Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary study on the efficacy of lacrimal duct reconstruction with pedicled conjunctival flap in the treatment of severe lacrimal canalicular obstruction with conjunctivochalasis.

AIM To determine the efficacy and safety of pedicled conjunctival lacrimal duct reconstruction in the treatment of severe obstruction of superior and inferior lacrimal canaliculi with conjunctivochalasis. METHODS This study was performed as a retrospective analysis of patients who received conjunctival dacryocystorhinostomy with pedicled conjunctival flap reconstruction combined with tube intubation due to severe superior and inferior lacrimal canalicular obstruction with conjunctivochalasis from January 2019 to October 2019. The clinical data included the degree of preoperative epiphora and postoperative relief, preoperative examination of lacrimal duct computed tomography and ultrasound biomicroscopy, postoperative evaluation of lacrimal duct function by chloramphenicol taste and fluorescein dye disappearance test, etc. Syringing was carried out to determine the reconstruction and patency of the lacrimal duct. RESULTS All 9 patients (9 eyes) had severe canalicular obstruction with conjunctivochalasis. The patients included 4 males and 5 females aged between 47-65y with an average age of 52.2±6.7y. At 3mo follow-up, the tube was removed and the patients were followed for a further 3mo. After tube removal, 6 patients showed no epiphora. These patients also had positive chloramphenicol tastes and normal fluorescein dye disappearance test results. Two patientshad epiphora. Also, syringing showed partial patency of the reconstructed lacrimal duct. One patient had no improvement in epiphora with negative chloramphenicol taste and fluorescein dye disappearance test results and obstruction of the reconstructed lacrimal duct. The total effective rate of the operation was 8/9, with no serious complications. CONCLUSION Pedicled conjunctival lacrimal duct reconstruction conjunctival dacryocystorhinostomy is safe and effective for superior and inferior canalicular obstruction with conjunctivochalasis.
Self-Supervision Interactive Alignment for Remote Sensing Image–Audio Retrieval

Cross-modal remote sensing image–audio (RSIA) retrieval aims to use audio or remote sensing images (RSIs) as queries to retrieve relevant RSIs or corresponding audios. Although many approaches leverage labeled samples to achieve good performance, the performance cost of labeled samples is high, because cross-modal remote sensing (RS) labeled samples usually require huge labor resources. Therefore, unsupervised cross-modal learning is very important in real-world applications. In this article, we propose a novel unsupervised cross-modal RSIA retrieval approach, named self-supervision interactive alignment (SSIA), which can take advantage of large amounts of unlabeled samples to learn the salient information, cross-modal alignment, and the similarity between RSIs and audios. Since self-supervised learning lacks the supervision of label information, we leverage the similarity between the input RSI information and audio information as the supervision information. Besides, to perform cross-modal alignment, a novel interactive alignment (IA) module is designed to explore fine correspondence relation for RSIs and audios. Moreover, we design an audio-guided image de-redundant module to reduce the redundant information of visual information, which can capture salient information of RSIs. Extensive experiments on four widely used RSIA datasets testify that the SSIA performance gains better RSIA retrieval performance than other compared approaches.
Soy Protein/Polyvinyl-Alcohol (PVA)-Based Packaging Films Reinforced by Nano-TiO2

This work was investigated to prepare a reinforcing composite packaging film composited of soy protein/polyvinyl alcohol (PVA) and nano-TiO2. First, different film compositions were designed by the particle size of nano-TiO2, concentration of nano-TiO2, concentration of polyvinylpyrrolidone (PVP, a dispersing agent for nano-TiO2), and pH of film casting solution. Then, the film composition that yielded the optimal physical properties was identified using orthogonal array design single-factor experiments, considering its physical properties, including tensile strength, elongation, water absorption, water vapor transmission, oxygen permeation, thermal property, and film morphology. The results displayed that the optimal film composition was (1) soy protein/PVA film with 2.5 wt% nano-TiO2, (2) 30 nm nano-TiO2 particle size, (3) 1.5 wt% PVP, and (4) pH 6.0 of film-forming solution. It yielded tensile strength of 6.77 MPa, elongation at break rate of 58.91%, and water absorption of 44.89%. Last, the films were characterized by scanning electron microscope (SEM) and differential scanning calorimetry (DSC). SEM analysis showed that compared with the film without TiO2, the film containing TiO2 has a smoother surface, and DSC determined that adding nano-TiO2 can improve the thermostability of soy protein/PVA film. Therefore, the film prepared in this paper is expected to provide a new theoretical basis for use in the packaging industry.
Histological Features of IgA Nephropathy in Pediatrics and the Magnitude of the Disease in Saudi Children

Objectives This review addresses the microscopic features of immunoglobulin A nephropathy (IgA nephropathy), its prognostic variables in children, and measures to which extent these features and variables differ from adults. Furthermore, it describes the extent of this disease process among children in Saudi Arabia and the rest of the Arab countries and compares it with the data from the West and the Far East. Method All the original work described the histological features of pediatric IgA nephropathy, and studies involved in developing the prognostic classification of IgA nephropathy, Oxford Classification, were reviewed. Moreover, the studies describing the crescent prevalence and outcome in pediatric IgA nephropathy in addition to thrombotic microangiopathy association were studied. National studies describing the prevalence of pediatric IgA nephropathy and pediatric crescentic glomerulonephritis were tracked with an overview of the regional data from the rest of the Arab world. Results IgA nephropathy in children showed more glomerular proliferative changes and less glomerular vascular and tubule-interstitial chronic injury compared to adults. The reference study that described the association between thrombotic microangiopathy and IgA nephropathy did not include the pediatric age group. Moreover, it was found that the data from the Middle East was not encountered in developing the original and updated IgA nephropathy Oxford Classification. Furthermore, the prevalence of IgA nephropathy in children is described in the regional literature, but its histological features were not well detailed. Finally, the percentage of crescentic glomerulonephritis (GN) due to IgA nephropathy is less in our country compared to the West and concords with the Far East findings. Conclusion A well-designed regional study addressing IgA nephropathy in Middle East children with a focus on histological features, association with crescent, and thrombotic microangiopathy and challenging the validity of the updated IgA nephropathy Oxford Classification is recommended.
A General Method for the Analysis and the Logical Generation of Discrete Mathematical Systems in Programmable Logical Controller

This paper deals with a general method for the analysis and the logical generation of discrete systems in Programmable Logical Controller (PLC). The Boolean operators are implemented with a generic and unique algebraic model as event-dependent discrete equations, which can be executed in a sequential order. With this method, a generator of sequential logical tables can be designed, simulated and executed for implementing discrete dynamical systems. The purpose of this research is to design self-rewrite operating systems in PLC, which automatically checks the logic of the implemented discrete dynamical systems, for simulation and execution of sequential operations.
Bio-Inspired Circular-Polarized UHF RFID Tag Design Using Characteristic Mode Analysis

This article presents a bio-inspired circularly polarized ultrahigh-frequency (UHF) radio frequency identification (RFID) tag antenna for metallic and low-permittivity substances. This tag design is based on a leaf-shaped radiator, two shorting stubs, and slots etched on F4B substrate. Initially, the tag antenna is designed using a characteristics mode analysis (CMA) by analyzing the first six characteristic modes (CM) and characteristic angles. The width of orthogonal slots is varied to get the resonance of CM modes in the required US RFID band. Moreover, the edges are blended to get orthogonal current distribution, which is necessary for circular polarization. In addition, the proposed tag design is optimized further using CST Microwave studio and an RFID chip is exploited as a capacitive coupling element (CCE) to run CM modes with the orthogonal current pattern. This tag can also be tunable to European RFID (EU) band (866–868 MHz) by changing the length of shorter diagonal slot. The tag design offers a read range of 7–4.5 m on $100\times100$ mm2 metals plate and low-permittivity substrates (for the 902–928-MHz band). In the EU band, the corresponding read ranges are 5.7 and 3.5 m above metal and low-permittivity objects, respectively. This circularly polarized tag antenna is advantageous in terms of cost, circular polarization feature, and ease of fabrication due to the absence of vias, shorting pins, and matching circuits. Therefore, this tag design is suitable for labeling various low-permittivity objects, industrial conveyer belt applications, baggage handling systems, and Internet of Things (IoT) applications.
Enhancing the interoperability of heterogeneous hardware in the Industry: a Multi-Agent System Proposal

The unstoppable growth of technological solutions, especially in relation to communication processes and industrial environments, has led to the rise of the term Industry 4.0. In this new reality, data converted into knowledge is the main protagonist. However, the existence of heterogeneous technologies results in complex processes and ends up hindering the implementation of new environments. In order to simplify this casuistry, this work defines an agent-based system that integrates mature technologies for the intercommunication of devices and de facto standards such as Modbus, as well as state-of-the-art solutions related to fields such as Computer Vision and other low-cost ones, such as those provided by those based on IoT. Finally, a case study is proposed that focuses on a circular economy model that, taking advantage of the benefits of the described mechanisms and in a simple way, shows the possibility of generating a positive impact on the environment. Specifically, the case proposes mechanisms for processing and sorting waste components - lithium batteries - making it easier to give them a second life before they become permanent and highly polluting waste.
WEAK CONVERGENCE OF THE LEAST SQUARES RANDOM FIELD IN THE SMOOTH CASE

Consider a nonlinear regression model Ŷ ^ = g^(e) + ns 1 where 6 e e cz R and {ê -} are independent random variables with mean 0 n 2 and finite variances. Define Q„(e) = l (Y,g.(e)) . It is shown that, " i=l ^ 1 after suitable normalization, the random field Qp(e) converges in distribution to a Gaussian random field and the asymptotic properties of the least squares estimator are obtained.
Ab-initio investigation of structural, optoelectronic, and transport properties of metal-alkali-based binary chalcogenides, X_2Te [X = Na, K, Rb]: Rb_2Te a potential candidate for UV-shielding and thermoelectric devices

We investigate the structural, optoelectronic, and thermoelectric properties of X_2Te (X = Na, K, Rb) chalcogenides. The energy-versus-volume fitted with the Birch–Murnaghan equation of state suggests the structural stability of these materials. We find that Na_2Te, K_2Te, and Rb_2Te have direct band gaps of 3.62, 2.26, and 0.60 eV, respectively. The optical properties revealed that Rb_2Te is reflective over a wide UV–Vis energy range while the K_2Te and Rb_2Te demonstrate reflectivity in the UV region, making these materials to be potential candidates for UV-light shielding. Furthermore, the effectiveness of Rb_2Te in thermoelectric energy converter technology is supported by its high Seebeck effect and high-power factor values. The Rb_2Te exhibits the highest value of the figure of merit and electrical conductivity among the three binary chalcogenides. We infer that Rb_2Te is the most promising among the studied binary chalcogenides for thermoelectric devices’ applications. Graphical abstract
Mining knowledge from unbalanced data based on ν-support vector machine

Learning from data sets that contain very few instances of the positive class usually produces biased classifiers that have a higher predictive accuracy over the negative class than over the positive class(usually the more important or interesting class). Based on ν-SVM and its elicitation,the bounds of both the SV number and BSV number were proposed.Further,the bounds of both the SV rate and BSV rate were presented and attested.Then the bounds mentioned above were extended to positive class and negative class respectively.Finally,it was proved that the SV rate and BSV rate of positive class are higher than those of negative class and that the positive class yields poorer classification and predictive accuracy than the negative class does.Experimental study based on German credit and Heart disease data sets showed that the hypothesis is reasonable and the conclusions are correct.
RANDES: A Ransomware Detection System based on Machine Learning

Ransomware is one of the most prevalent cybercrimes where an attacker steals or freezes the organizational data through the data encryption. Thus, the task of ransomware detection has great importance in the field of cyber security. One thing in common with the existing models today is that they treated the assemblies as one long text. While in the execution of real code, the program counter may jump in between lines, making it more like graph traversal than linear. Thus, we proposed a new deep learning model for ransomware detection based on the executable file disassembling analysis. We split the assemblies into non-branching sequences and apply per-sequence embedding. Then, we employed Graph Attention Network (GAT) to classify whether a suspect executable file is a ransomware. Finally, we conducted experiments to show that our proposed system is efficient for real deployment.
Real-time Speech Emotion Detection using Artificial Intelligence

The study of emotions has become a significant field of study that offers potential insights for a range of applications. Man-machine interfaces must be able to detect the user's emotional state and respond appropriately. Real-time applications of automatic emotion recognition include detecting the emotions of mobile phone users, call center operations, car drivers, pilots, etc. Emotions like happiness, joy, anger, sadness, neutral, boredom, disgust, fear and surprise can be predicted with the use of conventional algorithms such as the LSTM, Bayesian network using the Maximum Likelihood Estimation Principle and Support Vector Machine is proposed. MFCC (Mel-Frequency Cepstral Coefficients), Hamming/Hanning windows, Mel-Filter bank model were used in the proposed novel LSTM model. Implementation of novel LSTM model was performed and yielded an accuracy of 98(%). A dynamic webpage that can capture an individual's voice and predict their emotional state was implemented using the same.
A wireless sensor network design and implementation for vehicle detection, classification, and tracking

Vehicle intrusion is considered a significant threat for critical zones specially the militarized zones and therefore vehicles monitoring has a great importance. In this paper a small wireless sensor network for vehicle intrusion monitoring consists of a five inexpensive sensor nodes distributed over a small area and connected with a gateway using star topology has been designed and implemented. The system is able to detect a passage of an intrusive vehicle, classify it either wheeled or tracked, and track the direction of its movement. The approach is based on Vehicle’s ground vibrations for detection, vehicle’s acoustic signature for classification and the Energy- based target localization for tracking. Detection and classification are implemented by using different algorithms and techniques including Analog to Digital Conversion, Fast Fourier Transformation (FFT) and Neural Network . All of these algorithms and techniques are implemented locally in the sensor node using Microchip dsPIC digital signal controller. Results are sent from the sensor node to the gateway using ZigBee technology and then from the gateway to a web server using GPRS technology.
CENTRAL LIMIT THEOREMS FOR FUNCTIONAL Z-ESTIMATORS

We establish central limit theorems for function-valued estimators defined as a zero point of a function-valued random criterion function. Our approach is based on a differential identity that applies when the random criterion function is linear in terms of the empirical measure. We do not require linearity of the statisti- cal model in the unknown parameter, even though the result is most applicable for models with convex linearity that can be boundedly extended to the linear span of the parameter space. Three examples are given to illustrate the application of these theorems: a simplified frailty model which is nonlinear in the unknown parameter; the multiplicative censoring and double censoring models which are bounded convex linear in the unknown parameter.
INTERSECTION LOCAL TIME OF SUBFRACTIONAL ORNSTEIN-UHLENBECK PROCESSES

In this paper, we consider Ornstein-Uhlenbeck process dX t = −X t dt+ vdS t , X 0 = x, driven by a subfractional Brownian motion S . We prove that the subfractional Ornstein-Uhlenbeck process X is local nondeterministic and give some properties of this process. As an application, assume d ≥ 2, we prove that the intersection local time of two independent, d−dimensional subfractional Ornstein-Uhlenbeck process,X and X , exists in L if and only if Hd < 2. 2000 AMS Classification: 60G15, 60G18, 60H07.
Peptide Sodium Channels Modulator Mu-Agatoxin-Aa1a Prevents Ischemia-Reperfusion Injury of Cells

Ischemia-reperfusion injury (IRI) is an irreversible functional and structural injury. Restoration of normal oxygen concentration exacerbates the emergence and development of deadly cells. One of the possible moments of reperfusion damage to cells is an increase in the intracellular concentration of sodium ions. In this article, we study the mu-agatoxin-Aa1a, a modulator of sodium channels, on the processes of IRI cells damage. The toxin was synthesized using an automatic peptide synthesizer. Hypoxia was induced by reducing the content of serum and oxygen in the CHO-K1 culture. The influence of the toxin on the level of apoptosis; intracellular concentration of sodium, calcium, and potassium ions; intracellular pH; totality of reactive oxygen species (ROS), nitric oxide (NO), and ATP; and changes in the mitochondrial potential were studied. The experiments performed show that mu-agatoxin-Aa1a effectively prevents IRI of cells. Toxin reduces the level of apoptosis and prevents a decrease in the intracellular concentration of sodium and calcium ions during IRI. Mu-agatoxin-Aa1a contributes to the maintenance of elevated intracellular pH, reduces the intracellular concentration of ROS, and prevents the decrease in intracellular NO concentration and mitochondrial potential under conditions of reoxygenation/reperfusion. An analysis of experimental data shows that the mu-agatoxin-Aa1a peptide has adaptogenic properties. In the future, this peptide can be used to prevent ischemia/reperfusion tissue damage different genesis.
Network of Thrones

T he international hit HBO series Game of Thrones, adapted from George R. R. Martin’s epic fantasy novel series A Song of Ice and Fire, features interweaving plotlines and scores of characters. With so many people to keep track of in this sprawling saga, it can be a challenge to fully understand the dynamics between them. To demystify this saga, we turn to network science, a new and evolving branch of applied graph theory that brings together traditions from many disciplines, including sociology, economics, physics, computer science, and mathematics. It has been applied broadly across the sciences, the social sciences, the humanities, and in industrial settings. In this article we perform a network analysis of Game of Thrones to make sense of the intricate character relationships and their bearing on the future plot (but we promise: no spoilers! ). Driven by cause or circumstance, characters from the many noble families launch into arduous and intertwined journeys. Among these houses are the honorable Stark family (Eddard, Catelyn, Robb, Sansa, Arya, Bran, and Jon Snow), the pompous Lannisters (Tywin, Jaime, Cersei, Tyrion, and Joff rey), the slighted Baratheons (led by Robert’s brother Stannis) and the exiled Daenerys, the last of the oncepowerful House Targaryen.
Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Arabic’s complex morphology, orthography, and dialects make sentiment analysis difficult. This activity makes it harder to extract text attributes from short conversations to evaluate tone. Analyzing and judging a person’s emotional state is complex. Due to these issues, interpreting sentiments accurately and identifying polarity may take much work. Sentiment analysis extracts subjective information from text. This research evaluates machine learning (ML) techniques for understanding Arabic emotions. Sentiment analysis (SA) uses a support vector machine (SVM), Adaboost classifier (AC), maximum entropy (ME), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), logistic regression (LR), and naive Bayes (NB). A model for the ensemble-based sentiment was developed. Ensemble classifiers (ECs) with 10-fold cross-validation out-performed other machine learning classifiers in accuracy (A), specificity (S), precision (P), F1 score (FS), and sensitivity (S).
Robust technique using Novel Artificial Neural Network Classifier for detection and classification of Hepatitis C

Aim: The objective of this review is to compare the accuracy, sensitivity, and specificity of ANN (Artificial Neural Network) and SVM (Support Vector Machine) Classifiers in order to determine the presence of Innovative Hepatitis C Detection using contemporary techniques. Materials and Methods: Data for this study were gathered via the Kaggle website. Sample sizes were calculated to be $\boldsymbol{(\mathrm{N}=22)}$ for Artificial Neural Network Classifier and $\boldsymbol{(\mathrm{N}=22)}$ for Support Vector Machine Classifier, with an alpha error-threshold value of 0.05, enrollment ratio of 0.1, 95% confidence interval, $\mathbf{G}$ power of 80%, and total sample size. The accuracy, sensitivity, and specificity were determined using matlab programming and a common data set. Results: SPSS software is used to compare accuracy, sensitivity, and specificity using an independent sample t-test. The difference between ANN and SVM classifiers is statistically significant. In contrast to ANN, the SVM performed well with $\boldsymbol{\mathrm{p}=0.662},\boldsymbol{\mathrm{p} > 0.05. }$. accuracy (0.40%), $\boldsymbol{\mathrm{p}=0.473, \mathrm{p} > 0.05-}$ sensitivity (0.40%), and $\boldsymbol{\mathrm{p}=0.001, \mathrm{p} < 0.05}$ -specificity (0.510/0). Conclusion: SVM exhibited prosperous accuracy, sensitivity, specificity than ANN to predict Innovative Hepatitis C Detection in a rapid way.