Miracle-dz/UniFAR-FTUs huggingface dataset:
High-speed Core Loss Base Data Collection for Core Loss Calculation Under Power Electronics Converter Excitation

The calculation of core loss of power electronics converters requires a large number of core loss data, including the excitation frequency, ﬂux density ripple, and DC magnetic ﬁeld bias characteristic under rectangular voltage excitation. However, in the case of the conventional method (that employs a B-H analyzer with a chopper circuit), a very long time is required to collect this core loss base data, because circuit parameter tuning is needed for each measurement point. Therefore, we propose a high-speed core loss base data collection method for core loss calculation under power electronics converter excitation. The core loss base data collection time was substantially reduced upon usage of a pulse width modulated (PWM) inverter and a neural network because various core loss characteristics were expressed upon excitation of the PWM inverter, and these core losses were learned by the neural network. The core loss base data, as measured via the proposed and conventional methods, were compared to validate the measurement accuracy of the proposed method, and the di ﬀ erence was found to be lesser than 12%. Thus, the proposed method achieves high-speed core loss base data collection.
DLMP Calculation and Congestion Minimization With EV Aggregator Loading in a Distribution Network Using Bilevel Program

This article develops a distributional locational marginal pricing (DLMP) based electric vehicle (EV) aggregator scheduling framework minimizing the congestion in the network. The article has two parts: 1) calculation of DLMPs by solving actual DistFlow equations; and 2) EV aggregator scheduling in a distribution network formulated as a bilevel problem considering EV aggregator as a leader and distribution system operator (DSO) as a follower. The upper level problem is the cost minimization of EV aggregator, whereas the lower level problem is the social welfare maximization of DSO while satisfying the network constraints. The nonlinear bilevel problem is reformulated into the single-level problem using Karush–Kuhn–Tucker conditions and duality theorem. The framework considers the uncertainty of the EV aggregator by using robust programming and tested with the 15-bus radial distribution network for congested and uncongested cases.
Malassezia Species Associated With Dermatitis in Dogs and Their Antifungal Susceptibility

The yeast form was named as Pityrosporum and the mycelial form as Malassezia. However, in 1986 they were recognized to be two forms of the same organism and were thus collectively named Malassezia (Cannon, 1986). Malassezia considered as a normal cutaneous inhabitant of skin in humans, animals and birds. These lipophilic fungi colonize in the stratum corneum layer of skin, which is rich in lipids. These Dimorphic lipophilic fungi sometimes act as opportunistic pathogens and cause the dermatological infections such as seborrhoeic dermatitis and otitis in domestic animals. International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 7 Number 06 (2018) Journal homepage: http://www.ijcmas. com
Effect of Bellamya purificata on organic matter degradation in surface sediment as revealed by amino acids

The accumulation of organic matter (OM) in the sediment of aquaculture ponds is a potential threat for aquaculture ecosystems and the surrounding environment. The snail Bellamya purificata is a potential bioremediation species which may solve this problem. To investigate the effects of B. purificata on OM degradation in surface sediment, an 84 d experiment was carried out. The experimental setup entailed 6 glass tanks which were divided into B. purificata treatment (BPT) and control (CON). At the end of the experiment, a significantly lower degradation index (DI), reactivity index (RI), and carbon-normalized yield of amino acids were observed in BPT compared to CON, with mean ± SD values of −0.47 ± 0.43, 1.24 ± 0.01, and 6.24 ± 0.44, respectively. BPT showed higher oxidation−reduction potential and bacterial 16S rRNA gene copies than CON. Total organic carbon, total nitrogen, and total hydrolysable amino acid concentrations in the BPT treatment were 1.83 ± 0.10%, 0.07 ± 0.01%, and 22.38 ± 0.53 μmol g−1, respectively, all of which were significantly lower than in CON. A clustered heat-map of different indexes related to OM degradation in sediments showed the final BPT as one separate category, which was different from the initial samples and the final CON. Overall, B. purificata could effectively facilitate OM degradation by promoting oxidation−reduction potential and bacterial populations, and ultimately by inhibiting the OM accumulation in sediment. Our results therefore provide support for the application of B. purificata to reduce the risk of endogenous pollution caused by OM accumulation in aquaculture ponds.
The complete mitochondrial genome of a blue-tailed skink (Plestiodon tunganus) endemic to Sichuan Basin

Abstract The complete mitochondrial genome of Plestiodon tunganus from its type locality (Pengba town, Luding County, China) was determined using polymerase chain reaction and directly sequenced with a primer walking method. The mitogenome is 17,263 bp in length and comprises the standard set of one control region, two rRNAs, 22 tRNAs plus 13 protein-coding genes (PCGs). The PCGs were used to perform Bayesian phylogenetic analyses together with other scincid lizards with mitogenome data in GenBank. The resulting phylogenetic tree showed that P. tunganus clustered in a branch close to the species from the same genus. The mitogenome of P. tunganus will provide fundamental data to explore mitochondrial genome evolution in skinks.
Thermal photons at PHENIX experiment

Direct photons are a unique probe to study the properties of the medium created in heavy ion collisions. In particular low pT direct photons are of great importance since one expects that they are predominantly of thermal origin. In A+A systems PHENIX has observed a large yield of low pT direct photon that are emitted with a significant azimuthal anisotropy with respect to the reaction plane (v2). The mechanism responsible for the large yield and large v2 is not understood yet. Following recent evidence for collective behavior of charged particle production from small systems like p+A, d+Au, and 3He+Au, PHENIX has made systematic measurements of direct pho- tons with different collision energies and system configurations. It has been found that the low pT direct photon yield dNγ/dη is proportional to (dNch/dη)α. This behavior holds for beam energies measured both at RHIC and at the LHC in large-on-large systems, while data from small systems suggest an onset of QGP formation at low dNch/dα. In this talk, I will report recent measurements of thermal photon and related observables.
Information-Centric Semantic Web of Things

In the Semantic Web of Things (SWoT) paradigm, a plethora of micro-devices permeates an environment. Storage and information processing are decentralized: each component conveys and even processes a (very) small amount of annotated metadata. In this perspective, the node-centric Internet networking model is inadequate. This paper presents a framework for resource discovery in semantic-enhanced pervasive environments leveraging an information-centric networking approach. Information gathered through different Internet of Things (IoT) technologies can be exploited by both ubiquitous and Web-based semantic-aware applications through a uniform set of operations. Experimental results and a case study support sustainability and effectiveness of the proposal.
Antioxidant and Antibacterial Effects of Some Medicinal Plants of Iran

Medicinal plants used in the treatment of diseases earlier times are potential sources of new drugs. The present study was undertaken to study the chemical composition, antioxidant and antibacterial activity of certain medicinal plants of Iran by gas chromatography and mass spectrometry (GC/MS) , DPPH and disk diffusion method. According to the results of GC/MS, there are 46 kinds of chemical compounds including mucilage, fatty acids, flavonoid and diterpenes in flower of Echium khuzistanicum . There are aldehydes (7.9%), phenols (7.5%), fatty acids (5.8%) and furfural (5.4%) in the methanol extract of Echinops cephalotes . Furfural, steroids, vitamin B and flavonoids are the main compounds of Marrubium anisodan . Results of the antibacterial test showed that Staphylococcus aureus and Bacillus subtilis were more sensitive to methanol extract of Echium khuzistanicum root. Pseudomonas auruginosa was more sensitive to DMSO extract of Marrubium anisodan at 600 mg/ml concentration. Maximum flavonoid and phenol contents were belonging to Echinops cephalotes . Marrubium anisodan showed the best DPPH free radical scavenging activity.
Training American Bison (Bison bison) calves

Bison, like cattle, are routinely handled to maintain herd health and meet the requirements of various regulatory agencies for diseases such as tuberculosis and brucellosis. Injuries and death during handling are more frequent in bison than in cattle, which have been bred for calm temperaments. Bison often break off a horn cap, gore one another, attempt to jump out or smash through a holding pen, and even die due to excessive stress caused by handling. These bison are termed "self-culling" for if they manage to survive the experience, their owners are likely to sell them. Calm handling of bison, an excitable animal, requires attention to detail and strategies that differ greatly from traditional animal handling, including cattle handling.
A Low Walk Error Analog Front-End Circuit With Intensity Compensation for Direct ToF LiDAR

An analog front-end (AFE) circuit comprising an amplifier module, a peak detector, and a timing discriminator has been designed to facilitate the target identification for direct time-of-flight (dToF) LiDAR. The amplitude saturation error (ASE) is compensated in this article for the intensity determination, which is conducted based on the combination of the pulse width and peak detector. Together with the improved walk error compensation scheme, the proposed AFE circuit can attain the distance and intensity information simultaneously with lower cost and larger dynamic range. A specific frequency compensation method is proposed with a shunt feedback TIA, which improves the stability and mitigates the impact of the package parasitics. The measured -3-dB bandwidth, transimpedance gain, and the input-referred noise current are 281 MHz, 86 dB<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula>, and 4.68 pA/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz respectively. The proposed AFE circuit, which is fabricated in <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> CMOS technology, achieves the distance accuracy of ±30 ps and the intensity accuracy of ±4% in the dynamic range of 1:5000 without gain control scheme.
A Multi-Scale Attributes Attention Model for Transport Mode Identification

Transport mode identification (TMI), which infers the travel modes of user trajectories, is essential to facilitate an understanding of urban mobility patterns and passengers’ choice behaviors with the goal of improving urban transportation systems. To achieve higher accuracy, existing TMI methods usually rely on mobility features obtained from densely sampled GPS trajectory points (e.g. 1 second per GPS point) or data measurements of additional inertial measurement unit (IMU) sensors (e.g. accelerometer, gyroscope, rotation vector). However, these lead to high energy consumption of the users’ mobile devices. In this paper, we propose a novel deep learning framework, Multi-Scale Attributes Attention (MSAA) model, to extract discriminating trajectory features from GPS data only, without the need to increase its sampling rate. The proposed model first partitions the trajectories into different scales and extract the latent representation of local attributes at each scale. The MSAA model relies on Convolutional Neural Network (CNN) to capture the spatial correlation of different trajectory segments, and utilizes attention mechanism to select the most suitable local attributes on the different trajectory scales that can effectively characterize the various transport modes. Since the learned latent local attributes are significantly different from the global features (e.g. average/min/max travel speeds which are measurable quantities), an ensemble model based on Neural Decision Forest (NDF) is employed to fuse the heterogeneous features consisting of both measurable quantities and non-measurable elements for determining the transport mode. Experiments on real-world datasets demonstrate the competitive performance of the proposed approach compared to several state-of-the-art baselines, with average improvements in accuracy ranging from 0.76% to 6.4%. In addition, the proposed multi-scale local attributes well complement the global features. Our results show that by incorporating the local attributes, the detection performance improved by 2.3% on average compared to using only global features.
Deep Neural Network-Based Indoor Emergency Awareness Using Contextual Information From Sound, Human Activity, and Indoor Position on Mobile Device

Context-aware computing, which gathers information about the environment at a given time and adapts its behavior accordingly, can be effectively applied to a monitoring system that automatically detects and copes with emergency situations. In this paper, we propose an indoor emergency awareness alarm system using a deep neural network on a mobile device for at-risk people, such as the elderly and children. The proposed system detects emergency situations and quickly delivers the state of the person being monitored to a guardian using a mobile transmission system. To do this, three types of contextual information, including sound, human activity, and indoor location, are instantly recognized by the protected person’s mobile device. Both sound and human activity are used to improve the recognition accuracy of emergency situations provided to guardians’ mobile devices. Sound events are detected by a residual neural network, and human activities are recognized by applying the accelerometer and gyroscope signals of the mobile device to a deep spiking neural network. In addition, the indoor location where the emergency occurred is detected by the deep spiking neural network. The experimental results show the high accuracy of the indoor emergency awareness framework.
The genetics and genome-wide screening of perennialism loci in Zea diploperennis

Perennialism is common among the higher plants, yet we know little about its inheritance. To address this, six hybrids were made by reciprocally crossing perennial Zea diploperennis Iltis, Doebley & R. Guzman with three varieties/inbred lines of annual maize (Z. mays L. spp. mays). We specifically focused on the plant’s ability to regrow after flowering and senescence. All the F1 plants demonstrated senescence and regrowth for several cycles, indicating a dominant effect of the Z. diploperennis alleles. The regrowth ability was stably transmitted to progeny of the hybrids in segregation ratios that suggested the trait was controlled by two dominant, complementary loci. Genome-wide screening with genotyping-by-sequencing (GBS) identified two major regrowth loci reg1 and reg2 on chromosomes 2 and 7, respectively. GBS results were validated using a larger F2 population and PCR markers derived from the single nucleotide polymorphisms within the locus intervals. These markers will be employed to select near-isogenic lines for the two loci and to identify candidate genes in the loci in Z. diploperennis. Significance Statement Our study contributes to our general understanding of inheritance of perennialism in the higher plants. Previous genetic studies of the perennialism in Zea have yielded contradictory results. We take a reductionist approach by specifically focusing on the plant’s ability to regenerate new shoots after senescence without regard to associated traits, such as rhizome formation, tillering or environmental impacts. Using this criterion, inheritance of perennialism in Zea appears to be dominantly and qualitatively inherited. Importantly, our data indicate that there is no major barrier to transferring this trait into maize or other grass crops for perennial crop development, which enhances sustainability of grain crop production in an environmentally friendly way.
The Crystal and Molecular Structure of -Lactose

The crystal structure of β-lactose (C12H22O11) was solved by direct phasing methods with integrated photographic intensity data. The structure was refined by the anisotropic least-squares method to give a final R value of 0.068 for the observed reflections. The space group is P21 with Z=2 and with unit cell dimensions of a=10.839 (6), b=13.349 (6), c=4.954 (5) A, and β=91.31 (9)°. All the hydrogen atoms were located on different syntheses. The crystals are nearly isostructural with those of β-cellobiose except for the axial O(4) atom. The exocyclic C(5)–C(6) and C(5′)–C(6′) bonds are significantly shorter than the other C–C bonds. The lengths of the two ring C–O bonds are unequal in the galactose unit and equal in the glucose unit. The C(1)–O(1) and C(1′)–O(1′) bond lengths are shorter by 0.015 A and 0.030 A than the mean exocyclic C–O length of 1.417 A. The structure contains an intramolecular hydrogen bond and exhibits an asymmetrical twist about the bridge bond. All the oxygen atoms, except the bridge .
Edge Computing-Based Collaborative Vehicles 3D Mapping in Real Time

Cooperative vehicles are better able to detect the environment and self-localize than a single vehicle. Cooperative vehicles can quickly cover the entire environment by communicating and cooperating with each other and can also reduce localization and mapping error by merging the cooperative vehicle information from observation and navigation. In this paper, we propose a novel algorithm for an effective solution of navigation and mapping for cooperative vehicles in an unknown environment. We present an improved centralized and collaborative monocular simultaneous localization and mapping (CCM-SLAM) approach. The proposed algorithm can accurately compute the transformation matrix for cooperative vehicle maps and reduce the communication delay, data loss among vehicles and decrease the bandwidth demand. The quaternion and credibility similarity transformation (QC-Sim(3)) method we proposed is used to accurately merge the matched maps and accomplish loop closures. The sending messages at variable frequencies (SMVF) method we proposed and an improved detection and resending lost data (I-DRLD) method we proposed can improve the accuracy of pose estimation. SMVF solves the time-delay problem by sending messages to the vehicles at flexible frequencies while I-DRLD detects and resends the lost data. We also adopt Intra-frame Feature Compression (IFC) to decrease the bandwidth demand in the process of the transmitting data. The experiments demonstrate the superiority of our proposed algorithm compared with the state-of-the-art methods.
Kapur’s Entropy for Underwater Multilevel Thresholding Image Segmentation Based on Whale Optimization Algorithm

Multilevel thresholding is an effective and indispensable technology for image segmentation that has attracted extensive attention in recent years. However, the multilevel thresholding method has some disadvantages, such as a large computational complexity and low segmentation accuracy. Therefore, this paper proposes a whale optimization algorithm (WOA) based on Kapur’s entropy method to solve the image segmentation problem. The WOA can effectively balance exploration and exploitation to avoid falling into premature convergence and obtain the global optimal solution. To verify the segmentation performance of the WOA, a series of experiments on underwater images from the experimental pool of Harbin Engineering University are conducted, and the segmentation results are compared with those of the BA, the FPA, MFO, the MSA, PSO and WWO by maximizing the fitness value of Kapur’s entropy method. The fitness value, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), execution time and Wilcoxon’s rank-sum test are used to evaluate the overall performance of each algorithm. The experimental results reveal that the WOA is superior to the other comparison algorithms and has a higher segmentation accuracy, better segmentation effect and stronger robustness. In addition, the feasibility and efficiency of the WOA are verified.
Quantifying Nonrandomness in Evolving Networks

Complex systems have been successfully modeled as networks exhibiting the varying extent of randomness and nonrandomness. Network scientists contemplate randomness as one of the most desirable characteristics for real complex systems’ efficient performance. However, the current methodologies for randomness (or nonrandomness) quantification are nontrivial. In this article, we empirically showcase severe limitations associated with the state-of-the-art graph spectral-based quantification approaches. Addressing these limitations led to the proposal of a novel spectrum-based methodology that leverages configuration models as a reference network to quantify the nonrandomness in a given candidate network. Besides, we derive mathematical formulations for demonstrating the dependence of nonrandomness on three structural properties: modularity, clustering, and the highest degree node’s growth rate. We also introduce a novel graph signature (termed “cumulative spectral difference”) to visualize the nonrandomness in the network. Later, this article also discusses the relationship between the proposed nonrandomness measure and the diffusion affinity of networks. Toward the end, this article extensively discusses observations emerging from these signatures for both real-world and simulated networks.
GLICOPROTEOMA DEL INTESTINO DE Zabrotes subfasciatus ASOCIADO AL MECANISMO INSECTICIDA DE LA LECTINA PF2 DE Olneya tesota

Zabrotes subfasciatus (Boheman) is the main pest of common beans (Phaselous vulgaris). Wild legume seeds from Olneya tesota contain a lectin, PF2, that shows insecticidal activities against this insect. The binding of PF2 to midgut glycoproteins of 20-day-old larvae was evaluated using PF2 affinity chromatography. Sodium dodecyl sulfate−polyacrylamide gel electrophoresis (SDS−PAGE) analysis of the proteins retained on the gel revealed several putative glycoproteins, ranging in mass from 17 to 97 kDa. Subsequent protein digestion and analysis by liquid chromatography−tandem mass spectrometry (LC−MS/ MS) provided amino acid fragments that identified an α-tubulin, cytochrome c oxidase subunit I, an odorant receptor, and a lysozyme from available insect sequence databases. The potential of these proteins to serve as part of the mechanisms involved in the insecticidal activity of PF2 to Z. subfasciatus is discussed.