bluuebunny/crossref_metadata_2025 huggingface dataset:
Appearance of the hindrance to complete fusion in heavy-ion collisions
Authors: Nasirov A. K. , Kayumov B. M. , Ganiev O. K. , Khusanov E. D. , Yusupov A. R.
Published: October 2024

The small cross-section of the synthesis of the superheavy elements (SHEs) is caused by the hindrance to complete fusion and small survival probability of the compound nucleus against fission. The hindrance to complete fusion is related with such peculiarities of the entrance channel as the mutual orientation angles of the axial symmetry of the colliding deformed nuclei, their mass (charge) asymmetry, the shell effects in their microscopic structure and orbital angular momentum. The appearance of the hindrance to complete fusion has been analyzed in the framework of the dinuclear system (DNS) model by the description of the experimental data obtained in the reactions of the SHE synthesis.
Research progress on optical limiting performance of near-infrared laser protective materials
Authors: Zhou Haoran , Wang Chunrui , Du Xinchen , Zhang Yunfeng , Chen Fei , Guo Jin , Chen Xing
Published: October 2024

With the advancement of near-infrared laser technology, there is a significantly increased possibility of exposure of the human eye and optical devices to intense laser conditions. Consequently, research on protection against near-infrared lasers is becoming increasingly prevalent. This review has provided an overview of the recent advancements in the field of nonlinear optical materials, focusing on materials such as graphene, carbon nanotubes, inorganic metal clusters, transition metal sulfides, perovskites and copper niobate. These materials exhibit promising performance in near-infrared optical limiting applications, showcasing good broadband capabilities, but still face urgent issues in terms of structural control, performance regulation, high-purity preparation, and device applications.
Vibration Analysis of Rotating Functionally Graded Refined Shear Deformable Microbeams with Initial Geometric Imperfections
Authors: Jin Songye , Zhang Wuyuan , Li Jialing , Shen Huoming , Hu Biao , Wang Ling , Wang Yuxing
Published: October 2024

This study developed a rigid-flexible coupling vibration model for a rotating functionally graded geometrically imperfect microbeam, integrating refined shear deformation theory (RSDT), the von Kármán nonlinear assumption, and modified couple stress theory. The material properties are described by a power-law distribution along the thickness. Utilizing RSDT, deformation was characterized by axial tensile, chordwise bending, and shear variables. The energy of the microbeam was discretized through assumed modal functions that satisfy cantilever beam boundary conditions, and the governing equations were derived using the Lagrange equation, subsequently solved with the complex modal space method. After validating the model, the effects of shear and geometric imperfections on dimensionless natural frequencies and mode shapes were analyzed, taking into account dynamic stiffening, gradient index, and size effects. Key findings include: (i) shear and imperfections reduced the rotational speed, gradient index, and material length scale required for frequency locus veering; (ii) dynamic stiffening and size effects suppressed the influence of shear and imperfections; (iii) larger imperfections initially increased, then decreased, their impact on chordwise deformation modes as the gradient index increases; (iv) coupling between chordwise and axial motions leads to mode transition with increasing imperfection amplitude. This work provided theoretical guidance for addressing the disorder problem of system parameters in micro-electromechanical systems-based devices.
Limiting behavior of invariant measures of stochastic reaction–diffusion equations on thin domains
Authors: Li Dingshi , Lu Kening , Wang Bixiang
Published: October 2024

This paper deals with the limiting behavior of invariant measures of stochastic reaction–diffusion equations on thin domains. We first show the existence of invariant measures of the stochastic equations in a bounded domain in [Formula: see text] which can be viewed as a perturbation of a bounded domain in [Formula: see text]. We then prove that the set of all invariant measures of the perturbed equations is tight and any limit of invariant measures of the perturbed systems must be an invariant measure of the limiting system when the [Formula: see text]-dimensional thin domains collapses onto an [Formula: see text]-dimensional domain.
Symmetry phenomenon on quasitriangular Hopf algebras and its applications
Authors: Hu Naihong , Liu Gongxiang , Zhou Kun
Published: October 2024

Let [Formula: see text] be a Hopf algebra. The concept of a symmetric universal [Formula: see text]-matrix of [Formula: see text] is introduced (see Definition 2.1). Subsequently, we prove that symmetry phenomenon exists on some well-known quasitriangular Hopf algebras, including some infinite families of small quantum groups. Through an examination of symmetric universal [Formula: see text]-matrices, we present a simple method to determine universal [Formula: see text]-matrices of some Hopf algebras. Furthermore, as part of our applications, we demonstrate that the universal [Formula: see text]-matrices of [Formula: see text] (refer to Sec. 2 for their definition) are symmetric, where [Formula: see text] are families of abelian extensions which include the well-known eight-dimensional Kac algebra. Subsequently, we demonstrate how symmetry can be utilized to significantly simplify the determination of universal [Formula: see text]-matrices for [Formula: see text], ultimately yielding the complete set of universal [Formula: see text]-matrices for this Hopf algebra.
Convergence rates of regularized Huber regression under weak moment conditions
Authors: Tong Hongzhi , Ng Michael
Published: October 2024

Huber regression plays a critical role in robust learning owing to its robustness to outliers and heavy-tailed noises. In this paper, we investigate the theoretical foundation of an Ivanov regularized Huber regression within a Reproducing Kernel Hilbert Space (RKHS) framework. By imposing a weak moment condition on the conditional distribution, we show that Huber regression estimator with the scale parameter adapted to sample size can learn the underlying regression function very well. Specifically, we establish the explicit convergence rates of the prediction error even when the regression function is outside the associated RKHS. Our statistical analysis could greatly improve the understanding of the learning behavior of Huber regression in the absence of light-tailed assumption on the conditional distribution.
Nurse’s Knowledge Sharing in Time of COVID-19 Pandemic: A Systematic Review and Expert Opinion
Authors: Rahmany Kaywan , Karamali Mazyar , Yaghoubi Maryam
Published: October 2024

This study aimed to review the effective factors, tools and methods of knowledge sharing used by nurses during the COVID-19 pandemic. This mix-methods study was conducted in 2023 and included two stages. In the first stage, a systematic review of relevant literature was conducted. In the second stage, a panel of 10 experts justified and appended the findings of the previous stage. The main source of knowledge during the COVID-19 epidemic was social networks followed by official authorities, online training courses, media and colleagues. Different factors affecting knowledge sharing among nurses categorised into process-oriented factors, Human-oriented factors, Technology-oriented factors and organisation-oriented factors. Various known and innovative methods have been used by nurses to share knowledge during the COVID-19 pandemic including utilising social networks, Telementoring, team approaches for new staff and continuous training in line with the latest scientific findings. Nurses used both formal and informal sources to explore evidence supporting evolving clinical practices and social networks were a prominent source of knowledge during the pandemic. By recognising the significance of affecting factors, healthcare organisations can enhance nurses’ ability to effectively share knowledge, ultimately improving patient outcomes and overall healthcare response during similar crises in the future.
A Localized Collocation Method Based on Nonsingular Fundamental Solutions for Steady-State Heat Conduction Analysis in Heterogeneous Materials
Authors: Liu Shuainan , Fu Zhuojia , Liu Qingguo , Zhao Haitao
Published: October 2024

This paper further develops a localized nonsingular method of fundamental solutions (LNMFS) for effectively solving heat conduction problems in functionally graded materials (FGMs). In this numerical framework, the LNMFS approach is proposed as a solution for the inhomogeneous boundary value problems, where a pseudo-spectral Chebyshev collocation scheme (CCS) is employed for the approximation of the corresponding particular solutions. In the proposed LNMFS, the artificial boundary that is present in the existing method of fundamental solutions (MFS) is eliminated. Accordingly, the singularities are replaced by the normalized area integrals of the fundamental solution over small circular disks, which encompass the source nodes that intersect with the collocation nodes. The proposed method inherits the high accuracy of the nonsingular method of fundamental solutions (NMFS). Additionally, in contrast to the NMFS, the LNMFS not only possesses the capability to achieve solutions with a notable level of accuracy in nonharmonic boundary condition but also overcomes the constraint of exclusively simulating heat conduction issues in FGMs that exhibit specific spatial variations. The feasibility and accuracy of the recently developed method are demonstrated by means of comparing it with analytical solutions for several numerical examples.
Exploring Greenium and the Determinants of Green Bond Performance in Asia
Authors: Nurvita Tita , Achsani Noer Azam , Anggraeni Lukytawati , Malahayati Marissa , Novianti Tanti
Published: October 2024

This study examines the presence and determinants of the ‘greenium’ in Asian green bonds. The dataset includes green bonds issued by governments and corporations in Asian countries from 2016 to 2022. To analyse the determinants of the greenium, we applied a coarsened exact matching (CEM) model, an advanced form of the matching method. Ordinary least squares (OLS) regression was employed to assess the factors influencing the greenium. Our findings indicate that a greenium exists in green bonds issued in Asia, with significant effects observed in China, Korea, and Japan. Key drivers of the greenium include coupon rates, issuer type, oil prices, and the federal funds rate. This study contributes practical insights by highlighting that a greenium can be achieved through strategic project selection, transparent communication, and strong environmental commitment. Additionally, investors should account for coupon rates, issuer sector, and macroeconomic factors such as oil prices and the federal funds rate when investing in green bonds.
Measurement and Analysis of Medium to High Frequency Dynamic Stiffness for Rail Fastener Systems
Authors: Zheng Lixin , Yang Muyang , Sheng Xiaozhen , Thompson David
Published: October 2024

Rail fasteners are resilient components that connect the rails to sleepers or track slabs, ensuring the integrity of the track structure. Their dynamic characteristics play a crucial role in the noise and vibration performance of the track. The rail fastening system, as a resilient element (RE), can be described by a [Formula: see text] dynamic stiffness matrix based on the interaction between two rigid bodies and the RE. By applying the principles of momentum and angular momentum to the rigid bodies, an algebraic relationship between the dynamic stiffness of the resilient component and the frequency response functions (FRFs) of the assembly is established. Consequently, the dynamic stiffness of the resilient component can be determined based on the experimentally measured FRFs. Two methods are presented for calculating the dynamic stiffness from the FRFs, called the Complete Method and the Partial Method. The two methods are subsequently applied to the structurally complex DT-III type fastener. To do so, a “rail-like” block and a “sleeper-like” block were purposely designed so that they not only can be fastened by the fastener in the same way as in reality, but can also be treated as rigid bodies for frequencies up to 2000[Formula: see text]Hz. FRFs of the rail-like and sleeper-like blocks were obtained through impact tests, and the dynamic stiffness of the fastener in the frequency range 50–2000[Formula: see text]Hz was obtained using both methods. It turns out that, although the two methods have similarly good repeatability, the Partial Method gives more reliable result; in general the dynamic stiffness of the fastener increases with frequency, but exhibits multiple peaks and dips, indicating distinct modal characteristics of the fastener system. The results also show that there is a significant difference between dynamic stiffness seen by the rail and that by the sleeper.
Modeling and Simulation of Curing Kinetics and Stress of a Bonded Mirror with a Room-Temperature Curable Adhesive
Authors: Zhang Xiumin , Liu Jianhua , Xia Huanxiong , Ao Xiaohui , Zhao Yuxi , Zhou Jiechen , Liu Xin , Wang Yuhe , Yang Wang
Published: October 2024

Adhesive bonding widely appears in optomechanical products. Nevertheless, the occurrence of curing stress often leads to structure deformation, which significantly compromises the performance of optomechanical systems. This work focuses on the formation and evolution of curing stress and its influence on a multi-point face-bonded mirror. The adhesive’s degree of cure (DOC) was measured using the differential scanning calorimetry (DSC) technique. The functional relationship between DOC and glass-transition temperature ([Formula: see text]) was determined utilizing DiBenedetto’s equation. The time–DOC shift factor of the adhesive was obtained by stress relaxation tests on specimens with different DOCs. Subsequently, a viscoelastic constitutive model concerning the curing process and an accurate finite-element (FE) model of the bonded mirror were developed to simulate the surface-figure error (SFE) evolution. The predicted surface figure was compared with experiments, and the model was verified and validated. Further, simulation analyses were conducted on the performance of the multi-point face-bonded mirror, and the influences of adhesive thickness, bonding area, and spot number on the SFE of the mirror were examined.
Smart Cities and Human Development: Analysis Using Fuzzy Similarity Measures
Authors: Mordeson John N. , Malik Davender S. , Mathew Sunil
Published: October 2024

A smart city uses information and technology to provide a better quality of government service and citizen well being. A smart country also considers conditions to measure human development. In this paper, we use fuzzy similarity measures to determine the similarity of the rankings of countries and cities with respect to country human develop, city human development, and smart city.
Cooper–Hirschhorn type identities for three or more squares
Authors: Tang Dazhao
Published: October 2024

Let [Formula: see text] denote the number of representations of [Formula: see text] as a sum of [Formula: see text] squares. Hurwitz presented eleven cases in which the generating function of [Formula: see text] is a simple infinite product. In 2004, Cooper and Hirschhorn proved that the generating functions of some infinite families of arithmetic sequences in [Formula: see text] can be expressed as linear combinations of two given generalized eta-quotients. In this paper, by utilizing some [Formula: see text]-series manipulations, we prove that for any [Formula: see text] and [Formula: see text], the generating functions [Formula: see text], [Formula: see text] and [Formula: see text] can be expressed as linear combinations of certain given generalized eta-quotients. Further, for any [Formula: see text], we conjecture that these phenomena always exist for [Formula: see text].
Advances in Fluorescent Nanoprobes for Live-Cell Super-Resolution Imaging
Authors: Xu Peng , Dong Zexuan , Zhong Simei , Zhang Yu-Hui , Shen Wei
Published: October 2024

The rapid development of super-resolution microscopy has made it possible to observe subcellular structures and dynamic behaviors in living cells with nanoscale spatial resolution, greatly advancing progress in life sciences. As hardware technology continues to evolve, the availability of new fluorescent probes with superior performance is becoming increasingly important. In recent years, fluorescent nanoprobes (FNPs) have emerged as highly promising fluorescent probes for bioimaging due to their high brightness and excellent photostability. This paper focuses on the development and applications of FNPs as probes for live-cell super-resolution imaging. It provides an overview of different super-resolution methods, discusses the performance requirements for FNPs in these methods, and reviews the latest applications of FNPs in the super-resolution imaging of living cells. Finally, it addresses the challenges and future outlook in this field.
Neural-field-based image reconstruction for bioluminescence tomography
Authors: Zhang Xuanxuan , Cao Xu , Zhang Jiulou , Zhang Lin , Zhang Guanglei
Published: October 2024

Deep learning (DL)-based image reconstruction methods have garnered increasing interest in the last few years. Numerous studies demonstrate that DL-based reconstruction methods function admirably in optical tomographic imaging techniques, such as bioluminescence tomography (BLT). Nevertheless, nearly every existing DL-based method utilizes an explicit neural representation for the reconstruction problem, which either consumes much memory space or requires various complicated computations. In this paper, we present a neural field (NF)-based image reconstruction scheme for BLT that uses an implicit neural representation. The proposed NF-based method establishes a transformation between the coordinate of an arbitrary spatial point and the source value of the point with a relatively light-weight multilayer perceptron, which has remarkable computational efficiency. Another simple neural network composed of two fully connected layers and a 1D convolutional layer is used to generate the neural features. Results of simulations and experiments show that the proposed NF-based method has similar performance to the photon density complement network and the two-stage network, while consuming fewer floating point operations with fewer model parameters.
Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis
Authors: Wang Jiarui , Jiang Shiyan , Shi Jiaxin , Wang Jing , Du Shengrong , Huang Zufang
Published: October 2024

Male infertility affects 10–15% of couples globally, with azoospermia — complete absence of sperm — accounting for 15% of cases. Traditional diagnostic methods for azoospermia are subjective and variable. This study presents a novel, noninvasive, and accurate diagnostic method using surface-enhanced Raman spectroscopy (SERS) combined with machine learning to analyze seminal plasma exosomes. Semen samples from healthy controls ([Formula: see text]) and azoospermic patients ([Formula: see text]) were collected, and their exosomal SERS spectra were obtained. Machine learning algorithms were employed to distinguish between the SERS profiles of healthy and azoospermic samples, achieving an impressive sensitivity of 99.61% and a specificity of 99.58%, thereby highlighting significant spectral differences. This integrated SERS and machine learning approach offers a sensitive, label-free, and objective diagnostic tool for early detection and monitoring of azoospermia, potentially enhancing clinical outcomes and patient management.
Catalytic reduction of P-nitrophenol with nickel foam-supported NiO nanosheet array film
Authors: Tuo Qingwen , Zheng Lin , Li Gang
Published: October 2024

Catalyst-assisted chemical reduction of P-nitrophenol (PNP) with NaBH4 is an efficient detoxification method. However, there exist many defects in the use of conventional powdery catalysts. In this work, nickel foam-supported NiO nanosheet array film (NiO@NF), fabricated by the hydrothermal-calcination method, was used as a monolithic catalyst for PNP reduction. A PNP reduction conversion of 91.71% with the reaction rate constant of 0.0995 min[Formula: see text] was obtained within 25 min at 25[Formula: see text]. Moreover, during 13 continuous cycles, the reduction conversion of PNP always stayed above 80%. The results suggest that NiO@NF is a promising non-noble metal monolithic catalyst for PNP reduction owing to its superb catalytic properties, good stability and facile recovery.
Edge-criticality in trees upon outer connected independence
Authors: Fatima Mary M. , Sahul Hamid I.
Published: October 2024

In a graph [Formula: see text](not necessarily connected), an independent set [Formula: see text] is said to be an outer-connected independent set if [Formula: see text], where [Formula: see text] is the number of components in [Formula: see text]. The maximum cardinality of an outer connected independent set is called the outer-connected independence number and is denoted by [Formula: see text]. This concept was introduced in [I. Sahul Hamid, R. Gnanaprahasam and M. Fatima Mary, Outer connected independence in graphs, Discrete Math. Algor. Appl. 7(3) (2015), Article ID: 1550039, 11 pp., doi: 10.1142/S1793830915500391 ], and as a continuation of this work, the effect of [Formula: see text] on edge removal was reported in [M. Fatima Mary, A. Anitha and I. Sahul Hamid, Edge-criticality in unicyclic graphs upon outer connected independence, Adv. Appl. Math. Sci. 21(8) (2022) 4221–4238] and [I. Sahul Hamid and M. Fatima Mary, Criticality of outer connected independence upon edge removal, Electron. Notes Discr. Math. 63 (2017) 361–369]. This paper presents a further study of the critical edges upon [Formula: see text] in trees.
On the eccentricity spectra of chain graphs
Authors: Qiao Yaqiong , Lv Xuezheng
Published: October 2024

Chain graphs play a pivotal role in spectral graph theory and have a wide range of applications across the disciplines of computer science and probability theory. The eccentricity matrix [Formula: see text] of a graph [Formula: see text] is derived from its distance matrix by identifying the maximum value in each row and column, while all other entries are set to zero. In this paper, we explore and elucidate several properties pertaining to the eccentricity eigenvalues of chain graphs.
Fusing human action recognition and object detection for human-robot collaborative assembly
Authors: Liu Yongkui , Wang Sen , Yuan Mingyu , Du Jingli , Zhang Lin , Wang Lihui
Published: October 2024

Human-Robot Collaboration Assembly (HRCA) provides a solution to complex product assembly tasks in modern industrial manufacturing. A key point is how to enable robots to recognize assembly tasks being executed by assemblers through autonomous visual perception technology, and plan collaborative assembly tasks according to perception results in conjunction with assembly task procedures. First, to improve the cognitive ability of a robot in HRCA, an efficient method of assembly task recognition is proposed, which decouples assembly task recognition into two independent tasks: assembly action recognition and assembly object detection. Meanwhile, an integrated neural network model that is able to recognize assembly action while detecting assembly objects is proposed. Assembly task recognition is a prerequisite for a robot to plan and perform assisting tasks. Second, to improve response speeds and grasp success rates of a robot in HRCA, a two-stage object 6-DOF grasp pose detection algorithm is proposed. In the first stage, an object detection algorithm based on RGB images is used to recognize and locate target objects rapidly. In the second stage, the target object region is mapped into 3D point cloud data, which is used to predict stable grasp poses, so as to achieve orderly grasping of target objects. Finally, the effectiveness of the HRCA framework proposed is verified using a planetary gear reducer assembly scenario.
An intelligent simulation result validation method based on graph neural network
Authors: Yang Fan , Ma Ping , Li Wei , Yang Ming
Published: October 2024

With the rapid development of modeling and simulation, there has been a growing concern about the credibility of complex simulations. To validate complex simulation models or systems with dynamic and correlated outputs, an intelligent simulation result validation method based on graph neural network (GNN) is presented. A framework for simulation result validation is proposed, illustrating the process divided into three parts: graph structure modeling for validation data, feature extraction based on graph representation learning (GRL), and bayes factor (BF)-based model’s credibility assessment. A graph structure modeling method is introduced to provide a predefined graph structure for subsequent GRL primarily. Next, the interdependencies and dynamic evolutionary patterns among variables are captured by a Multi-level Feature Extraction-based Graph Representation Learning (MFEGRL) model. The similarity of the graph representations is then compared based on the BF to determine the model’s credibility. Finally, the effectiveness of this method is demonstrated through a case study focusing on validating simulation models about the terminal guidance stage of a flight vehicle.
A Transparency-Based Action Model Implemented in a Robotic Physical Trainer for Improved HRI
Authors: Aharony Naama , Krakovski Maya , Edan Yael
Published: October 2024

Transparency is an important aspect of human–robot interaction (HRI), as it can improve system trust and usability, leading to improved communication and performance. However, most transparency models focus only on the amount of information given to users. In this article, we propose a bidirectional transparency model, termed the transparency-based action (TBA) model, which allows the robot to take actions based on transparency information received from the human (robot-of-human and human-to-robot), in addition to providing transparency information to the human (robot-to-human). To examine the impact of a three-level (High, Medium and Low) TBA model on acceptance and HRI, we first implemented the model on a robotic system trainer in two pilot studies (with students as participants). Based on the results of these studies, the Medium TBA level was not included in the subsequent main experiment, which was conducted with older adults (aged 75–85). In that experiment, two TBA levels were compared: Low (basic information including only robot-to-human transparency) and High (including additional information relating to predicted outcomes with robot-of-human and human-to-robot transparency). The results revealed a statistically significant difference between the two TBA levels of the model in terms of
perceived usefulness
,
ease of use
, and
attitude
. The High TBA level was preferred by users and yielded improved user acceptance.
Leveraging the Hardware Resources to Accelerate cryo-EM Reconstruction of RELION on the New Sunway Supercomputer
Authors: Xu Jingle , Fu Jiayu , Gan Lin , Chen Yaojian , Sun Zhaoqi , Huang Zhenchun , Yang Guangwen
Published: October 2024

The fast development of biomolecular structure determination has enabled the fine-grained study of objects in the micro-world, such as proteins and RNAs. The world is benefited. However, as the computational algorithms are constantly developed, the enrichment of features increases the algorithmic complexity and brings more computationally unfriendly modules. It calls for efficient solutions to leverage the rich and various hardware resources from the world’s most state-of-the-art supercomputing systems, and to fully accelerate the performance of the applications. In this paper, we present our efforts on porting and optimizing the 3D reconstruction of RELION, one of the most popular cryo-EM software for biomolecular structure determinations, by leveraging different resources of the latest generation of Sunway heterogeneous supercomputer. Several novel approaches are proposed to resolve different challenges faced by the complex algorithm, including a multi-level parallel scheme and operator optimizations to smartly map and scale RELION, efficient strategies to largely address the memory bottlenecks and improve data locality, lock-free writing solutions to minimize write-write conflicts, and pipelining approaches to obtain excellent computation and communication overlap. Combining all proposed optimizations, the computation time is greatly reduced to under 2 hours, achieving 11.9 × and 8.9 × speedups on two different datasets. The overall design scales to 131,072 cores, increasing parallel efficiency from 33% to 61% and from 46% to 70%, respectively. To the best of our knowledge, this is the first work that fully optimized and scaled the 3D reconstruction of RELION using the latest Sunway system.
Image Recognition Tools for Blind and Visually Impaired Users: An Emphasis on the Design Considerations
Authors: Fernando Sandra , Ndukwe Chiemela , Virdee Bal , Djemai Ramzi
Published: October 2024

The existing digital image recognition technologies available for blind individuals are commercially accessible but still at an immature stage, necessitating enhancements in their capabilities. Areas requiring improvement in current image recognition tools for the visually impaired encompass information accuracy, information adequacy, appropriateness of information for blind individuals, functional sufficiency and ergonomic suitability. This research endeavours to explore technology employing an inclusive approach to overcome limitations inherent in current digital image recognition technologies for the visually impaired. To streamline the research process, the initial phase involves a critical evaluation of deficiencies present in existing image recognition technologies for blind/visually impaired users through primary and secondary investigations. This strategic evaluation aims to identify key deficiencies, guiding assistive technology developers in focusing their efforts. Simultaneously, a survey is conducted to establish a comprehensive checklist of usability features and requirements for the proposed technology, aligning with the ISO 9241-110 standard. The overarching goal of this research is to address the question, ‘What design considerations should be taken into account in designing image recognition technology for blind and visually impaired people?’ The research outcomes are intended to establish a standard for designers of digital products catering to blind/visually impaired users, fostering improved awareness and shaping attitudes toward individuals with visual disabilities in the development of image recognition software.
An Investigation of Multimodal Kinematic Template Matching for Ray Pointing Prediction for Target Selection in VR
Authors: Giordano Marcello , Grossman Tovi , Gupta Aakar , Clarke Daniel , Henrikson Rorik , Trowbridge Sean , Santosa Stephanie , Glueck Michael , Jonker Tanya , Benko Hrvoje , Wigdor Daniel
Published: October 2024

We explore the use of multimodal input to predict the landing position of a ray pointer while selecting targets in a virtual reality (VR) environment. We first extend a prior 2D Kinematic Template Matching technique to include head movements. This new technique, Head-Coupled Kinematic Template Matching, was found to improve upon the existing 2D approach, with an angular error of 10.0° when a user was 40% of the way through their movement. We then investigate two additional models that incorporated eye gaze, which were both found to further improve the predicted landing positions. The first model, Gaze-Coupled Kinematic Template Matching resulted in angular error of 6.8° for reciprocal target layouts and 9.1° for random target layouts, when a user was 40% of the way through their movement. The second model, Hybrid Kinematic Template Matching, resulted in angular error of 5.2° for reciprocal target layouts and 7.2° for random target layouts when a user was 40% of the way through their movement. We also found that using just the current gaze location resulted in sufficient predictions in many conditions. We reflect on our results by discussing the broader implications of utilizing multimodal input to inform selection predictions in VR.
Construction of consistent SysML models applied to the CPS
Authors: Khelifati Adel , Boukala-Ioualalen Malika , Hammad Ahmed
Published: October 2024

With the increasing complexity of cyber-physical systems (CPS), it is interesting to decompose a CPS into sub-systems. This provides greater modularity and flexibility so that each system can be developed independently, making it easier to maintain. Also, it can improve its fault tolerance. However, this decomposition of the system can lead to inconsistency. This paper proposes an approach for early verification of cyber-physical systems decomposition using SysML. We address the limitations of SysML as a semi-formal language by introducing syntax and static semantics for its structural diagrams. The aim is to verify structural consistency before defining behavioral aspects. For that, the proposed approach verifies a set of structural consistency rules within a refinement relation to ensure that sub-components offer at least the same services as the abstract block and require the same services. Furthermore, the sub-blocks must satisfy all the requirements that the abstract block is supposed to verify. We used the CyCab as a case study to demonstrate the effectiveness of this approach.
Terra incognita of the uncontrolled manifold
Authors: Latash Mark L.
Published: October 2024

The review addresses the central concept of the uncontrolled manifold (UCM) hypothesis, which has become a major framework for analysis of performance-stabilizing motor synergies. The major goals are to summarize the status quo in the field and to ask new questions stimulating new studies. We focus on a few main questions: What is the UCM? What are the likely neural origins of the UCM? How is the UCM reflected in movement patterns? Are properties of the UCM similar in all directions? We contrast experience-based features of movements seen very soon after the movement initiation versus those based on on-line sensory feedback signals. Furthermore, we address a number of poorly explored issues such as the differences in characteristic times of processes within the UCM and orthogonal to the UCM space, the interplay between movement stability and optimality, the origin of preferred sharing patterns of performance variables across abundant sets of elements and of their intertrial variability, problems with the UCM-based analysis in different spaces, and likely neurophysiological mechanisms contributing to the UCM formation. In particular, we focus on the UCM in spaces of hypothetical neural control variables, which we associate with the reciprocal and coactivation commands to the effectors. Analysis of performance-stabilizing synergies within the UCM framework in abundant spaces of kinetic, kinematic and electromyographic variables at the selected level of analysis may be practically useful. However, mapping findings in such studies onto neural control mechanisms has been challenging.
Textural Characteristics and Depositional Regime of the Shitalakshya River Sediments, Bangladesh
Authors: Ali Sohag , Roy Mrinal Kanti , Islam Md. Mahmodul , Rahman Zillur , Ahmed Faruk , Alim Abdul , Arafath Md. Yeasin
Published: October 2024

This study analyzes the depositional regime and textural properties of the sediments from the Shitalakshya River in Bangladesh, enabling us to comprehend how these sediments evolved in a river environment. For this investigation, 30 representative samples were taken from the Shitalakshya River, and their textures were analyzed. The cumulative frequency curve is obtained by using semilog graph paper to plot particle size (in phi scale) against cumulative percent. The statistical parameters such as median (Md), mode (Mo), mean (MZ), standard deviation (σi), skewness (SKi), and kurtosis (KG) were calculated using the percentile of phi values (1%, 5%, 16%, 25%, 50%, 75%, 84%, and 95%). The cumulative curves show that the sediments are deposited through the traction population (1.90%), saltation population (75.64%), and suspension population (22.46%). The median value varies between 0.7Ø and 4.85Ø, with an average of 2.738Ø indicating coarse sand to coarse silt. The analyzed samples have unimodal, bimodal, and polymodal distribution, which indicates the sediments are carried by different tributaries and distributaries in the Sitalakshya River system. The range of 1.46Ø–4.05Ø represented by the observed mean value suggests sand with medium to extremely fine grains. Most of the sediments indicate moderate sorting, which is shown by the standard deviation (sorting), which ranges from 0.399Ø to 1.48Ø. The skewness value ranges between −0.01Ø and 0.66Ø, suggesting near symmetrical skewed, while the kurtosis value ranges from 0.54Ø to 1.87Ø, indicating the sediments are 20% leptokurtic, 20% mesokurtic, and 60% platykurtic. According to the CM plot, the Shitalakshya River is mostly deposited in the area between the rolling and suspension fields, indicating a transportation regime of saltation. The scatter plots of skewness versus sorting and graphic mean versus sorting indicate that the sediments fall within the river sand zone. The energy process discriminant functions of the sediments show that they were deposited by a fluvial process.
Metaheuristic Algorithm‐Based Optimal Energy Operation Scheduling and Energy System Sizing Scheme for PV‐ESS Integrated Systems in South Korea
Authors: Park Sungwoo , Oh Jinyeong , Hwang Eenjun
Published: October 2024

To efficiently utilize the power generated by a photovoltaic (PV) system, integrating it with an energy storage system (ESS) is essential. Furthermore, maximizing the economic benefits of such PV‐ESS integrated systems requires selecting the optimal capacity and performing optimal energy operation scheduling. Although many studies rely on rule‐based energy operation scheduling, these methods prove inadequate for complex real‐world scenarios. Moreover, they often focus solely on determining the ESS capacity to integrate into existing PV systems, thereby limiting the possibility of achieving optimal economic benefits. To address this issue, we propose an optimal energy operation scheduling and system sizing scheme for a PV‐ESS integrated system based on metaheuristic algorithms. The proposed scheme employs a zero‐shot PV power forecasting model to estimate the potential power generation from a planned PV system. A systematic analysis of the installation, operation, and maintenance costs is then incorporated into the economic analysis. We conducted extensive experiments for comparing economic benefits of various scheduling methods and capacities using real electrical load data collected from a private university in South Korea and estimated PV power data. According to the results, the most effective metaheuristic algorithm for scheduling is simulated annealing (SA). Additionally, the optimal PV system, battery, and power conversion system capacities for the university are 13,000 kW each, 10% of the PV system capacity, and 60% of the battery capacity, respectively. The estimated annual electricity tariff calculated from the data used in the experiment is $3,315,484. In contrast, SA‐based scheduling in the optimal PV‐ESS integrated system achieved annual economic benefits of $875,000, an improvement of approximately 7% over rule‐based scheduling of $817,730.
Contemporary Surgical Approaches in Pediatric Aortic Valve Surgery: A Retrospective Comparison of Three Techniques
Authors: Komarov R. N. , Shumakov D. V. , Chernov I. I. , Belov V. A. , Chragyan V. A. , Ismailbaev A. M. , Tlisov B. M. , Enginoev S. T. , Kadyraliev B. K. , Didyk V. P. , Tarayan M. V. , Kurasov N. O. , Abzalova D. M. , Richmond Owusu
Published: October 2024

Background: Congenital aortic valve (AV) disease is viewed in 2% of the population and in 20 out of 1000 neonates are presented with bicuspid configuration. Treatment of the congenital AV diseases requires multiple interventions. The ultimate goal is to provide adequate close to physiological left ventricle outflow and minimization of regurgitation. The optimal operative management of AV disease in children and young adults remains controversial. AV neocuspidization with glutaraldehyde‐treated autologous pericardium may be a satisfactory alternative in pediatric cardiac surgery.

Objectives: The data of the AVNeo procedure results enable a comparative analysis of various AV reconstructive approaches in the pediatric cohort. In this article, we present the comparative study of various techniques in pediatric AV surgery, including the immediate and midterm outcomes of AVNeo, commissurotomy with aortic leaflet augmentation using pericardial patch (“free style” technique), and Ross procedure.

Study Design: We conducted a retrospective analysis of the early and midterm results of surgical AV disease treatment of 44 children in 5 cardiac surgery centers from 2014 to 2022. The patients were divided into 3 groups: group I: glutaraldehyde‐treated autologous pericardium AVNeo (n = 12), group II: “free style” technique (n = 10), and group III: Ross procedure (n = 22). We described the immediate and midterm outcomes. The study design was approved by the ethics committees of the participating centers.

Results: A total of 44 children after surgical treatment of the AV disease in 5 cardiac surgery centers were included in the study and were grouped as group I: AVNeo (n = 12), group II: “free style” technique (n = 10), and group III: Ross procedure (n = 22). The median follow‐up period was 57 ± 23.8 months. In‐hospital mortality was 4.5% (n = 2). In the midterm follow‐up, the peak pressure gradient was 18.45 ± 4.63 mmHg for group I and 8.9 ± 1.6 mmHg for group III (p = 0.00001), respectively. The cumulative freedom from reoperations was 100%, 30%, and 95.2% for 40 months of follow‐up and 100%, 30%, and 83.4% for 70 months of follow‐up in I, II, and III groups, respectively. No cases of midterm mortality were detected in all groups.

Conclusion: The AV neocuspidization with glutaraldehyde‐treated autologous pericardium has an enormous advantage in pediatric AV surgery, significantly surpassing the freedom from resurgery of repair procedures and having comparable results with the Ross procedure in the midterm follow‐up.
Comparative Study of Algorithms for the Conversion From Surface Mesh Models to Volume Mesh Models
Authors: Wang Youxi , Pang Qi , Li Meng , Liu Yang
Published: October 2024

The study delves into the performance of different algorithms in the process of converting surface mesh models (SMMs) into volume mesh models (VMMs) required for finite element analysis. The experiment compared the Initial mesh, Delaunay, Frontal, and HXT algorithms, analyzing their central processing unit (CPU) runtime, node count, element count, and model size when processing simple models under default control parameters. The results showed that the Frontal algorithm performed the best in the conversion of simple models with a CPU runtime of 0.001 s, while the Delaunay algorithm took relatively longer. For complex models, the Initial mesh, Delaunay, and Frontal algorithms were all successful in completing the conversion, but the HXT algorithm failed, highlighting the importance of algorithm selection. The Delaunay algorithm performed best in terms of efficiency and model size, while the Frontal algorithm had an advantage in model refinement. Although the Initial mesh algorithm had a more balanced number of nodes and elements, its longer CPU runtime indicates potential for efficiency improvement. In addition, the randomness of the HXT algorithm in mesh generation led to variations in results, which requires further optimization to improve the consistency and robustness of the algorithm. Future research will focus on algorithm optimization, particularly enhancing the HXT algorithm’s ability to handle complex models, developing automated parameter selection mechanisms, exploring the application of algorithms in other engineering fields, and achieving better integration of algorithms with computer‐aided design (CAD)/computer‐aided engineering (CAE) workflows.
Investigation of the Mechanism of Hidden Defects in Epoxy Asphalt Pavement on Steel Bridge Decks Under Moisture Diffusion Using Nondestructive Detection Techniques
Authors: Nie Wen , Wang Duanyi , Yan Junjian , Zhang Xiaoning
Published: October 2024

This study conducts a rigorous analysis of the moisture diffusion mechanism that undermines the adhesive layer of epoxy asphalt (EA) pavement on steel bridge decks, thereby fostering latent distresses. Furthermore, a novel and highly efficacious approach for detecting these concealed distresses is introduced. The findings of water vapor permeability tests conclusively reveal that the moisture diffusion coefficients of the upper and lower layers of the EA pavement stand at 0.1238 mm2/s and 0.0879 mm2/s, respectively, highlighting this disparity as the primary trigger for concealed issues like pavement delamination and swelling. Leveraging the combined capabilities of three‐dimensional ground‐penetrating radar (3D‐GPR) and infrared thermography, this research reliably detects, identifies, and pinpoints concealed defects at three strategic locations on the steel bridge deck. The integration of these two technologies has exhibited remarkable proficiency in identifying concealed damages. Consequently, this study lays a substantial foundation for evaluating and detecting concealed distress in EA pavements atop steel bridge decks.
Analysis of the HBV and TB Coinfection Model With Optimal Control Strategies and Cost‐Effectiveness
Authors: Teklu Shewafera Wondimagegnhu , Abebaw Yohannes Fissha
Published: October 2024

The coinfection of HBV and TB diseases affects millions of individuals throughout the nations in the world. The main objective of this study is to formulate and analyze the HBV and TB coinfection dynamical system, investigate the impact of time‐dependent optimal control measures and cost‐effectiveness analysis, and tackle the coinfection transmission dynamics in the population. Each of the submodels and the coinfection model’s qualitative analyses were carried out independently, whereas the TB‐only submodel and the coinfection model reveal the phenomenon of backward bifurcation. The coinfection model’s optimal control problem with five time‐dependent optimal control measures is formulated and analyzed. Using the parameter values described in the paper, we carried out numerical simulations and verified the models’ qualitative analysis results. To minimize invectives and the cost of the implementing effort toward the protection and the treatment, optimal control analysis is performed for the HBV and TB coinfection model using Pontryagin’s minimum principle. Numerical simulations with different combinations of efforts are then carried out to explore the effect of protection in the presence of treatment for both diseases. Numerical simulations emphasize the fact that to reduce coinfection from the population, programs to accelerate the protection of both diseases are also required along with the treatment. The results reveal that the implementation of the combination of protective and treatment‐optimal control strategies suppresses the occurrence of HBV and TB coinfection, and protective control measures are more effective than treatment control measures for individuals who are coinfected with HBV and TB diseases. Implementing all the proposed protective and treatment control measures significantly minimized the transmission dynamics of HBV and TB confection in the community. From the cost‐effectiveness analysis of the proposed time‐dependent control strategies, treatment of HBV is the most cost‐effective control strategy required to tackle the spread of the HBV and TB coinfection in the community.