Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics and Survival during Hepatocellular Carcinoma: Comparison between Hepatitis B and C Viruses

Introduction: Chronic hepatitis B and C remain the leading causes of cirrhosis and Hepatocellular Carcinoma (HCC) in Africa and South East Asia. In Tunisia, they account for more than 75% of the etiologies of this cancer. The purpose of this study was to compare the clinical features and survival in patients with Hepatitis B Virus-related Hepatocellular Carcinoma (HBV-HCC) and Hepatitis C Virus-related Hepatocellular Carcinoma (HCV-HCC). Patients and Methods: A retrospective study (January 2002-December 2017) including all cases of HCC developed on post viral B and C cirrhosis, diagnosed in Gastroenterology department of Habib Thameur hospital was conducted. Epidemiological, clinical, biological, morphological characteristics, therapeutic modalities and evolution were compared between HBC and HCV related HCC. Results: A total of 84 cases of HCC were included: 53 patients with HCV- HCC and 31 patients with HBV-HCC. The mean age of HBV-HCC patients was 60.1 years, while it was 68.9 years in HCV-HCC patients (p=0.03). The male/ female ratio was 9.3 in HBV-HCC, while it was 1.4 in HCV-HCC (p<0.001). The mean alpha-fetoprotein level in HBV-HCC was 16196ng/ml, while it was 28148ng/mL in HCV-HCC (p=NS). The mean tumor size was 50.1mm in HBV-HCC, while it was 44.5mm in HCV-HCC (p=NS). According to the BCLC classification, patients with HBV-HCC were classified in stage A in 6.4% of cases (N=2), in stage B in 22.5% of cases (N=7), in stage C in 38.7% of cases (N=12) and stage D in 32.2% of cases (N=10). Patients with HCV-HCC were classified in stage A in 26.4% of cases (N=14), in stage B in 30.1% of cases (N=16), in stage C in 20.7% of cases (N=11) and stage D in 22.6% of cases (N=12) (p=0.05). The mean survival was 8,6 months in HBV-HCC, while it was 23.9 months in HCV-HCC (p=0.03). Conclusion: Compared with HBV-HCC patients, HCV-HCC patients were older, had a lower male/female ratio, were diagnosed at a less advanced stage and had a better survival. evolutionary aspects between hepatitis B and C-related HCCs and those not linked to these viruses (secondary to immunological cirrhosis, alcoholic or non-alcoholic steatohepatitis). Most of them concluded that hepatitis B and C related HCCs had a poorer prognosis. However, few studies have focused on the impact of the viral etiology (B or C) of cirrhosis on clinical presentation and outcome of HCC.
The Impact of Human Capital on Non-Market Outcomes and Feedbacks on Economic Development

This paper develops the rationale and empirical methods for measuring the non-market social outcomes of education, plus their feedback effects on economic growth. It also seeks to identify and measure externalities, some of which are included in these feedback effects. As such, this paper situates human capital in a broader framework of measures of social outcomes generally desired for broader economic development and sustainability. Finally, the paper develops the conceptual framework for tracing and measuring the interrelations among these social outcomes, including those social outcomes which are also inputs and are aspects of social capital. The direct effects of education on each social outcome and on economic growth will be distinguished from the indirect feedback effects. The indirect effects are externalities because the effect on the outcome in question of a relatively small investment in education by one household is expected to be negligible and, therefore, is not taken into account by the individual or his/her family as they invest in education. Furthermore, the benefits of these indirect effects are not enjoyed by the individual as the direct result of how much he or she invests, but are freely available to all. Beyond this, if one assumes that there is not perfect information, a number of these indirect effects are very unlikely to be known by the average investor, and, therefore, cannot be taken into account as investment decisions in education are made. The feedback effects are also externalities freely available to others because the lags are very long so the benefits are frequently only enjoyed by
Dynamic Voltage Overshoot During Triggering of an SCR-Type ESD Protection

During triggering of an on-board ESD protection, e.g., an SCR, its voltage may temporarily rise to a large value. This work identifies and distinguishes the two major factors that contribute to this voltage overshoot: (1) the conductivity modulation in the silicon and (2) the inductance of the metal traces. The overshoot is shown to have a major impact on the residual current into the IC during a system level discharge (ESD gun test), in particular in high-speed interfaces, such as USB3. This work presents a physics-based behavioral model which accurately describes both contributions to the voltage overshoot. The conductivity modulation model is based on the premise that the doping concentration of a region in the SCR is insufficient to conduct the trigger current. It follows that a minimum charge must be injected into the low-doped region to enable conduction of the trigger current. SCR triggering is delayed until this minimum charge has been injected. During the delay time, the SCR voltage is determined by the high ( $\sim 1~\text{k}\Omega $ ) off-resistance of the device. After triggering, the voltage is determined by the low-impedance ( $\sim 0.25~\Omega $ ) on-resistance. The net effect is a voltage overshoot. This first overshoot is followed by a second inductive voltage overshoot at the time when L.dI/dt is maximum. The paper demonstrates how the resulting model can be used to improve system level robustness for a typical USB3 interface.
Plant Species Recognition Using Triangle-Distance Representation

Plant species recognition using leaf images is a highly important and challenging issue in botany and pattern recognition. A center problem of this task is how to accurately extract leaf image characteristics and quickly calculate the similarity between them. This article presents a new shape description approach called triangle-distance representation (TDR) for plant leaf recognition. The TDR descriptor is represented by two matrices: a sign matrix and a triangle center distance matrix. The sign matrix is used to characterize the convex/concave property of a shape contour, while the triangle center distance matrix is used to represent the bending degree and spatial information of a shape contour. This method can effectively capture the detailed and global characteristics of a leaf shape while keeping the similarity transformations (translation, rotation, and scaling) unchanged. In addition, this method is quite compact and has low computational complexity. We tested our method on four standard plant leaf datasets, including the famous Swedish, Smithsonian, Flavia, and ImageCLEF2012 datasets. The results confirm that our approach exceeds the prior state-of-the-art shape-based plant leaf recognition approaches. An extra experiment on the MPEG-7 shape dataset further shows that our method can be applied to general shape recognition.
Are healthcare teams meeting patient expectations1 Running head: ARE HEALTHCARE TEAMS MEETING PATIENT EXPECTATIONS Are interprofessional healthcare teams meeting patient expectations? An exploration of patients’ and informal caregivers’ perceptions

Poor teamwork skills in healthcare have been found to be a contributing cause of negative incidents in patient care, whilst effective teamwork has been linked to more positive patient outcomes. The aim of this research is to explore views of patients and informal caregivers on the key characteristics of effective healthcare teams and their experiences of healthcare teams using a qualitative approach. A focus group schedule was developed from existing literature to explore this. Topics included the purpose and value of teams in patient care, key attributes and their impact on patient care. Patients and informal caregivers were recruited via convenience sampling. Three focus groups were conducted. Thematic analysis identified a number of themes associated with effective teams. These themes included the perceived purpose of teams, perceptions about the structure of a team, team-based communication, the role of patients, delivery of care. Research participants noted the importance of key characteristics in effective teams, but felt that these were not always consistently present. Communication was considered to be the most important attribute in team working and also appeared to be the area in which the patient experience can be significantly improved. It is clear from the findings of this research that further improvements in teamwork skills in healthcare are needed to achieve effective collaborative practice, sustainable service delivery models and optimal patient care as outlined by the World Health Organization (2016).
HLMCC: A Hybrid Learning Anomaly Detection Model for Unlabeled Data in Internet of Things

The Internet of Things (IoT) is a network of distributed devices or sensors connected through the internet to allow gathering and sharing of data. The data generated by these devices is affected by anomalies or abnormal behaviour due to attack issues, or breakdown in devices, as examples. However, most current anomaly detection systems rely on labelled data, while the class labels for IoT data are usually unavailable. Furthermore, the manual labelling task is expensive and time-consuming to perform due to the need for domain experts. More importantly, the volume of data in the IoT is growing rapidly, creating a need to predict the classification labels for future data. This study proposes a Hybrid Learning Model which uses both Clustering and Classification methods (HLMCC) to automate the labelling process and detect anomalies in IoT data. The model consists of two practical phases, automatic labelling and detecting anomalies. First, the HLMCC groups the data into normal and anomaly clusters by adopting Hierarchical Affinity Propagation (HAP) clustering. Second, the labelled data obtained from the clustering phase is used to train the Decision Trees (DTs) and to classify future unseen data. The results show that the HLMCC is able to automate the labelling of data, which is beneficial to minimize human involvement. Moreover, HLMCC outperforms the DTs on the originally labelled datasets and the state-of-the-art model over a wide range of evaluation metrics based on the average ranks. HLMCC produces the highest average ranks against other models in terms of False Positive Rate (FPR), recall, precision and the Area Under the Precision-Recall curve (AUCPR) with 1.8, 1.6, 1.8 and 1.8, respectively.
Silver Nanoparticles SERS Sensors Using Rapid Thermal CVD Nanoscale Graphene Islands as Templates

Surface enhanced Raman scattering (SERS) sensors have been fabricated by rapid thermal chemical vapor deposition of high-density nanoscale discrete graphene islands on copper foils followed by electroless chemical plating of discrete, closely spaced, and irregularly shaped silver nanoparticles on the copper surface where it is not covered by graphene islands. By fine tuning of the size and distribution of graphene islands and adjusting the deposition time for silver nanoparticles, nanoscale gaps between silver particles are fabricated. SERS sensors exhibiting Raman scattering signal enhancement factors as high as 1014 in reference to a bare copper have been demonstrated. Raman scattering signal has been measured from as low as 10−16 M of R6G molecules in water. This article reports effects and optimization process of size and distribution of graphene islands on desirable morphology of chemically plated silver nanoparticles. The density of nanoscale gaps of a few nanometers in distance between neighboring silver nanoparticles is optimized, resulting in the demonstration of SERS sensors with very low detection limits for R6G molecules.
Deep Learning based Channel Code Recognition using TextCNN

The recognition of channel code of primary user signal is a important task for the full awareness of wireless environment in cognitive radio. Previous solutions to this problem usually suffer from high computational complexity that is not suitable for real-time applications and manual feature extraction that requires experience and expertise. This paper proposes a deep learning based channel code recognition algorithm that extracts features automatically and avoids complicated calculation. Three convolutional codes are considered as the candidate codes. To recognize which channel code has been adopted by the primary user, the received sequence is regarded as a text sentence and then understood by TextCNN. Experimental results show that the proposed algorithm works well and outperforms the max-log-MAP decoding algorithm in recognition accuracy.
The significance of tenascin-C expression in Egyptian urinary bladder carcinoma and its correlation with available prognostic clinicopathologic features

Objective Little data are available on the prognostic significance of the matricellular protein tenascin-C (TN-C) in urinary bladder carcinoma (UBC). The aim of the present study was to evaluate TN-C in Egyptian UBC and to correlate it with available prognostic clinicopathologic features. Materials and methods TN-C immunohistochemical staining was performed for 15 normal urinary bladder biopsies and for radical cystectomies of 25 urothelial carcinoma (UC) cases, 21 squamous cell carcinoma (SCC) cases, and four adenocarcinoma (ADC) cases. Results TN-C expression was not observed in normal urothelium or stroma; however, of the total studied malignant cases, 59% showed TN-C expression in malignant cells and 66% in tumor stroma. TN-C expression was observed in malignant cells of all ADC cases, 76% of UC cases, and 29% of SCC cases. TN-C positivity in tumor cells was significantly associated with older age, larger tumor size, and advanced pathologic stage. SCC cases showed lower H-score values in malignant cells as compared with UC and ADC cases (P=0.000 and 0.016, respectively). TN-C expression in stroma cells was associated with a higher mitotic index in tumor cells. Conclusion TN-C is associated with progression of UBC and could be used as a tool for differentiation between UC and SCC in the difficult cases.
Controlling Light- and Elevated-Temperature-Induced Degradation With Thin Film Barrier Layers

In this article, we investigate the extent of lifetime degradation attributed to light- and elevated-temperature-induced degradation (LeTID) in p-type multicrystalline silicon wafers passivated with different configurations of hydrogenated silicon nitride (SiNx:H) and aluminum oxide (AlOx:H). We also demonstrate a significant difference between AlOx:H layers grown by atomic layer deposition (ALD) and plasma-enhanced chemical vapor deposition (PECVD) with respect to the extent of LeTID. When ALD AlOx:H is placed underneath a PECVD SiNx:H layer, as used in a passivated emitter and rear solar cell, a lower extent of LeTID is observed compared with the case when a single PECVD SiNx:H layer is used. On the other hand, the LeTID extent is significantly increased when an ALD AlOx:H is grown on top of the PECVD SiNx:H film. Remarkably, when a PECVD AlOx:H is used underneath the PECVD SiNx:H film, an increase in the LeTID extent is observed. Building on our current understanding of LeTID, we explain these results with the role of ALD AlOx:H in impeding the hydrogen diffusion from the dielectric stack into the c-Si bulk, while PECVD AlOx:H seems to act as an additional hydrogen source. These observations support the hypothesis that hydrogen is playing a key role in LeTID and provide solar cell manufacturers with a new method to reduce LeTID in their solar cells.
Strain-Insensitive Biocompatible Temperature Sensor Based on DNA Solid Film on an Optical Microfiber

We demonstrated a biocompatible and highly sensitive temperature sensor using DNA-CTMA (DNA-Cetyl trimethyl ammonium) solid film deposited on micro-tapered fiber. The micro-tapered silica fiber provided a built-in interferometer that was inherently sensitive to the environment variation due to the enhancement of evanescent wave interaction. The tapered region was coated with DNA-CTMA film with a high thermo-optic coefficient enabling a high-temperature sensitivity higher than −0.9 nm/°C in the bio-medically important region from 35 to 80 °C in a water bath. The cross-sensitivity problem between the temperature-sensing and the strain-sensing was successfully resolved since the elastic properties of the DNA-CTMA film on the micro-tapered silica fiber resulted in a very low strain sensitivity of −7 pm/ $\mu \varepsilon $ . Detailed procedures for device fabrication and optical characterization of the proposed device were described.
Hybrid Blind Audio Watermarking for Proprietary Protection, Tamper Proofing, and Self-Recovery

The paper presents a lifting wavelet transform (LWT)-based framework for multi-purpose blind audio watermarking. The proposed schemes can be used to carry out robust watermarking for intellectual property protection as well as fragile watermarking for tamper detection and signal recovery. Following 3-level LWT decomposition of the host audio, the coefficients in selected subbands are partitioned into frames for watermarking. To expand applicability, the robust watermark comprising proprietary information, synchronization code, and frame-related data was particularly embedded in the approximation subband using perceptual-based rational dither modulation (RDM) and adaptive quantization index modulation (AQIM) at a payload capacity of 1523.9 bits per second. The fragile watermark is a highly compressed version of the audio embedded within the 2nd- and 3rd-level detail subbands using $2^{N}-\textrm {ary}$ AQIM. Hashing comparison and source-channel coding make it possible to identify tampered frames and restore affected regions. Experiment results indicate that the embedded robust watermark can withstand commonly-encountered attacks and the fragile watermark is highly effective in tamper detection and self-recovery. More importantly, the incorporation of a frame synchronization mechanism makes the proposed system resistant to cropping and replacement attacks, all of which were unsolvable using previous watermarking schemes. The perceptual evaluation revealed that the watermark caused only minor degradation. The proposed watermarking scheme is suitable for a wide range of ownership protection and content authentication applications.
Electrical Properties of CSS Deposited CdTe Thin Films for Solar Cell Applications

CdTe is a very potential absorber material for thin film solar cell application. In this work CdTe thin films (TF) were deposited on CdS thin films by close-spaced sublimation (CSS) technique at different source and substrate temperature in inert gas condition. To bring out the optimum temperature set for CSS deposited CdTe films, several experiments were done. The process pressure of the chamber during deposition was maintained at 1.5 Torr in a dynamic condition. The effects of deposition temperature on the electrical properties of the as-deposited CdTe films were investigated by Hall Effect measurement. The films were deposited at 610 °C, 630 °C, 650 °C and 670 °C source temperatures. The first three films showed p-type conductivity while n-type conductivity appeared in the film deposited at 670 °C. The hole concentration of the as-grown p-type CdTe films followed an upward trend with the increase of source temperature and it reached a peak value at 650 °C. The highest hole mobility was observed for the lowest source temperature. However, the resistivity of the CdTe films was found increasing with the increase of source temperature. Thus, the CdTe thin film deposited at 650 °C showed better electrical properties for solar cell applications.
Brain Tumor Detection Using Convolutional Neural Network

Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes unyielding. In this paper, we proposed a method to extract brain tumor from 2D Magnetic Resonance brain Images (MRI) by Fuzzy C-Means clustering algorithm which was followed by traditional classifiers and convolutional neural network. The experimental study was carried on a real-time dataset with diverse tumor sizes, locations, shapes, and different image intensities. In traditional classifier part, we applied six traditional classifiers namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Logistic Regression, Naïve Bayes and Random Forest which was implemented in scikit-learn. Afterward, we moved on to Convolutional Neural Network (CNN) which is implemented using Keras and Tensorflow because it yields to a better performance than the traditional ones. In our work, CNN gained an accuracy of 97.87%, which is very compelling. The main aim of this paper is to distinguish between normal and abnormal pixels, based on texture based and statistical based features.
Model Research on Forecast of Second-Hand House Price in Chengdu Based on XGboost Algorithm

In order to better and more accurately study the housing price of second-hand houses, this paper analyzed and studied 35417 pieces of data captured by Chengdu HOME LINK network. Firstly, the captured data were cleaned and the characteristics were selected. Then, multiple linear regression, decision tree and XGboost models were used to fit the predicted housing price score curve for these ten factors, and finally, the optimal prediction model was selected through parameter adjustment. The experimental results show that the accuracy of XGboost prediction is the highest, and the prediction accuracy score reaches 0.9251. Compared with linear regression and decision tree model, XGboost algorithm has better generalization ability and robustness in data prediction, and also prevents overfitting phenomenon, laying a solid foundation for the subsequent second-hand house price prediction.
Numerical Simulations of Portable Systems for Motion-Induced Eddy-Current Testing

This article presents two realizations of portable systems for motion-induced eddy-current testing using the rotational movement of permanent magnets (absolute and differential). The methodology of calculations using a quasi-stationary and transient approach in the finite-element method (FEM) is described in detail. Different procedures for calculating the induced voltage in the pick-up coils are also presented. Finally, the results of the measurements and the corresponding numerical simulations are analyzed.
The Increase of the Effectiveness of the System of Solar Heat Supply

The necessity of usage of resuming sources of energy in the system of heat supply is shown in the article. The indices of the effectiveness of the work of solar collector and the system of heat supply including regime are viewed. The effectiveness of solar collectors in different schemes of connection is examined. The possibility of maintenance of optimal indices of the effectiveness of the system by the application of different schemes of connection of solar collector in its composition is shown.