Miracle-dz/UniFAR-FTUs huggingface dataset:
AN EXPLANATION OF STUDENT PERFORMANCE USING HIERARCHICAL LINEAR MODEL FOR SCHOOLS IN PERNAMBUCO, BRAZIL

The hierarchical linear models or multilevels were developed for analysis of data which possess group structure, that is, a structure hierarchy whi ch takes in account the data variability inside and among each hierarchical level. By using data an alysis from SAEPE (2002 Educational Evaluation System of Pernambuco), hierarchical mode ls are presented with two levels of evaluation in mathematics and Portuguese language capplied to the 4 th and 8 th grades students of fundamental teaching and to 3 rd grade high school students. The results in this modeling are more appropriate due to data group str ucture DEFINITION OF HIERARCHICAL MODELS According to Bryk and Raudenbush (1992), hierarchical linear model (HLM) was named by Lindley and Smith (1972) as part of their work i n Bayesian estimation of linear models in which they introduced a general approach for grouped data with complex error structure. Until the middle of the 90s, the development of these models were concentrated in the areas of discrete output data, time series models, crossed classifica tion, missing data and nonlinear models (Goldstein, 1995). The hierarchical structure is ch aracterized by the presence of individual observation (people or objects in study) which are grouped in the highest level. The hierarchical models or multilevels provide a theoretical and con ceptual ground for treatment and analysis of data with hierarchical structure or grouping. They allow the measurement of how variables at different levels affect the variable output, and to quantify how much variability there has to be at each output level. The hierarchical model involves a statistical integration of the different models specified in the levels of interest. The simple int egration leads to added random coefficients in the model, where the regression coefficients of the lev el of the individual are considered as random variables in the level of the group, where the leve l of regression coefficient of an individual is derived from a probability distribution. In several studies, the hierarchical model has a different approach where hierarchy is based on the order of v ariable importance in the study. OBJECTIVE
An accelerated proximal alternating direction method of multipliers for robust fused Lasso

In the era of big data, much of the data is susceptible to noise with heavy-tailed distribution. Fused Lasso can effectively handle high dimensional sparse data with strong correlation between two adjacent variables under known Gaussian noise. However, it has poor robustness to nonGaussian noise with heavy-tailed distribution. Robust fused Lasso with l1 norm loss function can overcome the drawback of fused Lasso when noise is heavy-tailed distribution. But the key challenge for solving this model is nonsmoothness and its nonseparability. Therefore, in this paper, we first deform the robust fused Lasso into an easily solvable form, which changes the three-block objective function to a two-block form. Then, we propose an accelerated proximal alternating direction method of multipliers (APADMM) with an additional update step, which is base on a new PADMM that changes the Lagrangian multiplier term update. Furthermore, we give the O(1/K) nonergodic convergence rate analysis of the proposed APADMM. Finally, numerical results show that the proposed new PADMM and APADMM have better performance than other existing ADMM solvers.
Harris hawk optimization trained artificial neural network for anomaly based intrusion detection system

An integral part of security infrastructure is detecting and identifying malicious attacks commonly found in network environments. Despite its effectiveness at identifying anomalous network behaviors, an intrusion detection system (IDS) still has a low detection rate and a high rate of false alarms. This study proposes a novel effective anomaly IDS by integrating bio‐inspired optimization techniques, Harris hawk optimization (HHO), and an artificial neural network (ANN), called HHO‐ANN. Several experiments were conducted with other methods to verify the performance and capabilities of the proposed technique. The AWID, CIDDS001, NSL‐KDD, and NSL‐KDD datasets were used to benchmark the performance of the proposed method. Popular evolutionary algorithms, such as genetic algorithm, particle swarm optimization, moth‐flame optimization, and locust swarm optimization based on ANN trainer, were implemented to validate the result. Comparison with existing methods in the literature reveals that the proposed method offers more accuracy. Simulation results confirm that the proposed method has excellent speed convergence and a high‐reliability level due to a lower risk of getting stuck in the local minima region. The proposed method achieved accuracy of 96.71%, 98.03%, 98.25%, and 97.95% for AWID, CIDDS‐001, NSL‐KDD, and UNSW‐NB15, respectively.
De Novo Computational Design of a Lipase with Hydrolysis Activity towards Middle-Chained Fatty Acid Esters

Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta “inside-out” protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD1 exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD1–M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate—3.34 times higher than that of 1a8uD1. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD1 and the redesigned 1a8uD1–M8 were devised from scratch. More importantly, the designed 1a8uD1–M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions.
Non-Viral Gene Delivery to Hepatocellular Carcinoma via Intra-Arterial Injection

Purpose Hepatocellular carcinoma (HCC) has limited treatment options, and modest survival after systemic chemotherapy or procedures such as transarterial chemoembolization (TACE). There is therefore a need to develop targeted therapies to address HCC. Gene therapies hold immense promise in treating a variety of diseases, including HCC, though delivery remains a critical hurdle. This study investigated a new approach of local delivery of polymeric nanoparticles (NPs) via intra-arterial injection for targeted local gene delivery to HCC tumors in an orthotopic rat liver tumor model. Methods Poly(beta-amino ester) (PBAE) nanoparticles were formulated and assessed for GFP transfection in N1-S1 rat HCC cells in vitro. Optimized PBAE NPs were next administered to rats via intra-arterial injection with and without orthotopic HCC tumors, and both biodistribution and transfection were assessed. Results In vitro transfection of PBAE NPs led to >50% transfected cells in adherent and suspension culture at a variety of doses and weight ratios. Administration of NPs via intra-arterial or intravenous injection demonstrated no transfection of healthy liver, while intra-arterial NP injection led to transfection of tumors in an orthotopic rat HCC model. Conclusion Hepatic artery injection is a promising delivery approach for PBAE NPs and demonstrates increased targeted transfection of HCC tumors compared to intravenous administration, and offers a potential alternative to standard chemotherapies and TACE. This work demonstrates proof of concept for administration of polymeric PBAE nanoparticles via intra-arterial injection for gene delivery in rats.
Crowd Management Through Optimal Layout of Fences: An Ant Colony Approach Based on Crowd Simulation

The increasing population density in public places necessitates urgent attention to address safety concerns via effective crowd management. In many congested scenarios such as peak-hour subway stations, the utilization of fences to guide crowd movement has become a widely adopted approach to alleviate congestion. This work presents a method that combines crowd simulation and management, focusing on the optimization of the fence layout for efficient crowd guidance. First, a congestion probability social force model (CP-SFM) is introduced to simulate the irrational pedestrians and to evaluate the efficacy of different fence layouts. Second, based on CP-SFM, we are the first to formulate the fence layout problem as an optimization problem with the objective to minimize the congestion of pedestrians in public places. Third, we further propose an ant colony crowd intervention algorithm (ACCI) to optimize the layout of fences. Lastly, we illustrate the performance of proposed ACCI on 18 scenarios including two real-world subway stations. Compared with other optimization methods, ACCI demonstrates promising performance in avoiding crowd congestion.
Analysis of Phenolic Acids and Flavonoids in Rabbiteye Blueberry Leaves by UPLC-MS/MS and Preparation of Nanoemulsions and Extracts for Improving Antiaging Effects in Mice

Rabbiteye blueberry leaves, a waste produced after harvest of blueberry, are rich in polyphenols. This study aims to analyze phenolic acids and flavonoids in blueberry leaves by UPLC-MS/MS and prepare nanoemulsions for determining anti-aging activity in mice. Overall, 30% ethanol was the most suitable extraction solvent for total phenolic acids and total flavonoids. A total of four phenolic acids and four flavonoids were separated within seven minutes for further identification and quantitation by UPLC-MS/MS in selective reaction monitoring (SRM) mode, with 3-O-caffeoylquinic acid being present in the highest amount (6474.2 μg/g), followed by quercetin-3-O-galactoside (1943.9 μg/g), quercetin-3-O-rutinoside (1036.6 μg/g), quercetin-3-O-glucoside (867.2 μg/g), 5-O-caffeoylquinic acid (815.8 μg/g), kaempferol-3-O-glucoside (309.7 μg/g), 3,5-dicaffeoylquinic acid (195.3 μg/g), and 4,5-dicaffeoylquinic acid (60.8 μg/g). The blueberry nanoemulsion was prepared by using an appropriate ratio of soybean oil, Tween 80, glycerol, ethanol, and water at 1.2%, 8%, 2%, 2%, and 86.8%, respectively, and mixing with dried blueberry extract, with the mean particle size and zeta potential being 16 nm and −54 mV, respectively. A high stability was observed during storage of nanoemulsion for 90 days at 4 °C and heated at 100 °C for 2 h. An animal study revealed that this nanoemulsion could elevate dopamine content in mice brain as well as superoxide dismutase, glutathione peroxidase, and catalase activities in mice liver while reducing the contents of malondialdehyde and protein carbonyl in mice brains. Collectively, the high-dose nanoemulsion possessed the highest efficiency in improving mice aging with a promising potential for development into a health food.
A Heuristic Algorithm for Construction Euclidean Steiner Minimal Tree Inside Orthogonal Polygon

Steiner tree problem consists of finding the minimum tree that includes specific points and, if necessary, uses a number of auxiliary points to minimize the tree length. In this paper, a case study of Steiner tree in one whose vertices and edges are placed inside an orthogonal polygon. This problem is widely used in oil lines, highways and electronic integrated circuit design. This is an Np-hard problem and no polynomial-time algorithm has yet been discovered for it. We propose an algorithm to construct the Steiner tree using a graph and have reached optimal solutions in some cases, in this paper.
Pretreatment with salicylic acid and nitric oxide mitigated silver nanoparticles toxicity and enhanced their removal in medicinal Phlomis tuberosa plants

Since silver nanoparticles (AgNPs) are used as nanofungicides and nanopesticides in agriculture, the toxicity of AgNPs as well as AgNO 3 must be determined. Besides this, we evaluated the combined effects of salicylic acid (SA) and nitric oxide (NO) on responses of Phlomis tuberosa plants to Ag-induced stress. The results of growth parameters together with measurement of malondialdehyde (MDA) indicated that exposure to 1000 mg L –1 of AgNPs or AgNO 3 exerted more toxicity, which was closely associated with the over– accumulation of ROS and the reduction of photochemical functioning. However, SNP (NO) and SA addition successfully alleviated adverse impact of AgNPs on Phlomis seedlings. Maximum amelioration of Ag-induced stress was found by combined treatments of SA+NO. Phlomis plants primed with SA+NO exhibited higher synthesis of chlorophyll b and carotenoid pigments to ameliorate AgNP-induced adverse effects on chlorophyll fluorescence parameters. SA+NO led to high levels of proline under both AgNPs and AgNO 3 treatments. A further increase in antioxidants (phenolic compounds) was observed in NO-primed plants under AgNPs-induced stress, which was attendant with the high level of CAT and APX activities. Increase in total Ag translocation into shoot organs and cell survival were also enhanced by SA+NO under AgNPs stress. We concluded that SA+NO mitigated the inhibitory effects of AgNPs stress on the photosynthetic apparatus by increasing the phenolic compounds and carotenoids as well as by regulating accumulation of Ag, ROS and antioxidants. The present findings provide important knowledge to design strategies that minimize the negative
Surface and finite size effects impact on the phase diagrams, polar, and dielectric properties of (Sr,Bi)Ta2O9 ferroelectric nanoparticles

In the framework of the thermodynamic approach Landau-Ginzburg-Devonshire (LGD) combined with the equations of electrostatics, we investigated the effect of polarization surface screening on finite size effects of the phase diagrams, polar, and dielectric properties of ferroelectric nanoparticles of different shapes. We obtained and analyzed the analytical results for the dependences of the ferroelectric phase transition temperature, critical size, spontaneous polarization, and thermodynamic coercive field on the shape and size of the nanoparticles. The pronounced size effect of these characteristics on the scaling parameter, the ratio of the particle characteristic size to the length of the surface screening, was revealed. Also our modeling predicts a significant impact of the flexo-chemical effect (that is a joint action of flexoelectric effect and chemical pressure) on the temperature of phase transition, polar, and dielectric properties of nanoparticles when their chemical composition deviates from th.
Some Properties of the Aggregate Flow Model of Air Traffic

This paper examines the stability and response characteristics of the aggregate flow model of air traffic, described in the literature. It is shown that the transfer functions between the inputs and outputs are proper with fewer zeros than poles. Root loci of several input/output transfer functions are shown. Numerical results are presented to show the variation of eigenvalues, derived from the system matrices, as a function of time. It is shown that the modes of the system, eigenvalues, contribute significantly to different states at different times of the day.
Aqueous Extract of Cinnamon (Cinnamomum spp.): Role in Cancer and Inflammation

Cinnamon (Cinnamomum spp.; family Lauraceae), a plant widely used as a spice and flavoring agent and in the perfume industry, has high therapeutic value. However, the components and chemical properties of cinnamon extracts vary depending on the part of the plant, the method, and the solvent used for extraction. Green extraction methods using safe and green solvents have gained increased interest in recent years. Water is an environmentally friendly and safe green solvent widely used for preparing cinnamon extracts. This review focuses on the various preparation techniques for the aqueous extract of cinnamon, its major bioactive components, and their beneficial roles in different pathological conditions, specifically cancer and inflammation. The aqueous extract of cinnamon contains several bioactive compounds, such as cinnamaldehyde, cinnamic acid, and polyphenols, and exerts anticancer and anti-inflammatory properties by altering key apoptotic and angiogenic factors. The whole extract is a better anticancer and anti-inflammatory agent than the purified fractions, indicating a synergistic effect between various components. Studies have indicated that aqueous cinnamon extract has immense therapeutic potential, and to better understand its synergistic effects, extensive characterization of the aqueous extract and its potential to be used with other therapies should be explored.
Disruption of the Schizosaccharomyces japonicus lig4 Disturbs Several Cellular Processes and Leads to a Pleiotropic Phenotype

Gene targeting is a commonly used method to reveal the function of genes. Although it is an attractive tool for molecular studies, it can frequently be a challenge because its efficiency can be low and it requires the screening of a large number of transformants. Generally, these problems originate from the elevated level of ectopic integration caused by non-homologous DNA end joining (NHEJ). To eliminate this problem, NHEJ-related genes are frequently deleted or disrupted. Although these manipulations can improve gene targeting, the phenotype of the mutant strains raised the question of whether mutations have side effects. The aim of this study was to disrupt the lig4 gene in the dimorphic fission yeast, S. japonicus, and investigate the phenotypic changes of the mutant strain. The mutant cells have shown various phenotypic changes, such as increased sporulation on complete medium, decreased hyphal growth, faster chronological aging, and higher sensitivity to heat shock, UV light, and caffeine. In addition, higher flocculation capacity has been observed, especially at lower sugar concentrations. These changes were supported by transcriptional profiling. Many genes belonging to metabolic and transport processes, cell division, or signaling had altered mRNA levels compared to the control strain. Although the disruption improved the gene targeting, we assume that the lig4 inactivation can cause unexpected physiological side effects, and we have to be very careful with the manipulations of the NHEJ-related genes. To reveal the exact mechanisms behind these changes, further investigations are required.
A Lagrangian Relaxation Method for an Online Decentralized Assignment of Electric Vehicles to Charging Stations

This article addresses the issue of assigning electric vehicles to charging stations, minimizing the maximum completion time. It envisions the interaction between electric vehicles and the charging infrastructure to match supply and demand in a decentralized and collaborative fashion. For this reason, the assignment issue is regarded as a linear integer programming problem and a Lagrangian relaxation heuristic is proposed to solve it. Thus, each electric vehicle selects the charging station and the most convenient path to minimize its own completion time. The completion time of each electric vehicle is composed of the travel time (TT), the waiting time (WT) at the station, and the charging time. The Lagrangian relaxation heuristic results are more effective compared to other local heuristic procedures performances and demonstrate a fair allocation of the electric vehicles to the charging stations. The analysis of the time components of the solution on a real urban network highlights that the TT is negligible with respect to the WT and charging time, that are comparable. Therefore, a reservation policy is also considered.
COMPACTLY-ALIGNED DISCRETE PRODUCT SYSTEMS, AND GENERALIZATIONS OF ${\mathcal O}_\infty$

The universal C*-algebras of discrete product systems generalize the Toeplitz–Cuntz algebras and the Toeplitz algebras of discrete semigroups. We consider a semigroup P which is quasi-lattice ordered in the sense of Nica, and, for a product system p : E→ P, we study those representations of E, called covariant, which respect the lattice structure of P. We identify a class of product systems, which we call compactly aligned, for which there is a purely C*-algebraic characterization of covariance, and study the algebra which is universal for covariant representations of E. Our main theorem is a characterization of the faithful representations of when P is the positive cone of a free product of totally-ordered amenable groups.
Applicability of Compost and Mineral Materials for Reducing the Effect of Diesel Oil on Trace Element Content in Soil

Petroleum-derived substances have become the factor adversely affecting the soil quality and, also, crop production. However, the ability to immobilise contaminants is limited in anthropogenically altered soils. Therefore, a study was undertaken to evaluate the effects of soil contamination with diesel oil (0, 2.5, 5 and 10 cm3 kg−1) on the contents of trace elements in the soil and determine the suitability of different neutralising materials (compost, bentonite and calcium oxide) for the in situ stabilisation of soil contaminated with this petroleum derivative. In the soil contaminated with the highest dose of diesel oil (10 cm3 kg−1), a decrease in chromium, zinc and cobalt and an increase in the total nickel, iron and cadmium concentrations were found in the series without the addition of neutralising materials. Remediation with compost and mineral materials contributed to a significant reduction of nickel and iron, as well as cobalt, in soil (calcium oxide only). All materials used contributed to an increase in cadmium, chromium, manganese and copper in the soil. The above-mentioned materials (most notably calcium oxide) can be successfully used to reduce the effect of diesel oil on the contents of some trace elements in soil.
Some new results on reaction-diffusion equations and geometric flows

In this thesis we discuss the asymptotic behavior of the solutions of scaled reaction-diffusion equations in the unbounded domain Rn × (0 + ∞), in the cases when such a behavior is described in terms of moving interfaces. As first class of asymptotic problems we consider the singular limit of bistable reaction-diffusion equations in the case when the velocity of the traveling wave equation depends on the space variable, i.e. ce = ce(x), and it satisfies, in some suitable sense, ce/eτ → α, as e → 0+, where α is a discontinuous function and τ is an integer that can be equal to 0 or 1. The second part of the thesis concerns semilinear reaction-diffusion equations with diffusion term of type tr(Ae(x)D2ue), where tr denotes the trace operator, Ae = σeσte for some matrix map σe : Rn → Rn×(m+n) and Ae converges to a degenerate matrix. In order to establish such results rigorously, we modify and adapt to our problems the ”geometric approach” introduced by G. Barles and P. E. Souganidis for solving problems in Rn, and then partially revisited by G. Barles and F. Da Lio for reaction-diffusion equations in bounded domains. When it is possible we always consider the question of the well posedness of the Cauchy problems governing the motion of the fronts that describe the asymptotics we consider