, by extracting the most relevant qualities from the images to reach an optimal diagnosis. The performance test was based on Matlab glaucoma detection proposed method, showing satisfactory results with an accuracy of 94.61%, sensitivity of 94.57% and specificity of 95%. These percentages showed that glaucoma detection using Matlab is feasible.
All Frequency Direct Illumination Using Visibility Correspondence Generated With Spherical Voronoi Diagrams

Vectorized visibility is a powerful visibility representation for rendering direct illumination with all frequency quality. However, the existing rendering method requires the visibility functions to be synthesized three times, followed with three radiance evaluation, for each triangle of the 3D model. We propose an alternative method to enhance vectorized visibility, such that we can directly interpolate the vectorized visibility, and therefore, reduce the computation bottleneck, i.e. the radiance evaluation, to just once. To facilitate the interpolation, we need to generate the correspondence among the three sampled visibility functions for each triangular patch. This paper uses spherical Voronoi diagram as a tool to conduct a preliminary correspondence generation, instead of doing brute force search. Additional treatments are also implemented to ensure that the interpolated visibility functions have smooth transition across the 3D model. With our method, we have more flexibility to manipulate the visibility functions in the favour of rendering speed and render the all frequency direct illumination twice as fast as the previous method.