Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing

Mobile edge computing is an emerging paradigm that supplies computation, storage, and networking resources between end devices and traditional cloud data centers. With increased investment of resources, users demand a higher quality-of-service (QoS). However, it is nontrivial to maintain service performance under the erratic activities of end-users. In this paper, we focus on the service placement problem under the continuous provisioning scenario in mobile edge computing for multiple mobile users. We propose a novel dynamic placement framework based on deep reinforcement learning (DSP-DRL) to optimize the total delay without overwhelming the constraints on physical resources and operational costs. In the learning framework, we propose a new migration conflicting resolution mechanism to avoid the invalid state in the decision module. We first formulate the service placement under the migration confliction into a mixed-integer linear programming (MILP) problem. Then, we propose a new migration conflict resolution mechanism to avoid the invalid state and approximate the policy in the decision modular according to the introduced migration feasibility factor. Extensive evaluations demonstrate that the proposed dynamic service placement framework outperforms baselines in terms of efficiency and overall latency.
The Spanish Review of Financial Economics

This paper examines the mean-reversion property and volatility features of stochastic convenience yields for CO2 emissions allowances by using ADF, ECM-GARCH and ECM-TGARCH models. Empirical results show that the convenience yields for CO2 emissions allowances exhibit time-varying trends when different maturities are considered, and that convenience yields exhibit a linear mean-reverting process. We also find that the volatility of convenience yields exhibits a mean-reversion process and asymmetric leverage effect using ECM-GARCH (1,1) and ECM-TARCH (1,1) models. Unfavorable market information has a higher impact on this volatility than favorable market information, and unfavorable market information has a lower effect on the long-term volatility of convenience yields. © 2012 Asociación Española de Finanzas. Published by Elsevier España, S.L. All rights reserved.
A position sensing method for 2D scanning mirrors

This paper presents a cost-effective position sensing method for 2D scanning mirrors. The method uses only one 1D PSD (position sensitive detector) located at the backside of the 2D scanning mirror plate to retrieve the 2D rotation angle about the two axes separately in real time. Any 2D scanning mirror with resonant vibration about one axis and quasi-static vibration such as sinusoidal, saw tooth, triangular oscillation about the other axis can use this method. The two vibration axes are orthogonal to each other to form the scanning patterns, which are most desired in scanning 3D LiDAR systems. 3D scanning LiDAR is the targeted application for this research. The method uses timing measurement to measure the resonant vibration angle and Lagrange interpolation polynomial approximation to retrieve the quasi-static vibration angle. A prototype has been built to measure the 2D rotation angle of a 2D micromirror. The measured angle using the proposed method was verified using a 2D PSD. The largest errors for the vertical/horizontal angles were 9.6% and 5.36% respectively. The position sensing mechanism is also integrated to a scanning 2D micromirror based LiDAR system to demonstrate it as real time capability.
Use of Internet Technology among Older Adults in Residential Aged Care Facilities: Protocol for a Systematic Review and Meta-Analysis

Internet usage may help promote the physical and mental health of older adults living in Residential Aged Care Facilities (RACF). There is little evidence of how these older citizens use internet services. This systematic review aims to explore the trends and factors contributing to internet use among aged care residents. A systematic search will be conducted on nine online databases—MEDLINE, EMBASE, PsycInfo, CINAHL, AgeLine, ProQuest, Web of Science, Scopus, and the Cochrane Library. Two reviewers will independently conduct title and abstract screening, full-text reading, critical appraisal, and data extraction. Any discrepancies will be resolved by consensus. Methodological risk of bias will be assessed using the Effective Public Health Practice Project measure and Joanna Briggs Institute checklist. We will report a narrative synthesis of the evidence. Information on factors contributing to internet use and their strength of association will be reported. If feasible, we will undertake a meta-analysis and meta-synthesis. Our review will provide information on the factors predicting internet use among older adults in residential aged care facilities. The evidence from this review will help to formulate further research objectives and, potentially, to design an intervention to trial internet access for these groups. (Protocol Registration: PROSPERO-CRD 42020161227).
Fiber Ring Laser Based on Side-Polished Fiber MZI for Enhancing Refractive Index and Torsion Measurement

A fiber ring laser based on a side-polish fiber Mach-Zehnder interferometer (MZI) for effective improving refractive index (RI) and torsion sensing is proposed and investigated. The side-polished fiber MZI sensor can not only enhance the evanescent wave but also break circular symmetry of optical fiber face section, so it can be used for the surrounding RI and torsion sensing. A spectrum 3 <italic>dB</italic> bandwidth of less than 0.15 <italic>nm</italic> has been achieved which makes the fiber ring laser torsion sensing system to have higher measurement resolution. Experiment results show that the RI and torsion sensitivity of proposed sensor is dependent on the polish depth: the thicker the polish depth, the higher the sensitivity. For a sensor with side polish depth of <inline-formula> <tex-math notation="LaTeX">$47~\mu {m}$ </tex-math></inline-formula>, the measured RI sensitivity reaches −81.36 <italic>nm</italic>/RIU, the torsion sensitivity is as high as −0.019nm/<sup>O</sup>, the sensitivity converted to torsional rate is −0.267 <italic>nm</italic>/(<italic>rad</italic>.<inline-formula> <tex-math notation="LaTeX">${m} ^{-1}$ </tex-math></inline-formula>).
The Comparison of Various Correlation Network Models in Studying Mobility Data for the Analysis of Depression Episodes

Depression is a serious mental health disorder affecting millions of people around the world. Traditional diagnostic approaches are subjective including self-reporting feedback from patients and observational evaluation by a trained physician. However, altered motor activity is the central feature for depressive disorder. Moreover, recent studies show that the analysis of motor activity is the best predictor in characterizing psychological disorders including depression. With the advent of wearable devices, an individual’s motor activity can be monitored naturally using body worn sensors and feasible to distinguish depressed persons from healthy individuals. In this manuscript, we hypothesis to apply a methodology that takes advantage of motor activity recorded from wearable devices and process mobility patterns for a given group of subjects. Besides, employed a population analysis approach using correlation networks that evaluates mobility parameters of the population and identify subgroups that exhibit similar motor complexity. We have analyzed the mobility data of the given group by extracting three different sets of features using hour-wise, day-wise, and hybrid mobility data. Also, a comparison study of three models is presented by constructing a correlation graph and finding a cluster of individuals exhibiting similar mobility patterns. We found that mobility data using hour-wise features provides the best results compared to the other two models.
Electrically Injected UV-Visible Nanowire Lasers

There is strong interest in minimizing the volume of lasers to enable ultracompact, low-power, coherent light sources. Nanowires represent an ideal candidate for such nanolasers as stand-alone optical cavities and gain media, and optically pumped nanowire lasing has been demonstrated in several semiconductor systems. Electrically injected nanowire lasers are needed to realize actual working devices but have been elusive due to limitations of current methods to address the requirement for nanowire device heterostructures with high material quality, controlled doping and geometry, low optical loss, and efficient carrier injection. In this project we proposed to demonstrate electrically injected single nanowire lasers emitting in the important UV to visible wavelengths. Our approach to simultaneously address these challenges is based on high quality III-nitride nanowire device heterostructures with precisely controlled geometries and strong gain and mode confinement to minimize lasing thresholds, enabled by a unique top-down nanowire fabrication technique.
Long‐term outcome of immunologic autograft engineering

Our phase III trial reported that autograft‐absolute lymphocyte count (A‐ALC) improved survival post‐autologous peripheral blood hematopoietic stem cell transplantation (APBHSCT) for a short‐term follow‐up of 2 years. We evaluated retrospectively in our phase III trial patients that the A‐ALC still confers survival benefit with a longer follow‐up. With a median follow‐up of 127.6 months, patients infused with an A‐ALC ≥ 0.5 × 109 cells/kg experienced better overall survival (HR = 0.392, 95% confidence of interval [CI]: 0.224–0.687, p < 0.001) and progression‐free survival (HR = 0.413, 95% CI: 0.253–0.677), p < 0.0004). This study supports that A‐ALC provides long‐term survival benefit post APBHSCT.
Data Acquisition, Conditioning and Processing System for a Wearable-based Biostimulation

Data acquisition by electromyography, as well as the muscle stimulation, has become more accessible with the new developments in the wearable technology and medicine. In fact, for treatments, games or sports, it is possible to find examples of the use of muscle signals to analyse specific aspects related, e.g., to disease, injuries or movement impulses. However, these systems are usually expensive, does not integrate data acquisition with the muscle stimulation and does not exhibit an adaptive control behaviour that consider the pathology and the patient response. This paper presents a wearable system that integrates the signal acquisition and the electrostimulation using dry thin-film titanium-based electrodes. The acquired data is transmitted to a mobile application running on a smartphone by using Bluetooth Low Energy (BLE) technology, where it is analysed by employing artificial intelligence algorithms to provide customised treatments for each patient profile and type of pathology, and taking into consideration the feedback of the acquired electromyography signal. The acquired patient’s data is also stored in a secure cloud database to support the physician to analyse and follow-up the clinical results from the rehabilitation process.
Trans-Apical Transcatheter Aortic Valve Replacement in a Dialysis Patient with Systolic Heart Failure

Mortality and morbidity remain high following transcatheter aortic valve replacement (TAVR) in dialysis patients or those with low left ventricular ejection fraction. Therapeutic strategy for those with these comorbidities remains unestablished. We had a dialysis patient with peripheral artery disease and low left ventricular ejection fraction, who received successfully scheduled trans-apical TAVR following sufficient reverse remodeling by 3-month optimal medical therapy. Our strategy should be validated in a larger robust cohort.
A Subset of Acoustic Features for Machine Learning-based and Statistical Approaches in Speech Emotion Recognition

In this paper, a selection of acoustic features, derived from literature and experiments, is presented for emotion recognition. Additionally, a new speech dataset is built by recording the free speech of six subjects in a retirement home, as part of a pilot project for the care of the elder called E-Linus. The dataset is employed along with another widely used set (Emovo) for testing the effectiveness of the selected features in automatic emotion recognition. Thus, two different machine learning algorithms, namely a multi-class SVM and Naïve Bayes, are used. Due to the unbalanced and preliminary nature of the retirement home dataset, a statistical method based on logical variables is also employed on it. The 24 features prove their effectiveness by yielding sufficient accuracy results for the machine learning-based approach on the Emovo dataset. On the other hand, the proposed statistical method is the only one yielding sufficient accuracy and no noticeable bias when testing on the more unbalanced retirement home dataset.
DeFi: Shadow Banking 2.0?

The growth of so-called “shadow banking” was a significant contributor to the financial crisis of 2008, which had huge social costs that we still grapple with today. Our financial regulatory system still has not fully figured out how to address the risks of the derivatives, securitizations, and money market mutual funds that comprised Shadow Banking 1.0, but we are already facing the prospect of Shadow Banking 2.0 in the form of decentralized finance, or “DeFi. ” DeFi’s proponents speak of a future where sending money is as easy as sending a photograph—but money is not the same as a photograph. The stakes are much higher when money is involved, and if DeFi is permitted to develop without any regulatory intervention, it will magnify the tendencies towards heightened complexity, leverage, rigidity, and runs that characterized Shadow Banking 1.0. * Professor, American University Washington College of Law. This Article depicts the state of DeFi up to June 2022. Many thanks to participants in the William & Mary Law Review’s Cryptocurrency Symposium for their comments and perspectives, as well as to Ryan Clements, Stephen Diehl, Todd Philips, and David Rosenthal for helpful engagement with
Multilevel Ground Segmentation for 3-D Point Clouds of Outdoor Scenes Based on Shape Analysis

Ground segmentation of 3-D point clouds acquired by laser sensors plays a crucial role in many applications, such as environment perception, scene understanding, and environment modeling. This article proposes a novel multilevel framework of the ground segmentation for 3-D point clouds of outdoor scenes based on shape analysis. The local shape of the 3-D point cloud of an outdoor scene is captured by principal component analysis. Then, the 3-D point cloud is classified into scattered points, linear points, and surface points. The unit normal vectors of the surface points are calculated and mapped into a unit ball. On the normal ball, the normal vectors are clustered, which segments the surface points into some surface regions correspondingly. Each surface region is further divided into several surface fragments according to point positions. The surface fragment that meets the ground conditions is regarded as a part of the initial ground. Finally, the ground is obtained by using the 2-D Gaussian process regression. The proposed method explores both local shapes and multilevel structures of outdoor scenes and constructs a probabilistic ground model in order to improve the accuracy and adaptivity of ground segmentation. Experiment results demonstrate that the proposed method has good performance.
A Threat Intelligence Analysis Method Based on Feature Weighting and BERT-BiGRU for Industrial Internet of Things

The combination of 5G technology and the industrial Internet of things (IIoT) makes it possible to realize the interconnection of all things. Still, it also increases the risk of attacks such as large-scale DDoS attacks and IP spoofing attacks. Threat intelligence is a collection of information causing potential and nonpotential harm to the industrial Internet. Extracting network security entities and their relationships from threat intelligence text and constructing structured threat intelligence information are particularly important for IIoT security protection. However, threat intelligence is mostly text reports, which means the value information needs to be extracted manually by security analysts, and it is highly dependent on personnel experience. Therefore, this study proposes an IIoT threat intelligence analysis method based on feature weighting and BERT-BiGRU. In this method, BERT-BiGRU is used to classify attack behavior and attack strategy. Then, the attack behavior is weighted to make the classified result more accurate according to the relationship between attack strategy and attack behavior in ATT&CK for ICS knowledge. Finally, the possibility of attack and the harm degree of attack are calculated to form the threat value of the attack. The security analysts can judge the emergency response sequence by the threat value to improve the accuracy and efficiency of emergency response. The results indicate that the proposed method in this study is more accurate than the other standard methods and is more suitable for the unstructured threat intelligence analysis of IIoT.
A Study of Developments in the Routing Protocols in the Wireless Ad Hoc Networks (WANET)

Abstract: A mobile ad hoc network (manet) is a new name for an obsolete system: a network that doesn't require any pre-existing architecture to function. in the early 1970s, the darpa pr net and suran Programmes were predecessors of this system. the new twitch is the application of this approach in non-military communication circumstances. in addition, the research in this field has recently focused on various advanced aspects of this technology, such as multicasting and security. there have also been a number of unique solutions to "traditional" routing and medium access control problems proposed. this study attempts to characterize the state-of-the-art of networks technology in four areas: routing, intermediate identity management, multicasting, and security. when possible, reviews of the proposed techniques are offered. As part of this research, the Fuzzy based Flexible Energy Efficient Routing protocol (FIE2R) will be presented as a routing system for WANET. The decision maker is in charge of the whole FIE2R procedure, which is separated into two parts. It is the WANET graph start phase, which aids in the second phase's determination of each route's reward. That's from a comprehensive computation of FIE2R using MATLAB and NS2 and a comparative analysis of the same with other networking schemes, the recommended protocol improves performance in terms of energy consumption. Keywords: Adhoc networks, Manet, Wanet, MATLAB. Routing schemes
The Candidate Chromosomal Regions Responsible for Milk Yield of Cow: A GWAS Meta-Analysis

Simple Summary Milk production is one of the most important economic traits in dairy cattle. Therefore, determining the genomic regions influencing this trait can improve milk yield. In this study, we collected data from 16 articles associated with milk yield genome-wide association studies (GWAS) on different cattle breeds. Based on the information from the analysis and level of significance (p-value < 2.5 × 10−6), we identified different genomic regions on chromosomes with the highest marker density, markers with the highest effect, genes within or near these regions, chromosomes with the greatest effects on milk yield. Abstract Milk yield (MY) is highly heritable and an economically important trait in dairy livestock species. To increase power to detect candidate genomic regions for this trait, we carried out a meta-analysis of genome-wide association studies (GWAS). In the present study, we identified 19 studies in PubMed for the meta-analysis. After review of the studies, 16 studies passed the filters for meta-analysis, and the number of chromosomes, detected markers and their positions, number of animals, and p-values were extracted from these studies and recorded. The final data set based on 16 GWAS studies had 353,698 cows and 3950 markers and was analyzed using METAL software. Our findings revealed 1712 significant (p-value < 2.5 × 10−6) genomic loci related to MY, with markers associated with MY found on all autosomes and sex chromosomes and the majority of them found on chromosome 14. Furthermore, gene ontology (GO) annotation was used to explore biological functions of the genes associated with MY; therefore, different regions of this chromosome may be suitable as genomic regions for further research into gene expression.
Control Strategy of AC-DC Converter Based on Dual Active Bridge With Minimum Current Stress and Soft Switching

This article presents a control strategy for the single-stage dual active bridge (DAB) ac-dc converters. To reduce the conduction loss and achieve zero voltage switching (ZVS), the global optimal working modes are reviewed. Based on the global optimal working modes, the input current of the DAB ac-dc converter is analyzed for the sinusoidal input voltage. The control algorithm of the DAB ac-dc converter is proposed to achieve the unit power factor with the minimum current stress and ZVS. The range of the working modes is analyzed to meet the wide load power. The working mode is varied with the line-frequency angle and load power, which guarantees that the DAB ac-dc converter works in the optimal working modes. The conduction loss in the converter with the variation of the series inductance and the turns ratio of the transformer is analyzed. The series inductance and the turns ratio of the transformer are designed to further improve the conduction loss. The proposed control strategy is applied to a 1 kW DAB ac-dc converter with 220 V/50 Hz input voltage and 200 V output voltage. The experimental results demonstrate the working mode, the soft switching performance, fast dynamic response, high efficiency, and high power quality.