Miracle-dz/UniFAR-FTUs huggingface dataset:
elk-Defect Classification of Railway Fasteners Using Image Preprocessing and a 1 Lightweight Convolutional Neural Network

: Railway fasteners are used to securely fix rails to sleeper blocks. Partial wear or complete loss of these 4 components can lead to serious accidents and cause train derailments. To ensure the safety of railway transportation, 5 computer vision and pattern recognition-based methods are increasingly used to inspect railway infrastructure. In 6 particular, it has become an important task to detect defects in railway tracks. This is challenging since rail track images 7 are acquired using a measuring train in varying environmental conditions, at different times of day and in poor lighting 8 conditions, and the resulting images often have low contrast. In this study, a new method is proposed for the classification 9 of defects on rail track fasteners. The proposed approach uses image enhancement to first filter the rail images and obtain 10 a high contrast image. Then, the rail track and sleeper positions are determined from the high contrast image. The 11 location of the fastener is determined by applying the Line Local Binary Pattern method and the defects of the fastener 12 are classified using an improved lightweight convolutional neural network (LCNN) model. Features are extracted from 13 two fully connected layers of the developed LCNN model and the feature vector is constructed by concatenating these 14 layers. The concatenated features are processed using a number of machine learning methods and the optimum classifier 15 is chosen. Experimental results show that Cubic SVM gives the best results with a detection accuracy rate of 99.7%. 16
Combining dictionary- and rule-based approximate entity linking with tuned BioBERT

Chemical named entity recognition (NER) is a significant step for many downstream applications like entity linking for the chemical text-mining pipeline. However, the identification of chemical entities in a biomedical text is a challenging task due to the diverse morphology of chemical entities and the different types of chemical nomenclature. In this work, we describe our approach that was submitted for BioCreative version 7 challenge Track 2, focusing on the ‘Chemical Identification’ task for identifying chemical entities and entity linking, using MeSH. For this purpose, we have applied a two-stage approach as follows (a) usage of fine-tuned BioBERT for identification of chemical entities (b) semantic approximate search in MeSH and PubChem databases for entity linking. There was some friction between the two approaches, as our rule-based approach did not harmonise optimally with partially recognized words forwarded by the BERT component. For our future work, we aim to resolve the issue of the artefacts arising from BERT tokenizers and develop joint learning of chemical named entity recognition and entity linking using pre-trained transformer-based models and compare their performance with our preliminary approach. Next, we will improve the efficiency of our approximate search in reference databases during entity linking. This task is non-trivial as it entails determining similarity scores of large sets of trees with respect to a query tree. Ideally, this will enable flexible parametrization and rule selection for the entity linking search.
Detection of Pathologies in X-Rays Based on a Deep Learning Framework

The diagnostic process of respiratory diseases requires experience and skills to assess the different pathologies that patients may develop. Unfortunately, the lack of qualified radiologists is a global problem that limits respiratory diseases diagnosis. Therefore, it will be useful to have a tool that minimizes errors and workload, improves efficiency, and speeds up the diagnostic process in order to provide a better healthcare service to the community. This research proposes a methodology to detect pathologies by using deep learning architectures. The present proposal is divided into three types of experiments. The first one evaluates the performance of feature descriptors such as SIFT, SURF, and ORB in medical images with machine learning models as an introduction to the last experiment. The second one evaluates the performance of deep learning architectures such as ResNet50, Alexnet, VGG16, and LeNet. Finally, the third one evaluates the combination of deep learning and machine learning classifiers. Furthermore, a novel chest X-ray dataset called PathX_Chest, which contains 2,200 images of ten different classes, is presented. In contrast with the state of the art, good results were obtained in the three different approaches. However, the best performance was achieved by combining deep learning and machine learning: a 99.99 % accuracy was obtained with the combination of ResNet50 and SVM classifier. This methodology may be used to develop a CAD system to help radiologists have a second opinion and a support during the diagnostic procedure.
Semi-Supervised Analysis of the Electrocardiogram Using Deep Generative Models

Deep learning has gained increased impact on medical classification problems in recent years, with models being trained to high performance. However neural networks require large amounts of labeled data, which on medical data can be expensive and cumbersome to obtain. We propose a semi-supervised setup using an unsupervised variational autoencoder combined with a supervised classifier to distinguish between atrial fibrillation and non-atrial fibrillation using ECG records from the MIT-BIH Atrial Fibrillation Database. The proposed model was compared to a fully-supervised convolutional neural network at different proportions of labeled and unlabeled data (1%-50% labeled and the remaining unlabeled). The results demonstrate that the semi-supervised approach was superior to the fully-supervised, from using as little as 5% (5,594 samples) labeled data with an accuracy of 98.7%. The work provides proof of concept and demonstrates that the proposed semisupervised setup can train high accuracy models at low amounts of labeled data.
Combined treatment technology for storm runoff and circulating waters from vehicle transport enterprises

: The development of transport infrastructure strengthens the technogenic burden on the environment. Waste, thaw and rain waters from vehicle transport enterprises, such as car-washing installations, petrol stations, and car service stations may pollute ground and surface waters, and adjacent landscapes. The article presents quality parameters and suggests a number of measures permitting to minimize the harmful impact on the environment. The purpose is to improve the reagent treatment technology applicable to surface runoff from vehicle transport enterprises and the reuse of circulating waters by improving well-known methods with original technological procedures and chemical reagents. Research methods include the use of potentiometry, titrometry, and gravimetry. The investigation has shown the possibility to increase the efficiency of runoff treatment and removal of suspended particles and dissolved organic matter by 20–30%. This can be achieved by the application of a permanent magnetic field of 30–40 mT and the subsequent processing by the solution of aluminum chlorohydrate. Optimum parameters have been determined regarding magnetic field and processing conditions. It has been proven that the use of aluminum chlorohydrate in combination with polyhexamethyleneguanidine hydrochloride simplifies substantially the technological cycle. A better treatment can be achieved in comparison with the usual coagulant by 25%. Heavy metal ions are removed from water and the method includes microbiological disinfection and preservation of water in storage reservoirs. The improved technological scheme suggests the reagent treatment of storm and circulating waters for their repeated use.
Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features

Continuous non-invasive Blood Pressure (BP) monitoring is vital for the early detection and control of hypertension. However, this is yet not possible as all current non-invasive BP devices are cuff-based devices and hence precluding continuous monitoring. Several methods have been proposed to overcome this challenge, one of which utilises the Photoplethysmograph (PPG) signal in an effort to predict reliable BP values from this signal using various computational approaches. Although, good performance has been reported in the literature, it was mainly achieved on a small inadequate sample size using conventional models that are unable to account for the temporal variations in the input vector. To address these limitations, this paper proposes cuff-less and continuous blood pressure estimation using Long Short-term Memory (LSTM) and Gated Recurrent Units (GRU). The models were evaluated on 942 patients acquired from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) dataset. The proposed models produced superior results in comparison with conventional artificial neural network. In particular, the best performance was achieved by the GRU, with mean absolute error and standard deviation of 5.77 ± 8.52 mmHg and 3.33±5.02 mmHg for systolic (SBP) and diastolic blood pressure (DBP), respectively. Furthermore, the results comply with the international standards for cuff-less blood pressure estimation.
Slow and Easy: a Theory of Browsing∗

An agent needs to choose the best alternative drawn randomly with replacement from a menu of unknown composition. The agent is boundedly rational and employs an automaton decision rule: she has finitely many memory states, and, in each, she can inquire about some attribute of the currently drawn alternative and transition (possibly stochastically) either to another state or to a decision. Defining the complexity of a decision rule by the number of transitions, I study the minimal complexity of a decision rule that allows the agent to choose the best alternative from any menu with probability arbitrarily close to one. Agents in my model differ in their languages— collections of binary attributes used to describe alternatives. My first result shows that the tight lower bound on complexity among all languages is 3⌈log2(m)⌉, where m is the number of alternatives valued distinctly. My second result provides a linear upper bound. Finally, I call adaptive a language that facilitates additive utility representation with the smallest number of attributes. My third result shows that an adaptive language always admits the least complex decision rule that solves the choice problem. When (3/4) · 2n < m ≤ 2n for a natural n, a language admits the least complex decision rule if and only if it is adaptive.
Humboldt @ DrugProt: Chemical-Protein Relation Extraction with Pretrained Transformers and Entity Descriptions

—The detection of chemical-protein interactions is an important task with applications in drug design and biotechnology. The BioCreative VII - DrugProt shared task provides a benchmark for the automated extraction of such relations from scientific text. This article describes the Humboldt approach to solving it. We define the task as a relation classification problem, which we model with pretrained transformer language models and further use entity descriptions as an additional knowledge source. On the hidden test set of DrugProt, our model achieves 79.73% F1, yielding an improvement of over 17pp over the average score of all task participants.
An Absorbing Improvement for Space Infection Decompression: A Novel Drainage Device

Introduction Infection of the facial spaces and the associated exudate can often necessitate surgical intervention. Whilst traditional decompression methodologies have reduced the mortality rate from complications such as Ludwig’s Angina, there has been relatively little innovation in the procedure to minimize treatment times and patient distress. Negative pressure wound therapy, which can yield improvements to treatment time, wound healing and patient experience, has gained traction in abscess treatments in other parts of the body but seen limited adoption in maxillofacial surgeries. Methods A focused literature review explores the existing treatment methodologies for infected facial space decompression and identifies obstacles to implementing negative pressure wound therapy in maxillofacial surgeries. A novel drainage tool, which features a sleeved sponge over a perforated drainage tube, is proposed. Virtual prototyping and structural analyses of the novel drainage device including a parametric design study are presented. Results The parametric study validates the proposed tool’s biocompatibility in terms of overall flexural and axial stiffness between the tool and complex structures in the head and neck. Ultimately, this work presents a necessary first step in the development of specialized drainage tools to promote the adoption of negative pressure wound therapy for infections of facial spaces.
Is sustainable car making possible

Can the car really be made sustainable, or are we wasting our time? I will explore this question in the context of some of the guidelines for sustainability already available, such as those proposed by The Natural Step and those from the German Federal Parliamentary Commission on Protection of Humanity and the Environment. What is lacking is a clear ‘roadmap’ for the car and its use. We would need to establish the kind of world we want, the kind of cars we want and what motorised mobility needs we will require in this future world. We can then identify the characteristics needed. The next step would involve a careful plan – the roadmap – of how we go from what we have to what we will need at that future point. I will explore – briefly - the extent to which the current automobility system could adjust to drastic energy and resource use reduction or to a closed-loop economic system. If we explore the perceived extent of the changes needed and the implications of the changes to cars and car making this implies, we can see that current efforts on the part of the industry appear comparable to rearranging the ashtrays on the Titanic. However among the various actions the industry has been implementing to react to legislation and social pressure are a few gems that are more promising. Identifying and building on these may be more rewarding.
Which anthropometric and metabolic index is superior in hypertension prediction among overweight/obese adults?

Background Although the effectiveness of some combined anthropometric and metabolic scores were evaluated in hypertension prediction, none of them had addressed their accuracy in association with overweight/obese populations. This study examined the accuracy of several anthropometric parameters in this regard and compared the novel indices to the ancient ones. Methods Through a cross-sectional study, 5115 patients have been evaluated at the weight loss clinic. Data on demographic information, anthropometric indices, and biochemical measurements were assembled into a checklist. Multivariable regression modeling and the area under the receiver-operating characteristic (ROC) were analyzed using SPSS version 20. To find new combined scores, SEM (structural equation modeling) analysis was also adopted. P-values < 0.05 were considered statistically significant. Results Considering ancient indices, WHtR (waist-to-height ratio) showed a sufficient area under the curve in predicting hypertension among both genders concomitant with WC (waist circumference) in men, and BRI (body roundness index) in women as highest AUC. The highest odds ratio (OR) for the presence of hypertension, based on the age-adjusted model, was BRI in females (OR, 3.335; 95% confidence interval [CI], 1.58–7.28) and WC in males (OR, 13.478; 95% CI: 1.99–45.02). The combined scores were not superior to the single ones. Conclusion The most powerful association between hypertension and sufficient discrimination ability of normotensives from hypertensive patients was detected for BRI in women and WC among men. However, neither the BSI and BAI nor FMI and FFMI showed superiority to WC or WHtR in predicting the presence of hypertension.
The electromagnetic theory of coaxial transmission lines and cylindrical shields

A form of circuit which is of considerable interest for the transmission of high frequency currents is one consisting of a cylindrical conducting tube within which a smaller conductor is coaxially placed. Such tubes have found application in radio stations to connect transmitting and receiving apparatus to antennae. As a part of the development work on such coaxial systems, it has been necessary to formulate the theory of transmission over a coaxial circuit and of the shielding against inductive effects which is afforded by the outer conductor. This paper deals generally with the transmission theory of coaxial circuits and extends the theory beyond the range of present application both as regards structure and frequency.
RECURSOS E COMPETÊNCIAS RELEVANTES NA BUSCA PELA VANTAGEM COMPETITIVA: O DESENVOLVIMENTO DE VEÍCULOS DE BAIXO CONSUMO PELA VOLKSWAGEN DO BRASIL

O objetivo deste artigo e verificar que recursos e competencias tem sido mais relevantes no processo de desenvolvimento de novos veiculos de menor consumo energetico pela Volkswagen do Brasil na busca da vantagem competitiva, tendo em vista a sua adesao ao programa Inovar-Auto patrocinado pelo Governo Federal. Esta fundamentado na visao baseada em recursos e suportado por pesquisa descritiva de natureza qualitativa, que incluiu entrevistas com seis gestores, das areas de planejamento do produto, engenharia do produto e marketing, integrantes do processo de desenvolvimento de novos veiculos da montadora. As respostas foram tratadas e categorizadas por meio da analise de conteudo. Como resultado foi verificado que a empresa desenvolve pessoal capacitado em analise, simulacao e medicao, e conhecimento tecnico, utilizando recursos complementares especificos e outras competencias da empresa, focando as oportunidades e ameacas inerentes ao programa Inovar-Auto, adotando basicamente a estrategia de diferenciacao do produto.
Tratamento de esgoto sanitário em sistemas alagados construídos cultivados com lírio-amarelo

In this paper, it was evaluated the efficiency of four constructed wetlands (CWs) in the treatment of sewage after being subjected to preliminary treatment, and for some SACs, primary treatment (septic tank). In CWs planted with yellow lily (Hemerocallis flava), since the gravel #0 as the support medium, and submitted to organic loading rates (OLR) of sewage, in terms of BOD (kg ha-1 d-1): 44, 98, 230 and 395 kg ha-1 d-1 and hydraulic detention times (HDT) of 3.9, 2.0, 1.0 and 0.75 days, respectively. To evaluate the efficiency of systems, analysis of BOD, COD, TSS, total-N, total-P, K and Na were performed in the influent and effluent to the SACs for 6 months of operation of the system. In all the CWs there was predominance of the anoxic/aerobic environment, and high efficiencies in the removal of TSS, BOD and COD were obtained, meeting the effluent discharge standards set by the environmental legislation of the Minas Gerais State.
Nonporous, Strong, Stretchable, and Transparent Electrospun Aromatic Polyurea Nanocomposites as Potential Anticorrosion Coating Films

This study, for the first time, focused on the fabrication of nonporous polyurea thin films (~200 microns) using the electrospinning method as a novel approach for coating applications. Multi-walled carbon nanotubes (MWCNTs) and hydrophilic-fumed nanosilica (HFNS) were added separately into electrospun polyurea films as nano-reinforcing fillers for the enhancement of properties. Neat polyurea films demonstrated a tensile strength of 14 MPa with an elongation of 360%. At a loading of 0.2% of MWCNTs, the highest tensile strength of 21 MPa and elongation of 402% were obtained, while the water contact angle remained almost unchanged (89°). Surface morphology analysis indicated that the production of polyurea fibers during electrospinning bonded together upon curing, leading to a nonporous film. Neat polyurea exhibited high thermal resistance with a degradation temperature of 380 °C. Upon reinforcement with 0.2% of MWCNTs and 0.4% of HFNS, it increased by ~7 °C. The storage modulus increased by 42 MPa with the addition of 0.2% of MWCNTs, implying a superior viscoelasticity of polyurea nanocomposite films. The results were benchmarked with anti-corrosive polymer coatings from the literature, revealing that the production of nonporous polyurea coatings with robust strength, elasticity, and thermal properties was achieved. Electrospun polyurea coatings are promising candidates as flexible anti-corrosive coatings for heat exchanges and electrical wires.
A Study of 3d Coordinate Measuring Based on Binocular Stereo Vision

It is important to obtain the 3d coordinate in the field of measuring. How accurate, fast, convenient to obtain the 3d coordinate affects the accuracy and reliability of measurement directly. Through studying the basic theories of machine vision this paper focus on the study of a stereo vision measurement model based on the intersecting axis. In view of the parameters in the model of binocular stereo vision, this paper uses the Zhang Zhengyou calibration method to calibrate the system of Stereo vision. In order to test the accuracy of the system, this paper measures the distance of two standard circular. Finally, the machining experiment validates the proposed method.
Performance Analysis of IoT-based Healthcare Heterogeneous Delay-sensitive Multi-Server Priority Queuing System

Previous studies have considered scheduling schemes for Internet of Things (IoT)-based healthcare systems like First Come First Served (FCFS), and Shortest Job First (SJF). However, these scheduling schemes have limitations that range from large requests starving short requests, process starvation that results in long time to complete if short processes are continuously added, and performing poorly under overloaded conditions. To address the mentioned challenges, this paper proposes an analytical model of a prioritized scheme that provides service differentiation in terms of delay sensitive packets receiving service before delay tolerant packets and also in terms of packet size with the short packets being serviced before large packets. The numerical results obtained from the derived models show that the prioritized scheme offers better performance than FCFS and SJF scheduling schemes for both short and large packets, except the shortest short packets that perform better under SJF than the prioritized scheme in terms of mean slowdown metric. It is also observed that the prioritized scheme performs better than FCFS and SJF for all considered large packets and the difference in performance is more pronounced for the shortest large packets. It is further observed that reduction in packet thresholds leads to decrease in mean slowdown and the decrease is more pronounced for the short packets with larger sizes and large packets with shorter sizes. Keywords—Delay tolerant; delay sensitive; internet of things; mean slowdown; prioritized scheme
Modelling of thermo-hydraulic power generation processes using Modelica

The paper discusses current approaches to the modelling and simulation of thermo-hydraulic processes, to be used as a tool for system studies in thermal power plant control. After reviewing the desirable features of simulation environments, an approach based on the Modelica language is presented and motivated. Finally, the general concepts presented above are exemplified by modelling a simple process based on a heat exchanger.
Room temperature operation of microdisc lasers with submilliamp threshold current

Electrically pumped whispering-gallery mode microdisc lasers with singlemode operation and submilliamp threshold current at room temperature are demonstrated. The device consists of an InGaAs/InGaAsP multiple quantum well (MQW) structure formed into a 2-10 mu m diameter disc approximately 0.1 mu m thick. These microdiscs are supported on an InP pedestal.