Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards addressable organic impedance switch devices

The addressability of organic impedance switch devices as the memory elements of cross-point matrices is improved by introducing nonlinear behavior in their current-voltage characteristics. This is realized by adding a semiconducting layer of copper (II) phthalocyanine (CuPc) on top of the switch layer of Rose Bengal (RB) sodium salt. Leakage currents from unaddressed cells in a matrix are reduced by a factor of 17; thus improving the signal-to-background ratio, reducing driver currents and limiting the potential drop along the addressing lines. The matrix size of RB switch devices, using organic conductors, is predicted to be increased by 32 times when using the additional CuPc layer.
Increasing the flexibility of mixed models by using fractional polynomials

The class of regression models incorporating Fractional Polynomials (FPs), proposed by Royston and colleagues in the 1990’s, has been extensively studied and shown to be fruitful in the presence of nonlinearity between the response variable and continuous covariates. FP functions provide an alternative to higher-order polynomials and splines for dealing with lack-of-fit. Mixed models may also benefit from this class of curves in the presence of non-linearity. The inclusion of FP functions into the structure oflinear mixed models has been previously explored, though for simple layouts, e.g. a single covariate in the random intercept model. This paper proposes a general strategy for model-building and variable selection that takes advantage of the FPs within the framework of linear mixed models. Application of the methodto three data sets from the literature, known for violating the linearity assumption, illustrates that it is possible to solve the problem of lack-of-fit by using fewer terms in the model than the usual approach of fitting higher-order polynomials.
An ordered flow-shop sequencing problem with mean completion time criterion†

Abstract This paper investigates a subcategory of the classical n job m machine problem in which the processing times of different jobs are ' ordered '. An extremely simple algorithm is presented which obtains a sequence minimizing the mean completion time of all jobs. The proof of optimality is presented in the Appendix. The problem illustratos that although no efficient procedure exists to solve the classical flow-shop problem with the mean completion time criterion, it is possible to develop such a procedure for a specially structured problem.
Green synthesis of gold nanoparticles using Sambucus ebulus fruit extract, characterization, and antileishmanial, antibacterial, antioxidant, and photocatalytic activities

In this study, gold nanoparticles were synthesized using the fruit extract of Sambucus ebulus (S. ebulus) as a reducing, capping, and stabilizing agent. Biogenic synthesis of gold nanoparticles (Au nanoparticles) was accomplished using S. ebulus fruit extract in the presence of hydrogen tetrachloroaurate(III) trihydrate at a temperature of 65 °C and the solution stirred at 400 rpm. The characterization of the synthesized nanoparticles (SE-AuNPs) was performed using different analytical methods, such as scanning electron microscopy (FE-SEM), energy dispersion X-ray spectroscopy (EDS), Fourier transform infrared (FT-IR), X-ray diffraction analysis (XRD), and UV-vis spectroscopy. A strong absorption peak at 565 nm confirmed the formation of the gold nanoparticle. On the basis of the electron microscopy results, AuNPs were mostly spherical with an average size of 116.2 nm. The cubic crystalline structure of the prepared nanoparticles was confirmed using the XRD pattern and the average crystallite size was obtained at 28.471 nm. FT-IR analysis confirmed the presence of functional groups in the plant extract for the synthesis of nanoparticles. SE-AuNPs showed good antibacterial activity against Gram-positive and Gram-negative bacteria tested and exhibited potent antileishmanial activity. Furthermore, SE-AuNPs showed excellent antioxidant activity that inhibited DPPH radicals with an IC50 value of 21.976 µg/mL. The prepared AuNPs acted to degrade methyl orange (MO), which was performed in sodium borohydride and visible light.
Some Problems in the Theory of Perovskite Ferroelectrics

A brief review is given on the studies of ferroelectricity in perovskites. Early phenomenological theories of displacement-type ferroelectrics are considered. Two markedly different approaches based on density-functional theory are described. In one approach, the electron density of crystal is determined as a sum of Bloch functions. In the other, it is constructed as a superposition of the electron densities of individual ions. The latter approach gives a microscopic justification for the old phenomenological theories. We show that some phenomena in perovskites, which seemingly are of the order–disorder origin, can be explained by quasi-one-dimensional peculiarities of lattice dynamics.
POLARIMETRIC OBSERVATIONS OF SPIRAL GALAXIES

The extragalactic systems NCC 3031, NCC 4594, NGC 5194, and NGC 5457 have been observed polarimetrically. Linear polarization between 0.1 and 1.0 percent was detected in the spiral arms of all four galaxies. The planes of vibration indicate that reflection as well as interstellar absorption of starlight by the internal dust clouds of the observed galaxies contribute to the polarized light.
Integrating the Principles of Lean Management into the Design Process of Green Tall Buildings

- Tall buildings have become a sign of Economic and technological development in advanced countries, resolving different issues coming from the significant increase in the urban dwellers and land price. This type of buildings is characterized by its high levels of energy consumption and design complexity. The design process of tall buildings involves many professionals from several disciplines, requiring an efficient management to coordinate and interpret the interrelations between these various disciplines in the design stage taking into consideration the global awareness of energy consumption and the urgent need to reduce the quantity of energy used by this type of buildings. Lean process tools and methods have been adapted to the design management process in the AEC industry - known as Lean Design management (LDM) - to improve the context of design and overcome its deficiencies with minimum wastage with a greener comprehensive view of the whole project. Applying lean design management tools to the design process of tall buildings will help in overcoming the challenges coming from the complex design process of tall buildings. Thus, the objective of this paper is to set preliminary guidelines for the integration of the lean design management concept with the design process management of green tall buildings, through a literature review. More than 200 research paper -ranging from 2010 till present mostly indexed in Scopus database- including the aim of the study were selected for review. Research found a number of preliminary guidelines for optimizing the design process of green tall buildings through the adaptation of the lean design management concept. This research is meant to be the first phase of the study to assess the application of lean principles to the design process of tall buildings through practical analysis of case studies that will be carried out in further studies.
On dielectric breakdown in silicon-rich silicon nitride thin films

Observations of dielectric breakdown in Si-rich silicon nitride indicate that it is initiated by threshold field trap ionization. The films exhibit the charge transport mechanism of Poole–Frenkel emission with a compositionally dependent ionization potential ranging from 0.58 to 1.22 eV. Similar to silicon oxynitride, the barrier lowering energy at the point of dielectric breakdown is correlated with within ∼2kT of the ionization potential, thus revealing a dual role for bulk traps in the film: regulating charge transport and retarding hot electron generation. Additionally, a semiempirical expression is developed that accurately predicts the compositional dependence of the breakdown field.
The impact of managerial ownership on audit fees: Evidence from Portugal and Spain

Abstract This study examines the impact of managerial ownership on audit fees in a context of concentrated ownership and poor investor protection. Using samples of Portuguese and Spanish listed companies for the period 2010–2021, the results of this research suggest that there is a non-linear relationship between managerial ownership and audit fee which corresponds to a pattern of “alignment-entrenchment-alignment”. In Portugal and Spain, the convergence of interest effect dominates the entrenchment effect in the low and high ranges of managerial ownership, leading to the conclusion that a policy of providing management with amounts of equity within these ranges of managerial ownership should reduce agency costs and decrease audit fees. In contrast, in Spain, the entrenchment effect dominates the convergence of interest effect in the intermediate range of managerial ownership, which have the effect of increasing audit fees.
Diabetes diagnosis based on hard and soft voting classifiers combining statistical learning models

Diabetes mellitus is one of the deadliest incurable diseases globally, and its cases continue upward. The identification of the disease in an early way helps fight it; however, blood tests can be considered invasive, discouraging its accomplishment. In this vein, this work aims to build a model as an alternative to traditional exams to identify the disease. Statistical learning algorithms such as logistic regression, K-nearestneighbors, decision trees, random forest, and support vector machines were used for diabetes classification. These models were considered separately and combined via hard and soft voting classifiers. Themethods were applied to a widely known dataset of 768 individuals and nine variables, compared using several accuracy metrics based on the confusion matrix, and used to estimate the probability of diabetesfor a given profile.
Encrypted Storage Method of Oral English Teaching Resources based on Cloud Platform

—With the development of the times, the secure storage of educational resources has become one of the key security problems faced by colleges and universities. On the one hand, the cost of traditional resource storage is too expensive, on the other hand, its encryption and access efficiency are low. To solve this problem, this research takes the cloud platform serves as the main carrier for the encrypted storage of school teaching resources. On this basis, the convolutional neural network is encrypted and optimized, and the argmax algorithm is improved to improve the access efficiency of encrypted data. Finally, the effectiveness and superiority of the design method are compared and analyzed through the method of performance detection. The results show that the maximum consumption time of encryption and decryption of the encrypted storage model is no more than 20000ms, which is significantly less than that of the traditional model. The running time of the argmax output encryption module is 1.76ms and the running loss is 0.26 MB, which is less than that of the traditional model. It can be seen that the encrypted storage model has stronger encryption performance and access performance, and has a better application effect in the encrypted storage of oral English teaching resources with a large amount of access data and frequent updates.
Feedback Control of Minimum-Time Optimal Control Problems using Neural Networks

This paper presents an optimal feedback controller capable of driving a non-linear control system from an arbitrary initial state to a fixed final state in minimum time. The controller is based on a feedforward multilayer neural network trained repeatedly using open-loop optimal control data which densely span the field of extremals of the non-linear system. The effectiveness of the controller is clearly demonstrated by a simulation on a two-link robot manipulator. The effect of sensor/actuator noise and parameter variation is also included to confirm the robustness of the controller.
Discovery of a novel silicon photonic crystal waveguide modulator

Silicon, a material that has carried the entire electronics industry for the latter half of last century, has only recently been considered a favorable choice for functional optics. Silicon has no χ electro-optic effect, and converts injected electrons into light with extremely low efficiency. Both of these are great disadvantages for modulating and generating light. Thus far, integrated optical functions have primarily been performed on III-V materials such as gallium arsenide. This separation of photonics and electronics has been a big stumbling block for many important applications in telecommunications, optical interconnects, and consumer electronics. As the intrinsic material properties of silicon do not favor photonic applications, many techniques to artificially pattern silicon with extremely fine optical structures have been investigated. However—unless these fine structures can be produced with controllability, consistency, and high precision— these devices cannot serve their designed purpose. With current nano-fabrication technology, this remains a practical challenge for most material-engineering approaches. Silicon modulators based on micro-ring resonators can reduce the device size: however, their extremely narrow bandwidth (∼ 0.02nm) means that, for practical applications, they require a laser with accurate wavelength control. This is expensive. Photonic crystals, artificial dielectric periodic lattices on the wavelength scale, provide a solution to this problem. The lattice creates photonic bandgaps—similar to energy bandgaps for electrons in solids—that cause photons to be reflected regardless of their incident angle on the photonic crystal surface. This unique property provides a novel combination of abilities: to confine, guide, and decelerate light. A typical photonic crystal is made by etching arrays of holes with diameters of a few Figure 1. Sketch of the silicon Mach-Zehnder modulator on a siliconon-insulator chip.
Design of Deterministic Model of Signal Transduction between Neuronal Cells

AbstractMathematical model describing signal transduction between neurons has been presented using the system of nonlinear ordinary differential equations. The Cauchy problem for the given system has been solved numerically and system parameters were adjusted to match the results of experimental measurements of extracellular postsynaptic potentials in rat hippocampus slices. While fitting model to the experimental data some values of synaptic parameters have been determined.
A novel foam of geopolymer based on ZSM‐5 zeolite fabricated using templating emulsion/chemical foaming method and its application in batch and continuous dye removal systems

Geopolymers as sustainable and environmentally friendly “green materials,” can be synthesized by utilizing waste material and by‐products. A porous geopolymer foam adsorbent based on ZSM‐5 zeolite was prepared using templating emulsion/chemical foaming method in different conditions and used for removal of low‐concentration dye in batch and continuous systems. The parameters affecting the dye adsorption including temperature, concentration, pH, kinetics, isotherm, and thermodynamics of the process were investigated. The results of the geopolymer foam synthesis showed that thermal pretreatment of the zeolite has a positive effect on the strength and adsorption capacity. Moreover, the increase in sodium silicate more than the stoichiometric reduces the strength and adsorption capacity. The findings obtained from the batch adsorption process showed that the adsorption kinetics of the pseudo‐second‐order model and the adsorption isotherm of the Temkin model is adjusted with the experimental data. Thermodynamic results indicated that the process of dye adsorption with geopolymer foam is exothermic. The results from continuous experiments indicated more compatibility of the adsorption process with the models of Thomas and Bohart–Adams. The maximum adsorption capacity of methylene blue in batch and continuous processes was 9.82 and 8.17 mg/g. The adsorbent reduction was performed successfully by chemical and thermal processes.
Heat transport in amorphous silicon: Interplay between morphology and disorder

We present a theoretical study of the thermal conductivity (κ) of amorphous silicon (a-Si) based on molecular and lattice dynamics. We find that the majority of heat carriers are quasi-stationary modes; however the small proportion (≃3%) of propagating vibrations contributes to about half of the value of κ. We show that in bulk samples the mean free path of several long-wavelength modes is on the order of microns; this value may be substantially decreased either in thin films or in systems with etched holes, resulting in a smaller thermal conductivity. Our results provide a unified explanation of several experiments and show that kinetic theory cannot be applied to describe thermal transport in a-Si at room temperature.
Mevalonate pathway analysis of Saccharomyces cerevisiae during bioisoprene synthesis

Isoprene, synthesized through two complementary biosynthetic routes known as the mevalonate (MVA) pathway and the deoxy-xylulose phosphate pathway, is a valuable monomer that is used for rubber and several other chemical industries. Despite the recent interest in the industrial and biomedical applications of isoprene and its derivatives, the complexity of controlling its chemical synthesis due to the formation of greenhouse gases is a significant problem. To overcome the productivity and yield challenges, in addition to generating environmental and economic benefits, this study aimed to focus on the direct fermentation of cellulosic materials into bioisoprene. In this study, bioisoprene was synthesized via a biotransformation process through enzymatic hydrolysis of cassava peel using Aspergillus niger 11JK and Saccharomyces cerevisiae 19KB strain. The mevalonate (MVA) pathway (synthetic route) exploited during bioisoprene production by S. cerevisiae 19KB strain was investigated using the hydrolyzed cassava peel broth. The obtained crude extract was analyzed for bioisoprene yield and enzymatic activities using Gas chromatography. Furthermore, results of the size exclusion chromatography revealed the presence of polysaccharide hydrolyzing enzymes (e.g., amylase and cellulase), and mevalonate pathway enzymes, including isoprene synthase, mevalonate-5-diphosphate decarboxylase, and isopentyl phosphate kinase, in addition to isoprene, mevalonic acid (MVA), and its isomer dimethylallyl diphosphate (DMAPP). Based on the results obtained in this study, bioisoprene synthesis via direct fermentation of cheap and abundant carbon sources such as cassava peel using the S. cerevisiae 19KB strain will overcome the high production costs and low yield challenges of bioisoprene, thus generating significant environmental and economic benefits.
FDeep: A Fog-based Intrusion Detection System for Smart Home using Deep Learning

—Smart Home is an application of the Internet of Things (IoT) that connects smart appliances and the Internet. The emergence of Smart Home has caused many security and privacy risks that can lead to fatal damages to the user and his property. Unfortunately, Intrusion detection systems designed for conventional networks have shown their inefficiency when deployed in Smart Home environments for many reasons that rely basically on the resources-constrained devices and their inherent intermittent connectivity. So, an intrusion detection system designed for IoT and particularly Smart Home is mandatory. On the other hand, Deep learning shows its potential in enhancing the performance of Intrusion Detection Systems. According to recent studies, Deep learning-based intrusion detection systems are deployed either on the devices or in the Cloud. However, Deep learning models are greedy in terms of resources which makes it challenging to deploy them on Smart Home devices. Besides, in the IoT architecture, the IoT layer is far from the Cloud layer which may cause additional latency and jitter. To overcome these challenges, a new intrusion detection system for Smart Home deployed in the Fog Layer is proposed, it is called FDeep. FDeep will inspect the traffic using a Deep Learning model. To select the most accurate model, three Deep Learning models are trained using an IoT dataset named TON/IIOT, also the proposed models are compared to an existing one. The obtained results show that the long short-term memory model combined with the convolutional neural networks outperforms the other three models. It has the best detection accuracy compared to other Deep Learning models.
The Expression of Myoz1 and ApoB is Positively Correlated with Meat Quality of Broiler Chicken

Broiler chicken (Gallus gallus) is a source of animal protein with a high nutritional content. The purpose of this study was to evaluate the quality of broiler chicken meat (Gallus gallus) by analyzing its nutritional value, genetic profile, and protein level. The chicken meat samples were obtained from four different districts in Malang city, Indonesia. We analysed the proximate composition of chicken meat to detect its nutrition content. Furthermore, we have examined the sequence of the Myoz1 gene as well as the level of ApoB proteins in the same meat. The nutritional analysis of chicken meat showed that in the four locations different levels of protein, ash, water, and lipids were observed. The Myoz1 gene of femur chicken broilers from the second and third districts has five and twenty-one substitution mutations, respectively. The ApoB expression level in locations 2 and 3 was higher than that in the other districts. In conclusion, Myoz1 and ApoB levels were correlated with the nutritional content and quality of broiler chicken meat.
EFFECT OF GROWTH TIME ON STRUCTURE , OPTICAL AND PHOTO-RESPONSE CHARACTERISTICS OF ZnO NANORODS DEPOSITED ONTO VARIOUS SUBSTRATES

ZnO nanorod (NRs) were deposited by chemical bath deposition at different growth duration time onto a variety of substrates (glass, polyethylene terephthalate ( PET), n-type Si, and stainless steel). X-ray diffraction analysis confirms that all grown films crystallize within the hexagonal wurtzite-type structure with a strong preferred orientation along (002) for Si-substrate. Scanning electron microscopy observations reveal the formation of nanorods, where both density and length increase with the increase of deposition time. The optical band gap determined from PL spectra was found of ZnO (NRs) grown onto Si substrate at 2 and 3h is close to the value of the energy gap of bulk ZnO (Eg=3.37 eV), which can be associated with the low defects level. Raman spectroscopy analysis revealed the presence of a compressive stress within ZnO nanorods for all substrates. Metalsemiconductor-metal device was fabricated and UV photo-responsivity at room temperature was studied as function of the nature of substrate. The results showed that the UV photo-detector based on ZnO nanorods deposited (3h) onto n-Si showed a high sensitivity of 6250% at zero bias voltage.