Miracle-dz/UniFAR-FTUs huggingface dataset:
A measurement method for the characterization of the ferromagnetic bottom layer of cookware used in domestic induction heating

Design-oriented modeling approaches, such as finite element analyses (FEA), rely on accurate material data. However, manufacturers rarely provide the necessary data for cookware used in domestic induction heating. Due to the multi-layered structure of the cookware bottom, conventional measurement methods for the characterization of magnetic material are not applicable without great effort in preparation of suited material specimen. This contribution proposes a novel measurement method that allows determining the electromagnetic properties of the ferromagnetic bottom layer of cookware. The proposed measurement method is based on the minimization of deviation between measurement and simulation data in dependence of material properties. By characterization of the ferromagnetic bottom layer of a material specimen made from multiple layers, the feasibility of the method is shown. The results presented for the magnetic material characterization are validated using Epstein frame measurements. It is shown that the deviation between the magnetization curve determined with the proposed method and the tip of the hysteresis curve measured using the Epstein frame is approximately 3 %. To validate the results presented for the specific electrical resistance, a microhmmeter is used. The relative error between both measurements is approximately 16 %.
Virtual-Flux Finite Control Set Model Predictive Control of Dual-Three Phase IPMSM Drives

In this paper, a virtual-flux finite control set model predictive control (MPC) strategy for dual three-phase interior permanent magnet synchronous motors (DTP-IPMSM) is proposed. The technique is based on the conventional predictive current control, but it maps the measured variables into a virtual- flux domain, thus simplifying the prediction stage. The technique uses a flux-based cost function to track the estimated reference flux. The flux tracking cancels out to guarantee an improved current tracking, and thus, a better torque ripple. The proposed technique is validated through simulation of a 100 kW DTP- IPMSM in Matlab/Simulink. Results evidenced a reduced current control error, thus improving the torque tracking up to 38.9 % when compared to the conventional inductance based model predictive control.
Communication-Loss Tolerant Fault Diagnosis Approach for Large Scale Systems

Currently, the operation of modern industrial processes depends on many different components (systems and plant devices) all of which are tied together through communication systems. In large-scale systems, due to the difficulty of monitoring the communication infrastructure, the latter is vulnerable to external physical intervention, which could have, as a consequence, the loss of communication among process components. In some cases, to avoid loss of communication, redundancies or alternative paths are implemented, but this is not necessarily the most efficient solution. In this paper, a fault diagnosis approach based on structural analysis that makes effective use of its infrastructure in a communication-loss condition is proposed. This is accomplished by considering the communication structure in the proposed approach to apply optimization methods guaranteeing optimal use of the available communication structure. For this, diagnosers choose the best communication paths or, if necessary, change their design. The proposed approach is applied to a power distribution system achieving diagnosis goals in a communication-loss condition.
Verification of Coaching effect by Instructor-like Assistance System Based on Model Predictive Constraint Satisfaction

Safety and acceptability are the main concerns in the design of driver assistance systems. However, these two requirements sometimes conflict with each other depending on the situation and the driver. This conflict is particularly emphasized in the case of elderly drivers. To solve this problem, this paper proposes a driver-vehicle cooperation scheme, an "instructor-like assisting control" consisting of model predictive constraint satisfaction and a multi-modal human-machine inter-face. The proposed assisting scheme is expected to improve the drivers’ inherent driving characteristics, which is recognized as a "coaching effect" in cognitive science. This effect was verified by long-term experiments over one month using a driving simulator.
EveryWAn- An Open Source SD-WAN solution

Software Defined Wide Area Network (SD-WAN) was originally proposed as an alternative solution to redesign the architecture of the WAN. Like its technology precursor Software Defined Networking, SD-WAN was aiming at simplifying the management and operation of the networks (with a particular focus on WAN scenarios) by decoupling the networking hardware from its control programs and using software and open APIs to abstract the infrastructure and manage the connectivity and the services. The SD-WAN architecture leverages SDN principles to securely build interconnections between users and the applications hosted in the clouds or in remote branches, by leveraging any combination of transport services With this paper we shed some light on the SD-WAN scenario and describe an open-source implementation which can be taken as reference. We call this architecture EveryWAn.It has been designed with SDN and NFV principles in mind, and leverages Cloud best practices to deliver to the WAN customers and the MSP the same benefits and the agility of the Cloud service providers. Moreover, we strongly believe in the openness of the SDN/NFV paradigms which can ease the development of new services and can foster the innovation in the SD-WAN deployments.
“Stock‐touting” through spam e‐mails: a data mining case study

Purpose – Although the financial markets are regulated by robust systems and rules that control their efficiency and try to protect investors from various manipulation schemes, markets still suffer from frequent attempts to mislead or misinform investors in order to generate illegal profits. The impetus to effectively and systematically address such schemes presents many challenges to academia, industry and relevant authorities. This paper aims to discuss these issues.Design/methodology/approach – The paper describes a case study on fraud detection using data mining techniques that help analysts to identify possible instances of touting based on spam e‐mails. Different data mining techniques such as decision trees, neural networks and linear regression are shown to offer great potential for this emerging domain. The application of these techniques is demonstrated using data from the Pink Sheets market.Findings – Results strongly suggest the cumulative effect of “stock touting” spam e‐mails is key to under.
Ultra wideband patch antenna with a novel folded-patch technique

The authors present the results of a polygonal patch antenna for ultra-wideband applications covering a frequency band from 4.14 to 13.50 GHz. The fabricated antenna achieved a 10 dB impedance bandwidth in excess of 125 with an antenna size of 0.373lambda o times0.373lambda o times0.149lambda o at its centre frequency. The antenna's impedance bandwidth is 64% higher than what is currently obtainable with state-of-the-art folded-patch techniques. The proposed patch antenna has a polygonal-shape with a rectangular slot and shorting pins. The analysis of this antenna shows that bandwidth broadening is achieved by using a rectangular slot on the patch that is fed from a folded-patch feed, whereas the reduction in antenna size is achieved through the use of two shorting pins strategically located on the radiating patch.
Microblogs Deception Detection using BERT and Multiscale CNNs

Online news consumption has rapidly increased, and so has the proliferation of false information. People worldwide have mainly become dependent on social media networks to intake news about the happenings around them. Also, the data is profoundly contaminated with wrong information that harms society in uncountable ways. It is of huge importance to be able to identify a false message. The research society is contributing to solving the problem by developing machine learning and deep learning algorithms. With misinformation spreading ubiquitously, various data modalities have emerged that become carriers of such false news. Research trend is advancing towards multi-modal fake news detection to authenticate text, images, and videos on the web. Existing studies have elaborated on the successful use of RNNs and CNNs. Being a new NLP technique, BERT has been used by a limited number of studies, while multiscale CNNs have not been explored yet to apply fake news detection. This research proposes a novel framework using BERT and multiscale CNNs to perform multi-modal fake news classification and achieve results higher than the existing state-of-the-art techniques.
A Comparison of "Total Dust" and Inhalable Personal Sampling for Beryllium Exposure.

In 2009, the American Conference of Governmental Industrial Hygienists (ACGIH) reduced the Beryllium (Be) 8-hr Time Weighted Average Threshold Limit Value (TLV-TWA) from 2.0 {micro}g/m{sup 3} to 0.05 {micro}g/m{sup 3} with an inhalable 'I' designation in accordance with ACGIH's particle size-selective criterion for inhalable mass. Currently, per the Department of Energy (DOE) requirements, the Lawrence Livermore National Laboratory (LLNL) is following the Occupational Health and Safety Administration (OSHA) Permissible Exposure Limit (PEL) of 2.0 {micro}g/m{sup 3} as an 8-hr TWA, which is also the 2005 ACGIH TLV-TWA, and an Action Level (AL) of 0.2 {micro}g/m{sup 3} and sampling is performed using the 37mm (total dust) sampling method. Since DOE is considering adopting the newer 2009 TLV guidelines, the goal of this study was to determine if the current method of sampling using the 37mm (total dust) sampler would produce results that are comparable to what would be measured using the IOM (inhalable) sampler specific to the application of high energy explosive work at LLNL's remote experimental test facility at Site 300. Side-by-side personal sampling using the two samplers was performed over an approximately two-week period during chamber re-entry and cleanup procedures following detonation of an explosive assembly containing Beryllium (Be). more » The average ratio of personal sampling results for the IOM (inhalable) vs. 37-mm (total dust) sampler was 1.1:1 with a P-value of 0.62, indicating that there was no statistically significant difference in the performance of the two samplers. Therefore, for the type of activity monitored during this study, the 37-mm sampling cassette would be considered a suitable alternative to the IOM sampler for collecting inhalable particulate matter, which is important given the many practical and economic advantages that it presents. However, similar comparison studies would be necessary for this conclusion to be applied to other types of activities, where earlier studies have shown that the IOM sampler tends to collect higher concentrations of Be compared to the 37-mm cassette, which could complicate compliance with what is already an extremely low exposure limit. « less
Role of liquid biopsy in central nervous system tumors

Challenges in obtaining tissue specimens and tumor heterogeneity are major limitations for accurate diagnosis, molecular characterization, risk stratification, and development of biomarker-driven therapies in central nervous system (CNS) tumors. The potential of assessment of CNS tumors through analysis of corporeal fluids (liquid biopsy) is being explored to document tumor-related genetic/epigenetic alterations and protein expression to identify prognostic and therapeutic biomarkers. The quantity of circulating tumor DNA isolated also appears to be directly associated with tumor progression and response to treatment. In this review, we provide synopsis of the recent studies which have provided crucial insights into analyzing circulating tumor cells, cell-free nucleic acids, and extracellular vesicles for directing long-term disease control. We have also highlighted the stumbling blocks and gaps in technology that need to be overcome to translate research findings into a tool in the clinical setting.
Why Buildings Are Known

If we could predict how well buildings and structures in the city were known, planners and architects would be in possession of a powerful design tool, for we could begin to gain some control over that elusive communications medium, the urban environment. To do this, we have to go beyond Kevin Lynch’s (1960) identification of known urban element types. We must determine the reasons why these elements are known which means discovering the attributes that capture attention and hold a place in the inhabitant’s mental representation of his city. This is the subject of our investigation. During the planning of Ciudad Guayana, the newly growing industrial city in Venezuela, over three hundred interviews were carried out among the existing population of about 30,000 people, related to their perceptions of the city. One of our particular concerns was to check whether the
A new innovation in topical diabetic foot ulcer; hyaluronic acid platelet-rich fibrin (HAPRF) gel - a study in inflammation and angiogenesis

A new innovation in topical diabetic foot ulcer; hyaluronic acid platelet-rich fibrin (HAPRF) gel a study in inflammation and angiogenesis Ronald W. Kartika1, Idrus Alwi2, Em Yunir2, Sarwono Waspadji2, Saptawati Bardosono3, Suzzana Immanuel4, Todung Silalahi5, Saleha Sungkar6, Jusuf Rachmat7, Franciscus D. Suyatna8, Mirta Hediyati Reksodiputro9* Background: Indonesia ranks 6th out of ten countries with the highest number of diabetes patients. An increase followed this condition in the prevalence of diabetic foot ulcers by around 15%. This study demonstrated the ability of combination of Platelet Rich Fibrin (PRF) and Hyaluronic Acid (HA) to reduce inflammation in diabetic foot ulcer (DFU) healing. Methods: In the baseline, all patients received the same treatment including wound debridement. There were 20 subjects divided into two groups; HAPRF and PRF. A part of sample was processed into lysate and stored in -80oC. The rest is applied as a topical therapy in DFU base on intervention. We analyzed the inflammation using two methods; swab in DFU and fibrin gel of HAPRF or PRF and ELISA on day-3 and day-7. We also measure the granulation index by digital photograph and analyze use ImageJ at the same time. Result: There were 20 diabetic patients with DFUs, performed analysis of IL-6 lysate gel HAPRF decrease significantly compare with PRF on day 3 (p= 0.038) and day-7 (p = 0.034). Granulation index evaluation, in HAPRF increase on day-3 (p = 0.043), day-7 (p = 0.049), and day-14 (p = 0.041). Conclusion: A-PRF +HA administration leads to reduce IL-6 levels, and accelerated wound healing of DFUs patients. HAPRF directly aids epithelialization and granulation index.
UTHealth@BioCreativeVII: Domain-specific Transformer Models for Drug-Protein Relation Extraction

— It is important to automatically extract the relations between drugs and proteins from ever-growing biomedical literature, to build up-to-date knowledge bases in biomedicine. Through the DRUGPROT track at BioCreative VII, we developed automated methods to recognize drug-protein entity relations from PubMed abstracts. In this short system description paper, we outline and describe our proposed system submissions that leverage multiple transformer models pre-trained on biomedical data. The outputs of some of the systems have been combined using a decision based on majority voting. Our best system obtained 80.44% in precision and 74.96% in recall for an F1-score of 77.60%, demonstrating the effectiveness of deep learning-based approaches for automatic relation extraction from biomedical literature for the main track. We also participated in the Large-Scale Track - the micro-averaged precision, recall and F1-score of our best system being 79.49%, 75.27% and 77.32% respectively. ( Abstract )
Psychiatric Co-Morbidity and Substance Use Correlated with Medication Adherence Among People Living with HIV (PLWHIV) Attending the Virology Clinic of the University of Port Harcourt Teaching Hospital (UPTH)

HIV infection is an endemic communicable chronic disease, of enormous public health concern worldwide particularly, Sub-Saharan African. Substance use and associated psychiatric co-morbidity among the sufferers may affect medication adherence. The aim of this study, therefore, was to determine the effect of psychiatric comorbidity and substance use on medication adherence among people living with HIV at the University of Port Harcourt Teaching Hospital. A cross-sectional study was conducted among 230 Subjects Living with HIV. Sociodemographic questionnaire was used. A structured questionnaire was used to assess use of psychoactive substances. Psychiatric co-morbidity was assessed using the GHQ-12 in conjunction with the DSM 5. The Medication Adherence Rating Scale (MARS) was used to assess medication adherence. Data was analysed using the SPSS version 20. The prevalence of psychological co-morbidity among PLWHIV was 30.6%. Alcohol use disorders was the highest with 73 (31.7%), followed by cigarette smoking 38 (16.5%) while nicotine snuffing was the least with 1 (0.4%). Of the 43 (18.7%) with poor medication adherence, 31 (15.2%) had psychiatric co-morbidity, 26 (11.3%) had substance use, 25 (10.8%) had both psychiatric and substance use co-morbidity while 19 (8.2%) had none (p=0.004). Psychological co-morbidity and substance use are prevalent among PLWHIV, and may affect medication adherence.
End-to-end Piece-wise Unwarping of Document Images

Document unwarping attempts to undo physical deformations of the paper and recover a ’flatbed’ scanned document-image for downstream tasks such as OCR. Current state-of-the-art relies on global unwarping of the document which is not robust to local deformation changes. Moreover, a global unwarping often produces spurious warping artifacts in less warped regions to compensate for severe warps present in other parts of the document. In this paper, we propose the first end-to-end trainable piece-wise unwarping1 method that predicts local deformation fields and stitches them together with global information to obtain an improved unwarping. The proposed piece-wise formulation results in 4% improvement in terms of multi-scale structural similarity (MS-SSIM) and shows better performance in terms of OCR metrics, character error rate (CER) and word error rate (WER) compared to the state-of-the-art.
The Involvement of Leptospira Spp. in Swine Abortion and Susceptibility of the Pathogen to Antibiotics

The involvement of Leptospira spp. in thirty-five (35) out of fifty (50) cases of swine abortion was investigated which further required evaluation of pathogen susceptibility to a panel of antibiotics. Samples of urine from aborting sows were collected to recover the pathogen, amplify its 16S rRNA gene to confirm its identity and monitor cell density in McFarland Units (MFU) as a measure of susceptibility to antibiotics. The study permitted the isolation and evaluation of Leptospira spp. morphology, demonstration of 3 amplicon products of the 16S rRNA genes with molecular sizes of 1050 to 1080 bp and identification of 2 Leptospira spp. and L. hyos after gene sequencing. The susceptibility of the identified L. hyos to Cephalexin, Neomycin, Clindamycin, Ciprofloxacin and Sulfamethoxazole prepared at different concentrations (25 μg, 35 μg and 50 μg) and evaluated after 6 and 12 hour-exposure time was marked by lower cell densities of recovered L. hyos relative to the application of higher (35 μg and 50 μg) antibiotic concentrations. Significant reduction in cell densities at 12-hr compared to data obtained on the 6-hour were demonstrated and underscored the susceptibility of L. hyos as effects of antibiotic concentration and duration of exposure time. The bactericidal action of 25 μg Ciprofloxacin; and 25 and 35 μg Sulfamethoxazole against L. hyos were established at 6-hour exposure time and that this bactericidal action was maintained until the 12-hr with the application of Neomycin and Ciprofloxacin at 35 and 50 μg and Sulfamethoxazole at 50 μg. Arch Vet Sci Med 2021; 4 (3):71-84 DOI: 10.26502/avsm.027 Archives of Veterinary Science and Medicine 72 These data highlight usefulness of the antibiotics in the treatment of abortion-associated Leptospira infections when administered at the concentrations described.
Classifying the tracing di�culty of 3D neuron image blocks based on deep learning

Quickly and accurately tracing neuronal morphologies in large-scale volumetric microscopy data is a very challenging task. Most automatic algorithms for tracing multi-neuron in a whole brain are designed under the Ultra-Tracer framework, which begins the tracing of a neuron from its soma and traces all signals via a block-by-block strategy. Some neuron image blocks are easy to trace and their automatic reconstructions are very accurate, and some others are diﬃcult to trace and their automatic reconstructions are inaccurate or incomplete. The former are called low Tracing Diﬃculty Blocks (low-TDBs), while the latter are called high Tracing Diﬃculty Blocks (high-TDBs). We design a model named 3D-SSM to classify the tracing diﬃculty of 3D neuron image blocks, which is based on 3D Residual neural Network (3D-ResNet), Fully Connected Neural Network (FCNN) and Long Short-Term Memory network (LSTM). 3D-SSM contains three modules: Structure Feature Extraction (SFE), Sequence Information Extraction (SIE) and Model Fusion (MF). SFE utilizes a 3D-ResNet and a FCNN to extract two kinds of features in 3D image blocks and their corresponding automatic reconstruction blocks. SIE uses two LSTMs to learn sequence information hidden in 3D image blocks. MF adopts a concatenation operation and a FCNN to combine outputs from SIE. Neuron images of a whole mouse brain are segmented into many overlapped 3D image blocks along the gold standard reconstruction of each marked neuron. With the help of gold standard reconstructions and APP2 automatic reconstructions, 12732 training samples and 5342 test samples are constructed based on those 3D image blocks. The 3D-SSM achieves classiﬁcation accuracy rate 87.04% on the training set and 84.07% on the test set. The trained 3D-SSM is tested on samples from another whole mouse brain and its accuracy rate is 83.21%. 3D-SSM can be used as a stop condition of an automatic tracing algorithm in the Ultra-Tracer framework. With its help, neuronal signals in low-TDBs can be traced by the automatic algorithm and in high-TDBs may be reconstructed by annotators.