Miracle-dz/UniFAR-FTUs huggingface dataset:
Leishmania (Viannia) guyanensis Causing Localized Cutaneous Leishmaniasis in a Traveler

We report on a 66-year-old male who presented for evaluation of rapidly expanding lesions on his lower extremities. He first noticed these lesions following a trip to Costa Rica, in which he was bitten by several unidentified bugs. He was initially treated empirically with antibiotics in the outpatient setting with no improvement of his symptoms. His lesions continued to expand and spread locally which prompted further workup with a biopsy of one of his lesions. He was ultimately found to have Leishmaniasis (Viannis) guyanensis confirmed by microscopy and polymerase chain reaction. He was treated with aggressive wound care and amphotericin B due to the risk of progressing to involve his mucosa.
SEE‐MAC: Spectrum and energy efficient‐medium access control protocol for Internet of Things

In this paper, we have devised a method for spectrum‐and energy‐efficient wireless communication systems for the Internet of Things (IoT) devices that require continuous uninterrupted power for their operation. However, the potential application of the proposed scheme is vast and is relevant to any wireless communication systems deployed for the next generation connected systems that face challenges in spectrum availability and battery charging of the devices. To fulfill the demand of wireless devices, such as sensor nodes deployed in the IoT, we have proposed a spectral‐and energy‐efficient‐medium access control (SEE‐MAC) protocol with a radio‐frequency energy harvesting (EH) facility. The proposed protocol has integrated wireless power harvesting with cognitive sensor nodes to provide both the energy‐ and spectrum‐efficient wireless communication system. In the proposed SEE‐MAC, the cognitive sensor nodes take the advantage of both the idle and active licensed channels of the primary network for data transmission and EH. The proposed system identifies the active licensed channel of the network to be allotted to each cognitive sensor node for EH and idle channel recognition for data transmission. The mathematical analysis has also been performed representing allocation of primary user channels for the EH and data transmission. The closed‐form expressions obtained along with simulation results have illustrated the amount of harvested energy, throughput, and energy efficiency achieved by the cognitive sensor nodes of the network.
Improved Performance and Bias Stability of Al2O3/IZO Thin-Film Transistors with Vertical Diffusion

Several studies on amorphous oxide semiconductor thin-film transistors (TFTs) applicable to next-generation display devices have been conducted. To improve the poor switching characteristics and gate bias stability of co-sputtered aluminum–indium–zinc oxide (AIZO) TFTs, we fabricate Al2O3/indium–zinc oxide (IZO) dual-active-layer TFTs. By varying the Al2O3 target power and oxygen partial pressure in the chamber during Al2O3 back-channel deposition, we optimize the electrical characteristics and gate bias stability of the Al2O3/IZO TFTs. The Al2O3/IZO TFTs, which are fabricated under 50 W Al2O3 target power and 13% oxygen partial pressure conditions, exhibit a high electron mobility of 23.34 cm2/V·s, a low threshold voltage of 0.96 V, an improved on–off current ratio of 6.8 × 107, and a subthreshold swing of 0.61 V/dec. Moreover, by increasing the oxygen partial pressure in the chamber, the positive and negative bias stress values improve to +0.32 V and −2.08 V, respectively. X-ray photoelectron spectroscopy is performed to reveal the cause of these improvements.
Tracking Anti-Vax Social Movement Using AI-Based Social Media Monitoring

Anti-Vax social movement possesses a dire threat to governments’ aim of mass vaccination. Moreover, by propagating serious misconceptions and misinformation about COVID-19, Anti-Vaxxers directly and indirectly cause harm to the overall health and wellbeing of the society. In addition, the ongoing clashes between Anti-Vaxxers and Pro-Vaxxers have created great social conflicts in recent times. This article proposes an artificial intelligence (AI)-based solution to identify and monitor social groups, such as Anti-Vax and Pro-Vax, in ethical manner. The proposed solution uses AI-based sentiment analysis and named entity recognition (NER) to advice political scientist, social scientists, and policy makers to assess the influence and impact of social groups. The proposed solution was deployed via iOS, Android, and Windows App on a range of platforms integrating publicly available 40 857 Twitter data related to COVID-19 in 55 different languages from 15 June 2021 till 31 December 2021. Our system demonstrated that Anti-Vax and Pro-Vax social movements posted 72% and 65% more negative contents compared to average negative sentiments of global COVID-19 related posts during the monitored period. Moreover, Anti-Vax-related posts with “Hoax” keyword were found to have the highest level of social impact with 38 849 retweets and highest level of negativity (i.e., sentiment score of 0.87). We found out that Pro-Vax community engages in social conflict with Anti-Vaxxers referring them as ignorant (i.e., average sentiment score 0.91), stupid (i.e., average sentiment score 0.89), and confused (i.e., average sentiment score 0.88). Most importantly, the social conflict detection system reveals possible locations of conflict.
Experiments with Krylov subspace methods on a massively parallel computer

Summary. In this notě, we compare some Krylov subspace iterative methods on the MAS PAR, a massively parallel computer with 16K processors. In particular, we apply these methods to solve large sparse nonsymmetric linear systems arising from elliptic partial differential equations. The methods under consideration include conjugate gradient type methods, semiiterative methods, and a hybrid variant. Our numerical results show that, on the MAS PAR, one should compare iterative methods rather on the basis of total computing time than on the basis of number of iterations required to achieve a given accuracy. Our limited numerical experiments here suggest that, in terms of total computing time, semiiterative and hybrid methods are very attractive for such MASPAR implementations.
Seasonal carryover of water and effects on carbon dynamics in a dryland ecosystem

Net primary productivity in arid and semiarid regions is controlled by water availability for which rainfall has been a commonly used proxy at annual scales. However, the hydrological partitioning occurring through the water balance can also shape both seasonal and annual net ecosystem productivity. In this study, we used 10 years of water and carbon flux measurements in a mixed shrubland watershed of the Chihuahuan Desert to investigate the seasonal variability and controls on net ecosystem production. Over this period, the site exhibited a bimodal rainfall regime with an average of 211 and 67 mm for the wet (July – December) and dry (January – June) periods of the year, respectively. During the wet season, soil infiltration and channel transmission losses led to an average of 35.8 mm of water stored in the subsurface for subsequent dry periods. By contrast, dry seasons consumed 30.3
Environmental Interaction With Continuum Robots Exploiting Impact

Continuum robots offer unique potential benefits for environmental exploration, notably in using their maneuverability to navigate congested environments. However, significant challenges remain in environmental sensing using continuum structures, within which space for local sensing is often extremely limited. In this paper, we discuss the use of novel impulsive interaction using continuum robots to sense their environment. We introduce an impact model-based tapping approach with continuum robots which does not require the addition of specialized sensors, and demonstrate its utility in hardware. We contrast the impact model method to two alternative approaches to contact detection, “Finding Lost Wrenches” and the norm of the tendon length velocities. The methods are compared empirically on two different types of continuum robot hardware, a tendon actuated “Tendril” and the pneumatically actuated “OctArm”. The results identify relative strengths and weaknesses of the approaches. The statics-based “Finding Lost Wrenches” approach failed when the manipulators were in motion, and the length velocity norm method suffered in cases of reduced sensor resolution. The impact model approach is shown to include information not accessible to the other approaches.
Videolardaki Çevresel Sesleri Tanımak İçin Derin Öğrenme Tabanlı Bir Model Geliştirme

— Nowadays, decomposition of various environmental sounds for environment recognition has gained popularity. Various background sounds in videos could be classified with high success with deep learning and machine learning techniques. In this way, semantically enriched video scenes can be depicted. This work contains the process of developing a convenient deep learning neural network model for environmental sounds recognition. In training the developed model, ten main categories have been chosen from a dataset that has various data to test the model's prediction power by experiment. From the limited data available, first, spectrograms have been produced and then, these spectrograms have been enriched by the help of data augmentation techniques. In this way, attribute diversity that was gained from data has been increased. Also, with three different design approaches for training the model, source codes have been written. These codes have been created by using deep learning network model-based methods such as Convolutional Neural Networks, Long Short Term Memory, Gated Recurrent Unit. Seven different designed neural network models have been trained by experiments and achievement has been proved by tests. With the highest accuracy obtained from one of the generated models, approximately %87 of accuracy has been obtained. This work contains obtained experimental results and scientific evaluation.
Profit compensation mechanism and interest protection of minority shareholders in merger and reorganization

In the process of merger and reorganization transaction, major shareholders often make use of information asymmetry and control advantage to damage the interests of minority shareholders. In addition, in order to promote the smooth development of mergers and acquisitions, many enterprises often adopt the signing of performance commitments and profit compensation agreements to protect the interests of minority shareholders. However, even if the commitment subject undertakes profit compensation, can it ensure that the interests of minority shareholders will not be harmed? The article takes Jinyu Automobile City as an example to study whether the profit compensation mechanism in the merger and reorganization can protect the interests of minority shareholders. The research results show that the profit compensation mechanism essentially does not play a role in protecting the interests of minority shareholders. This paper has certain policy enlightenment to explore the influence mechanism of profit compensation mechanism on the interest protection of minority shareholders and improve the relevant laws and regulations on the interest protection of minority shareholders.
Magnetic fields: influence of their properties in the agricultural production process

The current efforts to achieve the Sustainable Development Goals to combat and eradicate hunger have led different specialized sectors of society to launch into the search for paths that lead to it. In this sense, the research, development, and innovation (R + D + i) approach has generated various optimized methods to obtain the greatest possible benefits, through important contributions. The objective of this document is to show the advances made in the application of magnetic fields as a tool to improve the productivity of different crops. Considering the main treatment methods, characteristics of the application, and the explanation of the possible effects caused in plant entities. An attempt has been made to use scientific articles from different databases, but with important levels of reliability, measured through the impact indices involved. As a tool to know the current situation of this issue, the review of documents published, preferably, during the last decade was focused, with few exceptions, based mainly on specialized conceptualizations.
Real-Time Wavelet-Based Grid Impedance Estimation Method

This letter presents a novel method to estimate the grid impedance based on stationary discrete wavelet packet transform (SDWPT). The proposed method uses a steady-state technique, by injecting an interharmonic current into the grid and measuring the voltage response at the point of common coupling to estimate the grid impedance. The proposed method employed a standard three-phase photovoltaic system interconnected to the grid to validate its effectiveness experimentally. Comparisons with a discrete Fourier transform- and continuous wavelet transform-based impedance estimation approaches demonstrate the performance of proposed method. Besides, the proposed SDWPT-based impedance estimation provided accurate experimental results, which make it viable for real-time applications.
Cost-Effectiveness of Postexposure Prophylaxis After Sexual or Injection-Drug Exposure to Human Immunodeficiency Virus

the practice of HIV/AIDS. In most cases, a comment is provided from the editor concerning interpretation, impact or further relevant information on the topic reviewed. This represents a modification of selected entries in the ‘‘What’s News’’ section of the Johns Hopkins website for ID HIV/AIDS (reprinted from www. hopkins-aids.edu with permission). John G. Bartlett, Johns Hopkins University, School of Medicine, Baltimore, MD.
Monitoring Chip Branch Failure in Multichip IGBT Modules Based on Gate Charge

The reliability of the multichip insulated gate bipolar transistor (IGBT) modules has been a concern. Condition monitoring is an effective approach to enhance reliability and improve the quality of customer service. This article proposes a method for monitoring the chip branch failure due to bond wires fatigue in the multichip IGBT modules. The idea is based on the cumulative effect of the gate charge deviation when the chip branch fails. By integrating the turn-on gate current of multiple switching cycles, the health state of multichip IGBT modules is characterized by the capacitor voltage as a health-sensitive parameter that can be extracted in the proposed monitoring circuit integrated into the gate driver. The capacitor voltage decreases significantly when the chip branch occurs due to bond wires fatigue. It is stable during sampling and has a strong immunity to junction temperature, collector current, and collector–emitter voltage. Besides, the method is nonintrusive and has the potential for real-time and online monitoring. These characteristics make the method reliable, easy to implement, and convenient for health status identification. The confirmatory experiment is carried out to verify the feasibility of the method.
DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression

With a small number of training range cells, sparse recovery (SR)-based space–time adaptive processing (STAP) methods can help to suppress clutter and detect targets effectively for airborne radar. However, SR algorithms usually have problems of high computational complexity and parameter-setting difficulties. More importantly, non-ideal factors in practice will lead to the degraded clutter suppression performance of SR-STAP methods. Based on the idea of deep unfolding (DU), a space–time two-dimensional (2D)-decoupled SR network, namely 2DMA-Net, is constructed in this paper to achieve a fast clutter spectrum estimation without complicated parameter tuning. For 2DMA-Net, without using labeled data, a self-supervised training method based on raw radar data is implemented. Then, to filter out the interferences caused by non-ideal factors, a cycle-consistent adversarial network (CycleGAN) is used as the image enhancement process for the clutter spectrum obtained using 2DMA-Net. For CycleGAN, an unsupervised training method based on unpaired data is implemented. Finally, 2DMA-Net and CycleGAN are cascaded to achieve a fast and accurate estimation of the clutter spectrum, resulting in the DU-CG-STAP method with unsupervised learning, as demonstrated in this paper. The simulation results show that, compared to existing typical SR-STAP methods, the proposed method can simultaneously improve clutter suppression performance and reduce computational complexity.
HOW CORPORATE SOCIAL RESPONSIBILITY EFFECTS COMPANY’S FINANCIAL PERFORMANCES

A successful business must pay attention not only to how to improve the welfare of shareholders, but also the welfare of all stakeholders. This study aims to see the effect of disclosure of Corporate Social Responsibility (CSR) on corporate financial performance. Financial performance is measured using three indicators, which are return on Equity (ROE), total stock return and Tobin’s Q. ROE represents company’s profitability, total stock return reflects company’s performance in the market and tobin’s Q shows firm value. CSR is measured using the Corporate Social Disclosure Index (CSRDI) which is based on the ISO 26000 standard. By using two controlling variables, company size and leverage, the results show that there is a negative and insignificant effect between CSR and ROE ratio. Although CSR practice together with control variables also has no effect on total stock returns, but there is a significant positive effect between CSR disclosures and firm value. These results can motivate company’s managers to include CSR as a management strategy to increase firm value by paying serious attention to stakeholders and sustainability issues.
Unsupervised Deep Learning-based End-to-end Network for Anomaly Detection and Localization

These days there is great demand for automatizing a visual inspection process in industrial companies since it is a tedious and time-consuming task. Recent progress in deep convolutional neural networks allowed to automatize visual inspection procedure. However, currently available supervised learning methods require large amount of labeled data, while the unsupervised learning techniques suffer from lack of accuracy. To address these problems, we propose a deep learning-based unsupervised learning method that exhibits fast and precise performance. The proposed unsupervised learning method based pseudo-labeling algorithm using graph Laplacian matrix that allows transferring computationally expensive autoencoder problem to classification task, the proposed system benefits from very fast convergence ability and significantly outperforms currently available deep learning-based AVI methods. In the conducted experiments using real-life fabric image datasets, the proposed method outperformed the currently available methods in terms of speed and accuracy.
An integral system-ergonomic approach for IT-based process management in complex work environments by example of manufacturing and health care

Recent trends in manufacturing and health care move these two work systems closer together from a system ergonomics point of view. Individual treatment of products, especially patients, by specialists in a distributed environment demand information technology (IT)-based support suitable for complex systems. IT-based support of processes in complex systems is difficult due to the lack of standard processes. IT support also means to rethink processes to use efficiency potentials. Close cooperation of users and software developers is needed to increase the ergonomic quality of the system. Therefore, suitable tools are needed: UML is available as the standard industry modeling language, Zope-Plone as the quasi-standard for content management systems, SimPy as an object-oriented simulation tool for event-triggered processes, and ACT-R as a powerful cognitive architecture for simulation of human information processes. The integration of these tools enables system-ergonomic support of processes in the complex work system as well as of the development and deployment process. It is the base of an integral system-ergonomic approach for IT-based process management. Knowledge gained during process analysis either enters models or leads to the extension and adaptation of the tool chain. The models serve as basis for discussion among system ergonomists, programmers, and specialists from the work system. Further, they are understood by simulation and process support tools. Transcoding efforts between humans with different professional backgrounds and machines are reduced, and the flexibility demanded by complex systems is met. © 2008 Wiley Periodicals, Inc.
Sensorless Control of SPMSM Based on High-Frequency Positive- and Negative-Sequence Current Dual-Demodulation

In surface-mounted permanent magnet synchronous motor (SPMSM) low-speed sensorless control, the high-frequency sinusoidal voltage injection method for rotor position estimation is affected by the low saturation salient rate of SPMSM and the system delay, resulting in estimation error. This article proposes a dual-demodulation method utilizing the high-frequency positive- and negative-sequence stator current of the motor. This method is based on the principle of maximizing the effect of the salient inductance component and minimizing the significant component of noise, which improves the utilization of the current containing angle information and increases the signal-to-noise ratio of the position angle error fed to the rotor position observer. The dc bias of the current caused by system delay is also eliminated so that the accuracy of position estimation is improved. The proposed method is verified by experiment, and the results show that the accuracy of the estimated rotor position and dynamic performance of the estimator is greatly improved compared to existing methods.
The Warps and Wefts of a Polyploidy Complex: Integrative Species Delimitation of the Diploid Leucanthemum (Compositae, Anthemideae) Representatives

Species delimitation—owing to the paramount role of the species rank in evolutionary, ecological, and nature conservation studies—is an essential contribution of taxonomy to biodiversity research. In an ‘integrative taxonomy’ approach to species delimitation on the diploid level, we searched for evolutionary significant units (the warps and wefts) that gave rise to the polyploid complex of European ox-eye daisies (Leucanthemum; Compositae-Anthemideae). Species discovery and validation methods based on genetic, ecological, geographical, and morphometric datasets were applied to test the currently accepted diploid morpho-species, i.e., morphologically delimited species, in Leucanthemum. Novel approaches were taken in the analyses of RADseq data (consensus clustering), morphometrics of reconstructed leaf silhouettes from digitized herbarium specimens, and quantification of species-distribution overlaps. We show that 17 of the 20 Leucanthemum morpho-species are supported by genetic evidence. The taxonomic rank of the remaining three morpho-species was resolved by combining genealogic, ecologic, geographic, and morphologic data in the framework of von Wettstein’s morpho-geographical species concept. We herewith provide a methodological pipeline for the species delimitation in an ‘integrative taxonomy’ fashion using sources of evidence from genealogical, morphological, ecological, and geographical data in the philosophy of De Queiroz’s “Unified Species Concept”.