Miracle-dz/UniFAR-FTUs huggingface dataset:
Security Monitoring of IEC 61850 Substations Using IEC 62351-7 Network and System Management1

Network and system management (NSM) plays an important role in ensuring end-to-end security of power systems. As defined in IEC 62351-7, NSM provides system security awareness through the collection of a large amount of data in order to monitor the power grid operational environments. In this paper, we follow the IEC 62351-7 guidelines to develop an NSM platform for IEC 61850 substations. Then, on top of the developed platform, we build a hybrid, deep learning and rule-based, anomaly detection system. Furthermore, considering IEC 61850 protocols, we develop a list of potential cyber attacks on the substation that are likely to impact the power grid availability. The effectiveness of the proposed anomaly detection system against the identified attacks is confirmed by testing it on an IEEE 8-Bus system in the presence of NSM using a smart grid testbed.1The research reported in this article has been supported by the NSERC/Hydro-Qubec Thales Senior Industrial Research Chair in Smart Grid Security
Deep Learning for C-Reactive Protein Prediction

Neural networks have been extensively utilised to perform biosignal prediction over the last two decades. This study proposes a long short-term memory-based recurrent neural network, to predict future state in a C-reactive protein (CRP) time series from a given cancer patient. Experiments are conducted to demonstrate that a LSTM-based RNN is capable of CRP time series forecasting using data obtained from ten patients with melanoma. Since CRP is biomarker of immune system activity, the ability to forecast can potentially guide clinical decisions in cancer treatments.
Plasmonics and SERS activity: beyond gold and silver

The search for new plasmonic nanostructures has received significant attention recently, owing to applications in diverse fields of nanoscience and nanotechnology, and being of interest for the life sciences. Metallic nanoparticles (NP) can strongly absorb and scatter light due to their ability to support localized surface plasmon resonances (LSPR) and, therefore, play an important role in plasmonic-based techniques such as surface enhanced Raman scattering (SERS) and metal-enhanced fluorescence (MEF). Interestingly, most research in the field is still confined to Ag, Au, and less intensively Cu nanostructures, and there is an open field for the investigation of other elements such as the poor metals. In this work, we investigate plasmonic properties of different metal nanoparticles, including the post-transition metals Bi, In, Pb, and Sn; the metalloid Sb; the transition metals Ni and Co; and the metal alloy Terfenol-D. All these nanoparticles were experimentally tested for LSPR and SERS, and our results indicate SERS responses and enhancement factors comparable to the ones obtained with gold NP. The nanoparticles used in this study were produced via laser ablation synthesis in solution (LASiS) and characterized by UV-Vis optical absorption, dynamic light scattering (DLS), transmission electron microscopy (TEM), atomic force microscopy (AFM) and surface area electron diffraction (SAED). These laser-synthesized NP present increased LSPR in the near UV spectral region, yet also reveal multi-peak LSPR extending towards the visible, an interesting feature to be considered in the development of plasmonic sensors. For the Raman experiments, several organic and biological molecules were tested, and SERS activity was demonstrated for diverse combinations of NP and test-analytes at varying concentrations, down to single molecule detection.
Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study

Drowsiness/sleepiness is a serious issue that needs to be addressed for improvement in the safety of road driving. Past statistical data on road accidents has shown enormous increases in car crashes due to drowsy/sleepy feelings. This study comprehensively summarizes all aspects of the drowsy state and its effects during car driving: its symptoms, causes, preventive actions, car accident statistics, sleep stages, and the behavioral, physiological and neural activation changes occurring during wakefulness and in the drowsy state. It considers drivers’ behavioral data and corresponding methodologies for its analysis, the biomedical signals of the human body (including neuronal signals in the forms of electrical and hemodynamic responses), and their use for drowsiness detection. All of the existing methodologies, their uses and pros and cons, are comprehensively summarized. A detailed survey of the data published by neuro-imaging methodology-, physiological signal- and behavioral methodology-based studies in addition to studies using electro-mechanical installed sensors are statistically and theoretically summarized. Additionally, the neuronal activity occurring during the drowsy and awake states are analyzed, and the important contributions of fNIRS, fMRI and EEG in this context are discussed in detail. Differing existing drowsiness-detection systems installed in popular car brands also are reviewed. Finally, the remaining challenges and future suggestions for drowsiness-detection systems are summarized as well.
The N = 8 Superconformal Bootstrap in Three Dimensions

We analyze the constraints imposed by unitarity and crossing symmetry on the fourpoint function of the stress-tensor multiplet of N = 8 superconformal field theories in three dimensions. We first derive the superconformal blocks by analyzing the superconformal Ward identity. Our results imply that the OPE of the primary operator of the stress-tensor multiplet with itself must have parity symmetry. We then analyze the relations between the crossing equations, and we find that these equations are mostly redundant. We implement the independent crossing constraints numerically and find bounds on OPE coefficients and operator dimensions as a function of the stress-tensor central charge. To make contact with known N = 8 superconformal field theories, we compute this central charge in a few particular cases using supersymmetric localization. For limiting values of the central charge, our numerical bounds are nearly saturated by the large N limit of ABJM theory and also by the free U(1)× U(1) ABJM theory. December, 2014 ∗e-mail: schester@Princeton.EDU †e-mail: jaehlee@MIT.EDU ‡e-mail: spufu@Princeton.EDU §e-mail: ryacoby@Princeton.EDU ar X iv :1 40 6. 48 14 v3 [ he pth ] 1 D ec 2 01 4
Understanding Consumer Purchase Intention in a Blockchain Technology for Food Traceability and Transparency context

From being associated with the financial services primarily, blockchain is now affecting other industries as well. Blockchain is attracting interest as a way to track and monitor food through the supply network, ensuring its origin. Consumers are increasingly demanding to know where their purchases come from and how they are made. In food indust’y, consumers can confirm the provenance of their purchases by blockchain. Despite some blockchain research on innovation adoption in fintech contexts, little is known about how consumers evaluate blockchain in food traceability and transparency. This study explores consumers’ motivations to purchase food with blockchain code and takes the Unified Theory of Acceptance and Use of Technology 2 (UTA UT2) as a reference and develops an extended model by considering perceived trust, since the capabilities of blockchain technology for food traceability and transparency. The results show that blockchain technology has the capability to build trust and affect purchase intention. Results also highlight purchase intention is significantly and positively influenced by performance expectancy, effort expectancy, and habit. The results further show that technographic can significantly enhance intention to the use of blockchain technology to trace food The research provides an insight to practitioners and researchers on how to increase intention towards blockchain.
CONDITIONS ASSOCIATED WITH THE RISK OF DEATH WITHIN 24 HOURS OF ADMISSION IN CHILDREN IN ZARIA, NIGERIA

While infant and childhood mortality rates have remained high in underdeveloped countries an important aspect of this mortality is the rate of death within 24 hours of admission to a Hospital. We retrospectively reviewed the records of 202 children who died over a 20-month period. Eighty one (40.1%) of these children died within 24 hours of admission with 30/81 (37.0%) of them receiving no drug treatment up to the time their death. A history of convulsion the presence of severe anaemia heart failure and impaired conscious level at admission were significantly associated with the risk of dying within 24 hours of admission to the hospital. Delay in commencement of treatment most likely caused by inability of parents or relatives to make available drugs for commencement of therapy is a major contributory factor for early death in our Hospital. (authors)
Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting

Traffic speed prediction, as one of the most important topics in Intelligent Transport Systems (ITS), has been investigated thoroughly in the literature. Nonetheless, traditional methods show their limitation in coping with complexity and high nonlinearity of traffic data as well as learning spatial-temporal dependencies. Particularly, they often neglect the dynamics happening to traffic network. Attention-based models witnessed extensive developments in recent years and have shown its efficacy in a host of fields, which inspires us to leverage graph-attention-based method to handling traffic network speed prediction. In this paper, we propose a novel deep learning framework, Spatial-Temporal Graph Attention Networks (ST-GAT). A graph attention mechanism is adopted to extract the spatial dependencies among road segments. Additionally, we introduce a LSTM network to extract temporal domain features. Compared with previous related research, the proposed approach is able to capture dynamic spatial dependencies of traffic networks. A series of comprehensive case studies on a real-world dataset demonstrate that ST-GAT supersedes existing state-of-the-art results of traffic speed prediction. Furthermore, outstanding robustness against noise and on reduced graphs of the proposed model has been demonstrated through the tests.
Shoulder Pain among Rehabilitated Spinal Cord Injured Persons Using Manually Propelled Wheelchairs in the Gaza Strip: A Survey

Shoulder pain among paraplegic persons has negative effects on their lives. The prevalence of shoulder pain among persons with spinal cord injury (SCI) varies from 30% to 70% in different studies and may be related to repetitive use of the shoulder during self-care and wheelchair-related activities. Purpose : This study focused on the prevalence of shoulder pain and examined its effects on activities of daily living and social participation, and on functional, work and recreational or athletic activities. It also aimed to detect the degree of satisfaction with shoulder functioning wheelchair users who were paraplegic due to spinal cord injury, in the Gaza strip. Methods: Cross sectional study design was used to collect data from 80 persons with paraplegia, post rehabilitation, who were still using manual wheelchairs (MWC) for ambulation. After giving informed consent, the selected persons were interviewed directly in their homes, and filled questionnaires which included the demographic data, Wheelchair User? s Shoulder Pain Index (WUSPI) and Shoulder Rating Questionnaire (SRQ). Results: The prevalence rate of shoulder pain among paraplegics who use manual wheelchairs was 62%. Pushing a wheelchair for 10 minutes or more, and propulsion up ramps or inclines outdoors were the most common activities that caused and exacerbated shoulder pain. Sixty four percent from among the study sample mentioned that they had no limitation in shoulder-using ability during daily personal and household activities, while the rest experienced different degrees of limitation. Seventy-four percent reported no limitation during recreational or athletic activities, while the rest (26%) agreed that pain has variably limited their participation in these activities. Fourteen percent from the sample rated the overall degree of satisfaction with their shoulder functioning as fair, and the rest rated their satisfaction from good to excellent. Conclusion : Shoulder pain, ranging from mild to severe, was highly prevalent among SCI paraplegics who use MWCs during their usual activities, and other activities which involve wheelchair propulsion. About two- thirds of the subjects reported no limitation in shoulder use during daily personal and household activities and in recreational or athletic activities. doi 10.5463/DCID.v23i2. 105
Fully Convolutional Pyramidal Residual Network for Material Discrimination of Spectral CT

The spectral computed tomography (CT) based on photon-counting detector performs energy-dependent image reconstruction of material attenuation coefficients, allowing for effective medical diagnosis and material discrimination. However, the spectral CT image quality is degraded in narrow energy bins as a consequence of low photon counts. Thus the edge information of some materials with similar attenuation cannot be well identified. To improve the accuracy of material discrimination of spectral CT, we proposed a deep-learning-based material discrimination method based on Fully Convolutional Pyramidal Residual Network (FC-PRNet). The FC-PRNet model can predict each pixel of spectral CT images and extract more edge information for different material components. We evaluated our method using mouse spectral CT data set, and experimental results demonstrated that the proposed method could efficiently discriminate different materials compared with traditional method based on post-reconstruction. Moreover, our algorithm has fewer parameters, faster convergent speed and higher accuracy, and achieves better quality of material discrimination than SegNet, FCN-8s and U-Net.
Comparative Evaluation of Short Read Alignment Tools for next Generation DNA Sequencing

The recent advancements in Next-generation sequencing technologies have resulted in an enormous amount of sequencing data with newer properties. An important step in all genomic analysis is aligning the reads to a reference genome. Numerous short read alignment tools are available to perform this time-consuming task. However, these tools have different trade-offs in terms of accuracy and speed. Hence, it is crucial to evaluate these tools based on various aspects to have a clear understanding of their performance. BWA MEM and Bowtie2 are selected to perform this evaluation mainly because they are designed to handle longer reads. The performance was evaluated using throughput and mapping percentage. The tools were put under several tests such as their ability to handle longer reads. The results indicate that one tool cannot outperform the other tool in all aspects. Bowtie2 achieved higher throughput in some cases while in other cases BWA MEM achieved higher throughput. However, BWA MEM had higher mapping percentage in most cases and handled longer reads more efficiently than Bowtie2.
Improving the Signal-to-Noise Ratio of Time Domain Fiber Bragg Grating Sensor Based on Hybrid Simplex and Golay Coding Technique

We report on the experimental results of the combination of unique auto-correlation properties of Golay complementary code and Hadamard matrix properties of standard simplex code. The combination of the two coding techniques results in improvement of the signal-to-noise ratio (SNR) of time domain multiplexed fiber Bragg grating (TDM-FBG) sensor for temperature measurement. Previously, we have analyzed the properties of both coding techniques when deployed separately in the TDM-FBG sensor. In this case, both coding techniques result in the same amount of SNR improvement for code length longer than 31 bits. In this paper, we demonstrate the combination and simultaneous deployment of the two techniques to measure multiple FBGs under room condition (25 °C) and 50 °C temperature. The two schemes combination results in remarkable improvement of SNR and still retains the original spatial property of the decoded FBG signals, confirming the successful deployment of the hybrid codes. From the measurement of two FBG sensors located after 16 km of fiber, the combination of 31 bits of simplex- and 64 bits of Golay codes has resulted in a total of 10.5 dB improvement of SNR.
Does Deep Super-Resolution Enhance UAV Detection?

The popularity of Unmanned Aerial Vehicles (UAVs) is increasing year by year and reportedly their applications hold great shares in global technology market. Yet, since UAVs can be also used for illegal actions, this raises various security issues that needs to be encountered. Towards this end, UAV detection systems have emerged to detect and further anticipate inimical drones. A very significant factor is the maximum detection range in which the system's senses can “see” an upcoming UAV. For those systems that employ optical cameras for detecting UAVs, the main issue is the accurate drone detection when it fades away into sky. This work proposes the incorporation of Super-Resolution (SR) techniques in the detection pipeline, to increase its recall capabilities. A deep SR model is utilized prior to the UAV detector to enlarge the image by a factor of 2. Both models are trained in an end-to-end manner to fully exploit the joint optimization effects. Extensive experiments demonstrate the validity of the proposed method, where potential gains in the detector's recall performance can reach up to 32.4%.
Identification of Vulnerability Within Front-Ends Chips Using 60 GHz Near-Field Injection

Following our previous demonstration of the effects of strongly out-of-band electromagnetic interferences on RF front-ends, an extended analysis is conducted on many chips. Rules of thumb are provided as a function of three parameters (frequency of operation, electronic function, package type) and a first explanation is given on the failure mechanism owing to simulations. Furthermore a care inspection is provided by raster-scanning of the chip surface allowing the identification of vulnerability areas. We focus here on this new methodology applied to commercial chips for which the exact layout remains unknown.
Proportional Derivative Observer Design for Nonlinear Singular Systems

This paper focuses on proportional derivative observer design for a class of Takagi-Sugeno fuzzy singular systems. According to the available knowledge on premise variables, first, observer design with known premise variables is considered, and explicit parametrization of the desired observer is also given. Moreover, observer design with unknown premise variables is further investigated. Some new conditions, which guarantee the error system to be robust stability, are derived. All the stability criterions are presented in linear matrix inequalities framework, which can be conveniently verified via Matlab. Finally, two illustrative examples are simulated to illustrate the correctness of the present schemes.
Effect of pH and Sucrose on Physical Properties of Drinking Yoghutt Stabilized by Whey Proteinconcentrate

The influence of pH (3.5, 4.0 and 4.5) and sucrose (0.0 and 1.0%, W/W) on physical properties of drinking yoghurt stabilized by whey protein concentrate (WPC) at 0.0, 0.5, 1.0, 1.5 and 2.0% (W/W) was investigated. Drinking yoghurt was then pasteurized and passed twice through a single-stage high pressure valve homogenizer. The obtained emulsions were measured and creaming stability, lightness, and apparent viscosity were determined. In the presence of WPC, the emulsions were more stable to droplet flocculation than those not stabilized by WPC which was attributed to high L* value, low apparent viscosity, and low creaming index. Emulsion instability was observed at pH 4.5. Addition of sucrose to the emulsions showed a negative significant effect on the creaming stability of emulsion (p<0.05). This study has important implications for the formulation and production of protein-stabilized drinking yoghurt.
Monostatic RCS measurements of a passenger car mock-up at 77 GHz frequency in virtual environment

The radar cross-section of a target is a critical parameter for evaluating the performance of an automotive radar in terms of target detection, discrimination, and tracking. Furthermore, it has to be emulated accurately when applied in an over-the-air radar sensor validation methodology in a hardware-in-the-loop approach. Therefore, it is essential to study the RCS patterns of traffic participants under conditions with least influence of ground reflections and background artefacts. Given that in publicly available literature there are few reports of high-quality RCS-data measured over the entire frequency band at 77 GHz, we have performed wideband RCS measurements of a sedan car mock-up including both horizontal and vertical polarizations. The measurements were performed in a semi-anechoic chamber and analyzed with advanced post-processing for improving the accuracy of measured data. Results indicate that the RCS values of the sides were around 10 dB larger than the rear face, and 15 dB larger than the front face of the car. Comparison between horizontal and vertical polarizations show a high degree of similarity over all azimuth aspect angles except for the rear of the car, where the difference in RCS values amounts to as much as 7 dB. Further, averaging over the bandwidth reduced the fluctuations, nearly preserving the peak values at the favorable detection angles.
Image and Video Super Resolution using Recurrent Generative Adversarial Network

Recently, the convolutional neural network with residual learning models achieves high accuracy for single image super-resolution with different scale factors. With adversarial learning model, effective learning of transformation function for the low-resolution input image to a high-resolution target image can be achieved. In this paper, we propose a method for image and video super-resolution using the recurrent generative adversarial network named SR2GAN. In the proposed model (SR2GAN) we use recursive learning for video super-resolution to overcome the difficulty of learning transformation function for synthesizing realistic high-resolution images. This recursive approach helps to reduce the parameters with increasing depth of the model. An extensive evaluation is performed to examine the effectiveness of the proposed model, which shows that SR2GAN performs better in terms of peak signal to noise ratio (PSNR) and structural self-similarity index (SSIM) as compared to the state-of-the-art methods for super-resolution. For source code and supplementary material visit: https://github.com/OmkarThawakar/SR2GAN/.
WGM Resonators for Conductivity Measurements of Graphene Films

The response of sapphire WGM resonator to a single-layer graphene introduced onto the dielectric substrate is studied in Ka-band. The experimental investigations were performed for two types of resonator modes: HE and EH. The modes differ by the prevailing component: electric or magnetic of electromagnetic field along the resonator longitudinal axis. The possibility of using these modes for finding graphene conductivity is demonstrated. Based on the experimental and the resonator model data obtained using COMSOL Multiphysics software the graphene conductivity values were obtained. The conductivity values calculated based on results of measurements performed on two mode types with different substrate position relative to the resonator are compared. The results demonstrate reproducibility. The possible source of some difference between the results obtained on different modes is discussed. The recommendations regarding the design of the most suitable configuration for graphene conductivity measurements are proposed.