Miracle-dz/UniFAR-FTUs huggingface dataset:
Epithelial–Mesenchymal Transition in the Resistance to Somatostatin Receptor Ligands in Acromegaly

Epithelial-mesenchymal transition (EMT) is a dynamic process by which epithelial cells loss their phenotype and acquire mesenchymal traits, including increased migratory and invasive capacities. EMT is involved in physiological processes, such as embryogenesis and wound healing, and in pathological processes such as cancer, playing a pivotal role in tumor progression and metastasis. Pituitary tumors, although typically benign, can be locally invasive. Different studies have shown the association of EMT with increased tumor size and invasion in pituitary tumors, and in particular with a poor response to Somatostatin Receptor Ligands (SRLs) treatment in GH-producing pituitary tumors, the main cause of acromegaly. This review will summarize the current knowledge regarding EMT and SRLs resistance in acromegaly and, based on this relation, will suggest new biomarkers and possible therapies to SRLs resistant tumors.
Smart manufacturing: a framework for managing performance

ABSTRACT The research proposes a metric referred to as Smart Manufacturing Performance Measures (SMPMs), which are mapped with manufacturing decisions (structural and infrastructural) and manufacturing outputs (cost, quality, delivery, flexibility, and innovativeness). The mapping between manufacturing decisions, manufacturing outputs, and SMPMs, facilitates the organization in choosing SMPMs to deliver desired output as represented by the competitive priority. The approach provides a direction for the practitioners to implement and operate smart manufacturing systems efficiently and effectively by devising a performance measurement system. A total of 22 performance measures are identified through an in-depth literature review. These are further classified into hard and soft measures, which allow decision-makers to measure SMPMs and focus on a specific performance measure. Further, SMPMs is linked to 13 enablers, which are the driving technologies for implementing smart manufacturing systems. Smart manufacturing is evolving, and its implementation is still in the nascent phase. The proposed approach plays a vital role in redefining the manufacturing strategy while deploying smart manufacturing systems. The research contribution is a step-in assessment of smart manufacturing to understand the ‘what’ and ‘how’ of technological changes are transforming the operations and driving outputs.
An improved memory networks based product model classification method

ABSTRACT Existing CAD model classification methods are usually performed by extracting the geometric and topological information of the models. This is not suitable for the classification of Model-based Definition (MBD) models because of the lack of non-geometric semantic information. They cannot solve the problem, as the geometry and topology are similar but the product manufacturing information (PMI) is completely different. Firstly, this paper proposed a multi-granularity classification model based on MBD attribute adjacency graph (MAAG). And then an improved Long Short-Term Memory (LSTM) neural network with a new loss function and adaptive training times is put forward. This neural network model is trained to achieve fast and accurate classification of the MBD models. Finally, the experimental results show that the proposed method not only improves the accuracy of classification, but also effectively reduces the training time cost of the model.
ALTERAÇÕES QUÍMICAS EM SOLO SUBMETIDO A DIFERENTES FORMAS DE MANEJO DO CAFEEIRO

Widespread marketing technologies recomend the use of high doses of chemical fertilizers and the control of pests and diseases to maintain the productive potential of farms. This affects the sustainability of the coffee agroecosystem, creating a total dependence of industrialized products. The objective of this study was to compare the alterations of some chemical, physical, and microbiological characteristics of a Typic Hapludox from the region of Santo Antonio do Amparo (Minas Gerais State, Brazil), in agroecosystems of "organic coffee", "in conversion", and "conventional" coffee production, to a native forest fragment. Data were collected throughout one year on two farms, under the influence of similar climate and relief conditions, and using the same cultivar (Acaia IAC474-19) of equal age (five years). Soil samples were collected twice (July 1999 and December 1999) at a depth of 0-20 cm. The principal component analysis allowed a comprehensive visualization of the characteristics that most influenced the soil behavior in the different studied systems. In general, the management forms for the production of organic, in conversion and conventional plantations increased the soil fertility in comparison to the soil under the native forest fragment. Larger chemical characteristic alterations were observed in the agroecosystem of organic coffee production in relation to the conventional; there were increments in the values of soil pH and Ca, Mg, K, P, Zn, B, CTC, base saturation, and decrease of exchangeable Al.
Adsorption isotherms ofm-xylene on activated carbon: measurements and correlation with different models

Abstract In this study, adsorption isotherms of m -xylene on activated carbon were measured by time integration of the breakthrough curves at T = (298.15, 308.15, 318.15, and 333.15) K using home-made apparatus developed for this purpose. At the temperature 333.15 K, isotherms were also measured using a microbalance in order to check the accuracy of the proposed apparatus. As expected, in keeping with the principle of Le Chatelier, the m -xylene loadings were observed to decrease with increasing temperature. Activated carbon was used as the adsorbent because it typically exhibits high adsorption capacities and shows potential as an adsorbent to effectively remove volatile organic compounds (VOCs) such as m -xylene. In a second step, the experimental data obtained were correlated with different existing adsorption isotherm models such as the Langmuir model, the Freundlich model, and the Dubinin equation. The Langmuir model was found to be well adapted to describe the adsorption of m -xylene onto activated carbon.
A Non-Isolated Bidirectional Buck-Boost Chopper With Auxiliary Converter for Battery Energy Storage Systems

This paper presents a non-isolated buck-boost bidirectional chopper with auxiliary converter for a battery energy storage system that is installed inside a light rail vehicle. The proposed chopper consists of two bidirectional chopper cells where one chopper cell is connected to each voltage side and an auxiliary converter that is connected in series with an inductor where the auxiliary converter consists of a cascade connection of multiple full-bridge cells. The proposed chopper is controlled such that the capacitors of the auxiliary converter store and release most of the converter energy instead of relying solely on a bulky and heavy inductor. As a result, the proposed chopper is lighter and smaller than the conventional four-switch non-inverting buck-boost converter. The operation of the proposed chopper has been validated by experiments performed on a downscaled experimental model.
Blended SPOC Teaching and Learning Model for Computer Programming Course: Insights and Defeating Challenges

To expose all budding engineers to the world of programming, generally, Computer Programming is a first-year compulsory course across all Engineering disciplines. While this course has been taught for a long in the university whose case study is presented here, the following challenges were faced over the past few years: (a) University expended itself from one campus to four geographically distant campuses. (b) Many (but not all) students now are already exposed to this programming subject at their respective 10+2 level, which poses a challenge to an instructor. (c) The focus of this course is on “Programming” and C-language is a tool to learn it, but often students feel the reverse. (e) Although all campuses shared a common handout that standardized the breadth coverage of this course, in-depth coverage could not be standardized. To overcome these problems, the university changed the teaching model of the course to a flipped classroom-based blended SPOC (Small Private Online Course) learning model. This paper shares the insights and experience of successive change in pedagogy over four years and relates it with intrinsic motivation vs. extrinsic motivation, standardizing in-depth coverage of any course across geographically distant campuses, and getting closer towards achieving the learning objectives.
Detection of Chipless RFID Tag Using a Single Antenna RFID Reader System

The detection of different types of chipless Radio Frequency Identification (RFID) tags using a single antenna reader is presented in this paper. The performance is evaluated through experiment and measured using Chipless RFID reader system with single antenna and compared with results from vector network analyser. It explains the operation of the reader and how the pic-microcontroller is programmed with the detection algorithm. Two types of tags have been tested, a printed tag which is flexible and will be in high demand for commercialisation and a copper tag which has more durability. The post processing of the measured results to obtain the tag ID is performed using MATLAB. Adaptive Wavelet based detection algorithm is used for the decoding of the tag ID. After the tag ID has been decoded it is sent to a display screen on the DSP unit and/ or via RS-232 to the host computer application. There is no need of human interaction for the reader to interrogate the tag as the user is able to save the tag data received by the reader in a specified database/server.
Performance Analysis of Mixed MIMO RF/FSO DF Relaying Based on Globally Coupled Low Density Parity Check (GC-LDPC) Codes

In this work, we analyze the performance of the use of error correcting codes in particular the LDPC codes with global coupling (GC-LDPC) in a double-hop relay system composed of links with multiple inputs and multiple outputs at radio frequency / free space optics (MIMO-RF/FSO). A multiantenna listener listens to the information by decoding the signals received from the source node. In addition, to decode the signals we use two-phase local-global decoding. To eliminate interference in the first hop we use the interference alignment technique (IA) we also assume that the source-relay link undergoes Rayleigh fading, and that the relay-destination link is affected by the degradations of the channel optics, including path loss, atmospheric turbulence, and pointing errors. Using DF relay technology, mixed MIMO-RF/FSO systems combine the advantages of RF and FSO communication technologies. The use of mixed MIMO-RF/FSO cooperative transmission systems can improve network reliability and transmission. Results demonstrate improved performance of MIMO-RF/FSO DF cooperative relay system based on GC-LDPC codes with multiple antennas compared to MIMO-RF/FSO systems without the use of GC-LDPC codes, but also to systems SISO (Single-Input Single-Output) RF-FSO DF proposed in the existing literature.
Critical Insights Into Fast Charging Techniques for Lithium-Ion Batteries in Electric Vehicles

The objective of this article is to illustrate the various fast charging techniques that are being used to charge the lithium-ion batteries in electric vehicles. Various charging protocols such as constant current, constant voltage, constant current constant voltage, multistage constant current, varying current method, pulse charging methods are critically reviewed and explained in their broader perspective of fundamental concepts to their modeling/simulation. Amongst, the constant current constant voltage charging approach is considered as a benchmark for other charging protocols in terms of the charging time, the charging efficiency, and battery life. A critical comparison among the various charging methods mentioned above are discussed and possible future research directions in the design and development of new fast charging techniques have been proposed based on the commercial and societal demands.
Zero-Forcing Precoding With Partial CSI in Multibeam High Throughput Satellite Systems

Precoding has emerged as a prominent technique to mitigate interbeam interference (IBI) in the forward link of a multibeam high throughput satellite system. In this paper, we consider zero-forcing (ZF) precoding to mitigate IBI. To reduce the channel state information (CSI) feedback from the user terminals, which consequently reduces the latency and signaling overheads, we design a ZF precoder with matrix and vector normalizations using partial CSI and analyze its ergodic SR performance. We consider a practical scenario where the links between the satellite and user terminals are affected by rain fading attenuation and derive an analytical expression, upper and lower bounds, and asymptotic (in terms of transmit power) approximations for the ergodic SR achieved by this ZF precoder. Many important insights are obtained from these results. Also, the robustness of this ZF precoder to CSI estimation errors is numerically analyzed.
Learning-Based Coronal Spine Alignment Prediction Using Smartphone-Acquired Scoliosis Radiograph Images

DICOM X-rays are not easily accessible for telemedicine, and existing learning-based automated Cobb angle (CA) predictions are not accurate on suboptimal X-ray images. To develop an automated CA prediction system irrespective of image quality, with no restrictions on curve patterns, 367 consecutive patients attending our scoliosis clinic were recruited and their coronal X-rays were re-captured using mobile phones. Five-fold cross-validation was conducted (each with 294 randomly selected images for training a neural network SpineHRNet to detect endplate landmarks and end-vertebrae, and the remaining 73 images for testing). The predicted heatmaps of vertebral landmarks were visualized to enhance interpretability of the SpineHRNet. Per-landmark Euclidean distance (L2) errors and recall of landmark detection were calculated to assess the accuracy of the predicted landmarks. Further computed CAs were quantitatively compared with spine-specialists measured ground truth (GT). The average L2 error and the recall of the detected endplates landmarks were 2.8 pixels and 0.99 respectively. The predicted CAs were all significantly correlated with GT ( $\text{p} < 0.01$ ). Compared with GT, the mean absolute error was 3.73-4.15° and standard deviation was 0.8-1.7° for the predicted CAs at different spinal regions. This is the first study on non-original X-rays to automatically and accurately predict endplate landmarks of the scoliotic spine and compute the CAs at different regions of the spine, irrespective of image qualities. SpineHRNet’s applicability is evidenced by five-fold cross-validations, which may be used with telemedicine to facilitate fast and reliable auto-diagnosis and follow-up.
Massive MIMO in Fixed-Point Arithmetic

Massive MIMO base stations are expensive to build due to the requirement for a large number of RF transceivers and high-resolution analog-to-digital converters. A way to reduce the implementation cost is to build the base stations with inexpensive hardware, resulting in the received signals to be coarsely quantized. To implement the data detection and decoding process in real time, fixed-point arithmetic with reduced precision is used. This article reports the minimum wordlength needed to maintain the Bit-Error Rate (BER) at acceptable levels. Specifically, the eigenvalue decomposition, which is the most computationally demanding portion of the receiver algorithm, can be calculated with wordlengths of 7 and 10 bits for eigenvectors and eigenvalues, respectively.
Migrating a Converter Design to GaN for Enhanced System Performance

Adopting GaN can improve system performance in efficiency, thermal capability, and system size. GaN-based system design has been demonstrated across a range of voltage and power levels, in many different topologies, and for a variety of applications. Migrating an existing Si-based design to GaN typically involves optimizing the PCB layout and gate driving circuits, characterization and modeling of power losses, fast overcurrent protection, and consideration of thermal performance. Furthermore, it is important to understand the key parameters and reliability standards for GaN when selecting an appropriate device. This paper reviews the state-of-the-art in commercial GaN transistors, addresses application challenges, then reviews examples of successful design migrations. Examples include hard-switching and soft-switching topologies based on 100 V and 600 V enhancement-mode GaN FETs.
Advanced Controls of HVDC Interconnection for Ancillary Services Support

This paper proposes advanced controls to improve ancillary services of High Voltage Direct Current (HVDC) interconnections. The converters control is a synchronverter-based control (SHVDC). The main goal is to improve the SHVDC controls to comply with the Grid Code Requirements (GCR) set by the Tunisian grid operator, the Tunisian Company of Electricity and Gas (STEG). Two control strategies are proposed. A local/decentralized control and a global/centralized control. The local control regulates voltage and frequency at the HVDC converters point of connection (PoC) to the grid. Frequency and voltage controls are based on conventional droop controls. The centralized control responds to control signals, emanating from the transmission system operator, to render the SHVDC system participate in the grid support as conventional generators. The proposed controls feature operation with energy reserves, emergency control and P/Q ramp-ups and ramp-downs controls. Centralized and decentralized controls are successfully implemented and tested on the HVDC interconnection between Tunisia and Italy.
Kinetics of transesterification reaction using CAO/AL2O3 catalyst synthesized by sol-gel method

Calcium oxide is one of the appropriate catalysts for biodiesel production. In this study, 40 wt. % CaO/Al2O3 catalyst was used. Transesterification reaction was performed in optimal condition presented in the previous study (0.05 molar nitric acid and gelation temperature of 70 °C) in a 250 mL two-necked flask. All the experiments were carried out in the presence of soybean oil, methanol (methanol to oil molar ratio of 12:1), and catalyst concentration of 6 wt. %. Stirrer speed was set at 350 rpm. This study investigated the effects of reaction temperature and reaction time on produced biodiesel conversion. Methyl ester conversion changes in all temperatures and across different times indicated pseudo-first order kinetic, so, first, the observed rate constant was obtained at various temperatures. Then, observed activation energy of soybean oil methanolysis in the presence of CaO/Al2O3 catalyst was determined. The results show that maximum errors of model are in primary times because methanol is insoluble in soybean oil. At the other times and by biodiesel and glycerol production, there was increased solubility of methanol in oil. The methyl esters production rate is related to diffusion of methanol in oil film and reaction rate constants which are calculated these constants.
Toward a Binarization Framework resolving the Maghrebian Font Database challenges

Historical documents are of major importance in conserving cultural and scientific heritage. The Maghrebian font of the Arabic language had dominated in different countries. Thousands of cultural and scientific documents were written using this style. In this paper, we intend to present the built Maghrebian font database with giving the different challenges facing the development of an Optical Character Recognition system able to treat it. Also, we are going to reveal the new developed binarization framework, based on Deep Learning theory, able to get the best accuracy compared to different studied binarization approaches. The proposed framework is based on Selectional Auto-Encoders and the Energy minimization theory. The obtained binarization result on the Maghrebian font using the F -measure is 98,1%. In this paper, we intend to present the built Maghrebian font database with giving the different challenges facing the development of an Optical Character Recognition system able to treat it. Also, we are going to reveal the new developed binarization framework, based on Deep Learning theory, able to get the best accuracy compared to different studied binarization approaches. The proposed framework is based on Selectional Auto-Encoders and the Energy minimization theory. The obtained binarization result on the Maghrebian font using the F -measure is 98,1%. Also, we are going to reveal the new developed binarization framework, based on Deep Learning theory, able to get the best accuracy compared to different studied binarization approaches. The proposed framework is based on Selectional Auto-Encoders and the Energy minimization theory. The obtained binarization result on the Maghrebian font using the F -measure is 98,1%.
Multiple 3D LiDARs Extrinsic Parameter Estimation Method Using Plane Features

Multiple LiDAR system has been suggested to many autonomous vehicles because of that LiDAR sensor price was cheaper than the past. It is essential to estimate extrinsic parameters to recognize the surrounding environment. However, it is difficult to extract and matching the features using low channel LiDAR due to the LiDAR resolution problem. In this paper, we propose an algorithm that can estimate extrinsic parameters using low channel LiDAR even without artificial markers. Our method automatically detects the features of the plane around the vehicle and proceeds with calibration. Our method automatically detects the plane features around the vehicle and proceeds with calibration. The proposed algorithm shows accurate calibration results in the experimental vehicle.
Advanced Cascaded Anisotropic Convolutional Neural Network Architecture Based Optimized Feature Selection Brain Tumour Segmentation and Classification

The purpose of the research is to find out how deep learning and the convolutional neural network will contribute to diagnosis, early detection and segmentation of brain tumors such as glioma, benign, malignant, etc. The aim is to achieve a higher degree of segmentation quality to resolve issues related to lack of the classification accuracy and poor performance in the segmentation and detection of tumors. The presented solution is an Advanced Cascaded Anisotropic Convolutional Neural Network (CA-CNN) architecture with an optimized feature selection method. The DFP (Data collection, feature extraction & selection and prediction) taxonomy is presented that involves data acquiring, data pre-processing, feature extraction, selection and prediction methods for effective tumor segmentation and detection. The presented system will enhance the prediction accuracy and involves the genetic algorithm for effective selection of features which prevents data redundancy and reduce the delay in the detection of tumors. The utilization of genetic algorithm minimizes the redundancy within input voxels and facilitates in the optimal selection of features which improves the classification accuracy of the solution. The research conducted is to improve the brain tumor segmentation and detection process in terms of accuracy, specificity and sensitivity using multi-scale prediction and cross-validation.