Miracle-dz/UniFAR-FTUs huggingface dataset:
Variance Risk Premium in Energy Markets: Ex-Ante and Ex-Post Perspectives

This paper introduces the ex-ante estimation of the variance risk premium. The novel methodology proposed is applied to forecast variance risk premium in energy markets, capturing the future degree of aversion of investors towards energy variance risks. We analyze the ex-ante variance risk premium of two energy indices, XLE and USO, during the period that spans from 2011 to 2022, and compare them to that of the SPX, the benchmark for the equity market. In the computation of the ex-ante variance risk premium, simple GARCH and Markov-switching GARCH models are exploited to forecast the realized variance, while variance swap rates are retrieved from the volatility indices VXXLE, OVX, and VIX of the three market indices. We find that the ex-ante variance risk premium succeeds to forecast the imminent periods of financial distress empirically detected in the abrupt surges and plunges of the ex-post variance risk premium. In particular,USO shows higher magnitudes of the variance risk premium than XLE and SPX, predicting that investors require on average higher premiums to bear oil variance risks.
Infrared Thermal Image Gender Classifier Based on the Deep ResNet Model

Gender classification from human face images has attracted researchers over the past decade. It has great impact in different fields including defense, human-computer interaction, surveillance industry, and mobile applications. Many methods and techniques have been proposed depending on clear digital images and complex feature extraction preprocessing. However, most recent critical real systems use thermal cameras. This paper has the novelty of utilizing thermal images in gender classification. It proposes a unique approach called IRT_ResNet that adopts residual network (ResNet) model with different layer configurations: 18, 50, and 101. Two different datasets of thermal images have been leveraged to train and test these models. The proposed approach has been compared with convolutional neural network (CNN), principal component analysis (PCA), local binary pattern (LBP), and scale invariant feature transform (SIFT). The experimental results show that the proposed model has higher overall classification accuracy, precision, and F-score compared to the other techniques.
Avaliação das Técnicas de Detecção do Ataque Sybil na Disseminação de Conteúdo da Internet das Coisas

A Internet das coisas (IoT) compreende uma diversidade de objetos heterogêneos. Eles atuam nestas redes coletando dados contínuos e discretos e disseminando estas informações para aplicações provendo assim um maior bem estar para as pessoas. O serviço de disseminação de dados na IoT utiliza o meio sem fio que é inseguro. Assim, este serviço pode ser prejudicado por diversos tipos de atacantes como o blackhole, sinkhole e o Sybil. Dentre esses, o ataque Sybil desponta como o mais crítico visto que ele atua na confidencialidade destes dados. Embora as soluções encontradas na literatura sejam eficazes contra o ataque Sybil, elas desconsideram a presença de dispositivos heterogêneos ou apresentam soluções complexas. Desta forma, este trabalho apresenta um estudo sobre a efetividade e eficiência das técnicas de detecção do ataque Sybil. Desta forma, esta avaliação usa métricas de eficácia e eficiência a fim de aferir a performance de um mecanismo com características próximas da IoT.
Supramolecular transmission of soliton-encoded bit streams over astronomical distances

A stream of optical pulses, transmitted over long distances in optical fiber, will be affected by a variety of noise sources, leading to degradation in the signal-to-noise ratio. This noise accumulation sets generic capacity limits of all fiber-based optical signal transmission systems and has long been regarded as unavoidable. We report that by tailoring long-range, non-covalent inter-pulse interactions, optical solitons in a fiber laser loop can robustly couple to each other and self-assemble into supramolecular structures that exhibit long-term stability, elementary diversity and the possibility of information encoding. We demonstrate error-free transmission of such self-assembled solitonic structures over many astronomical units without any active retiming, opening up the possibility of using bit-bit interactions to overcome noise accumulation in optical fiber telecommunications and bit-storage systems.
A Phase-Based Approach for ENF Signal Extraction From Rolling Shutter Videos

Electric Network Frequency (ENF) analysis has been an intriguing tool for multimedia forensics as former studies have paved the way for estimating ENF signals from digital audio, video, or even image files. However, for ENF signals to be widely used in extensive applications, supplementary research is needed so that ENF signals can be stably extracted without restrictions. In this letter, we propose a new phase-based approach for extracting ENF signals from CMOS sensor recordings. It uses phase differences between row signals from two consecutive frames, such that problems due to missing sample points during the idle periods are circumvented. The proposed method has substantial advantages in that it is applicable without a predefined read-out time and when the length of given videos is too short. Extensive experiments conducted with numerous devices demonstrate that the proposed method can take precedence over state-of-the-art methods because it robustly produces accurate ENF estimates in terms of alias frequency on the frame-level.
Lone Pair…π Contacts and Structure Signatures of r(UNCG) Tetraloops, Z-Turns, and Z-Steps: A WebFR3D Survey

Z-DNA and Z-RNA have long appeared as oddities to nucleic acid scientists. However, their Z-step constituents are recurrently observed in all types of nucleic acid systems including ribosomes. Z-steps are NpN steps that are isostructural to Z-DNA CpG steps. Among their structural features, Z-steps are characterized by the presence of a lone pair…π contact that involves the stacking of the ribose O4′ atom of the first nucleotide with the 3′-face of the second nucleotide. Recently, it has been documented that the CpG step of the ubiquitous r(UNCG) tetraloops is a Z-step. Accordingly, such r(UNCG) conformations were called Z-turns. It has also been recognized that an r(GAAA) tetraloop in appropriate conditions can shapeshift to an unusual Z-turn conformation embedding an ApA Z-step. In this report, we explore the multiplicity of RNA motifs based on Z-steps by using the WebFR3D tool to which we added functionalities to be able to retrieve motifs containing lone pair…π contacts. Many examples that underscore the diversity and universality of these motifs are provided as well as tutorial guidance on using WebFR3D. In addition, this study provides an extensive survey of crystallographic, cryo-EM, NMR, and molecular dynamics studies on r(UNCG) tetraloops with a critical view on how to conduct database searches and exploit their results.
Low-cost fabrication techniques for RF microelectromechanical systems (MEMS) switches and varactors

A novel low-cost microfabrication technique for manufacturing RF MEMS switches and varactors is proposed. The fabrication process entails laser microstructuring and non-clean room micro-lithography standard wet bench techniques. An optimized laser microstructuring technique was employed to fabricate the MEMS component members and masks with readily available materials that include, Aluminum foils, sheets, and copper clad PCB boards. The non-clean room micro-lithography process was optimized to make for the patterning of the MEMS dielectric and bridge support layers, which were derived from deposits of negative-tone photosensitive epoxy-based polymers, SU-8 resins (glycidyl-ether-bisphenol-A novolac) and photoacid activated ADEX™ dry films. The novel microfabrication technique offers comparatively reasonably yields without intensive cleanroom manufacturing techniques and their associated equipment and processing costs. It is an optimized hybrid rapid prototyping manufacturing process that makes for a reduction in build cycles while ensuring good turnarounds. The techniques are characterized by analysing each contributing technology and dependent parameters: laser structuring, lithography and spin coating and thin film emboss. They are developed for planar substrates and can be modified to suit specific work material for optimized outcomes. The optimized laser structuring process offers ablation for pitches as small as 75 µm (track width of 50 µm and gap 25 µm), with a deviation of 3.5 % in the structured vector’s dimensions relative to design. The lithography process also developed for planar and microchannel applications makes for the realization of highly resolved patterned deposits of the SU-8 resin and the laminated ADEX™ polymer from 1 µm to 6 µm and with an accuracy ±0.2 µm. The complete micro-fabrication technique fabrication techniques are demonstrated by realizing test structures consisting of RF MEMS switches and varactors on FR4 substrates. Both MEMS structures and FR4 substrate were integrated by employing the micro-patterned polymers, developed from dry-film ADEX™ and SU-8 deposits, to make for a functional composite assembly. Average fabrication yield up to 60 % was achieved, calculated from ten fabrication attempts. The RF measurement results show that the RF MEMS devices fabricated by using the novel micro-fabrication process have good figure-of-merits, at much lower overall fabrication costs, as compared to the devices fabricated by conventional cleanroom process, enabling it to be used as a very good micro-fabrication process for cost-effective rapid prototyping of MEMS.
Impulsive Formation Control Algorithms for Leader-following Second-order Nonlinear Multi-agent Systems* *This research is supported by the National Natural Science Foundation of China (Grant No.61174094), and the Program for New Century Excellent Talents in University of China (Grant No.NCET-10-0506

Abstract This paper investigates second-order nonlinear multi-agent systems with fixed and switching topologies. For the delay multi-agent system with fixed topology, an impulsive formation control strategy is proposed which only operates at instant times. Based on some impulsive time-delay differential inequalities, the sufficient conditions that ensure the nonlinear multi-agent systems converge to a desired formation are established. Furthermore, the results obtained are extended to the case when the interaction topology is switching. For the multiagent with disturbance under switching topology, the sufficient condition is obtained under the impulsive control. Finally, numerical examples are presented to illustrate the effectiveness of the theoretical results.
A Look Over on Prosperous Power Electronic Converters for Wind Turbine System

This paper essentially an introduction of power electronic and its appliances with the assertion on wind turbine systems. There is the far-reaching stretch use of wind turbines for power generation technologies. So progressively more power electronics stand-in as an intensity plant are connected instantly to the transmission and distribution systems. The tolerable induction generator is placed a vital role in power networks, prosperous power electronics systems and rein in methods declare to be introduced to get better efficiency of wind turbine system. This paper gives an analysis of suitable power electronic multilevel converter topologies for the latest wind energy system has been discussed with generators. As well as the corresponding power electronics converters are the reviewed and compared, current matrix converters more suitable technologies for renewable energy sources conversion and control purpose.
eHealth Literacy in German Skin Cancer Patients

The global incidence of skin cancer has steadily increased in recent years, and malignant melanoma still has one of the fastest-growing incidence rates among all malignant tumors in the western world. Thus, newly diagnosed patients have an increased need for health information concerning their disease. Using a standardized questionnaire, our study aims to investigate our patients’ primary sources of health-related information as well as their self-proclaimed eHealth literacy. We received 714 questionnaires. Regardless of age, the primary source of information was the treating dermato-oncologist, followed by the treating general practitioner and the Internet. However, with increasing age, the usage of the Internet decreased. Hence, younger participants were better equipped to find health-related information while using the Internet. Additionally, comprehending health-related information and gaining medical knowledge was significantly increased in better-educated participants. Overall, our study shows that with increased use of eHealth services, accessing web-based information increased, correlating with a better eHealth literacy of our patients. eHealth technologies are increasingly becoming more prevalent as a primary source of information in our modern health care system. Thus, it is crucial to educate cancer patients in eHealth literacy to make autonomous, informed decisions and gain more confidence in dealing with their disease.
Evaluation Anxiolytic Effect of Methanol Extract of Ceplukan Leaves (Physalis minima L.) in the Elevated Plus Maze Test through IL-6 Level Changes in Ovariectomized Rats

Ceplukan ( Physalis minima L.) has long been used to treat various conditions in traditional medicine. This study aims to demonstrate the anxiolytic effects of Methanol Extract of Ceplukan Leaves (MECL) in the Elevated Plus Maze (EPM) test and correlate to IL-6 level of ovariectomized rat brain. Total of 24 Wistar rats were divided into six groups: one normal group, one group of 1 month ovariectomized (ovx), one group of 2 months ovx, three groups of 2 months ovx (each given with MECL 500; 1500 and 2500 mg/kg doses for 1 month). The anxiety-like behavior level was measured by EPM test. After EPM test, the brain was removed to measure level of IL-6 by ELISA. The data were processed and analyzed by one-way ANOVA and Pearson correlation. We found that the MECL-treated rats enter the opened-arm higher than the control rats. It indicates that the MECL-treated rats are less anxious than the control rats. The results also show the decreased of IL-6 level in MECL-treated rats.
Packaging Solution Based on Low-Temperature Cofired Ceramic Technology for Frequencies Beyond 100 GHz

In this paper, a package concept based on low-temperature cofired ceramic (LTCC) technology is illustrated. The concept is specifically designed for frequencies beyond 100 GHz, taking into consideration the inherent LTCC manufacturing defects, e.g., shrinkage, layer misalignment, and warping. Simple techniques are used for package assembly which can be automated using standard industrial processes. The proposed concept is implemented by integrating a 122-GHz silicon-germanium (SiGe) radar transceiver chip in an LTCC-based package. The off-chip components of the package, operating at 122 GHz, include a transmit (Tx) and a receive (Rx) aperture-coupled patch antenna array, a stripline to grounded coplanar waveguide signal transition feeding each of the antenna arrays, and three parallel, self-matched ground-signal-ground wirebonds connecting the SiGe radar transceiver chip and the LTCC package on Tx and Rx side. The simulation and measurement results of the Tx and Rx antenna arrays along with the signal transitions in the frequency range of 110–140 GHz are shown. The radar front end (RFE) is encapsulated using an epoxy molding compound, Polytec TC 430-T with suitable dielectric characteristics in the desired frequency range. The encapsulated RFE is surface mounted on a baseband printed circuit board, thereby realizing a fully functional 122-GHz frequency modulated continuous wave (FMCW) radar. The FMCW radar is used to measure the distance of a target, and the measurement results are compared with those of a commercial 122-GHz RFE.
Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome

Background: Accurate outcome prediction is of great clinical significance in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect. This study intended to evaluate the performance of clinical text information (CTI), radiomics features, and survival features (SurvF) for predicting functional outcomes of patients with ischemic stroke. Methods: SurvF was constructed based on CTI and mRS radiomics features (mRSRF) to improve the prediction of the functional outcome in 3 months (90-day mRS). Ten machine learning models predicted functional outcomes in three situations (2-category, 4-category, and 7-category) using seven feature groups constructed by CTI, mRSRF, and SurvF. Results: For 2-category, ALL (CTI + mRSRF+ SurvF) performed best, with an mAUC of 0.884, mAcc of 0.864, mPre of 0.877, mF1 of 0.86, and mRecall of 0.864. For 4-category, ALL also achieved the best mAuc of 0.787, while CTI + SurvF achieved the best score with mAcc = 0.611, mPre = 0.622, mF1 = 0.595, and mRe-call = 0.611. For 7-category, CTI + SurvF performed best, with an mAuc of 0.788, mPre of 0.519, mAcc of 0.529, mF1 of 0.495, and mRecall of 0.47. Conclusions: The above results indicate that mRSRF + CTI can accurately predict functional outcomes in ischemic stroke patients with proper machine learning models. Moreover, combining SurvF will improve the prediction effect compared with the original features. However, limited by the small sample size, further validation on larger and more varied datasets is necessary.
A Long Short-Term Memory Based Framework for Early Detection of Mild Cognitive Impairment From EEG Signals

Mild cognitive impairment (MCI) is an irreparable progressive neuro-degenerative disorder, which seems to be a precursor to Alzheimer’s disease (AD) that may lead to dementia in elderly people. It is a major public health challenge for healthcare in the 21st century. Because there is no curative or therapy to halt or reverse the course of MCI, early identification is critical for successful treatment programs to enhance patients’ quality of life. Currently, Electroencephalography (EEG) has been emerged as an efficient tool to investigate MCI. Traditional methods for finding MCI from EEG data use shallow machine learning-based architectures that cannot find important biomarkers in deep, hidden layers of the data and also have trouble dealing with a large amount of EEG data. To reduce this issue, this research will use EEG data to provide a deep learning-based framework using the Long Short-term Memory (LSTM) model for effective identification of MCI individuals from healthy volunteers (HV). The suggested framework consists of four phases: denoising, segmentation, downsampling, uncovering deep hidden features using the LSTM model, and identifying MCI patients with the sigmoid classifier. This study has designed 20 different LSTM models and investigated them to a publicly available MCI database to find out the best one. After performing 5-fold cross validation, the best model achieved 96.41% of accuracy, 96.55% of sensitivity and 95.95% of specificity. The proposed LSTM-based deep learning model provides a robust biomarker and guide technologists to create a new automatic diagnosis system for MCI detection.
A Torque-Controlled Actuator With High Rigidity and Low Aspect Ratio

To ensure safe and accurate environment-robot interaction, a torque sensor is usually mounted at the output of an actuator to detect and control the output torque. Deformation-based sensors at the actuator output would significantly increase the actuator size and complexity while decreasing the output rigidity in off-axis directions. To reduce the extra complexity, space, and output compliance required for torque sensing, this article proposes a torque-controlled actuator that does not rely on deformation-based sensing at the actuator output. Instead, a torsion spring is mounted at the input of the harmonic drive. Encoders are used to sense the spring deformation to control the output torque with low sensing noise. Hence, the size of the actuator can be minimized while the output rigidity can be maintained. Using the torsion spring can also significantly reduce the reflected output inertia of the actuator. Experiments are provided to demonstrate torque control accuracy and bandwidth. Position control results also show a comparable response to actuators that do not have torque sensors. This new torque-controlled actuator can provide a more competitive solution for collaborative robots to interact with humans or the environment.
Tool fabrication for automated indirect optical micro-manipulation

Indirect optical micro-manipulation refers to mechanical manipulation of microscopic objects by means of optically trapped micro-tools. Two-photon polymerization is used to prepare the micro-tools, which are then trapped by focused laser beams through their spherical “handle” parts. Simultaneous control of several laser traps by Holographic Optical Tweezers (HOT) allows for positioning (both transfer and rotation) of the micro-tools in 3D. We report on the development of micro-tools and their testing in an automated HOT system. In order to facilitate the manipulation of objects in large systems (exceeding the field-of-view of the trapping microscope objective) the HOT apparatus is equipped with an additional low-resolution microscope. The two live images are processed with the system of several computers communicating with each other via local network and displayed side-by-side on remote client computer to allow interaction with the user. Initially, the user clicks the positions of laser traps matching micro-tool handles. Subsequently, traps are merged into a set with one representative control point serving for manual mouse operations (drag and drop, rotation and by mouse wheel). In the autopilot mode, the micro-tool moves in the given direction and velocity until it reaches the image border where it turns back. In the simulator mode, the manipulation is performed with animated micro-tools instead of real ones captured by camera.
Challenges and Limitations of Biological Network Analysis

High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms’ properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein–Protein Interaction (PPI) Networks, represent the biochemical interactions among proteins by using nodes that model the proteins from a given organism, and edges that model the protein–protein interactions, whereas pathway networks enable the representation of biochemical-reaction cascades that happen within the cells or tissues. In this paper, we discuss the main models for standard representation of pathways and PPI networks, the data models for the representation and exchange of pathway and protein interaction data, the main databases in which they are stored and the alignment algorithms for the comparison of pathways and PPI networks of different organisms. Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis. We have identified that network alignment presents a lot of open problems worthy of further investigation, especially concerning pathway alignment.