Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentence Comprehension as a Cognitive Process

Sentence comprehension - the way we process and understand spoken and written language - is a central and important area of research within psycholinguistics. This book explores the contribution of computational linguistics to the field, showing how computational models of sentence processing can help scientists in their investigation of human cognitive processes. It presents the leading computational model of retrieval processes in sentence processing, the Lewis and Vasishth cue-based retrieval mode, and develops a principled methodology for parameter estimation and model comparison/evaluation using benchmark data, to enable researchers to test their own models of retrieval against the present model. It also provides readers with an overview of the last 20 years of research on the topic of retrieval processes in sentence comprehension, along with source code that allows researchers to extend the model and carry out new research. Comprehensive in its scope, this book is essential reading for researchers in cognitive science.
Internet of Drones Intrusion Detection Using Deep Learning

Flying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET technology in their systems. However, FANET’s special roles made it complex to support emerging security threats, especially intrusion detection. This paper is a step forward towards the advances in FANET intrusion detection techniques. It investigates FANET intrusion detection threats by introducing a real-time data analytics framework based on deep learning. The framework consists of Recurrent Neural Networks (RNN) as a base. It also involves collecting data from the network and analyzing it using big data analytics for anomaly detection. The data collection is performed through an agent working inside each FANET. The agent is assumed to log the FANET real-time information. In addition, it involves a stream processing module that collects the drones’ communication information, including intrusion detection-related information. This information is fed into two RNN modules for data analysis, trained for this purpose. One of the RNN modules resides inside the FANET itself, and the second module resides at the base station. An extensive set of experiments were conducted based on various datasets to examine the efficiency of the proposed framework. The results showed that the proposed framework is superior to other recent approaches.
The role of miRNAs in regulating adrenal/gonadal steroidogenesis.

MicroRNAs (miRNAs) are endogenous noncoding single-stranded small RNAs of ~22 nucleotides in length that post-transcriptionally repress the expression of their various target genes. They contribute to the regulation of a variety of physiologic processes including embryonic development, differentiation and proliferation, apoptosis, metabolism, hemostasis and inflammation. In addition, aberrant miRNA expression is implicated in the pathogenesis of numerous diseases including cancer, hepatitis, cardiovascular diseases and metabolic diseases. Steroid hormones regulate virtually every aspect of metabolism and acute and chronic steroid hormone biosynthesis is primarily regulated by tissue-specific trophic hormones involving transcriptional and translational events. In addition, it is becoming increasingly clear that steroidogenic pathways are also subject to post-transcriptional and post-translational regulations including processes such as phosphorylation/dephosphorylation, and protein‒protein interactions and regulation by specific miRNAs, although the latter is in its infancy state. Here, we summarize the recent advances in miRNA-mediated regulation of steroidogenesis with emphasis on adrenal and gonadal steroidogenesis.
Multiobjective Optimal Dispatching Method of Generalized Power Active Distribution Network Based on Game Theory

To improve the operating dependability of a generalized power active distribution network, a multiobjective optimal scheduling approach based on game theory is proposed. The active distribution network's multistakeholder coordinated and optimal dispatching mode is then established, and the game interaction between various stakeholders in the generalized power active distribution network is evaluated. Using the multistakeholder game coordinated and optimal dispatching strategy, manage and regulate the distribution firms, distributed generation operators, and users engaging in demand response in the active distribution network, to guarantee that the distribution system runs smoothly and fairly. The rate of renewable energy usage, network loss, and user satisfaction are all factored into the multiobjective optimum scheduling objective function of a generalized power active distribution network. Using the multistakeholder game coordinated and optimal dispatching strategy, manage and regulate the distribution firms, distributed generation operators, and demand response participants in the active distribution network, to make sure the distribution system runs smoothly and equitably. Taking into account the rate of renewable energy usage, network loss, and user satisfaction, a multiobjective optimum scheduling objective function for a generalized power active distribution network is thus created.
A Pareto optimum approach and a power management strategy for a stand‐alone wind turbine‐PV‐hydrogen with reformer hybrid renewable energy system

In this study, a multi‐objective and constrained optimization problem is discussed. The SAHRESOpt (multi‐objective and constrained optimization algorithm for SA‐HRES) which includes size optimization and minimization of the loss of power supply probability was developed based on non‐dominated sorting genetic algorithm‐II (NSGA‐II). Additionally, a power management strategy was designed to ensure that the power flow between the system elements minimizes the unmet load and/or the excess energy. In this context, with the aim of obtaining uninterrupted hydrogen energy in the system, a reformer was used transforming methane gas produced by an anaerobic reactor into hydrogen. Thus, a stand‐alone hybrid renewable energy system (SA‐HRES) that does not generate CO2 emissions was designed. Furthermore, within the scope of size optimization, both the types of system elements and the numbers of them were optimized. Then, the positive effects of reformer existence on both of the objective functions, and also the effects of considering the waste energy percentage as a constraint are presented. Also, Pareto solutions of SAHRESOpt and NSGA‐II are compared. Finally, a case study including photovoltaic panels, wind turbines, fuel cells, electrolyzers, hydrogen tanks, and a reformer is presented to prove the effectiveness of the presented algorithm and validate the results.
Words as Visual Objects: Neural and Behavioral Evidence for High-Level Visual Impairments in Dyslexia

Developmental dyslexia is defined by reading impairments that are disproportionate to intelligence, motivation, and the educational opportunities considered necessary for reading. Its cause has traditionally been considered to be a phonological deficit, where people have difficulties with differentiating the sounds of spoken language. However, reading is a multidimensional skill and relies on various cognitive abilities. These may include high-level vision—the processes that support visual recognition despite innumerable image variations, such as in viewpoint, position, or size. According to our high-level visual dysfunction hypothesis, reading problems of some people with dyslexia can be a salient manifestation of a more general deficit of high-level vision. This paper provides a perspective on how such non-phonological impairments could, in some cases, cause dyslexia. To argue in favor of this hypothesis, we will discuss work on functional neuroimaging, structural imaging, electrophysiology, and behavior that provides evidence for a link between high-level visual impairment and dyslexia.
Identification of Signaling Pathways for Early Embryonic Lethality and Developmental Retardation in Sephs1−/− Mice

Selenophosphate synthetase 1 (SEPHS1) plays an essential role in cell growth and survival. However, the underlying molecular mechanisms remain unclear. In the present study, the pathways regulated by SEPHS1 during gastrulation were determined by bioinformatical analyses and experimental verification using systemic knockout mice targeting Sephs1. We found that the coagulation system and retinoic acid signaling were most highly affected by SEPHS1 deficiency throughout gastrulation. Gene expression patterns of altered embryo morphogenesis and inhibition of Wnt signaling were predicted with high probability at E6.5. These predictions were verified by structural abnormalities in the dermal layer of Sephs1−/− embryos. At E7.5, organogenesis and activation of prolactin signaling were predicted to be affected by Sephs1 knockout. Delay of head fold formation was observed in the Sephs1−/− embryos. At E8.5, gene expression associated with organ development and insulin-like growth hormone signaling that regulates organ growth during development was altered. Consistent with these observations, various morphological abnormalities of organs and axial rotation failure were observed. We also found that the gene sets related to redox homeostasis and apoptosis were gradually enriched in a time-dependent manner until E8.5. However, DNA damage and apoptosis markers were detected only when the Sephs1−/− embryos aged to E9.5. Our results suggest that SEPHS1 deficiency causes a gradual increase of oxidative stress which changes signaling pathways during gastrulation, and afterwards leads to apoptosis.
Femtosecond laser direct writing of a Fresnel zone plate in glasses for mid-infrared imaging applications

An important challenge in infrared imaging today consists in addressing the SWaP problem (Size, Weight and Power), for example by simplifying as much as possible the optical system before the sensor. The work presented in this paper takes advantage of recent techniques in femtosecond laser direct writing to imprint optical systems. This work aims at merging a lens array with a filter array by writing gradient index lenses with a femtosecond laser inside a dedicated glass substrate. A classical gradient index lens requires a huge refractive index variation, which cannot be reached today with femtosecond laser processing (Δ𝑛𝑚𝑎𝑥 ~ 0,05). So, we decided to turn towards writing a gradient index Fresnel lens. A first-order Fresnel lens was designed with a Δ𝑛𝑚𝑎𝑥 < 5.10−2 discretised into 8 index levels to guarantee a diffraction efficiency of 85% on the overall spectral bandwidth of the filters. We studied a chalcogenide glass because of his good properties in mid-IR optics. We compared the different laser responses of the material: threshold of permanent changes, quantitative phase and index variation. Finally, we showed that reaching a phase variation of π radians with femtosecond laser writing is possible even for a huge scanning speed (0,01 mm/s up to 25 mm/s). By overlapping the written lines, we were able to implement a maximum phase variation of 8 radians (with only one written layer), demonstrating the possibility to write quickly a GRIN Fresnel lens with a diameter of some millimeters.
Life Cycle Assessment of a Vapor Compression Cooling System Integrated within a District Cooling Plant

In standard district cooling (DC) plants, central chillers produce cold energy for space cooling throughout the district network. In recent times, the integration of the vapor compression system, which includes the functionalities of vapor compression chillers (VCC), and thermal energy storage (TES) tanks in the DC setup, has gained more implementation across the globe. This is due to the possibility of load shifting by using the VCC to produce chilled water for charging the TES tanks during off peak periods. Since the environmental implications of various energy intensive systems are largely determined by the amount of material and energy consumed throughout their life cycle, it is critical to conduct a sustainability assessment of these systems in terms of environmental contributions, and suggest design options to reduce these impacts. A cradle to grave life cycle assessment (LCA) model is created in response to these issues and in order to meet the project’s objectives. The life cycle impact assessment (LCIA) results of the analysis reveal that the carbon footprint per 1 RTh of the produced chilled water is estimated at 0.72 kg CO2 eq/RTh. The operation phase of the system’s life cycle accounted for the most impact, about 98%, with other life cycle phases having negligible contributions. In substantiating the study’s investigation, the environmental performance based on several design options were discussed and compared to the case study. Among the several scenarios considered, incorporating the Sweden mix technology provided the case study with the most significant environmental savings, of about 94%.
Different Formulations to Solve the Giesekus Model for Flow between Two Parallel Plates

This work presents different formulations to obtain the solution for the Giesekus constitutive model for a flow between two parallel plates. The first one is the formulation based on work by Schleiniger, G; Weinacht, R.J., [Journal of Non-Newtonian Fluid Mechanics, 40, 79–102 (1991)]. The second formulation is based on the concept of changing the independent variable to obtain the solution of the fluid flow components in terms of this variable. This change allows the flow components to be obtained analytically, with the exception of the velocity profile, which is obtained using a high-order numerical integration method. The last formulation is based on the numerical simulation of the governing equations using high-order approximations. The results show that each formulation presented has advantages and disadvantages, and it was investigated different viscoelastic fluid flows by varying the dimensionless parameters, considering purely polymeric fluid flow, closer to purely polymeric fluid flow, solvent contribution on the mixture of fluid, and high Weissenberg numbers.
Codh/Acs-Deficient Methanogens Are Prevalent in Anaerobic Digesters

Methanogens are archaea that grow by producing methane as a catabolic end product and thrive in diverse anaerobic habitats, including soil, sediments, oil reservoirs, digestive tracts, and anaerobic digesters. Methanogens have typically been classified into three types—namely, hydrogenotrophic, acetoclastic, and methylotrophic methanogens. In addition, studies have found methanogens that require both hydrogen/CO2 and organics, such as acetate, for growth. Genomic analyses have shown that these methanogens lack genes for carbon monoxide dehydrogenase/acetyl-CoA synthase (Codh/Acs), one of the oldest enzymes that catalyzes the central step in the Wood–Ljungdahl pathway. Since these methanogens have been found dominant in such habitats as digestive tracts and anaerobic digesters, it is suggested that the loss of Codh/Acs confers ecological advantages on methanogens in these habitats. Comparisons in genomes of methanogens suggest the possibility that these methanogens have emerged recently in anaerobic digesters and are currently under the process of prevalence. We propose that an understanding of the genetic and ecological processes associated with the emergence and prevalence of these methanogens in anaerobic digesters would offer novel evolutionary insights into microbial ecology.
Mercerizing Extraction and Physicochemical Characterizations of Lignocellulosic Fiber from the Leaf Waste of Mikania micrantha Kunth ex H.B.K

ABSTRACT Invasive plants can grow rampantly and spread fast in large amount, which can be economically invoked for generating value-added products. In this study, lignocellulosic fiber was extracted from the leaf waste of a luxuriant invasive plant, Mikania micrantha Kunth-ex H.B. K. by mercerization process. After the alkaline treatment, the lignocellulosic biomass remained at 38.54% resulting from the large removal of extractive impurities. The lignocellulosic fraction in the mercerized leaf fiber was improved from 56.59% to 83.96%. The chemical composition analysis showed the cellulose fraction was increased by 11.17% while the hemicellulose and lignin fractions were found to be decreased by 4.89% and 6.28%, respectively. The FT-IR and TGA results further affirmed the change in chemical composition of the lignocellulosic fiber. Furthermore, an increase in cellulose fraction raised the fiber crystallinity index from 11.0% to 36.7%. The SEM study revealed that the surface morphology of lignocellulosic fiber changed from smooth surface into rough corrugated ridges, which affirmed the increase in crystallinity, resulting from the removal of wrapped cementing materials. In subsequent, the lignocellulosic fiber exhibited more pervious to water attack with an increase in moisture absorption from 119.22% to 410.19%.
Video-based Car Make, Model and Year Recognition

: Fine-grained car recognition requires extracting discriminating features and certain car parts which can be used to distinguish between similar cars. This paper represents a full system for car make, model and year recognition in videos. We followed a multi-step approach for automatically detecting, tracking and recognizing them using deep Convolutional Neural Network (CNN). We also focused on the recognition stage where we managed to compare 4 state-of-the art Convolution Neural Networks and adapted them for extracting those features. Moreover, we modiﬁed the InceptionResnetv2 network and our results show our success as we managed to elevate the Top 1 accuracy to 0.8617 and Top 5 accuracy to 0.9751.
A Multi-Stage Approach Combining Feature Selection with Machine Learning Techniques for Higher Prediction Reliability and Accuracy in Cervical Cancer Diagnosis

Cervical cancer is the fourth most prevalent cancer in women which has claimed 3,41,831 lives and accounted for 6,04,127 new cases in 2020 worldwide. To reduce such a vast mortality rate, early detection of the disease is essential. A fast, accurate, and interpretable machine learning model is a research subject. Fewer features reduce the computational effort and improve interpretation. A 3-Stage Hybrid feature selection approach and a Stacked Classification model are evaluated on the cervical cancer dataset obtained from the UCI Machine Learning Repository with 35 features and one outcome variable. Stage-1 uses a Genetic Algorithm and Logistic Regression Architecture for Feature Selection and selects twelve features well correlated with the class but not among themselves. Stage-2 utilizes the same Genetic Algorithm and Logistic Regression Architecture for Feature Selection to select five features. In Stage3, Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Extra Trees (ET), Random Forest (RF), and Gradient Boosting (GDB) are used with the five features to identify patients with or without cancer. Data splitting, several metrics, and statistical tests are used, along with 10-fold cross validation, to do a comparative analysis. LR, NB, SVM, ET, RF, and GDB demonstrate improvement across performance measures by reducing the number of features to five. In the 66-34 split, all five machine learning methods except NB recorded 97% accuracy with 5 features. Also, the Stacked model produced higher than 96% accuracy with five features in 66-34 and 80-20 splits, and in 10-fold cross validation. Various performance aggregators have shown improved results with reduced features when compared to previous studies. Finally, with approximately 100% performance in classification results, the suggested ensemble model showed its promise. The output results were compared to those of other studies on the same dataset, and the proposed classifiers were found to be the most effective across all performance dimensions.
A Simulated Annealing based Energy Efficient Task Scheduling Algorithm for Multi-core Processors

In this paper we propose a Simulated Annealing (SA) based energy-efficient task scheduling algorithm for multi-core processors, the Simulated Annealing Energy Efficient Task Scheduling Algorithm (SAEETSA), and compare it with another algorithm, the Energy Efficient Task Scheduling Algorithm (EETSA). Our results show that for dual-core processors the SAEETSA algorithm is taking up to 16.78% less energy as compared to the EETSA algorithm, and for tri-core processors, the SAEETSA algorithm is taking up to 26.97% less energy as compared to the EETSA algorithm.
THERMODYNAMICS OF THE PROCESSES OF INTERACTION OF LIQUID METAL COMPONENTS IN Fe – Mg – Al – La – O SYSTEM

At the present time, rare-earth elements in metallurgy are used in the form of mischmetal – a rare-earth elements natural mixture (with atomic numbers from 57 to 71). It contains about 50 wt. % of cerium. The remaining elements are mainly lanthanum and niobium. The specific composition is determined by the ore deposit. Inconstant composition of the modifier containing rare-earth metals (REM) can significantly reduce its efficiency. Experimentally, for every branded steels composition the ratio of various REMs can’t be selected because of the high costs of obtaining technically pure rare-earth metals. The task of determining the each rare earth element optimum concentrations and complex ligature composition can be solved by thermodynamic modeling. In the framework of thermodynamic modeling, the interaction between magnesium, aluminum and lanthanum with oxygen in liquid iron is presented. And the thermodynamic model of steel deoxidation by these active metals composition is considered. On the basis of available literature data on the phase diagrams of the systems MgO – Al2O3 , MgO – La2O3 and La2O3 – Al2O3 , the coordinates of the invariant equilibria points in the system MgO – La2O3 – Al2O3 were determined. The phase diagram of the system MgO – La2O3 – Al2O3 was constructed. It made possible to establish all phase equilibria realized in the process of deoxidation of steel with magnesium, lanthanum and aluminum and to describe these phase equilibria by chemical reactions equations. The activity of the components in liquid oxide melts was determined using the theory of subregular ionic solutions, which takes into account the dependence of the coordination number of cations on the composition of the oxide melt. The activity of components in metal melts conjugated with oxide systems were determined by Wagner’s theory using the parameters of the first order interaction. Equilibrium constants values for the steel deoxidation reactions are installed indirectly by thermodynamic calculations. On the basis of the obtained data the components solubility surface in the metal melts of Fe – Mg – Al – La – O system was constructed, which allowed to determine the liquid metal composition regions associated with the corresponding oxide phase.