Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECTS OF HEAT-TREATMENT AND VARNISH APPLICATION ON THE COLOUR CHANGE OF PINE AND ASH LAMINATED VENEER LUMBERS

Isil islem sicakligi ve vernigin, cam (Pinus sylvestris L.) ve disbudak (Fraxinus americana) lamine edilmis levhalarinin (LVL) renk ozellikleri uzerindeki etkileri degerlendirilmistir. Ilk olarak, cam ve disbudak keresteleri, sirasiyla 1,5 ve 2 saat boyunca 190 ve 212 oC sicakliklarda isil isleme maruz birakilmistir. Isil islemden sonra LVL'ler, isil islem gormus cam ve disbudak kerestelerinden elde edilen tabakalar poliuretan (PU) ve melamin ureformaldehit (MUF) tutkallari ile yapistirilarak uretilmislerdir ve bir yuzeyleri yat vernigi ile verniklenmistir. Isil islem gormus ve verniklenmis numunelerin renk degisimini belirlemek icin bir spektrofotometre kullanilmistir. Numunelerde, isiya maruz kalma ve vernik uygulamasinin sonucu olarak belirgin renk degisimi gorulmustur. Numunelerin renk farki, tum islem sicakliklari nedeniyle artmistir. Vernik uygulamasi, her iki agac turu icin de onemli bir renk degisikligine sebep olmustur.
Improved Fault Analysis on RSA Based on Non-invasive Fault

First, by analyzing the previous fault analysis on RSA using square and multiply algorithm because these attacks require quite precise fault injections like a bit flip and the fault injection can’t be used effectively, this paper builds a new fault analysis model on RSA using square and multiply algorithm based on non-invasive fault, including the square operation fault and the multiply operation fault; Next, an improved fault analysis algorithm based on recovering key segment and a random fault analysis algorithm on RSA using SPA-FA resistant are proposed separately, these proposed fault analysis algorithms are proved in theory and the method of controlling the opportunity in fault injection is suggested; Finally, a fault attack is implemented on RSA in a Protues simulation environment, using the computer program simulation to inject the fault. Experimental results show that the proposed fault analysis algorithm based on recovering key segment can reduce the accuracy requirements of fault injection, and number of fault injection is reduced by about 80%; and the random fault analysis algorithm based on the multiply operation fault can cancel the dependence between the each key bit recover, further improve the feasibility of fault analysis.
University of Groningen Towards a feasible algorithm for tight glycaemic control in critically ill patients

Introduction Tight glycaemic control is an important issue in the management of intensive care unit (ICU) patients. The glycaemic goals described by Van Den Berghe and colleagues in their landmark study of intensive insulin therapy appear difficult to achieve in a real life ICU setting. Most clinicians and nurses are concerned about a potentially increased frequency of severe hypoglycaemic episodes with more stringent glycaemic control. One of the steps we took before we implemented a glucose regulation protocol was to review published trials employing insulin/glucose algorithms in critically ill patients. Methods We conducted a search of the PubMed, Embase and Cochrane databases using the following terms: 'glucose', 'insulin', 'protocol', 'algorithm', 'nomogram', 'scheme', 'critically ill' and 'intensive care'. Our search was limited to clinical trials conducted in humans. The aim of the papers selected was required to be glycaemic control in critically ill patients; the blood glucose target was required to be 10 mmol/l or under (or use of a protocol that resulted in a mean blood glucose = 10 mmol/l). The studies were categorized according to patient type, desired range of blood glucose values, method of insulin administration, frequency of blood glucose control, time taken to achieve the desired range for glucose, proportion of patients with glucose in the desired range, mean blood glucose and frequency of hypoglycaemic episodes. Results A total of twenty-four reports satisfied our inclusion criteria. Most recent studies (nine) were conducted in an ICU; nine others were conducted in a perioperative setting and six were conducted in patients with acute myocardial infarction or stroke. Studies conducted before 2001 did not include normoglycaemia among their aims, which changed after publication of the study by Van Den Berghe and coworkers in 2001; glycaemic goals became tighter, with a target range between 4 and 8 mmol/l in most studies. Conclusion Studies using a dynamic scale protocol combining a tight glucose target and the last two blood glucose values to determine the insulin infusion rate yielded the best results in terms of glycaemic control and reported low frequencies of hypoglycaemic episodes. Introduction Evidence is increasing that tight glycaemic control reduces morbidity and mortality in critically ill patients [1-3]. The study conducted by Van Den Berghe and coworkers in thoracosurgical intensive care unit (ICU) patients [1] yielded impressive results; glycaemic control to a mean blood glucose of 5.7 mmol/l lowered morbidity and mortality by nearly 50%. Following the publication of this trial in 2001, many attempts have been made to achieve strict glycaemic control in ICU patients, with varying and sometimes disappointing results. The glycaemic goals described by Van Den Berghe and coworkers appear difficult to achieve in a real life ICU. Furthermore, most clinicians and nurses are concerned about the potential for an increased frequency of severe hypoglycaemic episodes. Here
Treatment of Supraorbital Neuralgia Using Ultrasound-Guided Radiofrequency Thermocoagulation of the Supraorbital Nerve: A Retrospective Study

Purpose There is no standard clinical treatment protocol for supraorbital neuralgia patients who respond poorly to conservative treatment. Radiofrequency thermocoagulation, a neurologically damaging procedure, is a treatment for supraorbital neuralgia. However, assessments of its long-term efficacy are lacking. Thus, this study aimed to evaluate the long-term efficacy and safety of ultrasound-guided radiofrequency thermocoagulation for treating supraorbital neuralgia. Patients and Methods We retrospectively reviewed our clinical database for supraorbital neuralgia patients who underwent an ultrasound-guided radiofrequency thermocoagulation procedure. Demographic data and baseline characteristics, time of onset, postoperative pain intensity, time of recurrence, subsequent treatment, complications and side effects were collected and analysed. The Kaplan-Meier estimator was used to determine recurrence-free survival. Results A total of 53 supraorbital neuralgia patients were included in this study. All patients experienced complete pain relief within one month. The median follow-up time of the 53 patients was 36.0 months (IQR, 12.0–72.0 months). A total of 13 patients experienced pain recurrence, with a median recurrence-free time of 97 months according to the Kaplan-Meier estimator. The cumulative proportion of recurrence-free survival was 96.2% at 12 months, 88.4% at 24 months, 82.7% at 36 months, 70.0% at 48 months, 66.3% at 60 months, and 49.7% at 97 months. All but one patient with recurrent pain underwent a second or third radiofrequency thermocoagulation procedure and achieved complete pain relief. Numbness of supraorbital nerve innervation occurred in all patients. However, the numbness scores gradually decreased over time. Conclusion Ultrasound-guided radiofrequency thermocoagulation is a safe, effective treatment for supraorbital neuralgia patients who respond poorly to conservative treatments. These patients attained excellent long-term pain relief with a gradual reduction in numbness.
Socioeconomic inequality in smoking and its determinants in the Islamic Republic of Iran.

Background The role of socioeconomic inequality and related factors has not been well reported in tobacco consumption. Aims To investigate the socioeconomic inequality in smoking and its associated factors in the Islamic Republic of Iran. Methods Data were collected from surveillance for noncommunicable diseases in 2005, which included 89 404 people aged 15-65 years. Economic status was defined by principal component analysis on variables related to socioeconomic status. Concentration index and slope index of inequality were used to determine the inequality value. The gap between the high and low economic status groups was decomposed using the Oaxaca-Blinder decomposition method for explained and unexplained components. Results The total prevalence of smoking was 17.0%; 28.0% in males, and 5.8% in females, 15.8% in urban and 19.1% in rural areas. The concentration index was -0.032 in the whole of country; -0.098, in males, -0.246 in females, 0.014 in urban and -0.059 in rural areas and varied in different provinces of country. The smoking rate was 18.0% for the first quintile and 13.5% for the fifth quintile, a gap of 4.5%. The major part of this gap was related to differences in education level, sex, marital status and age in economic groups. Conclusion There was a pro-rich socioeconomic inequality in smoking, especially in females and in the southern provinces. Increase in education level and empowering females of low socioeconomic status are sound interventions for alleviating inequality and for tobacco control.
A Novel Model Reduction Algorithm for Static Security Assessment with Controlled Accuracy

This study proposes a novel algorithm to systematically identify a minimum set of external network elements to retain in the operator's internal model, to maintain targeted levels of accuracy in CA. The existing static network model reduction methods reduce the size of network preserving power flow in reduced model the same as in full model. None of the existing method reduce network with the objective to preserve results of contingency analysis. This paper introduces the first attempt to reduce the size of network by preserving results of power flow and enforcing the results of contingency analysis in reduced model deviate from the same results in the full model not more than the user selected thresholds for bus voltages and branch flow. The method can be applied for reduction of transmission and distribution networks. The effectiveness of the method is illustrated on 179-bus WECC system and actual ISO New England model.
Mathematical foundation for constructing accurate dynamic bipolar transistor compact models

For present and future mm- and sub-mm-wave applications, compact models need to capture the large-signal dynamic transistor behavior up to several harmonics, which are often far beyond the transistor cut-off frequencies. Since such such frequencies exceed the existing or usually affordable measurement capability, a physically and mathematically sound approach for developing compact models is required that guarantees a desired level of accuracy. This paper demonstrates the construction of physics-based equivalent circuits for the intrinsic transistor operation based on a closed-form solution of the DD transport and continuity equation. The solutions are verified based on device simulation.
Planning of EV Fast Charging Stations for Distribution System Coupled with Transportation Network

Encouragement of Electric Vehicles (EVs) due to environmental concerns calls for proper planning of charging infrastructure to promote its successful deployment. The charging infrastructure will affect both the transportation and distribution network. This paper proposes optimal siting and sizing of EV Fast Charging Station (FCS) with the aim of capturing as many charging demands as possible with consideration of customer convenience and satisfying the transportation and distribution system constraints. Therefore, the spatial-temporal charging demand is first identified and based on the queuing model, the size of the probable FCS is determined to minimize the number of FCS slots within the allowed waiting time. Then the constrained Mixed Integer Nonlinear Programming (MINLP) problem is formulated to capture the maximum EV flow and solved using Binary Grey Wolf Optimizer (BGWO) algorithm. The effectiveness of the proposed planning approach is investigated on IEEE 123 bus distribution system coupled with 25-node transportation network.
Quantification of Morphological Features in Non-Contrast-Enhanced Ultrasound Microvasculature Imaging

There are significant differences in microvascular morphological features in diseased tissues, such as cancerous lesions, compared to noncancerous tissue. Quantification of microvessel morphological features could play an important role in disease diagnosis and tumor classification. However, analyzing microvessel morphology in ultrasound Doppler is a challenging task due to limitations associated with this technique. Our main objective is to provide methods for quantifying morphological features of microvasculature obtained by ultrasound Doppler imaging. To achieve this goal, we propose multiple image enhancement techniques and appropriate morphological feature extraction methods that enable quantitative analysis of microvasculature structures. Vessel segments obtained by the skeletonization of the regularized microvasculature images are further analyzed to satisfy other constraints, such as vessel segment diameter and length. Measurements of some morphological metrics, such as tortuosity, depend on preserving large vessel trunks. To address this issue, additional filtering methods are proposed. These methods are tested on in vivo images of breast lesion and thyroid nodule microvasculature, and the outcomes are discussed. Initial results show that using vessel morphological features allows for differentiation between malignant and benign breast lesions (p-value < 0.005) and thyroid nodules (p-value < 0.01). This paper provides a tool for the quantification of microvasculature images obtained by non-contrast ultrasound imaging, which may serve as potential biomarkers for the diagnosis of some diseases.
Curtailment Contract Design for HVAC Systems and Benefits for Management of Uncertain Generation

This contribution proposes a curtailment contract design for HVAC systems and analyzes how such contracts can improve the performance of an aggregator with uncertain generation. We build upon a design brought forward by Cam-paigne and Oren (2016) and modify the approach for consumers with HVAC systems. Specifically, we include assumptions about the concavity of utility and time-interdependencies in multiperiod contracts and propose an online cut-off rule for the aggregator. We use mechanism design to formulate an incentive-compatible contract set for customers with different valuations for temperature deviations. Consumer welfare and aggregator profits are analyzed under different parametrizations.
Three-Phase AC-DC Converter with Asymmetrical Vienna Rectifier

In this paper, a 3-phase 3-switch 3-level 3-wire AC-DC converter topology with asymmetrical Vienna rectifier is Proposed. The asymmetrical Vienna rectifier is controlled using DCM (Discontinuous Conduction Mode) operation to achieve a simple and low cost control methods. At the same time, the advantage of the asymmetrical Vienna rectifier is studied using PSPICE simulation analysis. The aim of this paper is to provide a simple control and low cost solutions for high power factor 3-phase AC-DC converter applications. The voltage stresses on semiconductor devices, also the behavior of current and voltage waveforms in asymmetrical Vienna rectifier with DCM operation will be analyzed in this paper. The advantage of the proposed asymmetrical Vienna rectifier regarding the conduction losses is also discussed.
A framework for scheduling in cloud manufacturing with deep reinforcement learning

Cloud manufacturing is a novel service-oriented networked manufacturing paradigm that aims to provide on-demand manufacturing cloud services to consumers. Scheduling is a critical means for achieving that aim. Currently, research on scheduling in cloud manufacturing is still in its infancy, and current frequently adopted meta-heuristic algorithm-based approaches have some shortcomings, e.g. they require complex design processes and lack adaptability to dynamic environments. Deep reinforcement learning (DRL) that combines advantages of reinforcement learning and deep learning provides an efficient, adaptive and intelligent approach for solving scheduling problems in cloud manufacturing. However, to the best of our knowledge, there has been no application of DRL to scheduling in cloud manufacturing. This work conducts a preliminary exploration over this issue. First, a DRL-based framework for scheduling in cloud manufacturing is proposed. Then a DRL model for online single-task scheduling in cloud manufacturing is presented to demonstrate the effectiveness of the framework. DRL as a promising technique will find wide applications in cloud manufacturing, and this work can provide some reference for future research on this.
A New Similarity Computing Model of Collaborative Filtering

Collaborative filtering has become one of the most widely used methods for a variety of commercial recommendations. The key to collaborative filtering is use similarity calculation formula to find similar neighbors or projects. However, most similarity calculation methods only use the user common score and provide bad recommendations. This paper proposes a new similarity measure method, which effectively utilizes the user context information. The new method uses a singularity factor to adjust nonlinear equation and takes into account the user scoring habits. It can improve the accuracy of the prediction. The new method has been tested on the dataset and compared with other algorithms. The results show that the proposed method can improve the recommendation quality.
Optical Flow Feature Based for Fire Detection on Video Data

A fire detection on video data using optical flow feature is presented to improve the performance of detection when using only texture or color feature. We compare two kinds of optical flow that are dense optical flow using Farneback algorithm and sparse optical flow using the Lucas Kanade algorithm. The fusion of optical flow feature and Local Binary Pattern (LBP) as a texture feature is used to classify the video frame as fire or not fire using Support Vector Machine (SVM). There are three phases for fire detection in our framework. First, segmentation on each video frames based on Hue, Saturation, Value (HSV) color space is done to obtain the candidate of the fire area. Second, feature extraction using optical flow and LBP method is done to achieve the movement and texture features of the fire. Finally, the extracted features are classified to fire or not fire using the SVM method. The model is evaluated using stratified 10-folds cross-validation to be separated into learning process data and validation data. The best result is obtained using the Lucas Kanade optical flow feature and using a linear kernel SVM with 96.21% in accuracy.
A Scalable Multimode 24-GHz Radar Transceiver for Industrial and Consumer Applications in a 0.13µm SiGe BiCMOS Technology

This paper presents a 24-GHz radar system based on a low-power, low-noise, multichannel, scalable transceiver (TRX) monolithic microwave integrated circuit (MMIC). The MMIC features two homodyne quadrature downconversion receivers with a single-sideband noise figure (NFssb) of 8 dB and an adjustable conversion gain (CG) of up to 87dB using integrated programmable analog baseband (ABB) amplifiers. Integrated baseband bypass feature allows using the same MMIC for Doppler as well as Frequency-Modulated Continuous-Wave (FMCW) radar applications. The transmit section features a fundamental frequency voltage controlled oscillator (VCO) and two transmitters with bidirectional RF ports. The transmitter delivers a maximum output power of +5 dBm. The measured phase noise at 100kHz offset was -90 dBc/Hz. Designed in a production ready 0. 13µm SiGe:C BiCMOS technology, the chip consumes 165 mA from a 1.5V power supply with all blocks turned ON and consumes less than 100µA in the standby mode.
A SLAM-Integrated Kinematic Calibration Method for Industrial Manipulators with RGB-D Cameras

The accuracy of robot manipulator, one of the long-standing problem, is a major issue in the industry community. The manipulator may produce kinematic errors during operation. Traditional methods require expensive equipment with complex steps to calibrate kinematic parameters. Another issue is motion planning of the manipulator, which requires a map of the workspace. However, the mapping is time-consuming. In order to employ an efficient way to accomplish kinematic calibration and offer convenience to plan the motions of the manipulator, we study a new method called SKCLAM (Simultaneous Kinematic Calibration, Localization, and Mapping), which can calibrate the kinematic parameters of an industrial manipulator and achieve 3D environmental mapping simultaneously by employing an RGB-D camera attached to the end effector. In this paper, the true kinematic parameters were changed randomly to test and evaluate the effectiveness of our approach in simulation.
Modest Android Application Development for the Entrepreneurship in Art and Culture Organization

Currently, several platforms to develop an Android Application are available freely at the internet. People who do not have any major in computer science are able to create the mobile application by themselves. The technical aspects on creating the application have been successfully minimized and the creators do not need to type any programming code to create the application. However, most of them need to understand the pattern to create the menu and its function to work. The simplicity in making the Android application could help the managers of art and culture performances to spread the information and knowledge of their business to the modern users. This paper explores the possibility and technical aspects in developing a simple Android application for the entrepreneurship in art and culture organization. The approach in developing the Android application should be able to help the organization that manages art and culture in spreading the information to the people simultaneously.
Social Internet of Digital Twins via Distributed Ledger Technologies: Application of Predictive Maintenance

Predictive Maintenance in the era of Internet-of-Things is envisioned to be an important functionality of consumer electronics along with many other application areas. Challenges for Predictive Maintenance within Internet-of-Things ecosystem include heterogeneity, scalability, identification, constrained resources, mobility, security, and privacy. In this paper, we propose a reference model for Predictive Maintenance of commercial appliances, the digital twins of which interact socially via Distributed Ledger Technologies.
Non-Invasive Meningitis Diagnosis Using Decision Trees

Meningitis is one of the pandemic diseases that many less developed countries suffer, primarily due to the lack of economic resources to face it. The more severe types of meningitis, Meningococcal Disease, MD, demand immediate medical attention since delays increase the risk of mortality. This paper presents an open and integrated Clinical Decision Support System to assist physicians in the different stages of meningitis diagnostics through observable symptoms. Our system integrates three intelligent components which try to give support to physicians in early diagnostics of meningitis. These components are based on interpretable tree-based machine learning models and knowledge-engineering techniques. A dataset of 26,228 records of patients with a meningitis diagnosis in Brazil was used to construct and evaluate the system. The performance indicators of the decision models exhibit an outstanding classification performance for MD meningitis with a classification accuracy of 94.3%. In order to test the correct diagnosis of the system, an evaluation study with real patients’ data was performed. The experimental results concluded that excluding meningitis cases based only on observable symptoms is much more complicated than diagnosing it. However, the system properly diagnosed 88% of meningitis cases from the real database.