Minimum Number Research Articles

Comparison of Amount of Space Closure During En- Masse Retraction of Maxillary Anterior Teeth Using Three Accelerated Orthodontic Treatment Techniques- Micro Osteo-Perforation, Photo Bio-Modulation and Peizocision : A Retrospective Study

AIM: The study aimed at comparing the amount of space closure using peizocision (PZ) and micro- osteoperforation (MOP) and low-level laser therapy (LLLT). Materials and methods: A retrospective study consisted of 3 groups, sample number of minimum 8 patients per group were estimated using statistical model called an Analysis of Variance, or ANOVA model. Three sets of data were collected. Which included patients who were treated with upper first premolar extraction for en- masse retraction of maxillary anterior teeth using NiTi closed coil spring with regional acceleratory procedures. The data collected was divided into 3 groups, GROUP 1- micro osteo perforations (n=8), GROUP 2- photo bio modulation (n=8), GROUP 3- peizocision (n=8).sing digital Vernier calipers, a direct approach was used to measure all the stone casts obtained before canine retraction (T0) and after the retraction was completed after three months (T1). The statistical analysis was performed on the data acquired about the amount of space closure of en-masse retraction of maxillary anteriors in the PZ, LLLT, and MOP groups. Results: Compared to MOP (2.60mm) and LLLT (2.44mm), the PZ group had a greater mean amount of space closure (5.06mm). In conclusion, the quantity of space closure in piezocision was significantly higher than that in MOP and LLLT, although there was no statistically significant difference between the two. It is imperative that treatment duration be shortened. Finding the most effective and practical method to speed up tooth movement using the biomechanical mechanisms that are currently in place is therefore necessary

Quark and lepton modular models from the binary dihedral flavor symmetry

Inspired by the structure of top-down derived models endowed with modular flavor symmetries, we investigate the yet phenomenologically unexplored binary dihedral group 2D3. After building the vector-valued modular forms in the representations of 2D3 with small modular weights, we systematically classify all (Dirac and Majorana) mass textures of fermions with fractional modular weights and all possible 2 + 1-family structures. This allows us to explore the parameter space of fermion models based on 2D3, aiming at a description of both quarks and leptons with a minimal number of parameters and best compatibility with observed data. We consider the separate possibilities of neutrino masses generated by either a type-I seesaw mechanism or the Weinberg operator. We identify a model that, besides fitting all known flavor observables, delivers predictions for six not-yet measured parameters and favors normal-ordered neutrino masses generated by the Weinberg operator. It would be interesting to figure out whether it is possible to embed our model within a top-down scheme, such as \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb{T}}^{2}/{\\mathbb{Z}}_{4}$$\\end{document} heterotic orbifold compactifications.

Young’s double-slit interference with single hard X-ray photons

Double-slit interference experiments using monochromatic hard X-rays with the energy of 25 keV are presented. The experiments were performed at a synchrotron source with a distance of 110 m between the interferometer and the detector to produce an interference pattern with a sufficiently broad period that could be adequately sampled by a photon-counting detector with 75 micrometre pixels. In the single-particle version of the experiment, over one million image frames with a single registered photon in each one were collected. The sum of these frames showed a clear presence of the interference pattern with the expected period. Subsequent analysis provided an objective estimation of the minimal number of detected photons required to determine, in accordance with the Rose criterion, the presence of the photon interference. Apart from a general theoretical interest, these investigations were aimed at exploring the possibility of medical X-ray phase-contrast imaging in photon-counting regime at minimal radiation doses.

Rabinowitz Floer homology for prequantization bundles and Floer Gysin sequence

AbstractLet Y be a prequantization bundle over a closed spherically monotone symplectic manifold $$\Sigma $$ Σ . Adapting an idea due to Diogo and Lisi, we study a split version of Rabinowitz Floer homology for Y in the following two settings. First, $$\Sigma $$ Σ is a symplectic hyperplane section of a closed symplectic manifold X satisfying a certain monotonicity condition; in this case, $$X {{\setminus }} \Sigma $$ X \ Σ is a Liouville filling of Y. Second, the minimal Chern number of $$\Sigma $$ Σ is greater than one, which is the case where the Rabinowitz Floer homology of the symplectization $$\mathbb {R} \times Y$$ R × Y is defined. In both cases, we construct a Gysin-type exact sequence connecting the Rabinowitz Floer homology of $$X{\setminus }\Sigma $$ X \ Σ or $$\mathbb {R} \times Y$$ R × Y and the quantum homology of $$\Sigma $$ Σ . As applications, we discuss the invertibility of a symplectic hyperplane section class in quantum homology, the isotopy problem for fibered Dehn twists, the orderability problem for prequantization bundles, and the existence of translated points. We also provide computational results based on the exact sequence that we construct.

LEADERSHIP STYLES OF SCHOOL HEADS AND CLASSROOM PRACTICES OF PUBLIC SECONDARY SCHOOL TEACHERS

The study determined the extent of leadership styles of school heads and the extent of classroom practices of public secondary school teachers. This study employed a non-experimental quantitative research design utilizing the descriptive-correlation method. Validated questionnaires and a Universal sampling procedure were utilized considering the minimal number of teachers in the research locale. Two hundred fifty (250) public secondary school teachers were the respondents of the study. Using mean, pearson-r, and regression analysis, the findings revealed that the extent of leadership styles of school heads was extensive while the extent of classroom practices of public secondary school teachers was very extensive. Moreover, the overall results revealed that indicators for leadership styles of school heads have a strong positive relationship to the classroom practices of public secondary school teachers. Further, results from the regression analysis revealed the following have a strong influence on leadership styles of school heads on classroom practices of public secondary school teachers: Supportive, Participative, Achievement-Oriented, and Directive. It was recommended that school heads should manage change and develop a mechanism to improve the schools system through creative support of its manpower of teachers, staff, learners, and community. Education can be effective in an environment that is not conducive to teaching and learning. School heads conduct seminars and workshops on Different Leadership Styles to sustain high performance in managing and leading the school. KEYWORDS: Leadership style of school heads, classroom practices

Prediction of Student Performance Using Random Forest Combined With Naïve Bayes

Abstract Random forest is a powerful ensemble learning technique celebrated for its heightened predictive performance and robustness in handling complex datasets; nevertheless, it is criticized for its computational expense, particularly with a large number of trees in the ensemble. Moreover, the model’s interpretability diminishes as the ensemble’s complexity increases, presenting challenges in understanding the decision-making process. Although various pruning techniques have been proposed by researchers to tackle these issues, achieving a consensus on the optimal strategy across diverse datasets remains elusive. In response to these challenges, this paper introduces an innovative machine learning algorithm that integrates random forest with Naïve Bayes to predict student performance. The proposed method employs the Naïve Bayes formula to evaluate random forest branches, classifying data by prioritizing branches based on importance and assigning each example to a single branch for classification. The algorithm is utilized on two sets of student data and is evaluated against seven alternative machine-learning algorithms. The results confirm its strong performance, characterized by a minimal number of branches.

Preparation of nanocellulose-based flexible calcium carbonate to improve filler retention and combustion performance of cigarette paper

The addition of calcium carbonate to cigarette paper can enhance its combustion performance and apparent properties. However, it does come at the cost of reduced paper strength. In this study, a flexible calcium carbonate (FCC) was developed through the in-situ formation of calcium carbonate on nanocellulose surfaces. The incorporation of FCC into cigarette paper offered the potential to improve filler retention and combustion performance while maintaining high paper strength. The results of the study demonstrated that when compared to the addition of 30% precipitated calcium carbonate (PCC), the filler retention rate in cigarette paper increased by 31.5% when 30% FCC was used. Scanning electron microscopy images showed that only a minimal number of inorganic particles were observed between the fibers in the FCC-containing paper, indicating appropriate air permeability and fiber-fiber bond strength. Furthermore, when compared to PCC-containing paper, the FCC-containing paper exhibited an 8.8% increase in tensile strength and a 7.5% increase in folding resistance. Additionally, thermogravimetric analysis of the cigarette paper revealed that the incorporation of flexible calcium carbonate effectively lowered the thermal decomposition temperature, leading to a 10.3% increase in the combustion rate of FCC-containing paper. The inclusion of FCC into paper fibers has the potential to enhance air permeability and regulate cigarette burning performance. Consequently, the FCC developed in this study holds promise as a substitute for traditional calcium carbonate fillers in cigarette paper applications.

Plasma jet printing of copper and silver antennas operating at 2.4 GHz

Printing has emerged as a technically feasible and economically viable approach to fabricate antennas on a wide range of rigid and flexible substrates. Printing offers several advantages such as rapid prototyping, minimal number of processing steps and minimal equipment needs relative to conventional microfabrication and other antenna manufacturing techniques, versatility in the choice of substrates and low cost. Here we use a plasma-based printing approach to print self-sintered copper and silver antennas. The morphology of the printed films is continuous with nanoparticles agglomerated and fused together under the influence of the plasma. The printed antennas exhibit a reflection coefficient of about −20 dB or lower and bandwidth over 10% at a resonant frequency around 2.4 GHz. The results show the feasibility of printing high performance antennas on flexible substrates such as polyimide using plasma printing technology.

Implementation of a non-linear SVM classification for seizure EEG signal analysis on FPGA

An automatic detection method for disease diagnosis plays an important role in the medical field. A computer-aided diagnosis (CAD) system for seizure detection using electroencephalogram (EEG) data is significant in protecting patients' lives. This paper presents machine learning algorithm-based seizure detection approach implemented in both software and hardware using EEG recordings. The input EEG signal is subjected to pre-processing, decomposition using wavelet transform and classification by a support vector machine (SVM) classifier. The maximum and variance statistical values of the decomposed EEG sub-bands are computed to generate a feature vector, while classification accuracy and computational complexity are taken into account. Gaussian Radial Basis Function (RBF) kernel based Non-Linear SVM (NLSVM) is used in this proposed approach to solve both two-class and three-class classification problems. In three-class classification, the one-against-one (OAO) technique is used. The competency of the proposed technique is evaluated using two distinct datasets provided by Bonn University, Germany & Neurology and Sleep Centre, New Delhi. The adequate parameters of the NLSVM classifier model are determined by using MATLAB software in the training phase and the best performing NLSVM model is selected for implementation on the Field-Programmable Gate Array (FPGA). The proposed seizure detection method is implemented with Verilog code and examined on a Xilinx Zynq-7000 XC7Z020 FPGA board to evaluate its performance. The experimental results show that the proposed method with a minimal number of features achieved a good performance metric for seizure detection and is comparable with that of the state-of-the-art methods.

Cohen-Macaulayness of associated graded rings of Gorenstein monomial curves

Let C be a Gorenstein noncomplete intersection monomial curve in the 4-dimensional affine space with defining ideal I(C). In this article, we use the minimal generating set of I(C) to give a criterion for determining whether the tangent cone of C is Cohen-Macaulay. We also show that if the tangent cone of C is Cohen-Macaulay, then the minimal number of generators of the ideal I(C)⁎ is either 5 or an even integer of the form 2d+2, for a suitable integer d. Additionally, we provide a family of Gorenstein noncomplete intersection monomial curves C whose tangent cone is Cohen-Macaulay and the minimal number of generators of I(C)⁎ is large.

Development and validation of fractional constitutive models for viscoelastic-plastic creep in time-dependent materials: Rapid experimental data fitting

This study aims to address the core challenges in the field of fractional models: developing fractional viscoelastic models with clear physical meanings and efficient, precise methods for solving fractional differential equations. Through the analysis of experimental data, this study proposes several concise and accurate models to predict creep phenomena, characterized by a minimal number of parameters, each with a clear physical interpretation. These models demonstrate high accuracy in describing the nonlinear behavior during the accelerated creep stage and successfully overcome the difficulty of the fractional order exceeding 1. Additionally, the research introduces an improved memoryless technique for the numerical solution of fractional viscoelastic models, enhancing the efficiency of parameter identification. The methodology of this study is practical and feasible, offering valuable references for understanding and predicting the creep behavior of viscoelastic materials.

Bayesian Survey of the Dense Matter Equation of State Built upon Skyrme Effective Interactions

The nonrelativistic model of nuclear matter (NM) with zero-range Skyrme interactions is employed within a Bayesian approach in order to study the behavior of the neutron star (NS) equation of state (EOS). A minimal number of constraints from nuclear physics and ab initio calculations of pure neutron matter (PNM) are imposed together with causality and a lower limit on the maximum mass of an NS to all our models. Our key result is that accounting for correlations among the values that the energy per neutron in PNM takes at various densities and that are typically disregarded efficiently constrains the behavior of the EOS at high densities. A series of global NS properties, e.g., maximum mass, central density of the maximum mass configuration, minimum NS mass that allows for direct URCA, and radii of intermediate and massive NSs, appear to be correlated with the value of effective neutron mass in PNM at 0.16 fm−3. Together with similar studies in the literature our work contributes to a better understanding of the NS EOS as well as its link with the properties of dense NM.

Meta Heuristic Algorithm Based Novel Dstatcom Architecture for Power Quality Improvement

Electric utility systems are increasingly favoured as local energy systems due to their ability to integrate renewable energy, improve energy efficiency, and enhance the resilience of the power grid. However, while these benefits are significant, they also present certain complexities related to control and power quality (PQ). PQ is crucial within utility systems to ensure the proper functioning of connected devices and the overall health of the system. Therefore, both voltage quality and current quality are of utmost importance. Control strategies combined with advanced power electronics devices (PED) provide a robust framework to address PQ challenges. This paper introduces a novel approach for constructing a distributed Static Synchronous Compensator (DSTATCOM) utilizing innovative 125-level asymmetric multilevel DC link inverters to enable multilevel operation. A modified 125-level multilevel inverter aimed at enhancing power quality is proposed herein. By employing a minimal number of components, this inverter can generate 125-level voltages per phase, serving as a DSTATCOM to address power quality issues such as sag, swell, and harmonics. Bidirectional DC-DC converters are employed for purposes other than just providing power to the inverter's DC connections replacing DC sources. Additionally, the Coati Optimization Algorithm (COA), a newly introduced metaheuristic approach inspired by observed behavioural patterns in coatis in their natural habitat, is presented. COA is designed to emulate two primary behaviours of coatis: (i) their hunting strategy when pursuing iguanas and (ii) their evasion tactics when confronted by predators. To validate the effectiveness of the Coati Optimization algorithm, simulation is carried out with Matlab Simulink, and the outcomes of the simulation for the proposed scheme are provided in this paper. The efficacy of Solar-PV integrated 125-level asymmetric multilevel DC link inverter in enhancing power quality by adhering to the IEEE-519 Standards is demonstrated.

BiLSTM-CNN with fixed weight approach for tracking speech articulatory features

Speech production is a complex sequential process which involve the coordination of various articulatory features. Among them tongue being a highly versatile active articulator responsible for shaping airflow to produce targeted speech sounds that are intellectual, clear, and distinct. This paper presents a novel approach for predicting tongue and lip articulatory features involved in a given speech acoustics using a stacked Bidirectional Long Short-Term Memory (BiLSTM) architecture, combined with a one-dimensional Convolutional Neural Network (CNN) for post-processing with fixed weights initialization. The proposed network is trained with two datasets consisting of simultaneously recorded speech and Electromagnetic Articulography (EMA) datasets, each introducing variations in terms of geographical origin, linguistic characteristics, phonetic diversity, and recording equipment. The performance of the model is assessed in Speaker Dependent (SD), Speaker Independent (SI), corpus dependent (CD) and cross corpus (CC) modes. Experimental results indicate that the proposed model with fixed weights approach outperformed the adaptive weights initialization with in relatively minimal number of training epochs. These findings contribute to the development of robust and efficient models for articulatory feature prediction, paving the way for advancements in speech production research and applications.

Preliminary Insights from Las Chachalacas (SON S:8:004): A Pleistocene-Holocene Occupation Record from Quiriego, Sonora, Mexico

This article summarizes recent work at the site of Las Chachalacas, located in the Municipio of Quiriego, southern Sonora. Set within the western foothills of the Sierra Madre Occidental, Las Chachalacas was adjacent to an arroyo that periodically became impounded, producing a marsh like environment. Stratigraphic associations and a minimal number of 14C dates suggest the site was regularly utilized from the terminal Pleistocene through the Early Agricultural period. Results contribute to several enduring debates in the North American Southwest regarding Pleistocene landscape use, demographic continuity through the Holocene Climatic Optimum (7500–5000 cal BP), the sequence of mobility and subsistence changes in the Archaic/Early Agricultural periods, and the identities of the earliest farmers indexed by material culture such as projectile points.

Machine learning-powered estimation of malachite green photocatalytic degradation with NML-BiFeO3 composites

This study explores the potential of photocatalytic degradation using novel NML-BiFeO3 (noble metal-incorporated bismuth ferrite) compounds for eliminating malachite green (MG) dye from wastewater. The effectiveness of various Gaussian process regression (GPR) models in predicting MG degradation is investigated. Four GPR models (Matern, Exponential, Squared Exponential, and Rational Quadratic) were employed to analyze a dataset of 1200 observations encompassing various experimental conditions. The models have considered ten input variables, including catalyst properties, solution characteristics, and operational parameters. The Exponential kernel-based GPR model achieved the best performance, with a near-perfect R2 value of 1.0, indicating exceptional accuracy in predicting MG degradation. Sensitivity analysis revealed process time as the most critical factor influencing MG degradation, followed by pore volume, catalyst loading, light intensity, catalyst type, pH, anion type, surface area, and humic acid concentration. This highlights the complex interplay between these factors in the degradation process. The reliability of the models was confirmed by outlier detection using William’s plot, demonstrating a minimal number of outliers (66–71 data points depending on the model). This indicates the robustness of the data utilized for model development. This study suggests that NML-BiFeO3 composites hold promise for wastewater treatment and that GPR models, particularly Matern-GPR, offer a powerful tool for predicting MG degradation. Identifying fundamental catalyst properties can expedite the application of NML-BiFeO3, leading to optimized wastewater treatment processes. Overall, this study provides valuable insights into using NML-BiFeO3 compounds and machine learning for efficient MG removal from wastewater.

Frustraevo: a web server to localize and quantify the conservation of local energetic frustration in protein families.

According to the Principle of Minimal Frustration, folded proteins canonly have a minimal number of strong energetic conflicts in their native states. However, not all interactions are energetically optimized for folding but some remain in energetic conflict, i.e. they are highly frustrated. This remaining local energetic frustration has been shown to be statistically correlated with distinct functional aspects such as protein-protein interaction sites, allosterism and catalysis. Fuelled by the recent breakthroughs in efficient protein structure prediction that have made available good quality models for most proteins, we have developed a strategy to calculate local energetic frustration within large protein families and quantify its conservation over evolutionary time. Based on this evolutionary information we can identify how stability and functional constraints have appeared at the common ancestor of the family and have been maintained over the course of evolution. Here, we present FrustraEvo, a web server tool to calculate and quantify the conservation of local energetic frustration in protein families.

Chromatic numbers of certain hypercube variants

The total (vertex/edge) chromatic number of a graph G, χ ″ ( G ) ( χ ( G ) / χ ′ ( G ) ) is defined to be the minimum number of colours needed to colour the vertices and edges, together termed as elements (vertices/edges) of a graph in such a way that no two adjacent/incident elements (vertices/edges) are given the same colour. In this article, we determine the vertex, edge and total chromatic number of some hypercube variants such as folded hypercube, enhanced hypercube, augmented hypercube and exchanged hypercube.

HyDesign: a tool for sizing optimization of grid-connected hybrid power plants including wind, solar photovoltaic, and lithium-ion batteries

Abstract. Hybrid renewable power plants consisting of collocated wind, solar photovoltaic (PV), and lithium-ion battery storage connected behind a single grid connection can provide additional value to the owners and society in comparison to individual technology plants, such as those that are only wind or only PV. The hybrid power plants considered in this article are connected to the grid and share electrical infrastructure costs across different generation and storing technologies. In this article, we propose a methodology for sizing hybrid power plants as a nested-optimization problem: with an outer sizing optimization and an internal operation optimization. The outer sizing optimization maximizes the net present values over capital expenditures and compares it with standard designs that minimize the levelized cost of energy. The sizing problem formulation includes turbine selection (in terms of rated power, specific power, and hub height), a wind plant wake loss surrogate, simplified wind and PV degradation models, battery degradation, and operation optimization of an internal energy management system. The problem of outer sizing optimization is solved using a new parallel “efficient global optimization” algorithm. This new algorithm is a surrogate-based optimization method that ensures a minimal number of model evaluations but ensures a global scope in the optimization. The methodology presented in this article is available in an open-source tool called HyDesign. The hybrid sizing algorithm is applied for a peak power plant use case at different locations in India where renewable energy auctions impose a monetary penalty when energy is not supplied at peak hours. We compare the hybrid power plant sizing results when using two different objective functions: the levelized cost of energy (LCoE) or the relative net present value with respect to the total capital expenditure costs (NPV/CH). Battery storage is installed only on NPV/CH-based designs, while the hybrid design, including wind, solar, and battery, only occurs on the site with good wind resources. Wind turbine selection on this site prioritizes cheaper turbines with a lower hub height and lower rated power. The number of batteries replaced changes at the different sites, ranging between two or three units over the lifetime. A significant oversizing of the generation in comparison to the grid connection occurs on all NPV/CH-based designs. As expected LCoE-based designs are a single technology with no batteries.

A Spectrotemporal EEG Mapping Approach for Decoding Imagined Marathi Language Phonemes

Individuals facing verbal communication impairments resulting from brain disorders like paralysis or autism encounter significant challenges when unable to articulate speech. This research proposes the design and development of a wearable system capable of decoding imagined speech using electroencephalogram (EEG) signals obtained during the mental process of speech generation. The system’s main objective is to offer an alternative communication method for individuals who can hear and think but face challenges in articulating their thoughts verbally. The design suggested includes user-friendliness, wearability, and comfort for seamless integration into daily life. A minimal number of electrodes are strategically placed on the scalp to minimize invasiveness. Achieving precise localization of the cortical areas responsible for generating the EEG patterns during imagined speech is vital for accurate decoding. Literature studies are utilized to determine the cortical positions associated with speech processing. Due to the inherent limitations in EEG spatial resolution, meticulous experiments are conducted to map the scalp positions onto their corresponding cortical counterparts. Specifically, we focus on identifying the scalp location over the superior temporal gyrus (T3) using the internationally recognized 10-20 electrode placement system by employing a circular periphery movement with a 2 cm distance increment. Our research involves nine subjects spanning various age groups, with the youngest being 23 and the oldest 65. Each participant undergoes ten iterations, during which they imagine six Marathi syllables. Our work contributes to the development of wearable assistive technology, enabling mute individuals to communicate effectively by translating their imagined speech into actionable commands. This innovation ultimately enhances their social participation and overall well-being.