Miracle-dz/UniFAR-FTUs huggingface dataset:
Possi6le Role of Am6rosia psilostachya on Pattern and Succession in Old-Fields'

Ambrosia psilostachya (western ragweed) is a charac- teristic species found in the first stage of old-field succession and it persists through the later stages. Previous workers found that A. psilostachya was inhibitory to nitrogen-fixing algae and bacteria as well as to nitrifying bacteria. Field studies indicated a different pattern of vegetation around the A. psilostachya, and initial experiments suggested that the patterns were due neither to mineral nor physical properties of the soil nor to competition. The root exudate, leaf leachate and decaying leaves of A. psilostachya inhibited many of the early invaders of aban- doned fields. Andropogon ternarius, a bunch grass, was inhibited by de- caying material and root exudate of A. psilostachya. T'ridens flavus and Leptoloma cognatum, two species found more commonly near A. psilo- stachya in the field, were stimulated by leaf leachate and field soil collect- ed near A. psilostachya. Tridens flavus was also stimulated by root exudate of A. psilostachya. The differential patterns created by the A. psilostachya were probably due to the interaction of organic compounds released by leaf leachate, root exudate and decaying materials. The possibility of volatile materials from A. psilostachya affecting growth of associated plants was suggested. Two phytotoxins were isolated from overwintered A. psilostachya leaves but attempts to identify them were not suc- cessful.
Self-Repairing Hybrid Adder With Hot-Standby Topology Using Fault-Localization

Effective self-repairing can be achieved if the fault along with its exact location can be determined. In this paper, a self-repairing hybrid adder is proposed with fault localization. It uses the advantages of ripple carry adder and carry-select adder to reduce the delay and area overhead. The proposed adder reduces the transistor count by 115% to 76.76% as compared to the existing self-checking carry-select adders. Moreover, the proposed design can detect and localize multiple faults. The fault-recovery is achieved by using the hot-standby approach in which the faulty module is replaced by a functioning module at run-time. In case of 3 consecutive faults, the probability of fault recovery has been found to be 96.1% for a 64-bit adder with 8 blocks, where each block has 9 full adders.
Do Many Models Make Light Work? Evaluating Ensemble Solutions for Improved Rumor Detection

There have been many efforts to detect rumors using various machine learning (ML) models, but there is still a lack of understanding of their performance against different rumor topics and available features, resulting in a significant performance degrade against completely new and unseen (unknown) rumors. To address this issue, we investigate the relationship between ML models, features, and rumor topics to select the best rumor detection model under specific conditions using 13 different ML models. Our experiment results demonstrate that there is no clear winner among the ML models in all different rumor topics with respect to the detection performance. To overcome this problem, a possible way is to use an ensemble of ML models. Although previous work presents an improved detection of rumors using ensemble solutions (ES), their evaluation did not consider detecting unknown rumors. Further, they did not present nor evaluate the configuration of the ES to ensure that it indeed performs better than using a single ML model. Based on these observations, we propose to evaluate the use of an ES by examining their unknown rumor detection performance compared with single ML models but as well as different configurations of the ESes. Our experimental results using real-world datasets found that an ES of Random Forest, XGBoost and Multilayer perceptron overall produced the best F1 score of 0.79 for detecting unknown rumors, a significant improvement compared with a single best ML model which only achieved a 0.58 F1 score. We also showed that not all ESes are the same, with significantly degraded detection and large variations in performance when different ML models are used to construct the ES. Hence, it is infeasible to rely on any single ML model-based rumor detector. Finally, our solution also performed better than other recent detectors, such as eventAI and NileTMRG that performed similar to using a single ML model – making it a much more attractive solution to detect unknown rumors in practice.
Decoding Brain Dynamics in Speech Perception Based on EEG Microstates Decomposed by Multivariate Gaussian Hidden Markov Model

This study aims to reveal dynamic brain networks during speech perception. All male subjects were presented five English vowel [a], [e], [i], [o], and [u] stimuli. Brain dynamics were decoded using multivariate Gaussian hidden Markov model (MGHMM), which trained on spatiotemporal patterns of broadband multivariate event-related potential amplitudes to identify distinct broadband EEG microstates (MS), microstate source imaging, and microstate functional connectivity (<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>FC). Obtained results showed fluctuated cortical generators and <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>FC in eight microstates throughout the perception. Microstate source imaging revealed involvements of bilateral (left-side dominance) posterior superior temporal cortex (TC), inferior frontal gyrus (IFG), and supramarginal regions in perception. Precentral cortex where primary motor cortex located was also significantly activated. These regions were early appeared at 96–151 ms (left-side dominance) and at 186–246 ms (left hemisphere only) after the stimuli onset. Results from <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>FC revealed significant increases in delta (2.5-4.5 Hz), theta (4.5-8.5 Hz), alpha (12.5-14.5 Hz), beta (22.5-24.5 Hz), low gamma (30.5-32.5, 38.5-40.5 Hz) but decreases in high gamma (42.5-46.5 Hz) bands in perception. Increased FC were observed mainly at; (1) microstate segments 34–95 ms (MS2) and 96–151 ms (MS3) in early stages, (2) microstate intervals 186–246 ms (MS5) and 297–449 ms (MS6) in subsequent stages of perception. We found that stronger statistical FC differences in perception at TCs, with respect to left IFG (Broca’ area), left TC, and precentral areas. Furthermore, by conducting a comparative protocol measuring FC distinction degree, we showed performance improvements of 8.01% (<inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-value = 0.0162), 14.41% (<inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-value = 0.006) when compared MGHMM to well established Lehmann-based modified K-means, Atomize and Agglomerative Hierarchical Clustering and 8.791% (<inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-value = 0.0097) over the combination of K-means and sliding window methods, respectively. This study indicates the usefulness of EEG microstates to investigate broadband brain dynamics in speech perception. The current findings based on male subjects would be generalized more by future studies with a larger appropriate sample size including female subjects.
Comparison of Inline Crack Detection Systems for Multicrystalline Silicon Solar Cells

Cracks in silicon solar cells reduce cell efficiency and may lead to critical failures in cell and module production because of breakage. Detecting cracks in an early production stage is, therefore, advisable. Several approaches for crack detection systems have been developed in the past years. In order to compare the system performance, the rating done by the system has to be compared with a reference, i.e., a ground truth defining the crack's presence, position, and/or area. This information can also be used for training of detection algorithms based on supervised learning, e.g., convolutional neural networks. In the present article, we show that finding the ground truth is ambiguous and depends strongly on the operator and the applied metrology. For this end, we apply six crack detection tools to a set of 120 multicrystalline silicon passivated emitter and rear cells that were sorted out in industrial production because of cracks, and compare the human rating of six operators for every tool. The operators’ precision, i.e., the fraction of the contrasts detected as cracks that actually are cracks, ranges from 73% to 100% and their recall, i.e., the fraction of all actual cracks that have been found, from 30% to 92% for all tools and operators. To achieve the highest precision and recall, we suggest rating the images of an optical near-infrared system combined with luminescence imaging by three operators conjointly for the definition of ground truth for the present as well as for larger sample sets.
Optimum Resource Allocation for Full-Duplex Vehicular Communication Networks

In this article, we propose a resource allocation (RA) scheme for vehicular communication networks (VCNs). The proposed scheme exploits the spectral efficiency of full-duplex (FD) communications to fulfill the reliability constraints of vehicle-to-vehicle (V2V) links and the high capacity requirements of vehicle-to-infrastructure (V2I) links. Also, it is capable of coping with the fast variations of the channels due to the high mobility. Based on the links requirements, the RA problem is formulated as a non-convex problem, which is solved in two steps. First, the optimal power allocation (PA) is obtained by solving a system of linear equations. Second, the channel assignment (CA), which turns out to be a maximum weight bipartite matching problem, is solved using the Hungarian method. Also, a heuristic hybrid scheme, which combines the proposed FD scheme and the half-duplex (HD) scheme that optimally finds the RA, is proposed. Compared to the optimal HD-based scheme, simulation results show that the proposed FD scheme always offers higher sum of the V2I links’ capacities except for the case in which V2V links require low transmission rates, while the hybrid scheme ensures higher performance for all potential cases.
Multi-format speech perception hashing algorithm based on frequency band variance

Aiming at the problems of single speech format, no universal algorithm, low efficiency, low accuracy of tamper detection and location in small-scale of existing speech content authentication algorithms, a multi-format speech perception hashing algorithm based on frequency band variance is proposed. Firstly, the speech signal is preprocessed, framed and windowed, and the frequency band variance of each frame is calculated. Then, the hash sequence is constructed by difference hashing. Finally, the overvall security of the algorithm is improved by using logistic chaotic map to scramble and encrypt the hash of equal length. The experimental results show that the proposed algorithm not only has good efficiency, key dependence, robustness and discrimination, but also has a good compromise between robustness and discrimination.
Biopotency of Methionine Sources for Young Turkeys

Abstract L-Methionine (100%) and methionine hydroxy analogue-free aeid (88%) were evaluated for biopotency compared to DL-methionine (99%) in a starter diet for Large White turkeys during 7 to 28 days of age. The basal corn-soybean meal diet contained 28% protein, . 87% total sulfur amino acids, and 2974 kcal metabolizable energy/kg by calculation and . 865% sulfur amino acids by analysis. In Experiments 1 (males) and 2 (females), levels of methionine supplementation were 0, . 04, . 10, . 16, . 28, . 44, and 1.00%. In Experiment 3 (male and female poults), supplemental methionine levels were 0, . 12, . 22, . 32, and . 44%. Level of methionine significantly affected growth and feed efficiency in all experiments. A nonlinear regression model (exponential) was used to describe the body weight response to supplemental methionine (0 to .44%) for each source and to obtain biopotency estimates relative to DL-methionine (at 100). Based on the three studies the biopotency (± SE) of L-methionine was significantly superior to DL-methionine (131 ± 10%); the biopotency of the analogue was not significantly different from DL-methionine (96 ± 7%). Feed efficiency was similar for all sources in Experiments 1 and 3. In Experiment 2, differences in feed/gain were detected among the three sources, where the mean values were in the order: analogue>DL-methionine>L-methionine.
Effectiveness of Microbial and Chemical Insecticides for Controlling Cabbage Looper (Lepidoptera: Noctuidae) and Imported Cabbageworm (Lepidoptera: Pieridae) on Collards in Maryland

Field experiments were conducted from 1981 through 1984 to determine the effectiveness of several microbial agents for control of the cabbage looper, Trichoplusia ni (Hubner), and imported cabbageworm, Pieris rapae (L.), on collards. These agents included several nuclear polyhedrosis viruses (NPV), a microsporidian, a granulosis virus (GV) of T. ni , a GV of the cabbage butterfly, Pieris brassicae (L.), and commercial preparations and environmental isolates of Bacillus thuringiensis Berliner. Methomyl and fenvalerate were included as standards. Some NPV formulations and B. thuringiensis provided as good control of T. ni as methomyl. Reduced rates of B. thuringiensis and NPV when combined provided better control than normal rates of either. One of the noncommercial B. thuringiensis formulations provided better control than the B. thuringiensis commercial preparations. The P. brassicae GV was as effective as B. thuringiensis for control of P. rapae larvae. Fenvalerate was most effective of all treatments tested.
Apple Bruise Grading Using Piecewise Nonlinear Curve Fitting for Hyperspectral Imaging Data

Apple fruits can be easily damaged, and bruises occur on peels during harvest, transportation and storage, which could decrease fruit quality. This paper proposed an apple bruise grading method based on hyperspectral imaging (HSI). The spectral information of sound apples (Yantai Fuji 8) was first captured using a hyperspectral reflectance imaging device (386-1016 nm). These apples were then mechanically damaged by the same impact forces, and the bruised regions were exposed to room temperature for at most 120 min. The spectral data of the bruised apples at four different exposure times (30 min, 60 min, 90 min and 120 min) were obtained. The spectral data were preprocessed using Procrustes analysis (PA) to enable a more diverse distribution of the spectra among different patterns. To both accurately maintain the spectral information of different patterns and reduce the dimensions of the spectra, piecewise nonlinear curve fitting (PWCF) was presented using the least squares algorithm, where the resultant fitting coefficients from different spectral intervals were catenated into a low-dimension feature descriptor. The feature descriptors were then fed to an error-correction output coding-based support vector machine (ECOC-SVM) to grade the bruised apples. To further evaluate the performance of the presented PWCF, conventional algorithms, including the successive projections algorithm (SPA), genetic algorithm (GA), principal component analysis (PCA) and kernel principal component analysis (KPCA), were introduced for comparison. Experimental results showed that the proposed method obtained the best grading accuracy (97.33%) compared to the other methods.
Simultaneous Position and Orientation Estimation for Visible Light Systems With Multiple LEDs and Multiple PDs

Visible light communication (VLC) is seen as a supplement for fifth-generation (5G) wireless communication in short-range high data rate communication applications [1]. A reliable VLC system relies on an accurate estimate of the position and orientation of the receiver, which corresponds to the six-dimensional positioning problem mentioned in [2]. In this paper, we investigate the simultaneous position and orientation estimation (SPO) problem using received signal strength (RSS), for a visible light system containing multiple LEDs and multiple photodiodes (PDs) (MLMP). Although in general, the position and orientation of the receiver can be represented by a vector and a rotation matrix, respectively, the constraints imposed by the rotation matrix make the numerical optimization in the estimation process cumbersome, e.g, the commonly used constrained optimization method is often very complex and non-robust. Therefore, in this paper, we design two SPO algorithms using the principle of optimization on manifolds, which alleviates the constraints from the rotation matrix. In addition, we propose an initialization algorithm, based on the direct linear transformation (DLT) principle, to obtain an initial estimate in closed-form for the iterative algorithms. To evaluate the performance of the proposed RSS-based SPO algorithms, we derive the Cramer-Rao bound (CRB). In particular, the orientation error component of the CRB corresponds to the intrinsic CRB or the CRB on manifolds, which measures the error in the estimated rotation matrix in a physically meaningful way. Finally, computer simulations show an asymptotic tightness between the performance of the proposed algorithms and the theoretical lower bound, demonstrating the effectiveness of the proposed solutions.
Padded Coprime Arrays for Improved DOA Estimation: Exploiting Hole Representation and Filling Strategies

As a generalized coprime array structure, the coprime array with displaced subarrays (CADiS) allows a large minimum inter-element spacing by introducing a specific displacement between two sparse subarrays. While this structure can effectively reduce mutual coupling, the holes in its difference co-array greatly decrease the achievable number of uniform degrees of freedom (DOFs). In this paper, we first provide a complete characterization for the hole locations in the difference co-array generated by a tailored CADiS (tCADiS) as the union of four subsets of locations related via simple symmetry properties. We then introduce two representation approaches for the hole locations, revealing that the latter can be generated from the differences between sensor locations in the subarray of tCADiS and a small uniform linear array, referred to as a padded subarray. Subsequently, we propose novel padded coprime arrays (PCAs) by incorporating the padded subarray into tCADiS to enlarge the consecutive segments in the difference co-array. This not only contributes to increase the number of available uniform DOFs, but also helps mitigating the mutual coupling by limiting the number of sensor pairs with small separations. Finally, numerical simulation results are provided to demonstrate the superiority of PCAs over existing sparse array configurations in terms of DOF, mutual coupling and DOA estimation accuracy.
Predicting Students Academic Performance using an Improved Random Forest Classifier

Increasing demand in the education sector has paved the way to many research works which highly focuses on the student's academic performance and their behaviour analysis. Machine learning algorithms are applied in Education Data mining to find meaningful patterns and insights from the Educational datasets. Mostly, it is used to foretell the performance of students; to classify the features that impact the performance of students and also to cluster the students based on their performance. Introducing an interim mechanism to analyse and predict the student performance at the early stage help the institution and the students to take immediate action and improve the performance. In this research work, we have discussed the two most important factors – Factors that impact the scholastic achievement of students and aid in predicting the students at risk. This study also proposes a technique named improved random forest classifier. This technique aims to produce a higher accuracy rate in classification and prediction in comparison with the other algorithms such as Naive Bayes, Bagging, Boosting and Random Forest.
Effects of CO2 Enrichment and Supporting Material on Growth, Photosynthesis and Water Potential of Eucalyptus Shoots/Plantlets Cultured Photoautotrophically In Vitro

CO2施用の有無および支持材の種類 (寒天, ゲルライト, プラスチックネットおよびバーミキュライト) の異なる条件で6週間ユーカリを光独立栄養培養した. 支持材の種類にかかわらず, CO2施用によりシュート/小植物体の生長 (シュートおよび根の乾物重, 1次根長および発根率) および純光合成は促進された. CO2施用の有無にかかわらず, 支持材をバーミキュライトとした条件で生長は最大となり, 以下, プラスチックネット, ゲルライトおよび寒天の順に小となった. 培養開始2週間後における1次根数は葉の水ポテンシャルが高いほど多かった. 1次根長および発根率はin vitroにおける純光合成速度の増大とともに増大した.
New insights into the morphology and phylogeny of Heterosigma akashiwo (Raphidophyceae), with the description of Heterosigma minor sp. nov.

Abstract: Heterosigma akashiwo is one of the smallest raphidophytes and is known for its ability to form dense, fish-killing blooms. In many parts of the world, H. akashiwo is a permanent resident of the algal community, and blooms have caused extensive financial losses for the aquaculture industry. Different aspects of its biology have been the focus of numerous investigations over the last two decades but, perhaps because of its small size and fragile nature, few morphological studies have been carried out. We conducted phylogenetic and morphological studies of 24 strains of Heterosigma from the coastal waters of Europe, North America, Australia and New Zealand. Complete 18S, ITS region and the D1–D2 region of 28S rDNA were sequenced and compared with all available raphidophyte sequences, revealing a novel species of Heterosigma, Heterosigma minor sp. nov. All strains of H. akashiwo were genetically very similar across the rDNA region (> 99.8% similarity) and morphologically very similar in light microscopy and scanning electron microscopy. Heterosigma minor shared only 98.2% similarity with H. akashiwo, and there were several morphological differences: it was smaller, rounder and had fewer mucocysts than H. akashiwo. The secondary structure of ITS2 was reconstructed, revealing three compensatory base changes between the two species, further indicating that H. minor is a sister to H. akashiwo. The phylogeny of Raphidophyceae was reconstructed to include all currently described raphidophytes.
Investigation of dVDS/dt Controllability on Rg in SWITCH-MOS to Achieve Superior Turn-on Characteristics with Low dVDS/dt

In this study, we investigated dV<inf>DS</inf>/dt controllability on gate resistance (R<inf>g</inf>) in a 1.2 kV SWITCH-MOS. In the case of large R<inf>g</inf>, the SWITCH-MOS showed roughly the same dV<inf>DS</inf>/dt as a conventional SiC trench MOSFET. However, in the case of small R<inf>g</inf>, the SWITCH-MOS achieved higher dV<inf>DS</inf>/dt, suggesting that the SWITCH-MOS could improve the dV<inf>DS</inf>/dt controllability on R<inf>g</inf>. In the SWITCH-MOS, negative dI<inf>D</inf>/dt occurs in the initial phase of the drain-source voltage fall, owing to the fast reverse recovery. Therefore, the SWITCH-MOS has a large gate charging current in the initial phase, resulting in higher dV<inf>DS</inf>/dt with small R<inf>g</inf>. In the case of large $R_{g}$, this negative dI<inf>D</inf>/dt is so small that the SWITCH-MOS shows low dV<inf>DS</inf>/dt. Hence, the SWITCH-MOS demonstrates excellent dV<inf>DS</inf>/dt controllability on R<inf>g</inf>.