Miracle-dz/UniFAR-FTUs huggingface dataset:
A Gene-Based Machine Learning Classifier Associated to the Colorectal Adenoma—Carcinoma Sequence

Colorectal cancer (CRC) carcinogenesis is generally the result of the sequential mutation and deletion of various genes; this is known as the normal mucosa–adenoma–carcinoma sequence. The aim of this study was to develop a predictor-classifier during the “adenoma-carcinoma” sequence using microarray gene expression profiles of primary CRC, adenoma, and normal colon epithelial tissues. Four gene expression profiles from the Gene Expression Omnibus database, containing 465 samples (105 normal, 155 adenoma, and 205 CRC), were preprocessed to identify differentially expressed genes (DEGs) between adenoma tissue and primary CRC. The feature selection procedure, using the sequential Boruta algorithm and Stepwise Regression, determined 56 highly important genes. K-Means methods showed that, using the selected 56 DEGs, the three groups were clearly separate. The classification was performed with machine learning algorithms such as Linear Model (LM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Artificial Neural Network (ANN). The best classification method in terms of accuracy (88.06 ± 0.70) and AUC (92.04 ± 0.47) was k-NN. To confirm the relevance of the predictive models, we applied the four models on a validation cohort: the k-NN model remained the best model in terms of performance, with 91.11% accuracy. Among the 56 DEGs, we identified 17 genes with an ascending or descending trend through the normal mucosa–adenoma–carcinoma sequence. Moreover, using the survival information of the TCGA database, we selected six DEGs related to patient prognosis (SCARA5, PKIB, CWH43, TEX11, METTL7A, and VEGFA). The six-gene-based classifier described in the current study could be used as a potential biomarker for the early diagnosis of CRC.
Complementary Discriminant Analysis for Classification of Double Attributes

Real-world objects often have two or more significant attributes. For example, face images have attributes of persons, expressions, and so on. Even if we are interested in only one of those attributes, additional informations on auxiliary attributes can help recognition of the main one. In the present paper, the authors propose a method for pattern recognition with double attributes. A pair of classifiers are combined: each classifier makes a guess of its corresponding attribute, and it tells the guess to the other as a hint. Equilibrium point of this iteration can be calculated directly without iterative procedures.
A multifunctional all‐fiber device with a magnetic nanoparticles core mode blocker for wavelength bandpass filtering and multi‐to‐single mode conversion

A multifunctional all‐fiber device that can simultaneously serve as a multimode‐to‐single mode converter and a wavelength filter is presented in this article. The device has a concatenated structure consisting of a multimode fiber, a hollow core fiber, a single‐mode core mode blocker (made of magnetic nanoparticles), and a long period fiber grating fabricated in a single mode fiber. A quantitative theoretical analysis and experiments are carried out. A device with an extinction ratio of 25.7 dB and an insertion loss of −6.8 dB is obtained, and its performance should be able to be further improved by optimizing the fabrication process in future. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:740–744, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25834
Two-Factor Authenticated Key Exchange Protocol in the Three-Party Setting

This paper proposes a three-party authenticated key exchange protocol using two-factor including a password and a token. The proposed protocol allows two users to establish a session key through a trusted server with whom they both share a human-memorable password and a token. Over the past years, many three-party authenticated key exchange protocols have been proposed. However, many proposed protocols use smart cards with tamper-resistance property as tokens. It is not practical by using smart cards because of the high cost and the infrastructure requirements. Therefore, the proposed paper only uses a common storage device such as a USB memory stick. We believe the proposed protocol is suitable for practical scenarios.
Alginate NiFe2O4 Nanoparticles Cryogel for Electrochemical Glucose Biosensor Development

Glucose biosensors based on porous material of alginate cryogel has been developed, and the cryogel provides a large surface area for enzyme immobilization. The alginate cryogel has been supplemented with NiFe2O4 nanoparticles to improve the electron transfer for electrochemical detection. The fabrication parameters and operational conditions for the biosensor have also been optimized. The results showed that the optimum addition of NiFe2O4 nanoparticles to the alginate solution was 0.03 g/mL. The optimum operational conditions for the electrochemical detection were a cyclic voltammetry scan rate of 0.11 V/s, buffer pH of 7.0, and buffer concentration of 150 mM. The fabricated alginate NiFe2O4 nanoparticles cryogel-based glucose biosensor showed a linear response for glucose determination with a regression line of y = 18.18x + 455.28 and R² = 0.98. Furthermore, the calculated detection limit was 0.32 mM and the limit of quantification was 1.06 mM.
Self-Control at Meals in Order Not to Gain Weight and Its Relationship with Food Cravings in Low-Carb Dieters

Aims: This study’s objective was to verify the level of food cravings and cognitive restraint in women who reported having followed a low-carb diet. Methods: The volunteers filled out the binge eating scale, the cognitive restraint subscale, the food cravings trait and state questionnaires, and the food frequency questionnaire. This study has also compared participants according to the self-reported practice of a low-carb diet. Parametric tests were used to compare the groups and Pearson’s correlations between some variables of interest (p > 0.05 adopted). Results: According to the question regarding the practice of the low-carb diet, 39 participants had tried a low-carb diet in the last six months (46.2% of these with binge eating) and 48 did not (16.7% with binge eating). Dieters showed higher levels of binge eating, cognitive restriction, and food cravings (trait and state). Dieters consumed less rice and bread but did not present lower consumption of sweets. Correlations showed that for dieters who ‘Consciously hold back at meals in order not to gain weight’, it was negatively associated with ‘Intention and plans to eat’, ‘positive reinforcement’ and ‘relieving negative states’ and food cravings scores. Conclusions: Despite the higher levels of cognitive restriction in dieters, they are negatively correlated with food craving factors and negatively associated with the consumption of sweets.
Transmission line pulse system for avalanche characterization of high power semiconductor devices

Because of the increasing in power density of electronic devices for medium and high power application, reliabilty of these devices is of great interest. Understanding the avalanche behaviour of a power device has become very important in these last years because it gives an indication of the maximum energy ratings which can be seen as an index of the device ruggedness. A good description of this behaviour is given by the static IV blocking characteristc. In order to avoid self heating, very relevant in high power devices, very short pulses of current have to be used, whose value can change from few milliamps up to tens of amps. The most used method to generate short pulses is the TLP (Transmission Line Pulse) test, which is based on charging the equivalent capacitance of a transmission line to high value of voltage and subsequently discharging it onto a load. This circuit let to obtain very short square pulses but it is mostly used for evaluate the ESD capability of semiconductor and, in this environment, it generates pulses of low amplitude which are not high enough to characterize the avalanche behaviour of high power devices . Advanced TLP circuit able to generate high current are usually very expensive and often suffer of distorption of the output pulse. In this article is proposed a simple, low cost circuit, based on a boosted-TLP configuration, which is capable to produce very square pulses of about one hundreds of nanosecond with amplitude up to some tens of amps. A prototype is implemented which can produce pulses up to 20A of amplitude with 200 ns of duration which can characterize power devices up to 1600V of breakdown voltage. Usage of microcontroller based logic make the circuit very flexible. Results of SPICE simulation are provided, together with experimental results. To prove the effectiveness of the circuit, the I-V blocking characteristics of two commercial devices, namely a 600V PowerMOS and a 1200V Trench-IGBT, are measured at different operating temperature.
A new frequency tracking and phasor estimation algorithm for generator protection

Digital generator protection is a complex and difficult problem. Analog and solid state methods have been successfully applied to generator protection in the past and implementation of these functions in a digital device is a continuing trend. This paper explores a new method to implement frequency tracking and phasor estimation in a numerical relay. A new algorithm is presented which utilizes a variable window discrete Fourier transform (DFT) for frequency tracking. Use of the DFT to compute the phasor estimates at a frequency other than the assumed frequency is outlined first. Next, a new technique for tracking the frequency is outlined. The paper concludes with testing of the new algorithm.
Solution to the Problem of Inaccurate Duration-Estimation: Relational Database Model for Design/Build Organizations

For various participants of projects, accuracy in the estimation of project duration is vital at various stages of the building production process. The importance of the problem has been acknowledged by the industry worldwide, and various models have been proposed in the literature, but all have their limitations. This paper presents a performance-based duration-estimation model that utilizes historical knowledge, in particular information to estimate the worst, best and likely variances of current projects by using the unified structure of an information system developed for design/build organizations. The model allows the estimators to make use of data on past projects. Proposed organizational pattern, design/build, are vital for accessing the as-built schedules and variance information provided by construction groups in a design/build organizational pattern. Our final solution includes an integrated relational database model developed for design/build organizations that provides the integration of satellite software packages, thus eliminating additional efforts for obtaining the data needed in the estimation process.
The IMAGE 2 Hundred Year (1890 - 1990) Data Base of the Global Environment (HYDE)

Het testen met historische gegevens is een belangrijke stap tijdens de validatie van simulatiemodellen. Vanwege de brede strekking en omvang hebben 'global change' modellen een enorme behoefte aan historische data. Dit rapport presenteert de resultaten van een verzameling historische gegevens in de vorm van een honderdjarige database, die gebruikt kan worden voor het testen van 'global change' modellen. De database bestaat niet alleen uit algemene onderwerpen zoals de bevolking, het landgebruik, de veestapel, het nationaal inkomen en de toegevoegde waarde van industrie en diensten, maar bevat ook specifieke data wat betreft de energie/industrie sector, de atmosfeer en oceanen, en de terrestrische biosfeer. Waar mogelijk zijn de data op landenbasis verzameld voor de periode 1890 tot 1990. Sommige data zijn geografisch expliciet, echter dit zijn zeer voorlopige eerste schattingen.
Hybrid Convolutional Neural Network-Based Diagnosis System for Intracranial Hemorrhage

Early diagnosis of intracranial hemorrhage significantly reduces mortality. Hemorrhage is diagnosed by using various imaging methods and the most time-efficient one among them is computed tomography (CT). However, it is clear that accurate CT scans requires time, diligence, and experience. Computer-aided design methods are vital for the treatment because they facilitate early diagnosis of intracranial hemorrhage. At this point, deep learning can provide effective outcomes through an automated diagnosis way. However, as different from the known solutions, diagnosis of five different hemorrhage subtypes is a critical problem to be solved.This study focused on deep learning methods and employed cranial computed tomography scans in order to detect intracranial hemorrhage. The diagnosis approach in the study aimed to detect five subtypes of hemorrhage. In detail, EfficientNet-B3 and ResNet-Inception-V2 architectures were used for diagnosis purposes. Eventually, the study also proposed a two-architecture hybrid method for the diagnosis purpose. The obtained findings by the hybrid method were evaluated in terms of a comparative perspective.Results showed that the newly designed hybrid method was quite effective in terms of increasing classification rates of detecting intracranial hemorrhage according to the subtypes. Briefly, an accuracy of 98.5%, which is higher than those of the EfficientNet-B3 and the Inception-ResNet-V2, were obtained thanks to the developed hybrid method.
THE SIMULATION MODEL OF HIGHPRESSURE, SUSPENSION WATERJET CUTTING PROCESS WITH USE OF THE ARTIFICIAL NEURAL NETWORK

This article presents the use of artificial neural networks to simulation the process of hydroabrassive suspension jet cut whose pressure is reduced to 30 MPa. Three-ply layer perceptron type network with an error backpropagation learning algorithm was applied to describe this process. The article provides detailed description of neural network. This neural network simulates the limestone treatment process and predicts its efficiency due to given parameters. Impact of the most important parameters, as pressure, traverse speed, abrasive flow rate, length and diameter of nozzle was shown. Were determined in which cases this depth is biggest.
Who wants to wash away their sins? Guilt and shame proneness and behavioral moral cleansing endorsement: a pilot study

In our pilot cross-sectional study, we aimed to explore the associations between guilt and shame proneness and moral cleansing endorsement. Our sample consisted of 484 adults (73.3% females), aged 18 and 53 (M=24.09, SD=7.32). We used a novel approach to explore moral cleansing mechanisms, i.e., a two-item scale assessing behavioral cleansing endorsement (one's agreement with the idea that people must "wash away" their immoral acts by acting in ethical ways that would "clean" their moral debt). In addition to the significant associations that we found between moral cleansing endorsement and the guilt and shame proneness dimensions (i.e., negative behavior evaluation, repair action tendencies, negative self-evaluation, and withdrawal action tendencies), results also suggested that moral cleansing endorsement was significantly predicted by overall guilt and shame proneness. More specifically, we found that higher levels of guilt and shame proneness might account for higher moral cleansing endorsement levels. We also found important associations with participants' age: our findings suggested that the higher the age, the higher the endorsement for moral actions aimed to "clean" immoral deeds. Results are discussed in relation to cultural-related factors.
Productivity Prediction of Fractured Horizontal Well in Shale Gas Reservoirs with Machine Learning Algorithms

Predicting shale gas production under different geological and fracturing conditions in the fractured shale gas reservoirs is the foundation of optimizing the fracturing parameters, which is crucial to effectively exploit shale gas. We present a multi-layer perceptron (MLP) network and a long short-term memory (LSTM) network to predict shale gas production, both of which can quickly and accurately forecast gas production. The prediction performances of the networks are comprehensively evaluated and compared. The results show that the MLP network can predict shale gas production by geological and fracturing reservoir parameters. The average relative error of the MLP neural network is 2.85%, and the maximum relative error is 12.9%, which can meet the demand of engineering shale gas productivity prediction. The LSTM network can predict shale gas production through historical production under the constraints of geological and fracturing reservoir parameters. The average relative error of the LSTM neural network is 0.68%, and the maximum relative error is 3.08%, which can reliably predict shale gas production. There is a slight deviation between the predicted results of the MLP model and the true values in the first 10 days. This is because the daily production decreases rapidly during the early production stage, and the production data change greatly. The largest relative errors of LSTM in this work on the 10th, 100th, and 1000th day are 0.95%, 0.73%, and 1.85%, respectively, which are far lower than the relative errors of the MLP predictions. The research results can provide a fast and effective mean for shale gas productivity prediction.
Phase VI Advanced EVA Glove Development and Certification for the International Space Station

Since the early 1980’s, the Shuttle Extra Vehicular Activity (EVA) glove design has evolved to meet the challenge of space based tasks. These tasks have typically been satellite retrieval and repair or EVA based flight experiments. With the start of the International Space Station (ISS) assembly, the number of EVA based missions is increasing far beyond what has been required in the past; this has commonly been referred to as the “Wall of EVA’s”. To meet this challenge, it was determined that the evolution of the current glove design would not meet future mission objectives. Instead, a revolution in glove design was needed to create a high performance tool that would effectively increase crewmember mission efficiency.
Active tactile perception of object surface geometric profiles

A tactile probe is presented which was designed specifically to respond to a 3D geometric profile stimulus (measurand). In order to improve the tactile probe's compliance to the explored object surface, the authors have used an instrumented passively compliant wrist to attach the probe to the manipulator. It was found that special attention has to be paid in choosing the dimensions of the tactile sensing array. A large array would provide more parallel data per pose, which means that fewer poses will be required to explore a large object surface. On the negative side, a large array is a large rigid plane which will touch relatively fewer and higher peaks (and consequently will miss relatively minor surface details) than a smaller array. >
Kinematic and Force Analysis of Articulated Mechanical Hands

Bio BIO Salisbury worked on the development of the Stanford-JPL Robot Hand, the JPL Force Reflecting Hand Controller, the MIT-WAM arm, and the Black Falcon Surgical Robot. His work with haptic interface technology led to the founding of SensAble Technology, producers of the PHANToM haptic interface and software. He also worked on the development of telerobotic systems for dexterity enhancement in the operating room. His current research focuses on human-machine interaction, cooperative haptics, medical robotics, and surgical simulation.
Post-COVID-19 Patients Who Develop Lung Fibrotic-like Changes Have Lower Circulating Levels of IFN-β but Higher Levels of IL-1α and TGF-β

Purpose: SARS-CoV-2 infection induces in some patients a condition called long-COVID-19, herein post-COVID-19 (PC), which persists for longer than the negative oral-pharyngeal swab. One of the complications of PC is pulmonary fibrosis. The purpose of this study was to identify blood biomarkers to predict PC patients undergoing pulmonary fibrosis. Patients and Methods: We analyzed blood samples of healthy, anti-SARS-CoV-2 vaccinated (VAX) subjects and PC patients who were stratified according to the severity of the disease and chest computed tomography (CT) scan data. Results: The inflammatory C reactive protein (CRP), complement complex C5b-9, LDH, but not IL-6, were higher in PC patients, independent of the severity of the disease and lung fibrotic areas. Interestingly, PC patients with ground-glass opacities (as revealed by chest CT scan) were characterized by higher plasma levels of IL-1α, CXCL-10, TGF-β, but not of IFN-β, compared to healthy and VAX subjects. In particular, 19 out of 23 (82.6%) severe PC and 8 out of 29 (27.6%) moderate PC patients presented signs of lung fibrosis, associated to lower levels of IFN-β, but higher IL-1α and TGF-β. Conclusions: We found that higher IL-1α and TGF-β and lower plasma levels of IFN-β could predict an increased relative risk (RR = 2.8) of lung fibrosis-like changes in PC patients.
The Impact of Transport Infrastructure Investment on Energy Intensity: The Perspective of Fiscal Decentralization

Investment in China’s transport infrastructure has contributed to its rapid economic growth, which also consumes a great deal of energy and generates a significant amount of carbon emissions. In these circumstances, it is worthwhile to discuss the internal influence mechanisms behind these two outcomes’ similar growth trends. This paper selects panel data from 30 regions in China from 2009 to 2019 and uses the threshold spatial autoregressive (TSAR) model to analyze the impact of transport infrastructure investment on the energy intensity due to fiscal decentralization. While studies of the relationship between transport infrastructure investment and energy intensity exist, few studies examine the non-linear spatial relationship between the two. This paper fills this gap by using the TSAR Model. The results show the following: (1) the effect of transport infrastructure investment on the energy intensity under fiscal decentralization and heterogeneity expresses non-linear characteristic; (2) there is a positive relationship between infrastructure investment and energy intensity when the degree of attenuation is low, but when the degree of attenuation is higher than a particular threshold value, transport infrastructure investment negatively impacts energy intensity; (3) rising energy prices, increasing investment in technological innovation costs, and increasing foreign trade will help to drive the decline in energy intensity.
Proctor Plumbing Limited: A Case Study Of The Balanced Scorecard (BSC)

This paper presents a teaching case study on the balanced scorecard (BSC) for a plumbing firm. The case requires participants to apply BSC principles by producing a wall poster illustration of the BSC. The participants are then required to take a gallery tour of the wall poster illustrations, share their experiences and assess the illustrations for both content and presentation. The case is applicable in introductory and advanced management accounting courses.