Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Adaptive Weighted Least Square Support Vector Regression Algorithm-Based Identification of the Ship Dynamic Model

This study contributes to developing a novel hybrid identification method based on intelligent algorithms, i.e. the least support vector regression algorithm (LS-SVR) and the artificial bee colony algorithm (ABC), to deal with the identification of the simplified ship dynamic model while the outliers exist in the measurements. The ship dynamic model is directly derived from our previous work which has been well verified and validated. The outliers are detected by introducing the robust estimation method namely the <inline-formula> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula> principle and then deleted from the training data. The weighted version of LS-SVR (WLS-SVR) with spareness and robustness ability is used as the fundamental identification approach. To improve the performance of the WLS-SVR, the structural parameters involved in it are optimized by utilizing the artificial bee colony algorithm (ABC), and the weights of it are adaptively set with the use of the adaptive weight method. Two case studies including the simulation study on a container ship and the experimental study on an Unmanned Surface Vessel (USV) are carried out to test the proposed hybrid intelligent identification method. The simulation study demonstrates the effectiveness and the acceptable time complexity in terms of the engineering application of the proposed identification method through the comparison with the cross-validation method and particle swarm optimization algorithm optimized LS-SVR. In the experimental study, ABC-LSSVR, ABC-LSSVR with the <inline-formula> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula> principle (D-ABC-LSSVR), ABC-LSSVR with the adaptive weight (ABC-AWLSSVR), and ABC-LSSVR with both the <inline-formula> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula> principle and the adaptive weight (D-ABC-AWLSSVR) are applied to identify the steering model for the USV. The results indicate that the influence of the outliers on model identification is effectively diminished by the robust <inline-formula> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula> principle and the adaptive weight method and that the D-ABC-AWLSSVR outperforms over the other three identification methods in terms of the mean squared error (MSE) of the model predictions.
The Design and Implementation of Smart Agricultural Management Platform Based on UAV and Wireless Sensor Network

Traditional agricultural monitoring systems generally use static sinks to collect data that are often accompanied with some problems such as high construction costs, low precision, poor maintainability and so on. In this paper, we design and implement a smart agricultural management platform that can be divided into three layers: perception layer, network layer and application layer. In perception layer, a large number of ZigBee sensor nodes (SNs) are deployed to build ZigBee networks. In network layer, a mobile sink (MS) that consists of a gateway carried by an unmanned aerial vehicle (UAV) is used to gather data from the perception layer and send it to application layer. Besides, we deploy a full-featured web application in application layer to provide various kinds of services for users. The advice module of the web application is designed to provide users with countermeasures that ensure humidity and temperature are within the ideal range for plant growth. The experimental results show that an MS can gather data from a large number of lands in a short time, with excellent scalability and stable operability. These advantages allow the platform to apply very well to certain areas of particular terrain and it also alleviate the “energy hole” problem in the wireless sensor networks that use static sinks.
Full-Duplex Radio – Increasing the Spectral Efficiency for Military Applications

This paper summarizes the results of the NATO STO IST Panel’s Exploratory Team IST-ET-101. The team studied the full-duplex radio technology as an innovative solution to deal with the scarce and congested electromagnetic frequency spectrum, especially in the VHF and UHF bands. This scarcity is in strong contrast to the growing bandwidth requirements generally and particularly in the military domain. The success of future NATO operations relies more than ever on new real-time services going hand in hand with increased data throughputs as well as with robustness against and compatibility with electronic warfare. Therefore, future tactical communication and electronic warfare technologies must aim at exploiting the spectral resources to the maximum while at the same time providing NATO with an advantage in the tactical environment.
c-M2DP: A Fast Point Cloud Descriptor with Color Information to Perform Loop Closure Detection

We present c-M2DP, a fast global point cloud descriptor that combines color and shape information, and perform loop closure detection using it. Our approach extends the M2DP descriptor by incorporating color information. Along with M2DP shape signatures, we compute color signatures from multiple 2D projections of a point cloud. Then, a compact descriptor is computed by using SVD to reduce its dimensionality. We performed experiments on public available datasets using both camera-LIDAR fusion and stereo depth estimation. Our results show an overall accuracy improvement over M2DP while maintaining efficiency, and are competitive in comparison with another color and shape descriptor.
Determining Object Properties from Tactile Events During Grasp Failure

Robotic grasp planners are unable to successfully grasp objects 100% of the time. During the failures, inferring a better understanding of the in-hand item could lead to more robust regrasp strategies. This paper explores how relevant object parameters, such as its surface properties and its weight distribution, may be extracted from a time series of tactile feedback generated during a grasp failure. The surface texture of four known objects was classified with an accuracy of 90.22%. Objects whose weight distribution were top heavy, bottom heavy, or evenly distributed were distinguished with an accuracy of 86.9%. In both cases, only a small dataset was required to achieve a relatively high performance.
Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability

Adaptive processors can dynamically change their hardware configuration by tuning several knobs that optimize a given metric, according to the current application. However, the complexity of choosing the best setup at runtime increases exponentially as more adaptive resources become available. Therefore, we propose a polymorphic VLIW processor coupled to a machine learning-based decision mechanism that quickly and accurately delivers the best trade-off in terms of energy, performance, and reliability. The proposed system predicts the best processor configuration in 97.37% of the test cases and achieves an efficiency that is close to an oracle (more than 93.30% on all benchmarks).
Haptic feedback in the training of veterinary students

This paper reports on an initial study into the use of haptic (or touch) technology in the training of veterinary students. One major problem faced in veterinary education is that animals can be harmed by inexperienced students who are trying to learn the skills they need. The aim of the work described here is to provide haptic models to simulate internal examinations of horses so that students can learn the basic skills required on computer and then transfer to real animals with much less risk of doing them injury.
Hybrid III-V on Si Photonic Integrated Circuits for High-Speed Optical Communications: Roadmap and Perspectives

We review some of our latest contributions for on-chip high-speed optical communications using the hybrid III-V-on-Si platform. Fast tunable lasers for metro-access networks are demonstrated, showing wavelength switching of around ~ 1 ns, a tunable wavelength span of 45 nm and a coupled output power greater than 1 mW. Additionally, fully integrated coherent transceivers for long-haul telecommunications are also discussed, providing the first experimental demonstration of an integrated coherent transceiver that comprises, for the transmitter i) the laser, ii) the high-speed Mach-Zehnder modulator and iii) the semiconductor optical amplifier. For the receiver: i) the coherent 90° hybrid mixer, ii) the local oscillator and iii) an array of photodiodes. A figure of merit of VπLπ = 2.2 V cm was measured for the Mach-Zehnder modulator, with a 3 dB electro-optical bandwidth of 14 GHz and an open eye diagram (6.7 dB of extinction ratio) at 25 GHz. Regarding the coherent receiver, a maximum photodiode responsivity of 0.9 A/W was retrieved, with an in-quadrature symbol constellation presenting a quality factor of around 11.1 dB at 28 GBd. These results put in perspective the potential of the hybrid III-V on Si platform to implement compact on-chip high-speed communication systems that include the gain-chip components (lasers and SOAs) with unprecedented performance.
A New Electrostatic Gripper for Flexible Handling of Fabrics in Automated Garment Manufacturing

The handling of fabrics is a very challenging task throughout automated garment manufacturing. There are practical difficulties in designing and implementing a reliable gripper to efficiently handling fabric panels. In this paper, we present a new and flexible electrostatic gripper for the handling of fabrics. Our gripper consists of four flat pads and their embedded electrode patterns generate electrostatic adhesion fields. The coverage area varies with the expansion of the four electrostatic pads. This allows handling various size of fabric panels and flattening folded/wrinkled fabrics. We partially verified our new gripper in prototype form and experimentally evaluated its performance on a large number of fabric materials. Moreover, the proposed gripper can be used for handling and transporting garments while avoiding the damage of fabric surfaces.
Anomaly Detection in Network Management System Based on Isolation Forest

As an important part in network management system, rapid and accurate anomaly detection is one of the preconditions to guarantee the effective work of the network. In general, the network management system will monitor the data of multiple indicators, and the manifestations of network anomalies are complex in the data. Most existing anomaly detection approaches have encountered some trouble in network anomaly detection because of the periodicity of time series data or high time complexity and memory cost working on high-dimensional data. Aiming at the deficiency of present methods of network anomaly detection, we propose an adapted method based on iForest algorithm. We construct some feature extractors through statistical methods to highlight different abnormal behaviors in different indicator and then use the extracted feature data for the construction and prediction of iForest. Combining specific feature extractors, we can eliminate the effects of periodicity, or specify the detection of peaks or troughs to adapt to different indicators. By means of iForest algorithm which has linear time complexity and low memory requirement, our method makes a rapid detection with large dataset and works well in high dimensional network management data.
Analysis Of Factors That Affect The Re-Use of Carsharing Mobile Application

People interest in carsharing application keep growing due to the comfort and simplicity it provide. Carsharing system it self offer solutions for common problem that usually found in urban areas like traffic jam and pollution. PT XYZ is the company that providing carsharing through it's mobile application. However, the percentage of user that reusing the application is still low. This study aims to analyze factors that influence user intention to reuse the application using Expectation Confirmation Model (ECM) Usage sample were taken from 231 user who have done at least rent a car once from the application. The data samples were analyzed using Partial Least Square Method (PLS) with SmartPLS 3.0. The result showed that perceived usefulness, satisfaction and service quality directly affecting reuse intention. meanwhile, your confirmation task technology fit indirectly affecting reuse intention of Carsharing.
Deep Analysis of Quasi Z Source Inverter for Batteries Charging and Discharging Capabilities Across All DC Capacitors' Terminals

An energy-stored Quasi Z-source inverter (qZSI) is a single stage topology to smoothen and stabilize the fluctuation of DC power produced from the photovoltaic (PV) system. Moreover, qZSI allows to step up the double amount of DC output voltage than its DC input voltage compared to the conventional DC-AC boost converter. The existing topology consists of a battery, H-bridge inverter and a quasi-Z-source (qZS). The proposed control scheme is to control DC-link voltage and battery management to charge and discharge the battery effectively. In addition, the electronic circuit breaker (ECB) algorithm is also proposed to limit the battery from over-charging and over-discharging to protect the battery life. Furthermore, the control scheme and ECB can also maintain the qZSI with battery energy storage system (BESS) to work in continuous conduction mode (CCM) rather than discontinuous conduction mode (DCM). To compare and validate the performance of two different types of energy-stored qZSI in battery charging and discharging capabilities in this project, a simulation is carried out with a variable DC voltage source as inconsistent PV system for simplicity.
Toward Deep Learning Approaches for Learning Structure Motifs and Classifying Biological Sequences From RNA A-to-I Editing Events

RNA editing is a post-transcriptional RNA sequence modification that alters the mature RNA sequence from its template DNA sequences. RNA editing events are critical in various biological and biochemical mechanisms, and can expand the transcriptomic and proteomic diversity from altered gene regulation to mutations. A-to-I RNA editing is now being vastly detected and quantified on a global scale and gained much attention. A deeper understanding of this process with insufficient genomic annotations and prior knowledge-based filtering steps, such as high-throughput next-generation sequencing techniques, are needed, in addition to data regarding whether an editing location is located in one of the following three main classes of RNA editing sites: ALU in Alu repetitive elements, REP in non-Alu repetitive elements, and NONREP in non-repetitive regions. This study proposes deep learning approaches to ameliorate these issues by learning motif patterns and identifying regions of A-to-I editing events in biological sequences. Using datasets derived from the public REDI portal, 300,000 editing sites were equally divided and used for learning sequence and structure motifs visualized by convolutional kernels. We explored the RNA editing pattern changes using information of positional and class enrichment of learned motifs in the three aforementioned classes. We demonstrated that these newly investigated approaches using large-scale RNA sequencing data offer excellent classification accuracy with a well-optimized convolutional neural network and recurrent neural network classifiers that obtained average area under curves of 0.960 and 0.962, respectively. The findings further decipher the principles underlying RNA editing events and will facilitate more effective RNA sequencing research.
Solar Cell Simulations Made Easy

Numerical device simulations are a very powerful tool for the development of new types of highly efficient solar cells. The underlying theoretical framework describes many fundamental aspects of the complex flow of light through a photovoltaic device down to the atomic level, such that the most crucial simulation parameters can even be taken from first principles quantum mechanical simulations. But simpler approaches based on the Shockley diode equation are often sufficient to get an initial idea about the performance of a given basic material as part of a novel photovoltaic device. We discuss some of these simplified approaches and illustrate their applications, including the use of data from first principles simulations and/or experimental studies.
Physical exercise during pregnancy minimizes PTZ-induced behavioral manifestations in prenatally stressed offspring.

Stress during gestation has been shown to affect susceptibility and intensity of seizures in offspring. Environmental stimuli, such as maternal physical exercise, have shown to be beneficial for brain development. Although studies have demonstrated the deleterious influence of stress during pregnancy on seizure manifestation in offspring, very little is known on how to minimize these effects. This study verified whether physical exercise during the pregnancy associated with prenatal stress minimizes seizure susceptibility in offspring at the beginning of postnatal development. Pregnant rats and male pups were divided into the following groups: control, stress, stress/forced exercise, and stress/voluntary exercise. Behavioral manifestations were analyzed after injection of pentylenetetrazol (PTZ; 45 and 60 mg/kg) at ages P15 and P25. Increased behavioral manifestations and seizure severity was observed in the stress group compared with the control group at both ages. At the dose of 45 mg/kg, offspring of stressed mothers who performed both physical exercise models showed an increase in latency for the first manifestation and decrease in the seizures severity at both ages compared with the mothers groups who were only stressed. Prenatal restraint stress potentiated PTZ-induced seizure behavior, and both forced and voluntary exercise during gestation attenuates the negative effects of PTZ-induced offspring.
Extremely Low Birth Weight Predisposes to Impaired Renal Health: A Pooled Analysis

Background: A number of studies examined the association between preterm delivery and kidney size and function later in life. However, the number of cases in published cohort studies is low. This study was aimed at performing a multicenter collaboration to pool data to obtain more accurate results to quantify the extent of renal impairment in former extremely low birth weight (ELBW; <1,000 g) children. Methodology: We performed a subject-level meta-analysis to pool data from Cracow (64 cases/34 controls) and Leuven (93 cases/87 controls). We assessed and analyzed cystatin C, estimated glomerular filtration rate (eGFR), ultrasound kidney length, and blood pressure (BP) in 11-year-old ELBW children compared with controls born at term. The prevalence of hypertension (HT) and prehypertension (preHT) in both groups was also analyzed. Results: The study group comprised 157 former ELBW children (gestational age 23–33 weeks and birth weight 430–1,000 g) and 123 children born at term. Former ELBW children had lower mean eGFR (100.62 ± 16.53 vs. 111.89 ± 15.26 mL/min/1.73 m2; p < 0.001), smaller absolute kidney length (8.56 ± 0.78 vs. 9.008 ± 0.73 cm; <0.001), and higher systolic (111.8 ± 9.8 vs. 107.2 ± 9.07 mm Hg; p = 0.01) and diastolic (68.6 ± 6.8 vs. 66.3 ± 7.7 mm Hg; p = 0.03) BP. Smaller renal size in former ELBW children was positively associated with lower birth weight, shorter gestational age, and severity of perinatal complications (intraventricular hemorrhage, length of stay, mechanical ventilation, and oxygen therapy). Conclusion: ELBW is associated with lower eGFR and a high frequency of preHT and HT.
Fetal exposure to maternal inflammation interrupts murine intestinal development and increases susceptibility to neonatal intestinal injury

ABSTRACT Fetal exposure to chorioamnionitis can impact the outcomes of the developing fetus both at the time of birth and in the subsequent neonatal period. Infants exposed to chorioamnionitis have a higher incidence of gastrointestinal (GI) pathology, including necrotizing enterocolitis (NEC); however, the mechanism remains undefined. To simulate the fetal exposure to maternal inflammation (FEMI) induced by chorioamnionitis, pregnant mice (C57BL/6J, IL-6−/−, RAG−/− or TNFR1−/−) were injected intraperitoneally on embryonic day (E)15.5 with lipopolysaccharide (LPS; 100 µg/kg body weight). Pups were delivered at term, and reared to postnatal day (P)0, P7, P14, P28 or P56. Serum and intestinal tissue samples were collected to quantify growth, inflammatory markers, histological intestinal injury, and goblet and Paneth cells. To determine whether FEMI increased subsequent susceptibility to intestinal injury, a secondary dose of LPS (100 µg/kg body weight) was given on P5, prior to tissue harvesting on P7. FEMI had no effect on growth of the offspring or their small intestine. FEMI significantly decreased both goblet and Paneth cell numbers while simultaneously increasing serum levels of IL-1β, IL-10, KC/GRO (CXCL1 and CXCL2), TNF and IL-6. These alterations were IL-6 dependent and, importantly, increased susceptibility to LPS-induced intestinal injury later in life. Our data show that FEMI impairs normal intestinal development by decreasing components of innate immunity and simultaneously increasing markers of inflammation. These changes increase susceptibility to intestinal injury later in life and provide novel mechanistic data to potentially explain why preterm infants exposed to chorioamnionitis prior to birth have a higher incidence of NEC and other GI disorders. Summary: Fetal exposure to maternal inflammation alters normal development of neonatal goblet and Paneth cells, and significantly increases intestinal injury in the neonatal period through IL-6-dependent pathways.
Management of recurrent ovarian cancer: when platinum-based regimens are not a therapeutic option

Ovarian cancer relapses have been traditionally classified according to the platinum-free interval, leading to an arbitrary categorization of possible scenarios and treatment options. Its relevance in assessing treatment strategies has been revised in the last several years, as the panorama is constantly changing in the era of personalized medicine and targeted therapies. Factors to be considered while defining the best management of recurrent disease, and, consequently, the available treatment alternatives are increasing. Platinum remains one of the milestones of ovarian cancer treatment, but for some patients it might not be an ideal choice for several reasons other than limited platinum sensitivity. This review aims to analyze the scenarios in which platinum is not considered suitable in the management of patients with recurrent ovarian cancer, and the currently available alternatives.
Battle of epigenetic proportions: comparing Illumina’s EPIC methylation microarrays and TruSeq targeted bisulfite sequencing

ABSTRACT DNA methylation microarrays have been the platform of choice for epigenome-wide association studies in epidemiology, but declining costs have rendered targeted bisulphite sequencing a feasible alternative. Nonetheless, the literature for researchers seeking guidance on which platform to choose is sparse. To fill this gap, we conducted a comparison study in which we processed cord blood samples from four newborns in duplicates using both the Illumina HumanMethylationEPIC BeadChip and the Illumina TruSeq Methyl Capture EPIC Kit, and evaluated both platforms in regard to coverage, reproducibility, and identification of differential methylation. We conclude that with current analytic goals microarrays still outperform bisulphite sequencing for precise quantification of DNA methylation.