Miracle-dz/UniFAR-FTUs huggingface dataset:
Fractional Clique Collocation Technique for Numerical Simulations of Fractional-Order Brusselator Chemical Model

The primary focus of this research study is in the development of an effective hybrid matrix method to solve a class of nonlinear systems of equations of fractional order arising in the modeling of autocatalytic chemical reaction problems. The fractional operator is considered in the sense of Liouville–Caputo. The proposed approach relies on the combination of the quasi-linearization technique and the spectral collocation strategy based on generalized clique bases. The main feature of the hybrid approach is that it converts the governing nonlinear fractional-order systems into a linear algebraic system of equations, which is solved in each iteration. In a weighted L2 norm, we prove the error and convergence analysis of the proposed algorithm. By using various model parameters in the numerical examples, we show the computational efficacy as well as the accuracy of our approach. Comparisons with existing available schemes show the high accuracy and robustness of the designed hybrid matrix collocation technique.
Bow echoes : A tribute to T. T. Fujita

Abstract Bow echoes represent one of the unique and more well–known forms of severe convective organization, often being responsible for the production of long swaths of damaging surface winds and small tornadoes. They are identified by their characteristic bow shape as seen on radar reflectivity displays. Much of what is known about bow echoes originated with T. T. Fujita, whose observational insights and careful analyses two decades ago still guide research and forecasting of bow–echo phenomena today. This paper reviews Fujita's contributions to our understanding of bow echoes, and also summarizes more recent observational and numerical studies that have built on the foundation that he provided. Perhaps not surprisingly, the life cycle of bow echoes as first described by Fujita, consisting of an evolution from a symmetric line of convective cells to a comma–shaped echo with a dominant cyclonic vortex, is now recognized as one of the fundamental modes of mesoconvective evolution, for both severe and nons.
Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

The recent advancements in the surging field of Deep Learning (DL) have revolutionized every sphere of life, and the healthcare domain is no exception. The enormous success of DL models, particularly with image data, has led to the development of image-guided Robot Assisted Surgery (RAS) systems. By and large, the number of studies concerning image-driven computer assisted surgical systems using DL has increased exponentially. Additionally, the contemporary availability of surgical datasets has also boosted the DL applications in RAS. Inspired by the latest trends and contributions in surgery, this literature survey presents a summarized analysis of recent innovations of DL in image-guided RAS systems. After a thorough review, a sum of 184 articles are selected and grouped into four categories, based on the literature and the relevancy of the task in the articles, comprising 1) Surgical Tools, 2) Surgical Processes, 3) Surgical Surveillance, and 4) Surgical Performance. The survey also discusses publicly available surgical datasets and highlights the basics of the DL models. Furthermore, the legal, ethical, and technological challenges together with the intuitive predictions and recommendations related to the autonomous RAS systems are also presented. The study reveals that Convolutional Neural Network (CNN) is most widely adopted architecture, whereas, the JIGSAWS is most employed dataset in RAS. The study suggests fusing kinematic data along with image data, which produces better accuracy and precision, particularly in gesture and trajectory segmentation tasks. Additionally, CNN and long short term memory networks have shown remarkable performance, however, authors recommend employing these gigantic architectures only when simpler models have failed to produce satisfactory results. The simpler models, despite their limitations, are time and cost effective and yield considerable outcomes even on the smaller datasets.
Challenges of extreme load hexapod design and modularization for large ground-based telescopes

The hexapod is a parallel kinematic manipulator that is the minimum arrangement for independent control of six degrees of freedom. Advancing needs for hexapod performance, capacity and configurations have driven development of highly capable new actuator designs. This paper describes new compact hexapod design proposals for high load capacity, and corresponding hexapod actuator only mechanisms suitable for integration as structural motion elements in next-generation telescope designs. These actuators provide up to 90 000N load capability while preserving sub-micrometer positional capability and in-position stability. The design is optimized for low power dissipation and incorporates novel encoders direct manufactured with the nut flange to achieve more than 100000 increments per revolution. In the hexapod design we choose cardan joints for the actuator that have axis offsets to provide optimized stiffness. The additional computational requirements for offset axes are readily solved by advanced kinematic algorithms and modern hardware. The paper also describes the hexapod controller concept with individual actuator designs, which allows the integration of hexapod actuators into the main telescope structure to reduce mass and provide the telescope designer more design freedom in the incorporation of these types of motion systems. An adaptive software package was developed including collision control feature for real-time safety during hexapod movements.
Nanocellulose: A Fundamental Material for Science and Technology Applications

Recently, considerable interest has been focused on developing greener and biodegradable materials due to growing environmental concerns. Owing to their low cost, biodegradability, and good mechanical properties, plant fibers have substituted synthetic fibers in the preparation of composites. However, the poor interfacial adhesion due to the hydrophilic nature and high-water absorption limits the use of plant fibers as a reinforcing agent in polymer matrices. The hydrophilic nature of the plant fibers can be overcome by chemical treatments. Cellulose the most abundant natural polymer obtained from sources such as plants, wood, and bacteria has gained wider attention these days. Different methods, such as mechanical, chemical, and chemical treatments in combination with mechanical treatments, have been adopted by researchers for the extraction of cellulose from plants, bacteria, algae, etc. Cellulose nanocrystals (CNC), cellulose nanofibrils (CNF), and microcrystalline cellulose (MCC) have been extracted and used for different applications such as food packaging, water purification, drug delivery, and in composites. In this review, updated information on the methods of isolation of nanocellulose, classification, characterization, and application of nanocellulose has been highlighted. The characteristics and the current status of cellulose-based fiber-reinforced polymer composites in the industry have also been discussed in detail.
9 MJ laboratory gun and range at The University of Texas at Austin

The authors describe a 9 MJ laboratory gun and range program. The goal of the program is to demonstrate a single-shot EM (electromagnetic) gun suitable for establishing gun parameters for repetitive EM gun systems and for supporting projectile development. A description is given of the activities of phase I of this program, wherein the power supply has been prepared, the range has been designed and constructed, a one-half-scale and a full-scale gun have been designed and constructed, and the one-half-scale gun has been assembled and undergone initial tests. Of significance is that within a six-shot test sequence on the one-half-scale gun the electric gun has matched the muzzle velocity of the conventional gun that is operated on the M1E1 battle tank. >
Cohomology characterizations of non-abelian extensions of Hom-Lie algebras

In this paper, first we show that under the assumption of the center of h being zero, diagonal non-abelian extensions of a regular Hom-Lie algebra g by a regular Hom-Lie algebra h are in one-to-one correspondence with Hom-Lie algebra morphisms from g to Out(h). Then for a general Hom-Lie algebra morphism from g to Out(h), we construct a cohomology class as the obstruction of existence of a non-abelian extension that induce the given Hom-Lie algebra morphism.
Regulation of DNA Methylation by Cannabidiol and Its Implications for Psychiatry: New Insights from In Vivo and In Silico Models

Cannabidiol (CBD) is a non-psychotomimetic compound present in cannabis sativa. Many recent studies have indicated that CBD has a promising therapeutic profile for stress-related psychiatric disorders, such as anxiety, schizophrenia and depression. Such a diverse profile has been associated with its complex pharmacology, since CBD can target different neurotransmitter receptors, enzymes, transporters and ion channels. However, the precise contribution of each of those mechanisms for CBD effects is still not yet completely understood. Considering that epigenetic changes make the bridge between gene expression and environment interactions, we review and discuss herein how CBD affects one of the main epigenetic mechanisms associated with the development of stress-related psychiatric disorders: DNA methylation (DNAm). Evidence from in vivo and in silico studies indicate that CBD can regulate the activity of the enzymes responsible for DNAm, due to directly binding to the enzymes and/or by indirectly regulating their activities as a consequence of neurotransmitter-mediated signaling. The implications of this new potential pharmacological target for CBD are discussed in light of its therapeutic and neurodevelopmental effects.
What roles do civil society organizations play in monitoring and reviewing the Sustainable Development Goals? An exploration of cases from Ecuador, Colombia, and Argentina

ABSTRACT The UN 2030 Agenda and its Sustainable Development Goals (SDGs) constitute an international roadmap to end poverty, revert environmental degradation, and fight socio-economic inequalities. Monitoring and reviewing (M&R) processes can expose countries’ success or failure in achieving the SDGs. However, SDG M&R is a daunting task. Current mechanisms mainly rely on national statistics that lack the necessary spatial and temporal granularity. Building on policy and academic discussions about the potential of citizen science data to fill data gaps in compilations for the SDG framework, we study projects implemented by civil society organizations (CSOs) in Ecuador, Colombia, and Argentina. Through a theory-sensitive empirical analysis, we systematize five main roles through which CSOs engage in SDG M&R. These roles are (1) participation promoter, (2) information provider, (3) data innovator, (4) watchdog, and (5) advocacy. These roles encompass key activities such as making SDG-relevant data available to citizens, enhancing data literacy, promoting open data from governmental institutions to enhance transparency and accountability, producing counter-narratives, and encouraging collaboration for data collection. Despite differences in their political qualities and politicizing effects, all five roles contribute to the enabling environment for collective action that is needed in governance for the SDGs.
Study of Non-Invasive Methods of Measuring Glucose for Patients with Diabetes Mellitus

Diabetes is one of the most common diseases today. The late discovery of the disease is one of the key factors for the disease's spread. A diabetic patient, as well as a healthy individual, should test their blood sugar levels for further therapy. A non-invasive approach for checking blood sugar levels is one example. In the fields of medicine and science, this strategy is groundbreaking. Blood glucose levels are measured using this method. Daily testing and monitoring of a diabetic patient's health state will be achievable with the use of a non-invasive glucose measurement technology. Non-invasive glucose measurement has several advantages, including high accuracy and painless testing, which is very convenient for patients and prevents injury to body parts, particularly fingers, which must be pricked to collect blood. The goal of this article is to design a non-invasive method for monitoring glucose levels in diabetic patients by creating a model of a prototype device for testing the concentration of glucose in the blood.
Mechanism of action of paclitaxel for treating glioblastoma based on single-cell RNA sequencing data and network pharmacology

Paclitaxel is an herbal active ingredient used in clinical practice that shows anti-tumor effects. However, its biological activity, mechanism, and cancer cell-killing effects remain unknown. Information on the chemical gene interactions of paclitaxel was obtained from the Comparative Toxicogenomics Database, SwishTargetPrediction, Binding DB, and TargetNet databases. Gene expression data were obtained from the GSE4290 dataset. Differential gene analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology analyses were performed. Gene set enrichment analysis was performed to evaluate disease pathway activation; weighted gene co-expression network analysis with diff analysis was used to identify disease-associated genes, analyze differential genes, and identify drug targets via protein-protein interactions. The Molecular Complex Detection (MCODE) analysis of critical subgroup networks was conducted to identify essential genes affected by paclitaxel, assess crucial cluster gene expression differences in glioma versus standard samples, and perform receiver operator characteristic mapping. To evaluate the pharmacological targets and signaling pathways of paclitaxel in glioblastoma, the single-cell GSE148196 dataset was acquired from the Gene Expression Omnibus database and preprocessed using Seurat software. Based on the single-cell RNA-sequencing dataset, 24 cell clusters were identified, along with marker genes for the two different cell types in each cluster. Correlation analysis revealed that the mechanism of paclitaxel treatment involves effects on neurons. Paclitaxel may affect glioblastoma by improving glucose metabolism and processes involved in modulating immune function in the body.
Stress-dependent Peierls potential: Influence on kink-pair activation

Atomistic calculations based on the nudged elastic band method for a Lomer dislocation in aluminum evidence a dependence of the Peierls potential on the applied shear stress in such a way that the Peierls stress predicted from the zero-stress potential is half its true value for the case considered here. Stress-dependent Peierls potentials that are extracted are then introduced as substrate potentials in a string model with a line tension (LT) adjusted to match the dislocation kink width obtained from atomistic simulations. The LT model is found to predict accurately dislocation saddle configurations and corresponding kink-pair activation enthalpies for a wide range of stresses. In particular, it is shown that the stress dependence of the Peierls potential is required to model with accuracy the nonlinearity of the enthalpy-stress curve.
STUDY OF TaSOS4 GENE AND YIELD , CHEMICAL TRAITS IN SOME BREAD WHEAT GENOTYPES UNDER SALINITY CONDITIONS

This experiment was carried out at greenhouse in Centre of Biotechnology Researches/ University of Nahrin using soil clay loam texture, soil was in pots closed to study the effect of different salinity levels on detection TaSOS4 gene and yield traits in some bread wheat genotypes (Triticum aestivum L.). Wheat seeds were planted in Nov. 2016 with ten seeds in each pot. a factorial experiments within Randomized Complete Block Design with three replications was used and Four levels of salinity (4, 8 , 12 and 16 ds.m-1) with four genotypes (G1:Iraq, G2: 2H ,G3: 3H and G4: Hussein). The result showed non-significant differences among genotypes under first salinity levels (4 ds.m-1) in most traits. in second salinity level (8 ds.m-1), G2,G4 gave variation on G1 over 10 in No. grains. Spike-1 , 5-10 in grain 1000weight , over 2% in percentage of protein and K+/Na+ ratio value over 7. in third salinity level (8 ds.m-1), G2,G4 gave variation on G1 over 25 in No. grains. Spike-1, over 12 in grain 1000weight, over 100% in plant grains yield , 2-3% in percentage of protein , over 5 in K+/Na+ ratio value . in forth salinity level (16 ds.m-1), G2 gave variation on G1 over 25 in No. grains. Spike-1 , over 14 in grain 1000weight, over 100% in plant grains yield, over 3% in percentage of protein , over 5 in K+/Na+ ratio value. Molecular study showed TaSOS4 gene was found in G2 ,G3 and G4 genotypes but it did not appear in G1 genotype. This indicator refers to differences among genotypes in salinity tolerance and sensitive to salinity in G1 genotype.
Invariant Measures and Euler-Maruyama's Approximations of State-Dependent Regime-Switching Diffusions

Regime-switching processes contain two components: continuous component and discrete component, which can be used to describe a continuous dynamical system in a random environment. Such processes have many different properties than general diffusion processes, and much more difficulties are needed to be overcome due to the intensive interaction between continuous and discrete component. We give conditions for the existence and uniqueness of invariant measures for state-dependent regime-switching diffusion processes by constructing a new Markov chain to control the evolution of the state-dependent switching process. We also establish the strong convergence in the $L^1$-norm of the Euler-Maruyama's approximation and estimate the order of error. A refined application of Skorokhod's representation of jumping processes plays a substantial role in this work.
Bose-Einstein quantum phase transition in an optical lattice model

Bose-Einstein condensation (BEC) in cold gases can be turned on and off by an external potential, such as that presented by an optical lattice. We present a model of this phenomenon which we are able to analyze rigorously. The system is a hard core lattice gas at half of the maximum density and the optical lattice is modeled by a periodic potential of strength λ. For small λ and temperature, BEC is proved to occur, while at large λ or temperature there is no BEC. At large λ the low-temperature states are in a Mott insulator phase with a characteristic gap that is absent in the BEC phase. The interparticle interaction is essential for this transition, which occurs even in the ground state. Surprisingly, the condensation is always into the p=0 mode in this model, although the density itself has the periodicity of the imposed potential.
Diabetic Foot Ulcer Ischemia and Infection Classification Using EfficientNet Deep Learning Models

Motivation: Infection (bacteria in the wound) and ischemia (insufficient blood supply) in Diabetic Foot Ulcers (DFUs) increase the risk of limb amputation. Goal: To develop an image-based DFU infection and ischemia detection system that uses deep learning. Methods: The DFU dataset was augmented using geometric and color image operations, after which binary infection and ischemia classification was done using the EfficientNet deep learning model and a comprehensive set of baselines. Results: The EfficientNets model achieved 99% accuracy in ischemia classification and 98% in infection classification, outperforming ResNet and Inception (87% accuracy) and Ensemble CNN, the prior state of the art (Classification accuracy of 90% for ischemia 73% for infection). EfficientNets also classified test images in a fraction (10% to 50%) of the time taken by baseline models. Conclusions: This work demonstrates that EfficientNets is a viable deep learning model for infection and ischemia classification.
Regularized Jacobi Iteration for Decentralized Convex Quadratic Optimization With Separable Constraints

We consider multiagent, convex quadratic optimization programs subject to separable constraints, where the constraint function of each agent involves only its local decision vector, while the decision vectors of all agents are coupled via a common objective function. We focus on a regularized variant of the so-called Jacobi algorithm for decentralized computation in such problems. We provide a fixed-point theoretic analysis showing that the algorithm converges to a minimizer of the centralized problem under more relaxed conditions on the regularization coefficient from those available in the literature, and in particular with respect to scaled projected gradient algorithms. The efficacy of the proposed algorithm is illustrated by applying it to the problem of optimal charging of electric vehicles.
A multi-population coevolutionary approach for Software defect prediction with imbalanced data.

This paper proposes a cooperative coevolutionary approach namely COESDP to the software defect prediction (SDP) problem. The proposed method consists of three main phases. The first one conducts data preprocessing including data sampling and cleaning. The second phase utilizes a multi-population coevolutionary approach (MPCA) to find out optimal instance selection solutions. These first two phases help to deal with the imbalanced data challenge of the SDP problem. While the data sampling method aids in the creation of a more balanced data set, MPCA supports in the elimination of unnecessary data samples (or instances) and the selection of crucial instances. The output of phase 2 is a set of different optimal solutions. Each solution is a way of selecting instances from which to create a classifier (or weak learners). Phase 3 utilizes an ensemble learning method to combine these weak learners and produce the final result. The proposed algorithm is compared with conventional machine learning algorithms, ensemble learning algorithms, computational intelligence algorithms and an other multi-population algorithm on 6 standard SDP datasets. Experimental results show that the proposed method gives better and more stable results in comparison with other methods and it can tackle the challenge of imbalance in the SDP data.
Reconstructing Input Models in Stochastic Simulation

We investigate the problem of nonparametrically calibrating the input model in stochastic simulation, given only the availability of output data. While studies on simulation input uncertainty have focused on the use of input data, our inverse model calibration problem arises in various situations where resource and operational limitations hinder the direct observability of input data. We propose a moment-based, maximum entropy framework to infer the input model, by matching statistics between the simulation output and the real- world output. To bypass the methodological difficulties from the stochastic constraints in our formulation, we propose a stochastic quadratic penalty method that converts the problem into a sequence of least-square problems, where each element in the sequence can be solved by efficient stochastic approximation algorithms. We analyze the statistical properties of our method and demonstrate its ability to recover input models with numerical experiments.
Barrier Functionals for the Analysis of Complex Systems: An Optimization-Based Approach

We propose a methodology to address two analysis problems concerning complex systems, namely bounding state functionals of stochastic differential equations (SDEs) and verifying set avoidance of systems described by partial differential equations (PDEs). The proposed method is based on barrier functionals, which are functionals of the states of the studied systems. The proposed method does not require the approximation of solutions nor the stability of trajectories. In the case of SDEs, the formulation relies on a generalized version of the Feynman-Kac formula and results in moments bounds for nonlinear SDEs. Furthermore, we show that the analysis problems can be cast as optimization problems and can be solved by semi-definite programming.