bluuebunny/crossref_metadata_2025 huggingface dataset:
Wohlleben effect and quantum criticality in YBa1.4Sr0.6Cu3O6Se0.5 superconductor within a heterophase ceramic: Results for YBCO double-element substitution
Authors: Grinenko V. , Dudka A. , Nozaki S. , Muto A. , Clarke J. , Kilcrease J. , Hogan T. , Nikoghosyan V. , Napoletani D. , de Paiva I. , Dulal R. , Teknowijoyo S. , Chahid S. , Gulian A.
Published: November 2024

A novel material is obtained by modifying pristine YBCO superconductor via simultaneous partial substitution of Sr for Ba and Se for O. The intended stoichiometry was [Formula: see text]SrxCu3[Formula: see text]Sex with [Formula: see text], 0.75 and 1; and [Formula: see text] yielded the most interesting results considered in detail. We confirmed that Se atoms indeed enter the superconductor lattice cell. The obtained polycrystalline material contains five phases, with the major phase ([Formula: see text][Formula: see text]wt.%) being [Formula: see text][Formula: see text]Cu3O6[Formula: see text]. The synthesized superconductor demonstrates unique properties, including (i) two superconducting transitions with [Formula: see text][Formula: see text]K and [Formula: see text][Formula: see text]K yielding the Wohlleben effect; (ii) strong-coupling d-wave superconductivity (with [Formula: see text] 5); (iii) Schottky anomaly in the heat capacity; and, most importantly, (iv) a very unusual anomaly in the heat capacity which can be associated with the quantum criticality typically observable at much higher fields. The fact that the quantum criticality is visible in weaker fields ([Formula: see text][Formula: see text]T) may open opportunities for exploration of the interplay between superconductivity and pair density waves by wide research community particularly when its pure phase is available. To facilitate this, the stability of single-phase [Formula: see text][Formula: see text]Cu3O6[Formula: see text] is analyzed using machine learning methods.
Optical solutions to conformable nonlinear Schrödinger equation with cubic–quintic–septimal in weakly non-local media by new Kudryashov approach
Authors: Murad Muhammad Amin Sadiq
Published: November 2024

This paper analyzes the cubic–quintic–septimal nonlinear Schrödinger equation with conformable derivative utilizing the new Kudryashov approach. Through this algorithm, several exact solutions are constructed within the present nonlinear conformable Schrödinger model such as bell-shaped, singular, bright, dark, mixed dark–bright, and wave soliton solutions. The physical behavior of these solutions is depicted via contour, three-dimensional, and two-dimensional representations. Moreover, it is crucial to emphasize the importance of analyzing the cubic–quintic–septimal nonlinear Schrödinger wave equation, which finds utility across a spectrum of fields including optics, quantum mechanics, and the study of nonlinear wave propagation. Besides, the approach holds promise for investigating diverse sets of differential equations within multiple domains of applied sciences. The governing nonlinear conformable Schrödinger equation also has numerous applications in nonlinear optics, such as describing the propagation of laser beams through materials with nonlinear optical properties. The inclusion of these nonlinearities illustrates the interaction and behavior of light beams in weakly non-local media.
Dynamical Analysis and Circuit Implementation of HR-FHN Neuron Model Coupled by Locally Active Memristor
Authors: Li Xinying , Guo Wenhui , Du Yuxuan
Published: November 2024

In this paper, a novel locally active memristor is constructed. Based on the 3D Hindmarsh–Rose (HR) and 2D FitzHugh–Nagumo (FHN) neuron, a heterogeneous neuronal system is constructed by connecting the two neurons with the locally active memristor. The equilibrium point and stability of the system are investigated. The dynamic behavior of the system is numerically and experimentally revealed by utilizing dynamic analyses in terms of interspike interval bifurcation diagram, two-parameter bifurcation diagram and so on. The unique and abundant dynamic behavior is found in the proposed neuronal system by varying the coupling strength, external stimulus current, memristive parameter and other system parameters. Multiple bursting firing groups in the tongue-shaped domains have been discovered for the first time. Finally, in order to validate the numerical simulation, an analog equivalent circuit of the heterogeneous neuronal system is devised, which demonstrates that the system is physically realizable.
Chaos and Regularity in the Double Pendulum with Lagrangian Descriptors
Authors: Jiménez-López Javier , García-Garrido Víctor J.
Published: November 2024

In this paper, we apply the method of Lagrangian descriptors as an indicator to study the chaotic and regular behaviors of trajectories in the phase space of the classical double pendulum system. To successfully quantify the degree of chaos with this tool, we first derive Hamilton’s equations of motion for the problem in nondimensional form and demonstrate how they can be expressed compactly using matrix algebra. Once the dynamical equations are obtained, we conduct a parametric study based on the system’s total energy and other key parameters such as the lengths and masses of the pendulums, as well as gravity, to determine the extent of the chaotic and regular regions in the phase space. Our numerical results reveal that, for a given mass ratio, the maximum chaotic fraction of phase space trajectories is attained when the pendulums have equal lengths. Furthermore, we characterize how chaos grows and decays within the system in terms of the model parameters, and explore the hypothesis that the chaotic fraction follows an exponential law over different energy regimes.
Investigation of Spatial Pattern in SI Model with PD Control and Cross-Diffusion
Authors: Lu Yuanshan , Yao Yi , Huang Xia , Xiao Min , Jiang Guoping , Rutkowski Leszek
Published: November 2024

Spatial patterns are an important characteristic of ecological epidemic reaction–diffusion models, as they help predict the spatial dynamics of epidemic spread. However, there is currently a significant shortage in research on control strategies for generating and regulating these patterns. In this paper, we establish a Susceptible–Infectious (SI) model with Proportional-Derivative (PD) control and cross-diffusion. We analyze the stability of the model without diffusion and provide the conditions for Turing instability driven by diffusion. Using the cross-diffusion coefficient as the bifurcation parameter, we derive the amplitude equation of the two-dimensional Turing pattern at the Turing bifurcation threshold based on the multiple scale method, which determines the selection and stability of pattern formation. Numerical simulations in two-dimensional space show that the bifurcation parameter varies with changes in control parameters, and the PD control not only effectively alters the structure of Turing patterns but also suppresses Turing instability. Furthermore, it is found that more complex spatial patterns in three-dimensional space emerge under PD control.
Three-Dimensional Limit Cycles Generated from Discontinuous Piecewise Differential Systems Separated by Two Intersecting Planes
Authors: Baymout Louiza , Benterki Rebiha , Llibre Jaume
Published: November 2024

Due to their widespread applications in modeling natural issues, the study of piecewise linear differential systems has gained popularity in recent years. It is well known that the qualitative theory of piecewise linear differential systems heavily relies on limit cycles. By now, most studies have only considered planar systems by examining the presence and maximum number of limit cycles for piecewise differential systems. However, there have been few studies on this issue in [Formula: see text]. We recall the problem of the existence and the maximum number of limit cycles for planar discontinuous piecewise differential systems formed by linear differential centers separated by one or two parallel straight lines that have at most one limit cycle, respectively. Although, in [Formula: see text], the maximal number of limit cycles for the same problem is 0 when the separation surface is a plane and at most four if the separation surface is two parallel planes. In this paper, we mainly focus on the problem of the existence and the maximum number of limit cycles in [Formula: see text], when the separating surface is formed by two intersecting half-planes. First, we prove that when the entire space is divided into two regions, this family can have at most five limit cycles, where one limit cycle intersects the separation surface at two points and the remaining four limit cycles intersect the separation surface at four points. Second, when the entire space is divided into three regions, we prove that the maximum number of limit cycles intersecting the separation surface at three points and four points simultaneously is at most eight.
Conforming virtual element method for nondivergence form linear elliptic equations with Cordes coefficients
Authors: Bonnet Guillaume , Cangiani Andrea , Nochetto Ricardo H.
Published: November 2024

We propose and analyze an [Formula: see text]-conforming virtual element method (VEM) for the simplest linear elliptic PDEs in nondivergence form with Cordes coefficients. The VEM hinges on a hierarchical construction valid for any dimension [Formula: see text]. The analysis relies on the continuous Miranda–Talenti estimate for convex domains [Formula: see text] and is rather elementary. We prove stability and error estimates in [Formula: see text], including the effect of quadrature, under minimal regularity of the data. Numerical experiments illustrate the interplay of coefficient regularity and convergence rates in [Formula: see text].
Change Point Estimation Based on a Weighted Consensus Clustering Approach with Multiple Steps (A Real Case in Health Care: Diabetic Patients)
Authors: Gharegozloo Mona , Kamranrad Reza
Published: November 2024

One of the primary objectives of control charts is to accurately detect the occurrence of changes in statistical processes. This enables process analysts to identify the factors responsible for the change and take corrective actions. The clustering method is a practical approach that has been developed to estimate the time of change. In recent years, a new approach called weighted consensus clustering (WCC) has been introduced in the field of clustering analysis. This approach can be utilized to cluster different trends in process parameters, resulting in a final clustering that exhibits higher quality and stability compared to the initial clustering. One of the emerging aspects in the health care field is the use of new statistical approaches to rapid disease diagnosis. In this paper, a novel change point estimation scheme called WCC has been developed to detect changes in the diabetic patients’ processes. In addition, to demonstrate the advantages of the proposed scheme, its performance is compared with an existing method known as maximum likelihood of clusters (CMLE) through simulation studies as well as a real case study in the healthcare system.
COMPARISON OF THE PERFORMANCE OF INDIRECT EVALUATION OF FLANK WEAR FOR TURNING INCONEL-718 USING THE PROCESSED IMAGE AND ACOUSTIC WAVES
Authors: HANUMANTHAPPA HARISH , SHANMUGAM BHARATH KUMAR , KUMARI MONA , KUMAR AMIT , PRASAD DURGA C. , KUMAR MANISH , PRASHANTH K. S. , SINGH POOJA , GANGWAR PRASHANT KUMAR , GARIMELLA ADITHYA
Published: November 2024

This study investigates the tool wear for turning Inconel-718 using the titanium nitride carbide tool inserts. This research work aims to compare the performance of processed images and acoustic waves used as an indirect technique for evaluating tool wear. The work targets to capture the tool wear and tool image after turning and acoustic wave during turning for each experimental run. The pixel area of the processed picture, the root-mean-square (RMS) of the acoustic wave, and the microscope tool wear of the tool maker were taken into consideration as output parameters for the change of operational parameters including feed, speed, and depth of cut. The performance of wear, pixel area, and RMS was compared using the Box Behnken method. Further, the correlation between the performance of tool wear, image processed pixel area, and RMS for the variation in input variable was obtained from interaction and main effects plots. The results demonstrated that at lower speeds (280[Formula: see text]rpm), lower feed rates (0.04[Formula: see text]mm/rev), and medium depth of cut (0.2[Formula: see text]mm), there was less wear, pixel area, and RMS. Wear, pixel area, and RMS have all decreased as a result of the tool and workpiece having less surface friction due to the reduced speed, feed, and medium depth of cut. From the analysis, it was also clear that the indirect evaluation of the wear can be successfully carried out using digital image pixel area and acoustic wave RMS for turning Inconel-718 using a titanium nitride-coated carbide tool.
IMPROVED WEAR CHARACTERISTICS OF Al–B4C COMPOSITES FABRICATED BY ULTRA-SONIC ASSISTED STIR CASTING TECHNIQUE
Authors: UDAY K. N. , RAJAMURUGAN G. , GOGULRAJ G. , SENTHILRAJ K.
Published: November 2024

This work investigates the development and tribological performance of aluminum (Al) matrix composites reinforced with boron carbide (B4C) particles at 2, 4, and 6[Formula: see text]wt.%. These composites were fabricated using a novel ultrasonic-assisted stir-casting approach. The inclusion of B4C, a ceramic material known for its hardness and thermal stability, aimed to enhance the wear resistance of the Al matrix. Ultrasonic-assisted stirring ensured uniform dispersion of B4C particles and reduced agglomeration, addressing a common issue in composite production. The developed composites with varying B4C[Formula: see text]wt.% were subjected to dry sliding wear testing using a pin-on-disc device and a Box-Behnken design of response surface methodology, incorporating variables such as load, sliding distance, and reinforcement wt.%. XRD analysis confirmed the presence of B4C in the Al–B4C composite. A slight peak shift was observed as the weight percentage of B4C increased. The 2% Al–B4C composite exhibited a 29% decrease in porosity compared to cast Al 6061. The inclusion of 2[Formula: see text]wt.% B4C resulted in a significant 33% improvement in the composite’s hardness, reaching 86.6[Formula: see text]HV. Severe plastic flow and subsequent strain hardening were responsible for this increase in hardness. The ANOVA results primarily influenced the wear rate, with sliding distance and reinforcement weight percentage following closely behind. This study highlights the potential of Al–B4C composites in applications requiring exceptional wear performance, such as the automotive and aerospace industries.
A Note on Locally Primitive Arc-Transitive Graphs of Valency 8
Authors: Abedi Fateme , Reza Salarian M.
Published: November 2024

Let [Formula: see text] be a connected [Formula: see text]-locally primitive arc-transitive graph for some subgroup [Formula: see text] of [Formula: see text]. Suppose that [Formula: see text] is a [Formula: see text]-transitive graph. In this paper, we give a characterization of the vertex-stabilizer [Formula: see text] when [Formula: see text] has valency 8.
Higher Frobenius-Schur Indicators for Semisimple Hopf Algebras in Positive Characteristic
Authors: Wang Zhihua , Liu Gongxiang , Li Libin
Published: November 2024

Let [Formula: see text] be a semisimple Hopf algebra over an algebraically closed field [Formula: see text] of characteristic [Formula: see text]. We show that the antipode [Formula: see text] of [Formula: see text] satisfies the equality [Formula: see text], where [Formula: see text], [Formula: see text] and [Formula: see text] is a nonzero integral of [Formula: see text]. The formula of [Formula: see text] enables us to define higher Frobenius-Schur indicators for the Hopf algebra [Formula: see text]. This generalizes the notion of higher Frobenius-Schur indicators from the case of characteristic 0 to the case of characteristic [Formula: see text]. These indicators defined here share some properties with the ones defined over a field of characteristic 0. In particular, all these indicators are gauge invariants for the tensor category [Formula: see text] of finite-dimensional representations of [Formula: see text].
Arc-Transitive Graphs of Valency 11 and Order Four Times an Odd Square-free Integer
Authors: Ling Bo , Lan Ting , Yin Fugang
Published: November 2024

In this paper, we classify connected arc-transitive graphs of valency 11 and of order [Formula: see text], where [Formula: see text] is an odd square-free integer. In particular, there are four infinite families of such graphs, which have a minimal arc-transitive automorphism group isomorphic to [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] separately, where [Formula: see text] is a prime. Moreover, examples for each class are constructed.
Adding a Transformation to Imprimitive Transitive Groups
Authors: Li Wangwei , Li Xianhua
Published: November 2024

Let [Formula: see text] be a transitive group. We mainly prove that the group[Formula: see text]is primitive for a given special permutation[Formula: see text]and the semigroup[Formula: see text] is synchronizing for a given special transformation [Formula: see text].
Porphyrazine: An effective receptor for quantum dots
Authors: Dey Subhendu , Nayak Subrata , Bhattacharjee Sanyukta , Bhattacharya Shalmali , Bhattacharya Sumanta
Published: November 2024

CdSe/ZnS quantum dots (C-S QD) interact spontaneously with a designed free-base porphyrazine (H2Pz) in toluene. Spontaneous interaction between C-S QD and H2Pz is observed from UV-vis investigations and it may be formulated with the help of dipole-dipole transition. Fluorescence measurements reveal that the photoluminescence (PL) of C-S QD suffers a considerable extent of quenching at its emission maximum, i.e., at 612 nm following the interaction with H2Pz. The self-assembly of H2Pz with C-S QD is quantified with the help of the evaluation of the formation constant (K) value of C-S QD-H2Pz system, i.e., KC−S QD−H2Pz = 1.3 × 105 dm3 · mol−1. The estimated kq value (kq = quenching constant of C-S QD–H2Pz system) is determined to be ∼ 2.47 × 1012 dm3/mol.sec.) suggesting a static quenching mechanism for the photoexcited decay of C-S QD by H2Pz in solution. The results obtained from present investigations may be suitably utilized for the future application of porphyrazine as a selective host for quantum dots.
Index theory for Heisenberg elliptic and transversally Heisenberg elliptic operators from KK-theoretic viewpoint
Authors: Tian Minjie
Published: November 2024

This research comprehensively describes the basic theory of transversally Heisenberg elliptic operators, and investigates the index theory of Heisenberg elliptic and transversally Heisenberg elliptic operators from the perspective of [Formula: see text]-theory, applying Kasparov’s methodology. Moreover, the analysis methodically examines specific conditions, with a focus on the Fourier transform of the nilpotent group [Formula: see text]-algebra. We demonstrate enhanced methods for analyzing the hypoellipticity of operators, presenting a robust framework for defining and understanding transversal Heisenberg ellipticity in a [Formula: see text]-theoretic context. This work provides a solid foundation for future research into the properties of hypoelliptic differential operators in complicated manifolds.
Electrospun ZnO and GSH Co-loaded PU/CS Nanofibrous Films as Potential Antibacterial Wound Dressings
Authors: Al-Jamal Ahmed N. , Hussein Uday Abdul-Reda , Aljbory Najwa , Mahmoud Zaid H. , Mirani Abolfazl , Kianfar Ehsan , Maleknia Laleh
Published: November 2024

In this paper, polyurethane nanofibers (PU)/chitosan (CS)/zinc oxide nanoparticles (ZnO)/ Glutathione (GSH) were prepared as wound dressings using the electrospinning method. First, the antibacterial effect of ZnO nanoparticles against Escherichia coli and Staphylococcus aureus was investigated. Second, the ability of the nanofibers to release different amounts of antibiotics and nanoparticles to prevent wound infection was investigated. The morphology of the composite nanofibers was investigated using field emission electron microscopy (FESEM). The chemical structure of the nano-wound dressing was examined using infrared spectroscopy (FTIR), and the FTIR spectroscopy results showed that GSH and ZnO nanoparticles were successfully arranged within the nanofibers. with an increase in the amount of GSH (1,3,5 wt%) at a constant voltage of 0.4 V, the amount of current increased by 1.7, 2.1, and 6.2 [Formula: see text]A, respectively. According to the results of electrochemical analysis, the optimal amount of active ingredient is 5 wt%, but according to the images of FESEM analysis, due to the lack of nanofibers with the corresponding morphology, the amount of active ingredient is reduced to 5 wt% PU/Chi/.
Action of left identity on n-skew commuting 3-additive mappings
Authors: Alrehaili Suad , Shujat Faiza , Ansari Abu Zaid
Published: November 2024

In this paper, our aim is to prove the following result: consider a given integer [Formula: see text], and let [Formula: see text] be a [Formula: see text] torsion free ring possessing a left multiplicative identity e. If [Formula: see text] is a symmetric 3-additive map and [Formula: see text] is the trace of [Formula: see text] such that [Formula: see text] is n-skew commuting on [Formula: see text], then [Formula: see text].
Classification of weak del Pezzo pairs for surfaces
Authors: Miyanishi Masayoshi
Published: November 2024

A weak del Pezzo surface is a smooth projective surface whose anticanonical divisor [Formula: see text] is nef and big. We generalize this concept to weak del Pezzo pairs for surfaces and classify those with minimal and non-minimal properties.
Automated Roundoff Error Analysis of Probabilistic Floating-Point Computations
Authors: Constantinides George , Dahlqvist Fredrik , Rakamarić Zvonimir , Salvia Rocco
Published: November 2024

We present a detailed study of roundoff errors in probabilistic floating-point computations. We derive closed-form expressions for the distribution of roundoff errors associated with a random variable, and we prove that roundoff errors are generally close to being uncorrelated with their generating distribution. Based on these results, we propose a model of IEEE floating-point arithmetic for numerical expressions with probabilistic inputs and an algorithm for evaluating this model. Our algorithm provides rigorous bounds on the output and error distributions of arithmetic expressions over random variables, evaluated in the presence of roundoff errors. It keeps track of complex dependencies between random variables using an SMT solver, and is capable of providing sound but tight probabilistic bounds on roundoff errors using symbolic affine arithmetic. We implement the algorithm in the PAF tool, and evaluate it on FPBench, a standard benchmark suite for the analysis of roundoff errors in small kernels. Our evaluation shows that PAF computes tighter bounds than the current state of the art on almost all benchmarks.
A Systematic Review of Trust Assessments in Human–Robot Interaction
Authors: Campagna Giulio , Rehm Matthias
Published: November 2024

The integration of robots into daily life has increased significantly, spanning applications from social-care to industrial settings with collaborative robots. Ensuring a safe, secure environment and equitable workload distribution in human-robot collaborations is crucial. Trust is a key factor in these environments, essential for enhancing collaboration and achieving tasks while maintaining safety. Under-trusting robots can hinder productivity, while over-trusting them can lead to accidents. A comprehensive literature review of 100 publications from 2003 to 2023 analyzes trust and its influencing factors in industrial and social-care contexts. Findings reveal that in industrial settings, robot-related factors are more influential, whereas in social-care, human and environmental factors play a significant role. Furthermore, the review delves into gauging trust through observable behavior, while also exploring various trust evaluation methodologies. Results show that trust can be gauged through human behaviors, physical cues, and physiological measurements. Concerning trust evaluation methodologies, traditional questionnaires have limitations, opening new opportunities for machine learning and sensor-based approaches to real-time trust evaluation, as trust is a dynamic cognitive value that evolves over time. Notably, 97% of the reviewed articles were published in the last decade, underscoring a growing interest in human–robot interaction and trust within the scientific community.
The Use of Artificial Intelligence in Building Engineering for Historic Buildings Built in the Austro-Hungarian Monarchy
Authors: Dvornik Perhavec Daniela , Kamnik Rok
Published: November 2024

Knowledge discovery from databases (KDD) and data mining (DM) belong to the field of artificial intelligence (AI). The integration of AI into various segments of the construction industry is still in its infancy, but it is expected to be used more widely soon, driven by the development of databases and data warehouses. By using Building Information Modelling (BIM) technologies in the planning of new buildings, we will be able to obtain valuable data. The situation is different for old, existing buildings and the building engineering associated with these properties. Civil engineers, renovation planners and architects need knowledge of the building before renovation. This knowledge is much less than the possibilities that exist. Information about the building can be found in provincial archives. For historic buildings, 10%–15% of the plans, drawings, descriptions, or projects are available. The remaining 85% must be researched on site, which is a lengthy and costly process and hinders the construction process. The question arose as to how the findings from the study of buildings based on written and preserved sources can be applied to 85% of buildings for which no data is available. This paper presents the use of the collected data as an idea for an initiative to develop a database and modelling using AI algorithms. The research study investigates the feature “load-bearing wall” for residential buildings with basements and floors built between 1857 and 1948 in the former Austro-Hungarian Empire. The aim of this study is to create a model to predict the characteristics of a building for which no archival material is available. The study is based on the use of AI in the creation of decision trees to help engineers improve their knowledge of historic buildings in the former Austro-Hungarian Empire and building engineering for historic objects.
A SAM-guided Two-stream Lightweight Model for Anomaly Detection
Authors: Li Chenghao , Qi Lei , Geng Xin
Published: November 2024

In industrial anomaly detection, model efficiency and mobile-friendliness become the primary concerns in real-world applications. Simultaneously, the impressive generalization capabilities of Segment Anything (SAM) have garnered broad academic attention, making it an ideal choice for localizing unseen anomalies and diverse real-world patterns. In this paper, considering these two critical factors, we propose a SAM-guided Two-stream Lightweight Model for unsupervised anomaly detection (STLM) that not only aligns with the two practical application requirements but also harnesses the robust generalization capabilities of SAM. We employ two lightweight image encoders, i.e., our two-stream lightweight module, guided by SAM’s knowledge. To be specific, one stream is trained to generate discriminative and general feature representations in both normal and anomalous regions, while the other stream reconstructs the same images without anomalies, which effectively enhances the differentiation of two-stream representations when facing anomalous regions. Furthermore, we employ a shared mask decoder and a feature aggregation module to generate anomaly maps. Our experiments conducted on MVTec AD benchmark show that STLM, with about 16M parameters and achieving an inference time in 20ms, competes effectively with state-of-the-art methods in terms of performance, 98.26% on pixel-level AUC and 94.92% on PRO. We further experiment on more difficult datasets, e.g., VisA and DAGM, to demonstrate the effectiveness and generalizability of STLM. Codes are available online at
https://github.com/Qi5Lei/STLM
.
Congenital and Infantile Masses of the Head and Neck
Authors: Foust Alexandra M. , Krishnasarma Rekha , Robson Caroline D. , Snyder Elizabeth , Martin Dann , Cassella Katharyn D. , Johnstone Lindsey , Rispoli Joanne , Pruthi Sumit , Sarma Asha
Published: November 2024

The use of a location-based radiologic approach to assess congenital and infantile head and neck masses can aid radiologists in generating an accurate differential diagnosis and providing appropriate follow-up imaging recommendations.
Reduced cofilin activity as a mechanism contributing to endothelial cell stiffening in type 2 diabetes
Authors: Power Gavin , Lateef Olubodun M. , Ramirez-Perez Francisco I. , Lazo-Fernandez Yoskaly , Augenreich Marc A. , Ferreira-Santos Larissa , Soares Rogerio N. , Gonzalez-Vallejo Juan D. , Morales-Quinones Marianna , Norton Charles E. , Manrique-Acevedo Camila , Martinez-Lemus Luis A. , Padilla Jaume
Published: November 2024

Cell stiffening is an emerging contributor to endothelial dysfunction, a classic feature of type 2 diabetes (T2D). However, the mechanisms underlying endothelial stiffening remain largely unknown. This work provides evidence that oxidative stress-induced inactivation of cofilin, a key F-actin severing protein, may be implicated in increasing endothelial F-actin and cell stiffness in T2D.
Combined exercise hinders the progression of pulmonary and right heart harmful remodeling in monocrotaline-induced pulmonary arterial hypertension
Authors: Leite Luciano Bernardes , Soares Leôncio Lopes , Portes Alexandre Martins Oliveira , da Silva Bruna Aparecida Fonseca , Dias Taís Rodrigues , Soares Thayana Inácia , Assis Mirian Quintão , Guimarães-Ervilha Luiz Otávio , Carneiro-Júnior Miguel Araújo , Forte Pedro , Machado-Neves Mariana , Reis Emily Correna Carlo , Natali Antônio José
Published: November 2024

This study reveals that combined exercise improves tolerance to physical effort, prevents increases in pulmonary artery resistance, and conserves the right heart function during the progression of pulmonary arterial hypertension. Our analyses show that combined exercise hinders harmful right ventricular and lung structural remodeling and oxidative stress, which reflects in the maintenance of right ventricular myocytes’ contractile function by preserving the intracellular calcium cycling. An attenuated progression of the disease impacts positively on its prognosis.
Predictive Modeling of Surface Subsidence Considering Different Environmental Risk Zones
Authors: Li Yunsong , Qin Yongjun , Xie Liangfu , Yuan Yangchun , Ran Jie
Published: November 2024

Insufficient knowledge and control of the surrounding environmental risks in the process of subway tunnel construction will lead to different degrees of surface settlement during construction, and it is of practical significance to grasp the impact of environmental risks on surface settlement and make predictions on this basis. There is a close connection between the surrounding environment of subway tunnel construction and the settlement monitoring data, and the high precision prediction of surface settlement through the surrounding environment before subway tunnel construction can guarantee the safety of subway tunnel construction. Therefore, this paper proposes a surface settlement prediction model based on environmental risk zoning. Establish the integrated impact zoning of underground mining environmental risk through spatial superposition and risk quantification, and divide the construction environment into high‐risk, middle‐risk, and low‐risk region. Adopt four different noise reduction algorithms for data noise reduction on the raw data of the monitoring points at the intervals of different risk zones, and combine the time series prediction as well as the deep learning prediction method to get the prediction model for environmental risk zoning based on the environmental risk zoning. The monitoring data of Urumqi Metro Line 1 is analyzed as an example, and the suitable combination of prediction models for each region under the environmental risk zoning of the subway tunnel construction is obtained: high‐risk region (singular value decomposition (SVD) + long and short‐term memory (LSTM) neural network); middle‐risk region (wavelet transform/Kalman filter + back propagation neural network (BPNN)); and low‐risk region (mean filtering + autoregressive integrated moving average model (ARIMA)).
Prevalence of Patent Processus Vaginalis Diagnosed Using Laparoscopy during Peritoneal Dialysis Catheter Insertion and Subsequent Genital Edema: A Prospective Observational Study
Authors: Hayashi Terumasa , Miyazaki Susumu , Iwase Kazuhiro , Takatsuka Taisuke , Yoshimura Daisuke , Kawamura Tomohiro , Iwata Yukimasa , Okushima Hiroki , Ueda Yoshiyasu , Isaka Yoshitaka
Published: November 2024

Introduction: Inguinal hernia and genital edema are relatively common complications of peritoneal dialysis (PD). Although patent processus vaginalis (PV) is considered an important factor associated with these complications, the prevalence of patent PV at PD initiation and whether it leads to these complications has not been fully identified. Methods: A total of 71 patients were included in this study, 41 of whom underwent laparoscopy-assisted catheter placement. The remaining 30 patients did not undergo laparoscopy mainly because of a lack of patient consent. During laparoscopy, if a dimple or small canal toward the deep inguinal ring was observed, the groin was diagnosed as a patent PV. Results: Laparoscopy revealed that 9 of 41 patients (22%) had patent PV (male, 29.6%; female, 7.1%). Genital edema occurred in 2 of the nine patients with patent PV at 8.9 and 11.4 months after PD initiation, respectively. However, none of 32 patients without patent PV developed this complication. Two of 30 patients without laparoscopic inspection presented with genital edema at 6.7 and 12.4 months after PD initiation, respectively. Among the 71 patients, body mass index was significantly higher in patients with this complication than in those without (28.8 vs. 22.8, p 0.013). Conclusion: Although the number of patients with patent PV who manifested genital edema was small, our results suggest that patent PV at PD initiation may be an important contributor for genital edema in patients undergoing PD. Further studies are needed to determine whether the repair of patent PV could prevent subsequent genital edema.
Hub Metabolites Promote the Bioflocculant Production in a Biomass-Degrading Bacterium Pseudomonas boreopolis GO2
Authors: Xu Yijie , Feng Jiayin , Hu YuXuan , Chen Li , Qin Wensheng , Chen Chen , Yan Maocang , Guo Haipeng
Published: November 2024

Introduction: The low yield of bioflocculants has been a bottleneck problem that limits their industrial applications. Understanding the metabolic mechanism of bacteria that produce bioflocculants could provide valuable insights and strategies to directly regulate their yield in future. Methods: To investigate the change of metabolites in the process of bioflocculant production by a biomass-degrading bacterium, Pseudomonas boreopolis GO2, an untargeted metabolome analysis was performed. Results: The results showed that metabolites significantly differed during the fermentation process when corn stover was used as the sole carbon source. The differential metabolites were divided into four co-expression modules based on the weighted gene co-expression network analysis. Among them, a module (yellow module) was closely related to the flocculating efficiency, and the metabolites in this module were mainly involved in carbohydrate, lipid, and amino acid metabolism. The top 30 metabolites with the highest degree in the yellow module were identified as hub metabolites for bioflocculant production. Finally, 10 hub metabolites were selected to perform the additional experiments, and the addition of L-rhamnose, tyramine, tryptophan, and glutaric acid alone all could significantly improve the flocculating efficiency of GO2 strain. Conclusion: These results indicated that the hub metabolites were key for bioflocculant production in GO2 strain, and could help guide the improvement of high-efficiency and low-cost bioflocculant production.
Discriminative deep learning based hybrid spectro-temporal features for synthetic voice spoofing detection
Authors: Palsapure Pranita Niraj , Rajeswari Rajeswari , Kempegowda Sandeep Kumar , Ravikumar Kumbhar Trupti
Published: November 2024

Voice-based systems like speaker identification systems (SIS) and automatic speaker verification systems (ASV) are proliferating across industries such as finance and healthcare due to their utility in identity verification through unique speech pattern analysis. Despite their advancements, ASVs are susceptible to various spoofing attacks, including logical and replay attacks, posing challenges due to the sophisticated acoustic distinctions between authentic and spoofed voices. To counteract, this study proposes a robust yet computationally efficient countermeasure system, utilizing a systematic data processing pipeline coupled with a hybrid spectral-temporal learning approach. The aim is to identify effective features that optimize the model's detection accuracy and computational efficiency. The model achieved superior performance with an accuracy of 99.44% and an equal error rate (EER) of 0.014 in the logical access scenario of the ASVspoof 2019 challenge, demonstrating its enhanced accuracy and reliability in detecting spoofing attacks with minimized error margin. 
Efficient reconfigurable parallel switching for low-density parity-check encoding and decoding
Authors: Venkatesh Divyashree Yamadur , Mallikarjunaiah Komala , Srikantaswamy Mallikarjunaswamy
Published: November 2024

In the evolution of next-generation communication systems, the demand for higher data integrity and transmission efficiency has brought low-density parity-check (LDPC) codes into focus, particularly for their error-correcting prowess. Traditional LDPC encoding and decoding techniques, such as the belief propagation (BP), Min-Sum, and Sum-Product algorithms, are hampered by high computational complexity and latency. Our research introduces a groundbreaking approach: an efficient, reconfigurable highspeed parallel switching operation for a complexity-optimized low-density parity-check encoding and decoding model (CoLDPC-EC). This method leverages advanced parallel processing and reconfigurable computing to drastically enhance operational speed and efficiency. It significantly outperforms conventional algorithms by optimizing key parameters like decoding throughput and power consumption, ensuring swift, energy-efficient error correction ideal for cutting-edge communication technologies. Our comparison with traditional methods underscores our solution's superior speed, flexibility, and efficiency, promising a leap forward in reliable, highspeed data transmission for next-generation networks. As per the simulation analysis, the proposed system shows better performance compared to conventional methods by 10.35%, 3.56%, and 2.36% in terms of decoding throughput, power consumption, and energy efficiency error correction, respectively.
Dynamic spatio-temporal pattern discovery: a novel grid and density-based clustering algorithm
Authors: Meshram Swati Pramod , Wagh Kishor P
Published: November 2024

Clustering is a robust machine- learning technique for exploration of patterns based on similarity of elements over multidimensional data. Spatio-temporal clustering aims to identify target objects to mine spatial and temporal dimensions for patterns, regularity, and trends. It has been applied in humancentric applications, such as recommendation systems, urban development and planning, clustering of criminal activities, traffic planning, and epidemiology to identify the extent of disease spread. Although the existing research work in the field of clustering relies widely on partition and densitybased methods, no major work has been carried out to handle the spatiotemporal dimension and understand the dynamics of temporal variation and connectivity between clusters. To address this, our paper proposes an algorithm to mine clustering patterns in spatiotemporal dataset using an adaptive, dynamic hybrid technique based on grid and density clustering. We adopt spatio-temporal partitioning of the virtual grid for distribution of data and reducing distance computation and increasing efficiency. Grouping the higher density regions along with neighborhood cluster density attraction rate to merge the clusters. This method has been experimentally evaluated over the Indian earthquake dataset and found to be effective with clustering silhouette index up to 0.93.
Hyperparameter optimization of convolutional neural network using particle swarm optimization for emotion recognition
Authors: Rini Dian Palupi , Sari Tri Kurnia , Sari Winda Kurnia , Yusliani Novi
Published: November 2024

Emotion identification has been widely researched based on facial expressions, voice, and body movements. Several studies on emotion recognition have also been performed using electroencephalography (EEG) signals and the results also show that the technique has a high level of accuracy. EEG signals that detected by standart method using exclusive representations of time and frequency domains presented unefficient results. Some researchers using the convolutional neural network (CNN) method performed EEG signal for emotional recognition and obtained the best results in almost all benchmarks. Although CNN has shown fairly high accuracy, there is still a lot of room for improvement. CNN is sensitive to its hyperparameter value because it has considerable effect on the behavior and efficiency of the CNN architecture. So that the use of optimization algorithms is expected to provide an alternative selection of appropriate hyper parameter values on CNN. Particle swarm optimization (PSO) algorithm is a metaheuristic-based optimization algorithm with many advantages. This PSO algorithm was chosen to optimize the hyperparameter values on CNN. Based on the evaluation results in each model, hybrid CNN-PSO showed better results and achieved the best value in 80:20 split data which is 99.30% accuracy.
Accurate prediction of chronic diseases using deep learning algorithms
Authors: Cordova Ronald S. , Maata Rolou Lyn R. , Jawarneh Malik , Alshar'e Marwan I. , Agustin Oliver C.
Published: November 2024

In this paper, the researchers studied the effects of different activation functions in hidden layers and how they impact the overfitting or underfitting of the model in the multiclass prediction of chronic diseases. This paper also evaluated the effects of varying the number of layers, and hyperparameters and its impact on the accuracy of the model and its generalization capabilities. It was found that exponential linear unit (ELU) does not have a significant advantage over rectified linear unit (ReLU) when used as an activation function in the hidden layer. Additionally, the performance of softmax function, when used in the output layer, is the same as a classic sigmoid output activation function. In terms of the ability of the model to generalize, the researchers achieved a classification accuracy of 100% when the trained model was used to predict unseen data. Through this research, the researchers should be able to assist medical professionals and practitioners in Oman in the validation and diagnosis of chronic diseases in clinics and hospitals.
Optimizing seismic sequence clustering with rapid cube-based spatiotemporal approach
Authors: Hasana Silviya , Sari Wina Permana , Rojali Rojali , Fitrianah Devi
Published: November 2024

Due to their extensive volume and range of features, seismic data is regarded as highly complex data. Earthquakes that typically composed of foreshocks, mainshocks, and aftershocks, exhibit a unique sensitivity to temporal dimension, a characteristic that differs them from other natural hazards. Foreshocks and aftershocks that emanate from a similar epicenter, often display temporal patterns that contribute significantly to determining a sequence. This study introduces a density cube-based approach to cluster spatiotemporal seismic data. It addresses spatial irregularities observed in earthquake clusters and incorporates temporal aspects, acknowledging that seismic events originating from a similar epicenter could occur in separate time frames. We achieved the highest Silhouette score of 0.935 in daily-based clustering and 0.782 in weekly-based clustering. Notably, our analysis reveals a trend where weekly clustering lambda λ tend to be lower (λ=0.01) than in daily clustering (λ=0.1, λ=0.5), thus emphasizing the significance of temporal granularity where daily clustering requires higher λ to capture rapid fluctuations, while weekly clustering benefits from lower λ to cover broader trends. These findings enhance the understanding of the nuanced interplay of temporal dynamics in seismic sequence analysis.
Development of a 2 degree of freedom-proportional integral derivative controller using the hippopotamus algorithm
Authors: Dulyala Rattapon , Sa-ngiamvibool Worawat , Audomsi Sitthisak , Ardhah Kittipong , Buranaaudsawakul Techatat
Published: November 2024

This research project investigates the regulation of autonomous power generation in two interconnected regions using two hydroelectric power plants. It specifically addresses the challenges posed by significant electrical system issues. The hippopotamus optimization algorithm (HOA) has demonstrated enhanced gain value in research and designs of 2 degree of freedom (2DOF)-proportional integral derivative (PID) controllers. The objective is to provide efficient and uninterrupted functioning of the electrical network in both areas. Contemporary technology and methods enable the electrical system to efficiently and accurately fulfill user requirements, resolving any problems related to system balance and stability. This experiment evaluates the efficacy of several algorithms in accurately selecting optimal values. We evaluate performance using the integral of absolute error (IAE) and integral of time-weighted absolute error (ITAE) functions. This experiment evaluates and contrasts different algorithms. Summarizing the analysis using verifiable evidence. Optimization when evaluated using the ITAE measurement, the HOA earned the lowest result of 0.08744 for ITAE. Empirical research has demonstrated that this strategy is the most effective in reducing the ITAE. The sine-cosine algorithm (SCA) and whale optimization algorithm (WOA) have similar ITAE values, with SCA having an error of 0.08967 and WOA having an error of 0.08967. The numerical number is 0.08970.