Miracle-dz/UniFAR-FTUs huggingface dataset:
Micro-Doppler Effect and Determination of Rotor Blades by Deep Neural Networks

The paper deals with the analysis of simulated data, where thousands of samples of reflections from a radar target, a helicopter, with propellers were simulated. Simulations were performed for helicopters with 3, 4, 6, and 8 propeller blades. Data collection and evaluation were focused on the measurement of the Doppler Effect, specifically the Micro-Doppler effect for the rotating propeller section. The simulations have been divided into several sections for all types of helicopters differing in the number of propellers. The most considered was the change of Radar Cross Section (RCS), but changes in helicopter movement speed, changes in helicopter position relative to the radar, and changes in helicopter rotation speed have been considered as well. Moreover, a simulation of the change in radar carrier frequency across the microwave band was performed and the changes and effects on the Micro-Doppler measurement data were studied. However, the main task of this paper was to determine the number of propeller blades from any simulated signal sample with parameters corresponding to the Micro-Doppler, which was successfully done. Simulated data has been used to train a deep learning network to classify the number of propeller blades on a randomly selected measured/simulated sample. To detect the number of rotors, we have chosen to use Convolutional Neural Networks (CNN), which achieve good results for object recognition from images.
GBLD10+: a compact low-power 10 Gb/s VCSEL driver

We report the design and implementation of the GBLD10+, a low-power 10 Gb/s VCSEL driver for High Energy Physics (HEP) applications. With new circuit techniques, the driver consumes only 31 mW and occupies a small area of 400 μm × 1750 μm including the IO PADs and sealrings. These characteristics allow for multiple GBLD10+ ICs to be assembled side by side in a compact module, with each one directly wire bonded to one VCSEL diode. This makes the GBLD10+ a suitable candidate for the Versatile Link PLUS (VL+) project, offering flexibility in configuring multiple transmitters and receivers.
Extreme Event Detection and Management using Twitter Data Analysis

Events like the natural disasters are impossible to predict and are inevitable. The best course of action in that scenario is to effectively detect the event and respond to it accordingly. With quick detection and tangible information retrieval, casualties can be greatly reduced. Traditionally, this has been a tiring task as the data was siphoned from human observers or expensive equipment. In countries with scarce economies, the acquisition of these equipment can prove herculean and relying on individual human observers may lead to the spread of misinformation. An inexpensive and reliable solution to this problem can be found in the form of a system which facilitates the detection of extreme real-world events through the reactions of people on online social networks like Twitter. Unusual bursts in traffic from Twitter can be detected and this can be associated with an event. Moreover, the system can monitor the Twittersphere for rescue requests that appear on Twitter at the aftermath of an extreme event. Thanks to the containerized designed and hence its scalability, this model can undergo worldwide deployment with ease.
Looking at a Moving Target Defense of EthernetIP

SCADA and other industrial control systems have been successfully attacked and likely will continue to be attacked. These systems use many different, often proprietary, network application protocols for communications. Our research looks at applying a moving target defense (MTD) to an unsecured ethernetIP protocol used for communicating to a programable logic controller. Using this as a proxy for those protocols transmitted in the clear over TCP, we experiment with choosing various methods of encryption at random and periodically changing the scheme while keeping the system in motion, thus introducing a moving target defense of the protocol. Real time systems generally have deterministic timing requirements. Our research indicates that it may be possible to increase security with MTD of the underlying fieldbus protocol while maintaining deterministic temporal requirements suitable for some environments.
AMF-CSR: Adaptive Multi-Row Folding of CSR for SpMV on GPU

SpMV is a cost-dominant operation used in many iterative methods for solving large-scale sparse linear systems. However, irregular memory access of SpMV to the multiplied vector leads to low data locality and then harms the performance. This paper presents an adaptive multi-row folding of CSR (AMF-CSR) format for SpMV calculation on GPU. This new storage format supports the folding of the variable number of rows in order to achieve better load balancing in computation. AMF-CSR not only increases the density of non-zero elements in a folded row, thereby improving the access locality of the multiplied vector, but also merges an approximately equal number of nonzero elements in a folded row, hence achieving load balancing. The performance evaluation using 28 sparse matrices shows that the proposed SpMV algorithm based on AMF-CSR achieves the highest speedup of 4.11x and 3.62x on GTX 1080 Ti and Tesla V100 respectively against a fixed multi-row folding-based SpMV algorithm. Evaluation results using 450 regular sparse matrices and 450 irregular sparse matrices also show that AMF-CSR is superior to other SpMV implementations.
Change Detection for Large Distributed Sensor Networks With Multitriggered Local Sensors

The emergence of large sensor networks and the Internet of Things has reinvigorated interest into distributed quickest change detection. Important shortcomings of existing approaches are ease of design, flexibility in communication, and applicability to larger networks. The new approach proposed in this work features local sensors that can be triggered multiple times, i.e., can reset and continue monitoring after transmitting their decisions. With larger sensor networks as a focus, the system allows for multiple simultaneous transmissions to a fusion center within bandwidth limitations. The proposed system uses the cumulative-sum procedure at local sensors to binarize local decisions, which are then transmitted to the fusion center that also employs cumulative-sum quickest detection. Test overdesign due to sequential test overshoot is avoided, and global and local thresholds are chosen to meet a desired false alarm rate constraint using numerical computation of expected delay performance. The system compares favourably to several existing methods while offering greater flexibility in the amount of fusion center communication.
Finding the Best Repeater Position for Maximized Coverage in Outdoor Cellular Networks

Wireless propagation modelling is a key step in the design of an efficient wireless communication system. The power received at the receiver from the transmitter is largely dependent on the specific wireless channel since obstacles and path loss can result in signal attenuation. In this work, a section of Georgia Southern University’s campus is modelled and investigated for maximum cellular coverage, by use of an ideally placed repeater.
A Linear Stochastic Formulation for Distribution Energy Management Systems Considering Lifetime Extension of Battery Storage Devices

Recently, there has been a tremendous growth in the number of Battery Energy Systems (BESs) connected to the grid. These assets can alleviate some operational issues such as demand surges and occasional power fluctuations associated with the Renewable Energy Sources (RESs) connected to the grid. Nonetheless, both overcharging and frequent usage severely affect their health status and shorten their life expectancy. In this paper, an Energy Management System (EMS) framework with linearised algorithm and in-depth analysis on BES life extension is presented, which optimises the techno-economic aspects of an Active Distribution Network (ADN) connected to BESs and RESs. By applying a mathematical linearisation formulation, a Mixed-Integer Linear Programming (MILP) model is proposed for linearising the Optimal Power Flow (OPF) problem. This technique, which has the merit of fair accuracy while having high speed, is used for scheduling BESs to increase their durability as well as to decrease grid costs. To consider the inherent uncertainty associated with demand and RES generation, a two-stage Stochastic Programming (SP) method is implemented in our proposed model. In terms of battery Loss of Health (LoH) assessment, a linearised battery lifetime method is introduced. Ultimately, a modified 33-bus radial distribution test system with the day-ahead Real-Time Pricing (RTP) program was chosen to apply the proposed algorithm and assess its efficiency.
Constrained Multi-Agent Reinforcement Learning for Managing Electric Self-Driving Taxis

Electric self-driving taxis (es-taxis) draw great attention nowadays and hold the promise for future transportation due to their convenient and environment-friendly nature. However efficiently managing large-scale es-taxis remains an open problem. In this paper, we focus on scheduling es-taxis under charging budget constraint. Specifically, we design safe-controller to guarantee the satisfaction of budget constraint, and propose HAT framework to enlarge the sight for decision-making on deactivating es-taxis. As for the non-stationary induced by HAT, we analyze and limit its influence with theoretical guarantees. The overall framework Safe-HAT achieves superior performance in real-world data against other strong baselines.
Effect of OTS Selector Reliabilities on NVM Crossbar-based Neuromorphic Training

This paper studies the use of 1-selector-1-resistor (1S1R) devices as the cross-point components in a crossbar array for deep neural network (DNN) training. Two training algorithms—minibatch gradient descent (MBGD) and a low-rank approximation to MBGD—are evaluated with compact models of ovonic threshold switching (OTS) selectors considering several reliability issues, and resistive random-access memory (ReRAM), a type of emerging non-volatile memory (NVM) device. We explore various reliability concerns of the OTS selectors including the low on-state conductance, threshold voltage variation, and internal voltage offset, etc. and propose corresponding techniques to overcome the reliability challenges.
Mixed-Integer Linear Programming Model for Safe Zone Selection during Air Pollution Disaster

According to the air pollution disaster problem in Thailand, the human health problem becomes a significant issue. Many researchers and the government of Thailand attempt to help the people to avoid this problem. This paper aims to propose a mixed-integer linear programming model for safe zone selection during air pollution disasters. The mathematical model is applied to seek suitable locations for the case study under several conditions such as budget limit, capacity limit, bound of the number of selected locations in each area, and bound of total score obtained from the evaluation. The numerical data were generated for testing the proposed mathematical model. Based on the numerical data, the results found that the proposed mathematical model could generate the optimal solution and seek suitable locations for building safe zones in the air pollution disaster under the proposed limitations. This research will be of great significance in helping government consider the strategic placement of safe zones and helping people avoid the hazard of air pollution.
Overview of a Decade of Field Experiments with the Atmospheric Imaging Radar

Phased array radar (PAR) systems are widely considered the future of atmospheric science radar technology because they offer much faster and more versatile scanning than traditional, parabolic dish radars. The Atmospheric Imaging Radar (AIR) is a mobile PAR developed at the University of Oklahoma (OU) that uses digital beamforming to collect volumetric observations rapidly (5 to 10 s). Between 2011 - 2019, annual field campaigns were conducted to study thunderstorms, resulting in one of the largest collections of PAR data for weather research and extensive involvement of undergraduate and graduate students. In this study, the AIR system, past research findings, and new data examples are highlighted. In addition, the open-access AIR data archive is discussed, which allows interested users to download these unique PAR observations.
A Flexible and Inherently Self-Consistent Methodology for MOL/BEOL/MIMCAP TDDB Applications with Excessive Variability-Induced Degradation

In this work, we develop a flexible and inherently self-consistent grand methodology to resolve the excessive variability-induced degradation encountered in measurements of time-dependent dielectric breakdown (TDDB). This methodology is based on the underlying percolation principle in terms of thickness-dependence of characteristic breakdown time (T63) and Weibull slope (β). Starting from an ideal β thickness-vs-dependence, this methodology involves the use of an iteration procedure for the adjustment of the β -vs-thickness relation for the optimal coefficients to obtain the simultaneous agreement with multiple sets of experimental data. Moreover, we show different governing statistical distributions, (normal or Weibull), and their bimodality for dielectric thickness variation, can also play a great role in affecting final TBD failure distributions. Due to the rigor of our methodology, it can be used for both reliability assessment and lifetime projection.
BRCA1 Hypermethylation In Sporadic Breast Cancers: Discovering A Novel Pathway To Tumorigenesis Via Coordinate NBR2 Deregulation And TNBC Transformation

Women with a family history of mutations in the Breast cancer susceptibility gene, BRCA1 will have an increased risk of developing breast neoplasms. However, majority of the breast cancers are sporadic where BRCA1 mutations are very rare. Instead, 5-65% of sporadic cases manifest BRCA1 promoter hypermethylation and 30-40% of such cases develop into Triple Negative Breast Cancers. Even then, the molecular mechanism of BRCA1 hypermethylation mediated breast tumorigenesis has remained an enigma till date. Here, we present a novel tumorigenesis pathway for breast cancers that engenders from BRCA1 hypermethylation by generating site-specific methylations in the BRCA1 promoter using a modified version of CRISPR technology. We report that induction of site-specific methylation on BRCA1 promoter α effectuates a downregulation in BRCA1 expression via alteration in the balance between its alternate transcripts β and α. Induced BRCA1 hypermethylation is also responsible for the attenuation of a long noncoding RNA, NBR2 (Neighbour of BRCA1 gene 2), which is transcribed through the bidirectional BRCA1 promoter α in the reverse direction. Downregulation of NBR2 activates a feedback loop by leading to further downregulation of BRCA1 which is more evident under glucose starvation conditions and is associated with impaired DNA damage repair. BRCA1 hypermethylation also results in significant overexpression of β-hCG (human chorionic gonadotrophin), which was found to be associated with highly aggressive and drug-resistant forms of BRCA1 mutated breast cancers invitro & in vivo in our previous study. Further, we report a change in the hormone receptor levels as the tumor progresses which demonstrates how BRCA1 deficient cells modulate their expression of ER-α and ER-β to promote their proliferation in early stages of tumor development and at later stages, transform to a basal tumor subtype by shedding down the expression of ER-α & PR. Interestingly, we also discovered that modulation of ER-α expression upon BRCA1 hypermethylation is responsible for the alteration in BRCA1 transcript ratio. Finally, in in vivo mouse studies, BRCA1 hypermethylated tumors were found to be much larger, aggressive and invasive as compared to wildtype, BRCA1 and NBR2 knockdown tumors with downregulation of ER-α and PR; which explains the most probable reason behind high relapse rates in BRCA1 hypermethylated tumors. GRAPHICAL ABSTRACT
Exploratory meta-analysis of hypoxic transcriptomes using a precise transcript reference sequence set

Exploratory meta-analysis of hypoxic RNA-seq data using FANTOM-CAT as a reference transcriptome facilitates the evaluation of relationship between sense–antisense transcripts from the same locus. Gene expression studies are intrinsically biased, with many studies influenced by concomitant information such as gene–disease associations. This limitation can be overcome using a data-driven analysis approach without relying on ancillary information. The FANTOM CAGE–Associated Transcriptome project provides a comprehensive meta-assembly of the human transcriptome using coding and noncoding genes. Hypoxia strongly influences gene expression; in addition, noncoding RNA (ncRNA) metabolism is down-regulated in response to hypoxic stimuli. We evaluated the differential response of various transcripts to hypoxia by determining their hypoxia responsiveness scores. Enrichment analysis revealed that several genes associated with ncRNA metabolism, particularly those involved in ribosomal RNA processing, were down-regulated in response to hypoxia. Previously published information from the FANTOM CAGE–Associated Transcriptome project was suitable for meta-analysis of the transcriptome sequencing data from both coding and ncRNAs and to evaluate the hypoxia responsiveness of target transcripts and relationship between sense–antisense transcripts from the same locus. Our results may facilitate functional annotation of various transcripts including ncRNAs, allowing for both sense and antisense and coding and noncoding evaluations.
Impact localization by a multi-radio sink–based wireless sensor network for large-scale structures

With the rapid development of wireless sensor network technology, more and more researchers have been interested in taking advantages of wireless sensor network to reduce weight and cost and to solve the installing problem of the wired structural health monitoring systems. A number of wireless-based structural health monitoring methods have been developed over the years to ensure the safety of large-scale structures. However, little research has been reported on wireless impact monitoring of large-scale composite structures due to the limitations of ordinary monitoring methods and wireless sensor networks. In this article, a wireless multi-radio sink which can access multiple communication channels is developed and adopted to build an impact monitoring wireless sensor network. Besides, a corresponding network architecture with an energy-weighted factor–based localization method adopted is presented to enable impact localization within the whole monitoring scope of the network. To verify the performance of the impact monitoring wireless sensor network, the experiments are performed on complex aircraft composite structures and the experimental results prove the effectiveness of the proposed wireless sensor network.
Decreased in vitro dihydroartemisinin sensitivity in malaria parasites infecting sickle cell disease patients

Background Partial ACTs treatment failure in Plasmodium falciparum malaria has been previously reported in sickle cell patients. The main purpose of this study was to investigate the in vitro susceptibility of clinical isolates to DHA to find out hypothesis backing up the reason of this poor therapeutic response. Results A total of 134 clinical isolates from patients attending health centers in Abidjan with uncomplicated Plasmodium falciparum malaria were selected. Hemoglobin HbAS, HbSS, HbAC, HbSC and HbAA were identified. Parasitemia and hemoglobin level at inclusion were lower in sickle cell patients with major forms than in patients with normal phenotype. A significant number of parasites with survival rates ranging from 14.68 to 33.75% were observed in clinical isolates from the SS phenotype. At inclusion, these resistant clinical isolates showed lower parasite densities, and patients had lower red blood cell count and hematocrit levels compared to those with susceptible clinical isolates. A low rate of parasitic growth has more often occurred with AS sickle cell phenotype. However, the decrease in in vitro sensitivity to DHA was not associated with Kelch 13-Propeller gene polymorphism. Conclusion This study highlights an in vitro decreased sensitivity to DHA, for clinical isolates collected from sickle cell SS patients living in Abidjan (Côte d’Ivoire), which is not related to the Pfkelch13 gene mutations. These clinical isolates may represent a health threat for sickle cell disease patients especially during crisis. Moreover, these results could suggest additional mechanisms of artemisinin resistance that need to be explored.
Genome-wide CRISPR guide RNA design and specificity analysis with GuideScan2

We present GuideScan2 for memory-efficient, parallelizable construction of high-specificity CRISPR guide RNA (gRNA) databases and user-friendly gRNA/library design in custom genomes. GuideScan2 analysis identified widespread confounding effects of low-specificity gRNAs in published CRISPR knockout, interference and activation screens and enabled construction of a ready-to-use gRNA library that reduced off-target effects in a novel gene essentiality screen. GuideScan2 also enabled the design and experimental validation of allele-specific gRNAs in a hybrid mouse genome.
Non-Invasive Cancer Detection in Canine Urine through C. elegans Chemotaxis

Cancer is the leading cause of death of companion animals, and successful early treatment has been a challenge in the veterinary field. We have developed the Non-Invasive Cancer Screening (N.C.S.) Study to perform cancer detection through the analysis of canine urine samples. The test makes use of the strong olfactory system of the nematode C. elegans, which was previously shown to positively respond to urine samples from human cancer patients. We performed a proof-of-concept study to optimize the detection capability in urine samples obtained from dogs with naturally occurring cancers. In this study, we established a scale for identifying the cancer risk based on the magnitude of the chemotaxis index of C. elegans towards a canine urine sample. Through validation, the N.C. S. Study achieved a sensitivity of 85%, showing that it is highly sensitive to indicating the presence of cancer across multiple types of common canine cancers. The test also showed a 93% specificity to cancer samples, indicating a low rate of over-identifying cancer risk. From these results, we have demonstrated the ability to perform low-cost, non-invasive cancer detection in companion animals, a method which can increase the ability to perform cancer diagnosis and treatment.
Hypoxia‐mediated regulation of DDX5 through decreased chromatin accessibility and post‐translational targeting restricts R‐loop accumulation

Local hypoxia occurs in most solid tumors and is associated with aggressive disease and therapy resistance. Widespread changes in gene expression play a critical role in the biological response to hypoxia. However, most research has focused on hypoxia-inducible genes as opposed to those which are decreased in hypoxia. We demonstrate that chromatin accessibility is decreased in hypoxia, predominantly at gene promoters and specific pathways are impacted including DNA repair, splicing and the R-loop interactome. One of the genes with decreased chromatin accessibility in hypoxia was DDX5, encoding the RNA helicase, DDX5, which showed reduced expression in various cancer cell lines in hypoxic conditions, tumor xenografts and in patient samples with hypoxic tumors. Most interestingly, we found that when DDX5 is rescued in hypoxia, replication stress and R-loop levels accumulate further, demonstrating that hypoxia-mediated repression of DDX5 restricts R-loop accumulation. Together these data support the hypothesis that a critical part of the biological response to hypoxia is the repression of multiple R-loop processing factors, however, as shown for DDX5, their role is specific and distinct.