Miracle-dz/UniFAR-FTUs huggingface dataset:
Laminarin-triggered defence responses are geographically dependent in natural populations of Solanum chilense

Natural plant populations are polymorphic and show intraspecific variation in resistance properties against pathogens. The activation of the underlying defence responses can depend on variation in perception of pathogen-associated molecular patterns or elicitors. To dissect such variation, we evaluated the responses induced by laminarin, (a glucan, representing an elicitor from oomycetes) in the wild tomato species Solanum chilense and correlated this to observed infection frequencies of Phytophthora infestans. We measured reactive oxygen species burst and levels of diverse phytohormones upon elicitation in 83 plants originating from nine populations. We found high diversity in basal and elicitor-induced levels of each component. Further we generated linear models to explain the observed infection frequency of P. infestans. The effect of individual components differed dependent on the geographical origin of the plants. We found that the resistance in the southern coastal region, but not in the other regions is directly correlated to ethylene responses and confirmed this positive correlation using ethylene inhibition assays. Our findings reveal high diversity in the strength of defence responses within a species and the involvement of different components with a quantitatively different contribution of individual components to resistance in geographically separated populations of a wild plant species. Highlight Large-scale screenings reveal geographically distinct intraspecific differences in the dominant physiological pathogen defence responses upon glucan elicitor treatment in a wild tomato species.
Fiber-taper-coupled L-band Er3+-doped tellurite glass microsphere laser

A fiber-taper-coupled L-band microsphere laser was proposed and demonstrated. Er3+-doped tellurite glass was used to fabricate the microspheres. The microspheres were made by a novel spin method. The pump and emission light were coupled in and out of the microsphere modes with a tapered fiber. 975nm pumped L-band single/multi-mode microsphere lasers were demonstrated. The laser can be tuned in L-band. We call Er3+-doped microsphere laser as EDML.
Superconducting properties of A15 compounds derived from band-structure results

Calculations of the electron-phonon interaction, lambda, and T/sub c/ are presented for ten different A15 compounds, V/sub 3/X and Nb/sub 3/X, X = Al, Ga, Si, Ge, and Sn. The calculated temperature-dependent magnetic susceptibility of Nb/sub 3/Sn is also shown to be in good agreement with experiment. We argue that the low-temperature anomalies in many A15 materials are related to the very sharp structure in the density of states near the Fermi energy found in our ab initio band-structure results.
A New Power Management Control Strategy for PV-Battery Hybrid System

: : photovoltaic are limited in terms of available power due to their intermittent nature. Therefore, in order to balance the power di ff erence between PV and load in an islanded DC microgrid, a battery energy storage system should be used. An energy management control technique for a PV-battery hybrid system is examined in this paper in order to meet the load power needs at all moments. In order to minimize the losses, a unidirectional boost converter is utilized to track the maximum power point or curtail power from the PV array to the common DC load. A buck converter is proposed to connect in parallel with boost converter to charging the battery energy storage system; another boost converter is used to control battery storage’s discharge operation. The proposed approach for the power flow load and converters is used to preserve balance of power in the hybrid system, as well as charge / discharge of the battery storage to support the PV panel occurs depending on the PV power generated, state of charging, and the load power requirement. A double-loop control strategy is proposed for each converter taking into consideration the battery’s state of charge, battery charge rate limits, and adjusting operating point for PV array in order to avoid overcharge. Additionally, the control system is implemented without needing for programmatic elements or a state diagram. The proposed control strategy design, analysis, and validation results under several operating scenarios are presented and discussed. This work represents a simple method to make the hybrid system more e ffi cient and reliable.
Spherical harmonic analysis of IRAS galaxies: implications for the Great Attractor and Cold Dark Matter

The angular distribution of more than 8000 galaxies observed by IRAS on scales of 4°-180° was analyzed by expansion in spherical harmonics. This technique provides a powerful tool to study both cosmography and statistics of the local Universe. The harmonic analysis illustrates the existence of the «tug of war» between the Great Attactor/Centaurus complex and the Perseus-Pisces supercluster. A new concentration in Puppis, behind the Galactic plane is revealed by the harmonic analysis, in accord with a recent redshift survey
Towards Smart Tourism Development in City Branding Era in Indonesia

The advancement of technology recently urges city branding practi-tioners to implement smart tourism. In short, smart tourism refers to the usage of the latest information technology to support the tourism sector’s development. Enhancing the capability of utilizing the “Internet of Things” is deemed pivotal in tourism development. It makes things easier for tourists, but it can also widen potential markets for the tourism sector. This study aims to explore the extent of smart tourism development in Indonesia. It is done through content analysis of available literature, mainly focused upon a single city case study. By summa-rizing these kinds of literature using a comparative technique, this study ﬁnds that most Indonesian cities still use mobile applications. Other than this, there are many indicators in smart tourism yet to be achieved, such as smart vehicle scheduling, crowd handling, Intelligent-Guide System, travel safety protection. This study uses three dimensions in evaluating smart tourism: technology, people, and institutional dimensions. This study found some challenges, such as human resources’ capabilities in using technology and funding limitations. Suggestions to overcome these challenges and improve smart tourism implementation are pro-posed, such as collaboration between the private sector and the government to enhance technology’s service quality and target speciﬁc potential customers using social media.
Penilaian Esai Pendek Otomatis Berdasarkan Similaritas Semantik dengan SBERT Semantic Similarity-Based Short Essay Auto Scoring using SBERT

Essay questions are considered better in measuring students' understanding than multiple choice questions. However, essay answers require more time and effort to evaluate and there are often inconsistencies. Therefore, we need an automated essay scoring system that can assist evaluators in providing grades faster and more consistently. This study aims to evaluate the performance of the automatic essay scoring model where the students’ answer essay and evaluator answer key are compared semantically to find out how similar is between the students’ answer and evaluator’s answer key. The semantics of the essay text are obtained by word embeddings using the Siamese-BERT (SBERT) pretrained language model which transforms the essay text into a 512-length vector. This automatic essay scoring process begins with preprocessing the text by applying case folding, then word embeddings on the text that has been preprocessed with SBERT. The numerical vectors of the answer keys and the test answers of the word embeddings are then compared with Cosine Similarity to obtain the semantic similarity as well as the essay skor which is the output of the model. The evaluation of this automatic essay scoring model is done by comparing model skor output with human evaluator skor. The metrics used to measure the performance of this automatic essay scoring is to calculate the Mean Absolute Error (MAE) and Pearson Correlation, where the results of this study show an average MAE value of 0.26 and an average correlation of 0.78.
The Numerical Solution of Higher Index Linear Time Varying Singular Systems of Differential Equations

A family of Taylor-type methods is given for solving numerically a large class of singular systems of differential equations of the form $A(t)x'(t) + B(t)x(t) = f(t)$. The advantages, disadvantages, and implementation of these methods are discussed and several analytical and numerical examples are given.
A survey on the challenges of data integration

The field of data is undergoing a great development, which is growing more and more in parallel with the Internet. This development has created many types of data for us, which are difficult to integrate. It is still necessary to find ways to process the data and to solve the semantic and structural integration problems, resulting from the heterogeneity of the sources. The objective of this paper is to survey the problems posed by data integration, and to discuss the solutions proposed by researchers in this field. Next, we will discuss our proposed methods for solving the record linkage problem with the LSTM model and for identifying duplicate records using graphs.
The Blackhole Attack on Vehicular Network

One of the most well-known security risks in wireless mobile ad hoc networks is the Black Hole Attack. Because the route-finding procedure is required and unavoidable, the intruders take advantage of the loophole to carry out their nefarious activities. Many researchers have experimented with various detection approaches in order to suggest various detection strategies. In this study, we review current solutions and explore cutting-edge routing techniques. We categorize these approaches into single and collaborative Black Hole Attacks, we also assess the categories of these solutions. In anticipation, we anticipate providing additional researchers with comprehensive work.
An Optimised Deep Learning Approach of Lane Detection in Complex Indian Environment

Lane detection systems are useful to avoid road accidents that are due to wrong-lane driving. Such systems have the target to detect the lanes and warn the driver in case the vehicle enters the wrong lane. In this paper, we've proposed a lane detection technique that increases the accuracy and stability of the vehicle while tracing the position of the lane in a complex driving environment. Based on the existing methods like Canny edge detection and the Hough transform method, the proposed technique has an excellent detection ability and can detect key lanes in complex road environments such as darkness, traffic jams, and uneven lighting.
Automated segmentation of glands to facilitate quantitative analysis in CD138 whole slide images using a KNet deep learning framework

Segmentation of glands in immunohistochemical (IHC) images is the top priority for automated evaluation of CD138 positive cells, which could exclude irrelevant regions and improve accuracy and effectiveness. In this paper we propose a novel patch-based pipeline with an integrated KNet-like deep learning framework to perform automated gland segmentation for CD138 whole slide images. The patch-based pipeline is composed of patch decomposition, tissue patch extraction, gland segmentation, patch merging, and gland mask padding. The KNet deep learning framework introduces SwinTransformer to extract multiscale patch features, and integrate Uper Net as basic semantic kernels into KNet framework to refine the gland segmentation results. The integrated framework is trained in an end-to-end way with a weighted cross-entropy loss and dice loss. The experimental results show that our proposed framework achieves state-of-the-art performance, and could produce accurate gland masks for real whole slide images, which lays a solid foundation for automated quantitative analysis of CD138 images.
Named Entity Annotation Corpus for Commercial Opportunity Mining

With the rising number of enterprises in China, enterprise data present explosive growth. Mining valuable commercial opportunity information from massive enterprise data is called commercial opportunity mining. Commercial opportunity information has rich investment reference value, it can help enterprises and investors to make better decisions, so it has important research significance. In order to solve the problem of the lack of Chinese named entity annotation corpus suitable for commercial opportunity mining research, this paper constructs the Commercial Opportunity Information named entity annotation Corpus (COIC). Based on the demand of commercial opportunity mining, we collect six types of commercial information. By deeply analyzing the characteristics of commercial texts, we define nine categories of commercial entities, and formulate the entity annotation specification. We use entity annotation platform to conduct pre-annotation, manual annotation, and manual proofreading of entities in commercial texts with more than 720,000 characters. The COIC we constructed contains 36,787 entities, and the annotation consistency reaches 0.8613. The current mainstream algorithms in the field of named entity recognition are selected for preliminary experiments, and various entities in the corpus are evaluated, which lays the data foundation for the follow-up research on commercial opportunity mining.
Pulsed X-Ray Detector Based on an Unintentionally-Doped High Resistivity ε-Ga₂O₃ Film

Gallium Oxide (Ga2O3), an emerging ultra-wide band gap semiconductor, has drawn great attention for application in radiation detection. In this letter, ultrafast X-ray detectors have been fabricated using a high resistivity unintentionally-doped (UID) <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-Ga2O3 film grown on sapphire by metal-organic chemical vapor deposition (MOCVD). The detector featuring a lateral metal–semiconductor–metal (MSM) structure exhibited a low dark current < 2 nA at 100 V and its sensitivity was as high as 28.6 nC/Gy or ~<inline-formula> <tex-math notation="LaTeX">$1.0\,\,\times 10^{6}$ </tex-math></inline-formula> nC/(<inline-formula> <tex-math notation="LaTeX">$\text {Gy}\cdot \text {cm}^{3}$ </tex-math></inline-formula>) at 40 V and an X-ray dose rate of 0.383 Gy/s. A stable and repeatable transient response was observed for the detectors under switching X-ray illumination. Furthermore, the detector achieved a pulsed X-ray detection with 50 ns in full width and its time resolution was revealed to be ~7.1 ns. These results imply the great potential of the MOCVD-grown high-resistivity UID <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-Ga2O3 film for ultrafast X-ray detection.
Angular Domain-Based Importance Sampling Estimator for Linear Block Codes over the AWGN Channel with M-PSK Modulation

In this paper, the problem of efficient performance evaluation of linear block codes over the AWGN channel with M-PSK modulation is considered. Based on the geometric structure of the channel, we define the tangent of the half-angle of the circular cone, whose central line passes through the transmitted signal vector and the origin, as a random variable and refer to its sample space as the angular domain. We propose a minimum-variance importance sampling (IS) estimator by deriving the optimal IS distribution in the angular domain. Besides, we derive the asymptotic IS gain of the proposed estimator compared to the Monte Carlo method as SNR tends to infinity. The effectiveness of the proposed estimator and the accuracy of the asymptotic IS gain are verified through simulation.
Effects of Nitrate and Pathogenic Nanoparticles on Reproductive Losses, Congenital Hypothyroidism and Musculoskeletal Abnormalities in Mares and Other Livestock: New Hypotheses

Spontaneous abortions, congenital hypothyroidism and musculoskeletal abnormalities were attributed to high nitrate in the diet of pregnant mares on Thoroughbred farms in central Kentucky. These fetal losses, with an unknown etiology, and associated with the Mare Reproductive Loss Syndrome (MRLS) have plagued horse farms in central Kentucky for decades. Fetal losses occur in mares grazing spring pastures affected by climatic and environmental factors including droughts, cold-stress, nitrogenous fertilizers, and herbicides. These factors may cause nitrate to accumulate in pasture forages. On a Thoroughbred horse farm, mares affected with the MRLS, pregnant Boer goats grazing high nitrate pastures also were affected with fetal losses. When spring pastures were not fertilized with nitrogen, herbicides not applied, protein reduced in the ration and the diet increased in sodium, fetal losses did not occur and foals were normal at birth. Excessive nitrate, ammonia and sulfate in the diet were associated with the formation of toxic and pathogenic abiotic nanoparticles in the amnionic fluid and pathognomonic placental lesions consistent with the MRLS. Pathogenic nanoparticles were found in aborted fetuses of other livestock. The discovery of these toxic pathogenic abiotic micro and nanoparticles in developing fetuses is unique. This novel mechanism of action for the pathogenesis of fetal losses may be a predisposing factor for a host of opportunistic diseases in livestock. The pathogenic nanoparticles collect in the vessels of the placenta and other organs to form niduses that predispose fetuses to a host of opportunistic microorganisms.
An Extensible Covert Communication Scheme Over the AWGN Channel With Feedback

We consider a problem of communication over a physically-degraded additive white Gaussian noise wiretap channel. A covertness constraint is imposed, which says that the output at the wiretapper must statistically resemble pure noise. Previous works have shown that the total amount of information that can be transmitted over n uses of the channel is proportional to n, but the communication schemes in those works are designed to transmit only one message of predetermined length. We propose a feedback-aided scheme that, at the cost of less-than-optimal message length, is easily extensible to transmitting multiple messages and an unbounded amount of information.
The reliability challenge of SiC Power Modules in Automotive Applications

Silicon carbide (SiC)- based power modules in automotive applications are becoming more and more important in the framework of battery and hybrid vehicles. Consequently, the reliability concerns related to these products must be carefully assessed, considering the harsh environment of automotive applications. The aim of this work is to give some insights into the reliability assessments during the design stage of SiC modules devoted to traction applications, considering different aspects such as power cycle, thermal characterization, and solder joint reliability.
High-Precision Fixated Hovering and Pinpoint Landing of Quadrotors by Visual Navigation

One of the key requirements in many UAV applications is the capability of precise positioning and pinpoint landing. Precise positioning is essential for maintaining task-advantageous positions of UAV relative to a ground object with minimum deviation, and is crucial for task actions, such as geo-referencing, image capturing, and payload delivery. Pinpoint landing helps assure successful task conclusion and machine safety. In this paper, we present a technical approach to achieve precise positioning, fixated hovering, and pinpoint landing by autonomous visual navigation. Different visual processing methods, such as fiducial detection, object tracking, and visual odometry, are combined and integrated with navigation control. A visual-based autonomous control system has been developed and field tested with successful performance. The system is capable of achieving high-precision positioning with visual navigation in the process of changing altitude. The average lateral deviation at 100-meter altitude by a drone product with better flight stability is only 0.25 meter.
Adaptive Motion Control of a Terrain-Adaptive Self-Balancing Leg-Wheeled Mobile Robot over Rough Terrain

This paper proposes an adaptive motion control approach that incorporates the backstepping sliding-mode motion control, side tilting control and impedance control method to steer a terrain-adaptive self-balancing leg-wheeled mobile robot over rough and uneven terrain. The proposed robot improves a hybrid design of bipedal walking and wheel-driven modes by adding a new locomotion mechanism. Different from ordinary two-wheeled self-balancing robots, the improved mobile robot has two extra knee joints to keep the body balancing when encountering rough terrain. Because of knee joints, the robot's center of gravity also changes when the robot's posture changes, so motion control of the robot is decomposed into three parts: self-balancing for front tilting, trajectory tracking control, side tilting, and center of gravity (COG), and knees joint's impedance control. In particular, two nonlinear backstepping sliding-mode controllers are proposed to achieve self-balancing, speed tracking and orientation control for the robot moving over flat terrain. The robot's mechatronic design uses a low-level controller with an OpenCR control board and Dynamixel motors, where the OpenCR control board is programmed via Arduino. Simulations and experimental results of the mobile platform traveling around its workspace demonstrate the feasibility and effectiveness of the proposed control strategy.
A Compliance Control Strategy for Stand-to-squat Locomotion with Lower-Limb Exoskeletons

This paper proposes an admittance control algorithm for lower-limb exoskeletons to assist healthy wearers in energy-efficient stand-to-squat locomotion. A two-degree-of-freedom (DOF) model dedicated to the stand-to-squat locomotion is derived, which takes into account the wearer's torso inclination angle and the vertical displacement of the hip joint. The torso inclination angle is estimated from an inertial measurement unit (IMU) located at the backpack of the exoskeleton through a second-order complementary filter. Then a model-based disturbance observer (DOB) is used to estimate the wearer's joint torques as an indication of the motion intention. The exoskeleton complies with the intention by following the desired joint angular velocity which is generated from the estimated joint torque through an admittance function. Experiments with different admittance parameters are conducted, and the energy consumption is evaluated in terms of the normalized energy consumption index (NECI) proposed in previous works. The results show that the proposed method allows the wearer to conduct the stand-to-squat locomotion freely with tunable energy consumption by setting the admittance parameters appropriately.
Feature Selection for Lung and Breast Cancer Disease Prediction Using Machine Learning Techniques

Early detection of cancer is essential for a favorable prognosis because it is the biggest cause of death globally. After lung cancer, breast cancer ranks as the second most prevalent cause of death. With the fast expansion of the populace, the risk of mortality from lung and breast cancer is increasing rapidly. Early cancer prediction is challenging because there are few signs of this disease at an early stage. An automated sickness identification system provide accurate, efficient and quick response while assisting medical workers in identifying disorders and decreases death rates. In this research, we proposed PSO-FS (particle swarm optimization-based feature selection) method to select the features for several machine learning techniques to categorize accessible lung and breast cancer data. The best classifier approach for predicting both cancer diseases is considered to be the forest (RF) and deep learning (DL) classifier, which has high accuracy of 99.7% and 97%, respectively. Hence feature selection approach can increase performance by selecting only significant features.