Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Framework Using Two Layers of Missing Value Imputation

In this study we present a novel framework that uses two layers/steps of imputation namely the Early-Imputation step and the Advanced-Imputation step. In the early imputation step we first impute the missing values (both numerical and categorical) using existing techniques. The main goal of this step is to carry out an initial imputation and thereby refine the records having missing values so that they can be used in the second layer of imputation through an existing technique called DMI. The original DMI ignores the records having missing values. Therefore, we argue that if a data set has a huge number of missing values then the imputation accuracy of DMI may suffer significantly since it ignores a huge number of records. In this study we present four versions of the framework and compare them with three existing techniques on two natural data sets that are publicly available. We use four evaluation criteria and two statistical significance analyses. Our experimental results indicate a clear superiority of the proposed framework over the existing techniques.
Asphalt Concrete Mix Design Optimization Using Autoencoder Deep Neural Networks

Asphalt concrete (AC) balanced mix design (BMD) is based on the selection of aggregate gradation, component volumetrics, and binder content to control pavement cracking and rutting potential. The Illinois Flexibility Index Test (I-FIT) and the Hamburg Wheel Tracking Test (HWTT) results, used to predict cracking and rutting potential, respectively, are used in the BMD approach. However, BMD generally relies on a trial-and-error process to identify the aggregate gradation and binder content needed to meet volumetrics and optimize I-FIT and HWTT results. Minimizing or eliminating the trial-and-error process would increase productivity and accuracy. Therefore, this study proposes an autoencoder deep neural network (ADNN) to develop optimized AC mix design alternatives that can meet a prescribed flexibility index (FI) and rut depth (RD). Autoencoders are a type of neural network designed for representation learning composed of an encoder and a decoder. The encoder detects a structured pattern in the original input data to create a compressed representation of the AC mix design. The decoder reconstructs the compressed representation. The proposed autoencoder is composed of an encoder of five hidden layers, a latent space of one node, and a five-hidden-layer decoder. Models were created from a database of 5,357 data sets that include mix properties, I-FIT FI, and HWTT RD (after data preprocessing was conducted). An autoencoder was then trained to predict the total binder content, and aggregate gradation based on a target mix type, FI, and RD.
Quantitative measures of pivot shift on knee rotatory instability

11 One of the key prerequisites to returning to sports activities after knee injury is restored rotatory instability. The pivot shift test is the standard way to evaluate rotatory instability. However, manual testing methods reliant on the subjective judgment of examiners may fail to discriminate the dynamic stages of rotatory instability, especially because the pivot shift phenomenon occurs as a result of combination of translation and rotation movements of the tibiofemoral joint. To this end, several studies have investigated novel ways to quantitatively measure kinematics of the pivot shift test by developing new measurement devices. Measurement devices, such as navigation, electromagnetic, and inertial sensors, and image analysis systems have been developed. Low-cost, non-invasive, and self-contained devices are gaining popularity for their ease of use. Here, we review the advantages and disadvantages of current measurement systems of pivot shift tests and summarize the relevant scientific knowledge of this field for future research.
Un risultato relativo ad equazioni ellittiche del secondo ordine di tipo non variazionale

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Power quality enrichment using enhanced adaptive control-detuned-LC proposal in voltage source control conquered distributed generation with hardware implementation

This paper presents the collective operation and comparative assessment of artificial neural network (ANN)-based adaptive controller with detuned-inductor capacitor (LC) filter facility in grid-tied voltage source control (VSC) system. In order to facilitate proper shaping of VSC outputs and to avoid voltage surge or current surge issues that may occur during the synchronization, the controlling action should reflect importance of total impedance (Zt) effect for: (i) accurate online weight updating, (ii) generation of correct references for proper shaping of VSC outputs, (iii) accurate assessment and exclusion of current harmonics and (iv) robust in defending any system perturbation. This impedance is taken into consideration during the run-time weight updation process through extended control steps in order to pass over various losses that certainly occurs in transformers, filters, line parameters and so forth. Performance of the system is well improved with an inclusion of total impedance (Zt) measured between the VSC and point of common coupling (PCC). A detuned-LC filter is predominantly intended for reactive power compensation, power factor correction, prompt and accurate alleviation of the harmonics. A comparative assessment in between enhanced and conventional adaptive controllers that are designed in MATLAB/Simulink clarifies the robust performances of the proposed control design under sundry system turbulences. The verification of the proposed enhanced controller is approved with the hardware results obtained using dSPACE RTI 1202 kit.
How Healthy Is It to Fortify Cocoa-Based Products with Cocoa Flavanols? A Comprehensive Review

(1) Background: Cocoa’s healthy benefits may be attributed to the potent antioxidant activity of cocoa polyphenols, mainly flavanols, which have been characterised as existing in a high concentration in cocoa. However, the phenolic composition of cocoa and cocoa-derived products is highly variable, and manufacturing processes might significantly reduce their phenolic content. For that reason, the full characterisation of cocoa and cocoa-derived products before evaluating their bioactivity is crucial. The aim of this review is to analyse the available evidence on the effect of flavanol-fortified cocoa-derived products on human health. (2) Methods: Forty-eight clinical trials focused on the health effect of consuming flavanol-fortified drinks, bars and chocolate have been reviewed, with a total of 1523 subjects. (3) Results: Although studies differ widely in methodology, dosage, duration, and target population, beneficial effects of flavanol-rich cocoa consumption have been observed at doses ranging from 45.3 mg/d to 1078 mg/d, especially on cardiovascular health and cognitive function. (4) Conclusions: Considering the high consumption and acceptability of cocoa and cocoa-derived products, the fortification of cocoa products as well as other highly consumed foods with cocoa flavanols could be an effective strategy for health promotion.
Highly Resistant Zero‐Order Waveplates Based on All‐Silica Multilayer Coatings

Waveplates, used to modify polarization state of light, are integral part of high‐power lasers. Classical approach to waveplate manufacturing is based on combination of birefringent crystalline materials and deposition of nonpolarizing antireflection (AR) coatings. Their limitation to withstand maximal peak power is determined by laser‐induced damage (LID) phenomena, mainly determined by low band‐gap materials used in AR coatings. In this study, a novel multi‐layer approach of high band‐gap birefringent coatings was proposed and investigated to overcome this limitation. Three eligible candidate materials, namely LaF3, Al2O3, and SiO2 are investigated. Structural and optical analysis reveal superior properties of silica for UV spectral range. Zero‐order thin film retarders based on all‐silica nano‐structures are fabricated by oblique angle deposition process. Low optical losses and high transparency (T ∼ 99%) are demonstrated while indicating potential to withstand high laser fluence of 40 J cm−2 in nanosecond regime at 355 nm wavelength. Such waveplates can essentially improve maximal tolerated peak power and thus allow production of more compact optical systems, even when high laser power is used.
WearTrack – Tracking Valuables using Near Field Communication Stickers

In modern era, people will not be satisfied with technology until it not only reminds us of our day to day work but also does it for us without draining much of our energy. Any person will acknowledge the fact that we need smarter technology that reminds us what things are to be taken wherever we go or whenever we leave a place. It makes life so much easier if we are told to do things rather than thinking up things to be done on our own.People irrespective of their age, have always been in situations where they have lost their valuable things or have the fear of losing them.So in this paper an attempt is made how a user gets alarmed whenever they leave a place forgetting to take their valuables. The user has a sensorand othersensorsare connected to things which they want to forget to take as they leave a place. All the sensing devices consist of Bluetooth to recognize the user's sensor or to be recognized by it. An alarm is connected in the circuit which switches on whenever they leave the place without the required things. The evaluation results show that the sensors will give 98%accuracy towards the complete Bluetooth connection.
An Extended Version of the Natario Warp Drive Equation Based in the Original $3+1$ $adm$ Formalism Which Encompasses Accelerations and Variable Velocities

Warp Drives are solutions of the Einstein Field Equations that allows superluminal travel within the framework of General Relativity. There are at the present moment two known solutions: The Alcubierre warp drive discovered in 1994 and the Natario warp drive discovered in 2001. However the major drawback concerning warp drives is the huge amount of negative energy density able to sustain the warp bubble.In order to perform an interstellar space travel to a ”nearby” star at 20 lightyears away in a reasonable amount of time a ship must attain a speed of about 200 times faster than light.However the negative energy density at such a speed is directly proportional to the factor 10 which is 1.000.000.000.000.000.000.000.000 times bigger in magnitude than the mass of the planet Earth! With the correct form of the shape function the Natario warp drive can overcome this obstacle at least in theory.Other drawbacks that affects the warp drive geometry are the collisions with hazardous interstellar matter that will unavoidably occurs when a ship travels at superluminal speeds and the problem of the Horizons(causally disconnected portions of spacetime). The geometrical features of the Natario warp drive are the required ones to overcome these obstacles also at least in theory.However both the Alcubierre or Natario warp drive spacetimes always have a constant speed in the internal structure of their equations which means to say that these warp drives always travel with a constant speed.But a real warp drive must accelerate from zero to a superluminal speed of about 200 times faster than light in the beginning of an interstellar journey and de-accelerate again to zero in the end of the journey.In this work we expand the Natario vector introducing the coordinate time as a new Canonical Basis for the Hodge star and we introduce an extended Natario warp drive equation which encompasses accelerations. ∗spacetimeshortcut@yahoo.com
Influence of carrier localization at the core/shell interface on the temperature dependence of the Stokes shift and the photoluminescence decay time in CdTe/CdS type-II quantum dots

Right ©2017 American Physical Society. This article may be downloaded for personal use only. Any other use requires prior permission of the author and American Physical Society. The following article appeared in Phys. Rev. B 96, 035305 and may be found at https://doi.org/10.1103/PhysRevB.96. 035305 URI http://dlisv03.media. osaka-cu.ac. jp/il/meta_pub/G0000438repository_ 24699969-96-035305 DOI 10.1103/PhysRevB.96. 035305
Leveraging supplementary modalities in automated real estate valuation using comparative judgments and deep learning

PurposeIn this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.Design/methodology/approachThe authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.FindingsThe results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.Originality/valueTo the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.
Statistical inference for structural models in high dimension. Sparse generalized linear models, PLS through orthogonal polynomials and community detection in graphs

This thesis falls within the context of high-dimensional data analysis. Nowadays we have access to an increasing amount of information. The major challenge relies on our ability to explore a huge amount of data and to infer their dependency structures.The purpose of this thesis is to study and provide theoretical guarantees to some specific methods that aim at estimating dependency structures for high-dimensional data. The first part of the thesis is devoted to the study of sparse models through Lasso-type methods. In Chapter 1, we present the main results on this topic and then we generalize the Gaussian case to any distribution from the exponential family. The major contribution to this field is presented in Chapter 2 and consists in oracle inequalities for a Group Lasso procedure applied to generalized linear models. These results show that this estimator achieves good performances under some specific conditions on the model. We illustrate this part by considering the case of the Poisson model. The second part concerns linear regression in high dimension but the sparsity assumptions is replaced by a low dimensional structure underlying the data. We focus in particular on the PLS method that attempts to find an optimal decomposition of the predictors given a response. We recall the main idea in Chapter 3. The major contribution to this part consists in a new explicit analytical expression of the dependency structure that links the predictors to the response. The next two chapters illustrate the power of this formula by emphasising new theoretical results for PLS. The third and last part is dedicated to graphs modelling and especially to community detection. After presenting the main trends on this topic, we draw our attention to Spectral Clustering that allows to cluster nodes of a graph with respect to a similarity matrix. In this thesis, we suggest an alternative to this method by considering a $l_1$ penalty. We illustrate this method through simulations.
Prediction of Foot Ulcers Using Artificial Intelligence for Diabetic Patients at Cairo University Hospital, Egypt

Introduction In Egypt, diabetic foot ulcers markedly contribute to the morbidity and mortality of diabetic patients. Accurately predicting the risk of diabetic foot ulcers could dramatically reduce the enormous burden of amputation. Objective The aim of this study is to design an artificial intelligence-based artificial neural network and decision tree algorithms for the prediction of diabetic foot ulcers. Methods A case–control study design was utilized to fulfill the aim of this study. The study was conducted at the National Institute of Diabetes and Endocrine Glands, Cairo University Hospital, Egypt. A purposive sample of 200 patients was included. The tool developed and used by the researchers was a structured interview questionnaire including three parts: Part I: demographic characteristics; Part II: medical data; and Part III: in vivo measurements. Artificial intelligence methods were used to achieve the aim of this study. Results The researchers used 19 significant attributes based on medical history and foot images that affect diabetic foot ulcers and then proposed two classifiers to predict the foot ulcer: a feedforward neural network and a decision tree. Finally, the researchers compared the results between the two classifiers, and the experimental results showed that the proposed artificial neural network outperformed a decision tree, achieving an accuracy of 97% in the automated prediction of diabetic foot ulcers. Conclusion Artificial intelligence methods can be used to predict diabetic foot ulcers with high accuracy. The proposed technique utilizes two methods to predict the foot ulcer; after evaluating the two methods, the artificial neural network showed a higher improvement in performance than the decision tree algorithm. It is recommended that diabetic outpatient clinics develop health education and follow-up programs to prevent complications from diabetes.
Aldehyde Dehydrogenase: An Off-Label Marker of Endothelial Activation in Oral Squamous Cell Carcinoma

Introduction: The vascular endothelial (VE) expression of aldehyde dehydrogenase (ALDH) 1/2 family in oral leukoplakia (OL) and oral squamous cell carcinoma (OSCC) cases has not been studied so far. The aim of this study was to illustrate the “off-label” endothelial expression of cancer stem cell (CSC) biomarker, ALDH1/2, adjacent to oral potentially malignant and malignant lesions in order to shed some light on the mechanisms leading to oral carcinogenesis. Materials and methods: The expression of CSC protein-biomarker ALDH1/2 was detected through immunohistochemistry (IHC) in 30 paraffin-embedded samples of OL and 21 samples of OSCC compared to five samples of normal oral mucosa. Statistical analysis was done using SPSS, Pearson Chi-square, and Fischer’s exact test. The significance level was set at 0.05 (p≤ 0.05). Results: In oral mucosal vessels, ALDH1/2 was not expressed. It was expressed significantly more in the vessels of OSCCs compared to the OLs (Fisher’s exact test, p-value= 0,001). However, when endothelial expression of ALDH1/2 in the vasculature of OLs was compared with that of the normal oral mucosa, no significant change was noticed (Fisher’s exact test, p-value=1.000). Discussion: The IHC VE expression of ALDH1/2 in OSCC vasculature but not in OL indicates a possible significantly stronger activation of endothelial cells during carcinogenesis, which could be an indicator of the role of inflammation in the development of field cancerization and of prognostic value for (vascular/lymphatic) metastasis.
Correlation of Brownian Motions and Its Impact on a Reinsurer’s Optimal Investment Strategy and Reinsured Proportion under Exponential Utility Maximization and Constant Elasticity of Variance Model

This work investigated a reinsurer’s optimal investment strategy and the pro-portion he accepted for reinsurance under proportional reinsurance and expo-nential utility preference in the cases where the Brownian motions were corre-lated and where they did not correlate. The reinsurer invested in a market in which the price process of the risky asset is governed by constant elasticity of variance (CEV) model. The required Hamilton-Jacobi-Bellman Equations (HJB) were derived using the Ito’s lemma from which the optimal investment strategy and reinsured proportion were calculated. Also investigated were the implications of the correlation coefficient.
Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives

Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems’ outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future.