bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Education and Teaching of Physical Education Dance Courses in Colleges and Universities Based on Deep Learning
Authors: Liu Yang , Yang Gaoming , Feng Xuwen
Published: December 2024

Teaching reform is usually a unified adjustment based on the current teaching objectives, lacking pertinence. In order to improve the teaching quality and students’ learning quality of sports dance course in colleges and universities, according to the teaching quality evaluation data, this paper proposes the teaching method of sports dance course in colleges and universities based on in-depth learning. According to the sports dance action image after the visual error correction, the OpenPose human posture estimation algorithm is used to implement the dancer’s motion posture estimation, obtain the sequence data of the dancer’s skeleton joint points, and use the depth convolution neural network optimized by particle swarm optimization algorithm to build the sports dance action recognition model, with the obtained sequence data of the dancer’s skeleton joint points as the effective input. The result of sports dance movement recognition is obtained through training, which is used as the basis for students to correct their dance movements and improve their professional skills. According to the teaching process organized by teachers and the goal of cultivating students’ professional skills, a sports dance teaching evaluation index system is constructed, and the deep convolution neural network optimized by particle swarm optimization algorithm is used to build a sports dance teaching evaluation model. With the quantified data of the secondary indicators in the teaching evaluation index system as the model input, the sports dance teaching evaluation results are obtained through training. The findings will serve as a dependable foundation for educators to enhance the quality of instruction and assist students in enhancing their proficiency in sports dance. The experimental outcomes demonstrate that this approach is adept at identifying sports dance movements effectively and precisely evaluating sports dance classes, thereby establishing a solid footing for substantially boosting the instructional impact of sports dance courses in higher education institutions.
Neural Networks and English Learning: Design and Implementation of Personalized Learning Paths
Authors: Zhao Pengxiang , Song Yumeng
Published: December 2024

An instructional approach identified as a personalized learning path aims to adjust regulations to the particular interests’ necessities, talents, and skills of each pupil. Research on personalized path recommendation is critical for the development of sophisticated learning systems. The challenge emerges from the difficulty in developing personalized learning paths that properly fit every pupil’s unique characteristics and learning needs. This paper examines personalized educational routes and how deep learning (DL) algorithms can be used to construct them, with a particular emphasis on English language learning. This research proposed a novel fruit fly optimizer-tuned adjustable recurrent neural network (FFO-ARNN) to employ the students learning performance and effectively personalized English learning. For this study, gathering data on student’s demographics and English proficiency levels through assessments and educational records, in that Data on the linguistic competency of English linguistic Learners (ELLs) in grades 8–12 was collected. The data were pre-processed using tokenization for the obtained data. Term Frequency-Inverse Document Frequency (TF-IDF) is a method using extracts the feature from pre-processed data. These personalized learning approaches encourage self-directed learning and independence while increasing student engagement and academic achievement. The proposed method is implemented using Python software. In comparative analysis, the proposed method evaluates various performance metrics. The results show that the proposed method achieved superior performance in personalized learning paths in English learning. This study demonstrates how DL has the potential to revolutionize language instruction by providing customized solutions for every student.
Application of Interactive Evolutionary Computing in Personalized Interior Design
Authors: Dai Bifeng
Published: December 2024

Design is a complex and clever process with multiple concepts attempting to explain it. To validate ideas and quantitatively characterize the design process, design experiments, and data analysis are required and predictable. Applying data analysis in design experiments can be challenging, and data are often underutilized. In this study, we develop an experimentation Interactive Evolutionary Computing Design (IECD) model using Upgraded Particle Swarm Optimization (UPSO) within the personalized interior design to iteratively analyze the new color combinations based on 116 designer’s preferences. The study proposed data-infused research into interior design via experimental techniques and the proposal system is evaluated, with additional feedback collected from experimented 116 designers, and data visualization analysis using Color Histogram Analysis and Auto encoders for data’s structure and patterns of the data, thus enhancing user preferences. A proposal system is developed using the collected data to suggest design options that align with individual user preferences and then integrated into the IECD framework to generate personalized design solutions that are more efficient and intelligent. The results are the effectiveness of data analysis and visualization in revealing design patterns and the impact of the data-infused IECD system to enhance the personalization and efficiency of interior design solutions. The IECD system enhances interior design efficiency and personalization through a proposal system, revealing design patterns, and aligning options with individual user preferences.
Multimodal Learning Data Analysis and Algorithmic Teaching Effectiveness Evaluation Model Construction
Authors: Tu Lihui , Hong Huanjie
Published: December 2024

Introduction:A combination of internal characteristics, such as motivation, personality, beliefs, and dispositions that combine with external circumstances to affect student results is referred to as teaching efficacy. The English-speaking environment becomes a major problem as students learn by interaction. To communicate effectively, they do not acquire the speaking and listening abilities necessary in English.

Objective:This study examines the impact of computer programming courses and learning analytics on student’s computer programming skills.

Methods:In this study, we proposed a novel Dung Beetle Optimized Flexible Random Forest (DBO-FRF) to evaluate the teaching effectiveness. In this study, 175 students’ data were collected for teaching effectiveness. Using student data, prediction model was constructed based on the attributes of the students, their past academic records, their interactions with online resources, and their advancement in laboratory work related to programming. The proposed method is compared to other traditional algorithms.

Results and conclusion:The proposed method is implemented using Python software. The expected performance in the course and, in the instance that any submitted programs failed to meet the requirements, a programming recommendation from one of the class’s top students. The result shows the proposed method achieved better performance in terms of accuracy, precision, recall, and [Formula: see text]1-score. This decreased the performance gap between students who performed lower and those who performed higher, enabling students who adjusted their programs to learn more.
Deep Learning and Market Trends: Building Future Economic Forecasting Models
Authors: Pang MaoQian
Published: December 2024

Economic market forecasting involves predicting future market conditions based on various data. By analyzing the market trends, organizations can make informed decisions, and strategize for potential economic shifts. Accurate forecasting is crucial for minimizing risks and maximizing opportunities. In this research, we aim to develop an innovative future economic forecasting model through a deep learning (DL) algorithm. For this, we propose a novel Drosophila Food Search-drivenGate Adjusted Long Short-Term Memory[Formula: see text]DFS-GA-LSTM[Formula: see text] for analyzing the market trends and providing extensive forecasting results of future economics. We obtained a dataset comprising historical financial market data and economic indicators from various media sources. It includes stock prices and economic growth metrics to train and evaluate the proposed model. Data normalization is utilized to pre-process the gathered raw information. Kernel principle component analysis (kernel-PCA) is utilized to remove the crucial features from the processed data. In our proposed model, the DFS optimization algorithm iteratively fine-tunes the GA-LSTM architecture to enhance forecasting accuracy. The established model is implemented using Python software. During the outcome analysis phase, we evaluate our model’s performance across several parameters like RMS (0.19), RMSE (0.009), MAE (0.00011), MSE 0.003, and MAPE (26.7%). In addition, we conducted comparative analyses using diverse existing methodologies. The result reveals the superiority and efficiency of the suggested future economic forecasting model.
Influences of Media-Induced Awareness and Sanitation Practices on Cholera Epidemic: A Study of Bifurcation and Optimal Control
Authors: Pal Kalyan Kumar , Rai Rajanish Kumar , Tiwari Pankaj Kumar
Published: December 2024

Media has a notable impact on reducing disease prevalence, while sanitation measures and heightened awareness can effectively control epidemics by diminishing bacterial growth rates and limiting direct contact with infected individuals. In this study, we propose and analyze an epidemic model to explore how media and sanitation practices influence the dynamics of diseases transmitted through direct contact between susceptible and infected individuals, as well as via bacteria in the environment. Our study entails a combined approach involving both analytical and numerical analyses of the system. We observe that the disease-free and endemic equilibria of the system are interconnected through a forward transcritical bifurcation. We estimate the most important model parameter using authentic cholera data from Sudan for calibration. Our numerical findings suggest that regulating disease transmission through direct contact and environmental bacteria can significantly decrease disease prevalence. Additionally, we note that the growth rate of social media advertisements, along with efforts made by government officials and informed individuals to eliminate bacteria through sanitation coverage, introduces destabilizing effects. However, system stability is reestablished when the baseline number of social media advertisements exceeds a specific threshold. The dissemination of awareness among susceptible individuals, as well as the rate of transfer of informed people to the susceptible class, initially leads to destabilization but eventually stabilizes the system. Disease eradication becomes feasible when the rate of transfer of informed individuals to the susceptible class is very low. Moreover, higher initial values for awareness programs and the dissemination rate of awareness could also eliminate the disease from society. Furthermore, we see that increasing the treatment rate of infectives plays a significant role in achieving disease eradication. Moreover, we investigate an optimal control problem that integrates sanitation interventions and awareness protocols.
Global Analysis of a Duopoly Game with Privatization of State-Owned Enterprises
Authors: Yue Xiao-Le , Zhu Yi-Fan , Ai Zhi-Chao , Xu Yong
Published: December 2024

This paper constructs a nonlinear dynamical model of a mixed ownership market duopoly game for environmental tax collection within the framework of bounded rationality expectations. A stability analysis of the proposed model is conducted, and a Nash equilibrium stability region is estimated using the Jury criterion. The effects of the degree of privatization and environmental tax rate on the stability are investigated through numerical simulations. Subsequently, the comprehensive global analysis is carried out using the composite cell coordinate system method. Two types of crisis phenomena, namely, interior and boundary crises, have been identified. The former is caused by the collision between chaotic or periodic attractors and interior chaotic saddle within the basin of attraction, and the latter is caused by the collision between chaotic attractor and periodic saddle on the basin boundary. Meanwhile, the global dynamic behavior of two private enterprises engaged in synchronized output adjustment is investigated. When the output adjustment speed varies, the structure of the basin of attraction undergoes a transformation as changes are made to the number of coexisting attractors. The sudden disappearance of one attractor is due to the collision of the chaotic attractor with the saddle, on the basin boundary, merging into a larger saddle and resulting in a boundary crisis. The emergence of numerous “holes” can also be observed. It is found that the presence of periodic or chaotic saddles enriches the dynamical phenomena. Finally, the chaotic phenomenon occurring in the mixed ownership market is controlled using the time-delay feedback control method, stabilizing the system in a stable state, which implies a steady-state of total market output. The study of this model can provide theoretical guidance for the decision-making process of enterprises, helping minimize profit losses while stabilizing the market.
Analyzing Hippocampal Neural Dynamics Under Acetylcholine Deficiency Related to Alzheimer’s Disease
Authors: Yang Hao , Yang XiaoLi , Li YuPeng
Published: December 2024

Acetylcholine, as an important neurotransmitter, regulates neural excitability and discharge property distinctly, whose deficiency in the hippocampus can deteriorate cognitive and memory functions in Alzheimer’s Disease (AD). By establishing a hippocampal small-world network of pyramidal neurons and interneurons, this work aims to explore how acetylcholine deficiency influences the hippocampal neural dynamics from the perspective of neurocomputation. Based on some experimental results that acetylcholine can inhibit several [Formula: see text] channels in the pyramidal neuron via muscarinic receptors, we simulate acetylcholine deficiency by increasing the conductance [Formula: see text] of M-type (or [Formula: see text] of A-type) [Formula: see text] channel. The numerical results indicate that the enhancement of [Formula: see text] or [Formula: see text] can affect the pyramidal neuron dynamics in the individual node and network levels significantly. For the individual pyramidal neuron, its excitability and mean firing rate gradually decrease upon increasing [Formula: see text] or [Formula: see text], in particular, the neuron transitions from a firing state to a resting state when [Formula: see text] or [Formula: see text] increases over a threshold. Through dynamic bifurcation analysis, we find that the underlying dynamical mechanism behind these changes is attributed to sub-critical Andronov–Hopf or saddle-node bifurcations induced by acetylcholine deficiency. For pyramidal neurons in the network scale, acetylcholine deficiency can shift the network encoding mode from collective regular firing to bursting, and more importantly, the averaged mean firing rate and sample entropy of the network decrease significantly with severe acetylcholine deficiency. These results are consistent with previous studies reported in the electrophysiological experiments of AD. This work may help to deeply understand the AD pathogenesis of cholinergic hypothesis.
PREDICTIVE MODELING OF REAL-TIME COLORECTAL CANCER VIA HYPERPARAMETER CONFIGURATION WITH DEEP LEARNING USING PUBLIC HEALTH INDICATOR ANALYSIS
Authors: ALSAMRI JAMAL , QUIAM FATIMA , MAASHI MASHAEL , ALASHJAEE ABDULLAH M. , ALAMGEER MOHAMMAD , SALAMA AHMED S.
Published: December 2024

Public health indicator analysis plays a crucial part in colorectal cancer (CRC) recognition by leveraging epidemiological data and health metrics to recognize trends, disparities, and risk factors in diagnosis and screening. Public health officials use screening, incidence, and mortality rates to analyze current screening programs, target interventions for underserved populations, and improve early recognition and treatment results. With growing case numbers, CRC ranks second in women and third in men. The growing diagnostic workload and variability in biomarker assessment have prompted the exploration of robust machine-based models for pathology labs. Diagnosing CRC from histopathological images involves analyzing tissue samples for cancerous cellular changes. These images offer elaborate insights into tissue microstructure, aiding pathologists in examining cellular characteristics such as size, shape, and organization. Artificial Intelligence (AI) and fractals algorithms have proven advanced in the healthcare field, having the inherent ability for medical applications. The incorporation of AI in CRC diagnosis aims to improve the efficiency of pathologists, reduce diagnostic errors, and ultimately contribute to better patient outcomes through early and accurate detection of cancerous lesions. The study proposes a Golden Jackal Fractal Optimization with Deep Learning Assisted CRC Detection (GJODL-CRCD) technique on HIs. The GJODL-CRCD technique mainly examines the HIs to recognize and identify the CRC. The GJODL-CRCD method applies the Gabor filtering (GF) approach as a noise removal procedure to achieve this. Furthermore, the GJODL-CRCD technique employs the DenseNet model for learning and capturing intrinsic feature patterns from the preprocessed imageries. For hyperparameter selection of the DenseNet model, the GJODL-CRCD approach uses the GJO fractal algorithm. Finally, the GJODL-CRCD method uses the Elman Neural Network (ENN) model to identify and classify CRC. The performance evaluation of the GJODL-CRCD technique takes place on benchmark medical image data. The experimental results pointed out the betterment of the GJODL-CRCD model over other methods.
FORTIFYING ANDROID SECURITY: HYPERPARAMETER TUNED DEEP LEARNING APPROACH FOR ROBUST SOFTWARE VULNERABILITY DETECTION
Authors: ALZABEN NADA , ALASHJAEE ABDULLAH M. , MARAY MOHAMMED , ALOTAIBI SHOAYEE DLAIM , ALHARBI ABEER A. K. , SAYED AHMED
Published: December 2024

Detecting software vulnerabilities is a vital component of cybersecurity, concentrating on identifying and remedying weaknesses or flaws in software that malicious actors could exploit. Improving Android security includes using robust software vulnerability detection processes to identify and mitigate possible threats. Leveraging advanced methods like dynamic and static analysis and machine learning (ML) approaches with fractals theories these models early scan Android apps for vulnerabilities. Effectual software vulnerability detection is critical to mitigate safety risks, security systems, and data from cyber-attacks. Android malware detection employing deep learning (DL) supports the control of neural networks (NNs) for identifying and mitigating malicious apps targeting the Android and Complex Systems platforms. DL approaches, namely recurrent neural networks (RNNs) and convolutional neural networks (CNNs) can be trained on massive datasets encompassing benign and malicious samples. This study develops a Hyperparameter Tuned Deep Learning Approach for Robust Software Vulnerability Detection (HPTDLA-RSVD) technique. The primary aim of the HPTDLA-RSVD technique is to ensure Android malware security using an optimal DL model. In the HPTDLA-RSVD technique, the min–max normalization method is applied to scale the input data into a uniform format. In addition, the HPTDLA-RSVD methodology employs ant lion fractal optimizer (ALO)-based feature selection (FS) named ALO-FS methodology for choosing better feature sets. Besides, the HPTDLA-RSVD technique uses a deep belief network (DBN) model for vulnerability detection and classification. Moreover, the slime mould algorithm (SMA) has been executed to boost the hyperparameter tuning process of the DBN approach. The experimental value of the HPTDLA-RSVD approach can be examined by deploying a benchmark database. The simulation outcomes implied that the HPTDLA-RSVD approach performs better than existing approaches with respect to distinct measures.
SECURING INTERNET OF THINGS-ASSISTED CONSUMER ELECTRONICS USING BLOCKCHAIN WITH DEEP LEARNING-BASED USER AUTHENTICATION
Authors: ALAMRO HAYAM , NEMRI NADHEM , ALJEBREEN MOHAMMED , ALRSLANI FAHEED A. F. , ALSHUHAIL ASMA , SALAMA AHMED S.
Published: December 2024

Internet of Things (IoT)-assisted consumer electronics refer to common devices that are improved with IoT technology, allowing them to attach to the internet and convey with other devices. These smart devices contain smart home systems, smartphones, wearables, and appliances, which can be monitored remotely, gather, and share data, and deliver advanced functionalities like monitoring, automation, and real-time upgrades. Safety in IoT-assisted consumer electronics signifies a cutting-edge technique to improve device safety and user authentication. Iris recognition (IR) is a biometric authentication technique that employs the exclusive patterns of the iris (the colored part of the eye that surrounds the pupil) to recognize individuals. This method has gained high popularity owing to the uniqueness and stability of iris patterns in finance, healthcare, industries, complex systems, and government applications. With no dual irises being equal and small changes through an individual’s lifetime, IR is considered to be more trustworthy and less susceptible to exterior factors than other biometric detection models. Different classical machine learning (ML)-based IR techniques, the deep learning (DL) approach could not depend on feature engineering and claims outstanding performance. In this paper, we propose an enhanced IR using the Remora fractals optimization algorithm with deep learning (EIR-ROADL) technique for biometric authentication. The main intention of the EIR-ROADL model is to project a hyperparameter-tuned DL technique for automated and accurate IR. For securing consumer electronics, blockchain (BC) technology can be used. In the EIR-ROADL technique, the EIR-ROADL approach uses the Inception v3 method for the feature extraction procedures and its hyperparameter selection process takes place using ROA. For the detection and classification of iris images, the EIR-ROADL technique applies the variational autoencoder (VAE) model. The experimental assessment of the EIR-ROADL algorithm can be executed on benchmark iris datasets. The experimentation outcomes indicated better IR outcomes of the EIR-ROADL methodology with other current approaches and ensured better biometric authentication results.
An Enhanced Time Delay Smith Predictor of Sliding Mode Structural Vibration Controller Based on Extended State Observer
Authors: Zhai Juan , Li Shengquan , Li Bohan , Kawaguchi Takahiro , Hashimoto Seiji
Published: December 2024

Considering the total disturbances such as system time delay, model uncertainty, actuator effect and dynamic nonlinearities, a composite control strategy is proposed to suppress vibrations in an all-clamped plate structure, which combines sliding mode control based on extended state observer with time delay compensation (TDCSMCESO). First, a second-order state-space representation of the all-clamped plate, equipped with an inertial actuator, is formulated through the analysis of the dynamic partial differential equation and the physical model of the thin plate. Second, due to the inevitable system time delay and the noise amplification of the traditional differentiator, an enhanced differentiator is proposed to make the system without time delay. Additionally, a sliding mode control approach based on extended state observer (SMCESO) is designed to estimate and compensate for the total disturbance through a feedforward mechanism. Third, the closed-loop system’s stability is established through the application of the Lyapunov stability method. Finally, a semi-physical experimental platform utilizing an NI-PCIe6343 acquisition card and Matlab/Simulink environment is constructed to assess and compare the effectiveness of the proposed TDCSMCESO with SMCESO and traditional predictor-based SMCESO (PSMCESO). Comparative experiments show that the TDCSMCESO controller exhibits improved disturbance resistance and vibration reduction capabilities.
Effects of Folding Degree and Mass Fraction on the Static and Natural Frequency Characteristics of Functionally Graded Graphene Origami-Enabled Auxetic Metamaterials Annular Plates
Authors: Yao Yujuan , Arshid Ehsan
Published: December 2024

This study addresses a critical gap in the literature by investigating the static and natural frequency characteristics of functionally graded (FG) auxetic metamaterial annular plates reinforced with graphene origami (GOri), a novel area previously unexplored in the context of composite constructions, particularly for circular plates. The governing equations are derived utilizing higher-order shear deformation theory along with Hamilton’s principle, and solved using the finite element approach. For the first time, a comprehensive parametric study including the folding degree and mass fraction, and distribution pattern of GOri, is investigated on the static and natural frequency properties of annular plates. It is found that the natural frequency generally increased with higher mass fractions and decreased with greater folding degrees, though the X and V patterns at a 3% mass fraction showed an atypical increase in frequency with higher folding degrees. The impact of distribution patterns varied with weight fraction: the X-pattern caused the highest deflection at 1% weight fraction but the lowest at 3%, while the O-pattern caused the least deflection overall.
Effect of Geometric Imperfections on the Free Vibration Behavior of L-shaped Functionally Graded Beam with Cracks
Authors: Shi Yinghui , Tang Huaiping
Published: December 2024

In this study, the effects of cracks and geometric imperfections on the free vibration characteristics of L-shaped functionally graded beams are comprehensively investigated. The governing equations are established based on the Timoshenko beam theory and Hamilton’s principle. The cracks are modeled using massless rotating springs, and the material properties vary along the beam’s thickness direction. The free vibration frequencies of the system are obtained by using the differential quadrature method. The results are verified by comparing them with the findings in the existing literature. Additionally, the influence of deformation type, geometric imperfection mode, crack location, and crack depth on the frequency of L-shaped functionally graded beams are analyzed.
On Low-Frequency Vibration Suppression Characteristics of Corrugated Metamaterial Beams
Authors: Liu Chunchuan , He Liuhai , Lu Qifa , Song Hongwei
Published: December 2024

A class of corrugated metamaterial beams is constructed by attaching local resonators into the available internal space of a corrugated sandwich beam, and the low-frequency vibration suppression characteristic is realized and researched using the spectral element method (SEM) and finite element method (FEM). The transmission coefficient and vibration distribution of the corrugated metamaterial beam are obtained, and the results demonstrate that the low-frequency locally resonant band gap can be opened and utilized in low-frequency vibration control of corrugated sandwich beams. Further research is conducted on the parameter design and band gap coupling of corrugated metamaterial beams, and vibration suppression performance is achieved over a broader low-frequency range. Vibration experiment on the corrugated metamaterial beam is also carried out, which validates the effectiveness of low-frequency vibration suppression characteristics of the corrugated metamaterial beam. The present research can provide a novel idea of metamaterial design for the low-frequency vibration suppression of corrugated sandwich structures.
Experimental Study on The Influence of Twin-Box Girder Attachments on the Driving Wind Environment of a Long-Span Coastal Bridge Under Crosswind
Authors: Lin Xiaobo , Zhuo Weidong , Lin Li , Wang Huaifeng , Xia Dandan , Liu Zhixin , Yuan Zhiqun
Published: December 2024

The influence of twin-box girder attachments on the driving wind environment of a long-span coastal bridge under crosswind was experimentally studied in a boundary layer wind tunnel. First, the particle image velocimetry (PIV) technique and anemometers were used to determine the optimal PIV window size. Then, the effects of attachments and vehicles on average wind speed, turbulent kinetic energy, Reynolds stress, and equivalent wind speed reduction coefficient on the driving wind environment were analyzed. Results indicate that bridge attachments alter both the magnitude and distribution of average wind speed on the windward side of the vehicle. Turbulent kinetic energy peaks were observed near the windward side of attachments, particularly in the wake region above the vehicle. Reynolds stress peaks were primarily located on the windward side of the attachments; increasing the height of the attachments expanded the peak area above the vehicle while reducing the wake region’s area. The coefficient of equivalent wind speed reduction provides a reasonable quantification of changes in the driving wind environment. However, the difference between conditions with and without the vehicle was significant, suggesting that practical wind barrier solutions should prioritize aerodynamic measurements of vehicles behind wind barriers.
Performance of Different Damping Devices for Mitigating Vortex-Induced Vibration of Long Span Bridges: A Comparative Study
Authors: Wu Sizhe , Fang Genshen , Ge Yaojun , Wen Zuopeng , Zhou Rui , Xu Kun , Xu Shengyi
Published: December 2024

Vortex-induced vibration (VIV) of long-span bridges is frequently observed in various types of bridges at low or medium wind speeds, causing disruption of traffic, fatigue issues of the bridge, and even panic in society. This study compares the VIV mitigation performance of the conventional tuned mass damper (TMD) and three novel damping devices, i.e. the negative-stiffness damper (NSD), inerter-based damper (IBD), and nonlinear energy sink (NES). The governing motion equations of four damper-bridge systems are established, and the optimal parameters of each damper are obtained. The semi-analytical solutions of controlled responses in the frequency domain are derived using the harmonic balance method (HBM). These four dampers are applied to mitigate the VIV of a parallel box girder bridge, which was observed and studied in the wind tunnel tests. Their performance is examined using semi-analytical solutions in the frequency domain and numerical solutions in the time domain based on the Runge–Kutta method, respectively. The control efficiency, the stroke of the auxiliary block, and the robustness of these four systems are compared. The strengths and weaknesses of different dampers used for VIV mitigation in bridge girders are discussed. The findings of this study provide the basis for determining the most favorable damper devices for VIV mitigation to adapt the specific requirements of different bridges.
State Prediction Model of Steel Structure Based on VMD-CNN Method
Authors: Li Xinmei , Xue Tianxiang , Suo Shuai , Li Wen , Bai Yunyan , Wang Peijun , Sun Lele
Published: December 2024

With the rapid pace of economic and social development, the complexity and diversity of building structures continue to evolve. Over their operational lifespan, structures are subjected to various forms of degradation, including material aging and environmental erosion, which can significantly diminish their durability. Consequently, the development of robust structural health monitoring systems becomes imperative. These systems not only track the evolving performance trends of structures but also predict potential failures, extending their operational lifespan and ensuring the safety of occupants and assets. This study addresses the challenges inherent in extracting detailed structural feature information and enhancing the accuracy of predictive models. Focused on the cantilever beam test model as a research framework, the research explores an innovative approach to structural state prediction. It integrates Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN) methodologies to effectively analyze and forecast structural conditions. The study highlights a significant improvement in prediction accuracy and overall model performance by comparing CNN’s predictions using original structural signals with those processed through VMD. The results show that when the number of modes [Formula: see text], compared with the original signal (when [Formula: see text]), the growth rate of the [Formula: see text]-value at each measuring point can reach a maximum of 288.13%, and the average growth rate is 101.07%. This indicates that the VMD-CNN can significantly improve the prediction performance of the model.
Vibration Characteristics of a Rotating Composite Beam with Pitch Action and Hygrothermal Effects
Authors: Yuan Ling , Li Liang , Zhu Weidong , Wang Long , Cui Jianguo , Lu Xiaoyu , Xue Changguo , Li Yinghui
Published: December 2024

The blades of helicopters and wind turbines undergo pitch motion in hygrothermal environments. Few works have studied the effect of this movement on blade frequencies. This paper focuses on the bending characteristics of a pitching thin-walled beam, including the effects of hygrothermal environments, rotor speeds, and composite materials. The harmonic pitch function is introduced into the dynamics model of a rotating beam. The method of multiple scales and the Galerkin method are used to solve these equations. Results indicate that the pitch amplitude A dramatically influences natural frequencies. Conclusions are obtained: (1) The flapping frequency varies periodically with pitch amplitude A, and monotonically changes every 2/[Formula: see text]. The pitch amplitude A significantly impacts frequencies, while the pitch frequency [Formula: see text] and pitch phase C don’t influence frequencies. (2) An increase in temperature or humidity will decrease the flapping frequencies of a pitching beam. (3) The ply angle, coupled with the dynamic pitching angle, dramatically influences the flapping frequencies of the composite beam. The larger the ply angle, the greater the effect of pitch amplitude A on frequencies.
A Case of Pseudohyponatremia in the Setting of Pegaspargase‐Induced Hypertriglyceridemia in an Adult T‐Cell Lymphoblastic Leukemia Patient
Authors: Ahmad Asim , Joseph Matthew
Published: December 2024

We present a rare case of pseudohyponatremia in a 20‐year‐old male patient with adult T‐cell acute lymphoblastic leukemia (ATLL). The patient was admitted for a mediastinal mass with superior vena cava syndrome and was receiving pegaspargase therapy. The pseudohyponatremia was found to be secondary to hypertriglyceridemia associated with the pegaspargase treatment. In this case report, we discuss the important considerations in the management of adult ATLL patients receiving pegaspargase therapy, and the specific approaches taken to care for the patient described in this case.
Analysis of Internal Controls, Individual Morality and Information Asymmetry Against Fraud Tendencies At PT. Ambassador Mayanksari in Surabaya
Authors: Dwi Dewianawati  , Erry Setiawan  , Darmayanti Novi
Published: December 2024

This study aims to examine the influence of internal control, individual morality, and information asymmetry on the tendency of fraud in PT. Ambassador Mayanksari located in Surabaya. Fraud is a serious problem because it can harm a company both financially and reputationally. In this study, a quantitative approach was used with a survey method involving 57 respondents, who were employees of PT. Ambassador Mayanksari. The collected data was analyzed using multiple linear regression techniques to identify the relationship between independent and dependent variables. The findings show that all three variables—internal control, individual morality, and information asymmetry—together exert a significant influence on fraud propensity. Separately, internal control has been proven to have a positive influence on fraud tendencies. The same is true for individual morality and information asymmetry, both of which have a significant impact on the occurrence of fraud. This study suggests that the implementation of good internal control, the improvement of individual morality, and the reduction of information asymmetry can play an important role in reducing the potential for fraud in companies.
Affective Determinants of Mathematics Learning: A Literature Review
Authors: Travero Arnel S. , Japos Genaro V.
Published: December 2024

This study examined scientific articles as the basis for a literature review on affective determinants of mathematics learning. These af-fective determinants are the affective factors that are shown to affect mathematics learning, performance and achievements. The study used qualitative method using literature review that applies the con-tent of scientific articles from reputable databases such as ERIC, Web of Science, and PsycINFO. The findings indicate that students’ attitude towards mathematics, goal setting, motivation, self-concept, self-efficacy, self-regulated learning, and study habits significantly impact their mathematical performance and achievement, thereby influenc-ing their mathematics learning. However, the complex and bidirec-tional relationship between these factors and their influence on vari-ous factors, such as parental influences and teaching methods, remain unexplored. The study highlights the need for future research to in-vestigate these research gaps, providing a more comprehensive un-derstanding of the interplay between affective determinants and their impact on mathematics learning in diverse educational settings. Through addressing these gaps, researchers and educators can devel-op targeted interventions and strategies to enhance students’ math-ematical performance, achievement and overall learning experience.
Impacts of the MSU-Sulu College of Agriculture Patikul Extension Program on Students Residing within the Municipality of Patikul
Authors: . Sammah Merhama M , Marajuli Fatima Sheirene M. , Sammah Ronnie H. , Ahajani Nedzfa A.
Published: December 2024

This study focused on the positive impact of the Patikul Ex-tension Program of MSU-Sulu College of Agriculture on the enrolled students residing within the boundaries of Patikul Municipality. Specifically, the study assessed the impacts of the program in three areas: Socio-Cultural, Economic, and Envi-ronmental aspects. To gather the necessary data, the re-searchers used a self-devised structured survey questionnaire, which underwent a reliability test. The questionnaire consist-ed of two parts. The first part determined the profile of the respondents using the quantitative descriptive method of fre-quency distribution. The second part assessed the impact of the program through selected questions, with the data ana-lyzed using weighted means and inferential statistics. A one-way ANOVA was employed to examine the significant differ-ences in the responses across different respondent profiles.Results showed that most of the respondents were 25 years old or older, female, first-year students, and residents of Patikul, Sulu, aged 21 or above. In terms of impact, the Socio-Cultural aspect had a weighted mean of 4.58, interpreted as "Strongly Agree"; the Economic aspect had a weighted mean of 4.42, interpreted as "Agree"; and the Environmental aspect had a weighted mean of 4.46, interpreted as "Agree." Regard-ing significant differences, the responses on the Socio-Cultural, Economic, and Environmental impacts of the Patikul Exten-sion Program varied across the profiles, with the results being mostly significant according to the one-way ANOVA test.Based on these findings, the researchers recommend the con-tinuous development of the program through faculty devel-opment, additional facilities, and laboratory improvements, as well as conducting further studies on the impact of faculty de-velopment on the stakeholders of the institution.
Couldn’t Be Happier: The Non-Identity Intuition and Valuing People
Authors: Fischer Jessica J.T.
Published: December 2024

This paper raises a worry about the non-identity intuition. As one part of the non-identity problem, the non-identity intuition tells us that when choosing between bringing a person with a good life into existence and bringing a person with a great life into existence, we have moral reason to bring the person with a great life into existence. But there is a worry to be had about the non-identity intuition. Because the non-identity intuition compares future persons exclusively in regard to their well-being, the non-identity intuition values future persons solely for the well-being enjoyed by each of them. Therefore, or so the worry goes, the non-identity intuition fails to be compatible with what it means to value persons for their own sake.
LTH 6. Literatura Taxonomica Herpetologica.
6. Year 1761
Authors: DUBOIS ALAIN , FRÉTEY THIERRY
Published: December 2024

This paper presents the analysis of three works by three authors (Linnaeus, Vandellius and Kalm) published in 1761 that contain classifications of amphibians and reptiles. The nomenclatural status of the herpetological nomina in these works, particularly regarding their availability and taxonomic allocation, is clarified.
Third time’s the charm: nomenclatural availability provided to the nomina of two Bornean Cyrtodactylus (Squamata, Gekkonidae)
Authors: DAVIS HAYDEN R. , NASHRIQ IZNEIL , WOYTEK KYRA S. , WIKRAMANAYAKE SHANELLE A. , BAUER AARON M. , KARIN BENJAMIN R. , BRENNAN IAN G. , ISKANDAR DJOKO T. , DAS INDRANEIL
Published: December 2024

Davis et al. (2023a) published the descriptions of two new Bornean species of the highly speciose gekkonid genus Cyrtodactylus Gray, 1827 in the journal Zoologica Scripta. The descriptions themselves actually appeared in the online supplementary information to this paper as Appendix S1, accessible as a link under “Supporting Information” on the Wiley Online Library page associated with the paper’s DOI. Life Science Identifiers (LSIDs) for ZooBank registration entries for the new nomina were provided in this Appendix but were not given in the body of the main paper, which was published electronically on 1 February 2023. As noted by Davis et al. (2023b), however, the Appendix is a separate document from the main paper and it exists only in an editable Word format, not in a fixed PDF/A format, and as such, the conditions of Article 8.1.3.2 of the amended Code (Anonymous 2012), that nomenclatural acts be available as “widely accessible electronic copies with fixed content and layout”, were not met. Because of this lack of ZooBank registration in the online version of the paper, it cannot be considered as validly promulgated, nor can the nomina therein (Article 8, as amended in 2012). The subsequent publication of the print version of the manuscript in the May 2023 issue of Zoologica Scripta (actual date of publication not stated) did meet the criteria for the promulgation of the nomina C. kapitensis and C. hutan, but did not meet the requirement of Article 13.1.1 that “a new nomen published after 1930 must be accompanied by a description or definition that states in words characters that are purported to differentiate the taxon”, as the diagnostic and descriptive data supporting the recognition of these two taxa were present only in the ephemeral Appendix S1, leaving these nomina nomenclaturally unavailable. This situation is illustrative of some of the numerous previously identified problems with the inclusion of nomenclatural acts in online supplements (see also Dubois et al. 2013, 2022).
Etymology of the nomina of chelonians of the world.1. Genus-series nomina
Authors: FRÉTEY THIERRY
Published: December 2024

We provide a catalogue of the genus-series nomina of chelonians of the world, including all (343) extant and extinct genera or subgenera since the Holocene (present geological epoch, beginning about 12,000 years ago), currently recognized as valid (107) or not (236).
In order to solve some nomenclatural cases, we had to resort to 29 Articles of the Code (8.1.3, 8.4.2.2, 9.12, 11.6.1, 11.8, 11.10, 23.9, 23.9.1.1, 23.9.1.2, 23.9.2, 24.1, 28, 29.3, 29.3.1.1, 29.4, 30.1.1, 30.1.2, 30.1.3, 30.1.4.2, 30.1.4.3, 30.1.4.4, 30.1.4.5, 30.2.2, 30.2.3, 30.2.4, 32.5.2.1, 56.2, 67.8 and 67.13) and to 63 technical nomenclatural terms.
Some nomenclatural consequences result from our observations: [1] we fix onomatophores (by subsequent designation) for 15 genera in order to place them in synonymies; [2] we validate two nomina of genera (Chrysemys Gray, 1844 and † Hesperotestudo Williams, 1950) and one of species (Emys kachuga Gray, 1831) as mnemokyronyms (nomina protecta) by showing that their older synonyms should be considered as lethakyronyms (nomina oblita); [3] we act as first reviser by choosing Parapelodiscus Hoser, 2021 as lectoprotograph; [4] the genus Chelonemys Gray, 1864 is a synonym of Peltocephalus Duméril & Bibron, 1835, not of Podocnemis Wagler, 1830 as usually indicated; [5] the family-series nomen Rhinoclemmydinae Gray, 1873 must be corrected into Rhinoclemmyinae Gray, 1873; [6] the nomen Testudo muhlenbergii Schoepff, 1801 (currently Glyptemys muhlenbergii) must be corrected into Testudo muehlenbergii Schoepff, 1801 (now Glyptemys muehlenbergii); [7] under a rigorous application of the Code, the subgenus nomen Supremechelys Hoser, 2014 should take precedence over Chelydera Thomson & Georges in Shea, Thomson & Georges, 2020, but this is controversial; [8] we remind that, without a decision of the Commission, two nomina (Trachemys Agassiz, 1857 and Trionyx Geoffroy Saint-Hilaire, 1809) are threatened, as the first subsequent designations of their type species result in these nomina applying to taxa very different from those which are currently recognized. Finally, the search for distribution dates of publications led us to correct the dates of promulgation of several genus-series nomina.
A survey of mite lifespans: part 3 (Cover)
Authors: ZHANG ZHI-QIANG
Published: December 2024

(Cover)
survey of mite lifespans: part 3 (Table of Contents)
Authors: ZHANG ZHI-QIANG
Published: December 2024

Table of Contents
survey of mite lifespans: preface to part 3
Authors: ZHANG ZHI-QIANG
Published: December 2024

This volume is part 3 of a series of surveys of mite lifespans and related life history data. The first part in 2021 covered the suborder Oribatida (excluding Astigmata), Winterschmidtiidae, Eriophyoidea, Tydeidae, Anystidae, Cheyletidae, and Blattisociidae (Zhang 2021). The second part (Zhang 2022) included reviews of life history data for three important families of economic importance: Tetranychidae (Li et al. 2022), Phytoseiidae (Han et al. 2022), and Laelapidae (Zhang et al. 2022). I am pleased to introduce Part 3 of this series, with three reviews for the superorder Parasitiformes: the order Ixodida, or the ticks (Chen et al. 2024), the family Parasitidae (Lewandowski & Pińkowska 2024), and the family Rhodacaridae (Momen et al. 2024). About 10% of the 960 tick species are of importance in animal and human health, and we have life history data reviewed for 12.2% of the species (Chen et al. 2024). It is noteworthy that the longest-living mite is a soft tick—Argas brumpti Neumann—which had survived for 27 years in the laboratory (Shepherd 2022).