bluuebunny/crossref_metadata_2025 huggingface dataset:
Unsupervised PolSAR Image Classification Based on Superpixel Pseudo-Labels and a Similarity-Matching Network
Authors: Wang Lei , Peng Lingmu , Gui Rong , Hong Hanyu , Zhu Shenghui
Published: November 2024

Supervised polarimetric synthetic aperture radar (PolSAR) image classification demands a large amount of precisely labeled data. However, such data are difficult to obtain. Therefore, many unsupervised methods have been proposed for unsupervised PolSAR image classification. The classification maps of unsupervised methods contain many high-confidence samples. These samples, which are often ignored, can be used as supervisory information to improve classification performance on PolSAR images. This study proposes a new unsupervised PolSAR image classification framework. The framework combines high-confidence superpixel pseudo-labeled samples and semi-supervised classification methods. The experiments indicated that this framework could achieve higher-level effectiveness in unsupervised PolSAR image classification. First, superpixel segmentation was performed on PolSAR images, and the geometric centers of the superpixels were generated. Second, the classification maps of rotation-domain deep mutual information (RDDMI), an unsupervised PolSAR image classification method, were used as the pseudo-labels of the central points of the superpixels. Finally, the unlabeled samples and the high-confidence pseudo-labeled samples were used to train an excellent semi-supervised method, similarity matching (SimMatch). Experiments on three real PolSAR datasets illustrated that, compared with the excellent RDDMI, the accuracy of the proposed method was increased by 1.70%, 0.99%, and 0.8%. The proposed framework provides significant performance improvements and is an efficient method for improving unsupervised PolSAR image classification.
Adaptive Beamforming for On-Orbit Satellite-Based ADS-B Based on FCNN
Authors: Xiang Yiran , Li Songting , Chen Lihu
Published: November 2024

Digital multi-beam synthesis technology is generally used in the on-orbit satellite-based Automatic Dependent Surveillance–Broadcast (ADS-B) system. However, the probability of successfully detecting aircraft with uneven surface distribution is low. An adaptive digital beamforming method is proposed to improve the efficiency of aircraft detection probability. The current method has the problem of long operation time and is not suitable for on-orbit operation. Therefore, this paper proposes an adaptive beamforming method for the ADS-B system based on a fully connected neural network (FCNN). The simulation results show that the calculation time of this method is about 2.6 s when more than 15,000 sets of data are inputted, which is 15–80% better than the existing methods. Its detection success probability is 10% higher than those of existing methods, and it has better robustness against large amounts of data.
The Neurological and Hemodynamics Safety of an Airway Clearance Technique in Patients with Acute Brain Injury: An Analysis of Intracranial Pressure Pulse Morphology Using a Non-Invasive Sensor
Authors: Souza Daniela de Almeida , Devetak Gisele Francini , Branco Marina Wolff , Melo Reinaldo Luz , Tonial Jean Lucas , Delattre Ana Marcia , Valderramas Silvia Regina
Published: November 2024

Patients with acute brain injury (ACI) often require mechanical ventilation (MV) and are subject to pulmonary complications, thus justifying the use of Airway Clearance Techniques (ACTs), but their effects on intracranial pressure (ICP) are unknown. This study investigates the neurological and hemodynamics safety of an ACT called ventilator hyperinflation (VHI) in patients with ACI. This was a randomized clinical equivalence trial, which included patients aged ≥ 18 years with a clinical diagnosis of hemorrhagic stroke, with symptom onset within 48 h. The participants were randomly allocated to the Experimental Group (EG, n = 15), which underwent VHI followed by tracheal aspiration (TA), and the Control Group (CG, n = 15), which underwent TA only. Neurological safety was verified by analyzing the morphology of the ICP wave through the non-invasive B4C sensor, which detects bone deformation of the skull, resulting in a P2/P1 ratio and TTP, and hemodynamics through a multi-parameter monitor. Evaluations were recorded during five instances: T1 (baseline/pre-VHI), T2 (post-VHI and before TA), T3 (post-TA), T4 and T5 (monitoring 10 and 20 min after T3). The comparison between groups showed that there was no effect of the technique on the neurological variables with a mean P2/P1 ratio [F (4,112) = 1.871; p = 0.120; np2 = 0.063] and TTP [F (4,112) = 2.252; p = 0.068; np2 = 0.074], and for hemodynamics, heart rate [F (4,112) = 1.920; p = 0.112; np2 = 0.064] and mean arterial pressure [F(2.73, 76.57) = 0.799; p = 0.488; np2 = 0.028]. Our results showed that VHI did not pose a neurological or hemodynamics risk in neurocritical patients after ACI.
Efficient Small Object Detection You Only Look Once: A Small Object Detection Algorithm for Aerial Images
Authors: Luo Jie , Liu Zhicheng , Wang Yibo , Tang Ao , Zuo Huahong , Han Ping
Published: November 2024

Aerial images have distinct characteristics, such as varying target scales, complex backgrounds, severe occlusion, small targets, and dense distribution. As a result, object detection in aerial images faces challenges like difficulty in extracting small target information and poor integration of spatial and semantic data. Moreover, existing object detection algorithms have a large number of parameters, posing a challenge for deployment on drones with limited hardware resources. We propose an efficient small-object YOLO detection model (ESOD-YOLO) based on YOLOv8n for Unmanned Aerial Vehicle (UAV) object detection. Firstly, we propose that the Reparameterized Multi-scale Inverted Blocks (RepNIBMS) module is implemented to replace the C2f module of the Yolov8n backbone extraction network to enhance the information extraction capability of small objects. Secondly, a cross-level multi-scale feature fusion structure, wave feature pyramid network (WFPN), is designed to enhance the model’s capacity to integrate spatial and semantic information. Meanwhile, a small-object detection head is incorporated to augment the model’s ability to identify small objects. Finally, a tri-focal loss function is proposed to address the issue of imbalanced samples in aerial images in a straightforward and effective manner. In the VisDrone2019 test set, when the input size is uniformly 640 × 640 pixels, the parameters of ESOD-YOLO are 4.46 M, and the average mean accuracy of detection reaches 29.3%, which is 3.6% higher than the baseline method YOLOv8n. Compared with other detection methods, it also achieves higher detection accuracy with lower parameters.
Classification and Identification of Frequency-Hopping Signals Based on Jacobi Salient Map for Adversarial Sample Attack Approach
Authors: Zhu Yanhan , Li Yong , Wei Tianyi
Published: November 2024

Frequency-hopping (FH) communication adversarial research is a key area in modern electronic countermeasures. To address the challenge posed by interfering parties that use deep neural networks (DNNs) to classify and identify multiple intercepted FH signals—enabling targeted interference and degrading communication performance—this paper presents a batch feature point targetless adversarial sample generation method based on the Jacobi saliency map (BPNT-JSMA). This method builds on the traditional JSMA to generate feature saliency maps, selects the top 8% of salient feature points in batches for perturbation, and increases the perturbation limit to restrict the extreme values of single-point perturbations. Experimental results in a white-box environment show that, compared with the traditional JSMA method, BPNT-JSMA not only maintains a high attack success rate but also enhances attack efficiency and improves the stealthiness of the adversarial samples.
Deep-Learning-Based Automated Building Construction Progress Monitoring for Prefabricated Prefinished Volumetric Construction
Authors: Chua Wei Png , Cheah Chien Chern
Published: November 2024

Prefabricated prefinished volumetric construction (PPVC) is a relatively new technique that has recently gained popularity for its ability to improve flexibility in scheduling and resource management. Given the modular nature of PPVC assembly and the large amounts of visual data amassed throughout a construction project today, PPVC building construction progress monitoring can be conducted by quantifying assembled PPVC modules within images or videos. As manually processing high volumes of visual data can be extremely time consuming and tedious, building construction progress monitoring can be automated to be more efficient and reliable. However, the complex nature of construction sites and the presence of nearby infrastructure could occlude or distort visual data. Furthermore, imaging constraints can also result in incomplete visual data. Therefore, it is hard to apply existing purely data-driven object detectors to automate building progress monitoring at construction sites. In this paper, we propose a novel 2D window-based automated visual building construction progress monitoring (WAVBCPM) system to overcome these issues by mimicking human decision making during manual progress monitoring with a primary focus on PPVC building construction. WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. This is achieved by detecting windows within the input image at two scales by using YOLOv5 as a backbone network for object detection before using a window detection filtering process to omit irrelevant detections from the surrounding areas. Next, a rectification module is developed to account for missing windows in the mid-section and near-ground regions of the constructed building that may be caused by occlusion and poor detection. Lastly, a progress estimation module checks the processed detections for missing or excess information before performing building construction progress estimation. The proposed method is tested on images from actual construction sites, and the experimental results demonstrate that WAVBCPM effectively addresses real-world challenges. By mimicking human inference, it overcomes imperfections in visual data, achieving higher accuracy in progress monitoring compared to purely data-driven object detectors.
Local–Global Feature Adaptive Fusion Network for Building Crack Detection
Authors: He Yibin , Yuan Zhengrong , Xia Xinhong , Yang Bo , Wu Huiting , Fu Wei , Yao Wenxuan
Published: November 2024

Cracks represent one of the most common types of damage in building structures and it is crucial to detect cracks in a timely manner to maintain the safety of the buildings. In general, tiny cracks require focusing on local detail information while complex long cracks and cracks similar to the background require more global features for detection. Therefore, it is necessary for crack detection to effectively integrate local and global information. Focusing on this, a local–global feature adaptive fusion network (LGFAF-Net) is proposed. Specifically, we introduce the VMamba encoder as the global feature extraction branch to capture global long-range dependencies. To enhance the ability of the network to acquire detailed information, the residual network is added as another local feature extraction branch, forming a dual-encoding network to enhance the performance of crack detection. In addition, a multi-feature adaptive fusion (MFAF) module is proposed to integrate local and global features from different branches and facilitate representative feature learning. Furthermore, we propose a building exterior wall crack dataset (BEWC) captured by unmanned aerial vehicles (UAVs) to evaluate the performance of the proposed method used to identify wall cracks. Other widely used public crack datasets are also utilized to verify the generalization of the method. Extensive experiments performed on three crack datasets demonstrate the effectiveness and superiority of the proposed method.
Two-Stream Modality-Based Deep Learning Approach for Enhanced Two-Person Human Interaction Recognition in Videos
Authors: Akash Hemel Sharker , Rahim Md Abdur , Miah Abu Saleh Musa , Lee Hyoun-Sup , Jang Si-Woong , Shin Jungpil
Published: November 2024

Human interaction recognition (HIR) between two people in videos is a critical field in computer vision and pattern recognition, aimed at identifying and understanding human interaction and actions for applications such as healthcare, surveillance, and human–computer interaction. Despite its significance, video-based HIR faces challenges in achieving satisfactory performance due to the complexity of human actions, variations in motion, different viewpoints, and environmental factors. In the study, we proposed a two-stream deep learning-based HIR system to address these challenges and improve the accuracy and reliability of HIR systems. In the process, two streams extract hierarchical features based on the skeleton and RGB information, respectively. In the first stream, we utilised YOLOv8-Pose for human pose extraction, then extracted features with three stacked LSM modules and enhanced them with a dense layer that is considered the final feature of the first stream. In the second stream, we utilised SAM on the input videos, and after filtering the Segment Anything Model (SAM) feature, we employed integrated LSTM and GRU to extract the long-range dependency feature and then enhanced them with a dense layer that was considered the final feature for the second stream module. Here, SAM was utilised for segmented mesh generation, and ImageNet was used for feature extraction from images or meshes, focusing on extracting relevant features from sequential image data. Moreover, we newly created a custom filter function to enhance computational efficiency and eliminate irrelevant keypoints and mesh components from the dataset. We concatenated the two stream features and produced the final feature that fed into the classification module. The extensive experiment with the two benchmark datasets of the proposed model achieved 96.56% and 96.16% accuracy, respectively. The high-performance accuracy of the proposed model proved its superiority.
Age-Related Influence on Static and Dynamic Balance Abilities: An Inertial Measurement Unit-Based Evaluation
Authors: Lin Tzu-Tung , Cheng Lin-Yen , Chen Chien-Cheng , Pan Wei-Ren , Tan Yin-Keat , Chen Szu-Fu , Wang Fu-Cheng
Published: November 2024

Balance control, a complex sensorimotor skill, declines with age. Assessing balance is crucial for identifying fall risk and implementing interventions in the older population. This study aimed to measure age-dependent changes in static and dynamic balance using inertial measurement units in a clinical setting. This study included 82 healthy participants aged 20–85 years. For the dynamic balance test, participants stood on a horizontally swaying balance board. For the static balance test, they stood on one leg. Inertial measurement units attached to their bodies recorded kinematic data, with average absolute angular velocities assessing balance capabilities. In the dynamic test, the younger participants had smaller average absolute angular velocities in most body parts than those of the middle-aged and older groups, with no significant differences between the middle-aged and older groups. Conversely, in the single-leg stance tests, the young and middle-aged groups outperformed the older group, with no significant differences between the young and middle-aged groups. Thus, dynamic and static balance decline at different stages with age. These results highlight the complementary role of inertial measurement unit-based evaluation in understanding the effect of age on postural control mechanisms, offering valuable insights for tailoring rehabilitation protocols in clinical settings.
Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution
Authors: Kumari Pooja , Keck Shaun , Sohn Emma , Kern Johann , Raedle Matthias
Published: November 2024

This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep-learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields.
Steady-State Visual Evoked Potential-Based Brain–Computer Interface System for Enhanced Human Activity Monitoring and Assessment
Authors: Chen Yuankun , Shi Xiyu , De Silva Varuna , Dogan Safak
Published: November 2024

Advances in brain–computer interfaces (BCIs) have enabled direct and functional connections between human brains and computing systems. Recent developments in artificial intelligence have also significantly improved the ability to detect brain activity patterns. In particular, using steady-state visual evoked potentials (SSVEPs) in BCIs has enabled noticeable advances in human activity monitoring and identification. However, the lack of publicly available electroencephalogram (EEG) datasets has limited the development of SSVEP-based BCI systems (SSVEP-BCIs) for human activity monitoring and assisted living. This study aims to provide an open-access multicategory EEG dataset created under the SSVEP-BCI paradigm, with participants performing forward, backward, left, and right movements to simulate directional control commands in a virtual environment developed in Unity. The purpose of these actions is to explore how the brain responds to visual stimuli of control commands. An SSVEP-BCI system is proposed to enable hands-free control of a virtual target in the virtual environment allowing participants to maneuver the virtual target using only their brain activity. This work demonstrates the feasibility of using SSVEP-BCIs in human activity monitoring and assessment. The preliminary experiment results indicate the effectiveness of the developed system with high accuracy, successfully classifying 89.88% of brainwave activity.
Development and Design of an Online Quality Inspection System for Electric Car Seats
Authors: Wei Fangjie , Wang Dongqiang , Zhang Xi
Published: November 2024

As the market share of electric vehicles continues to rise, consumer demands for comfort within the vehicle interior have also increased. The noise generated by electric seats during operation has become one of the primary sources of in-cabin noise. However, the offline detection methods for electric seat noise severely limit production capacity. To address this issue, this paper presents an online quality inspection system for automotive electric seats, developed using LabVIEW. This system is capable of simultaneously detecting both the noise and electrical functions of electric seats, thereby resolving problems associated with multiple detection processes and low integration levels that affect production efficiency on the assembly line. The system employs NI boards (9250 + 9182) to collect noise data, while communication between LabVIEW and the Programmable Logic Controller (PLC) allows for programmed control of the seat motor to gather motor current. Additionally, a supervisory computer was developed to process the collected data, which includes generating frequency and time-domain graphs, conducting data analysis and evaluation, and performing database queries. By being co-located with the production line, the system features a highly integrated hardware and software design that facilitates the online synchronous detection of noise performance and electrical functions in automotive electric seats, effectively streamlining the detection process and enhancing overall integration. Practical verification results indicate that the system improves the production line cycle time by 34.84%, enabling rapid and accurate identification of non-conforming items in the seat motor, with a detection time of less than 86 s, thereby meeting the quality inspection needs for automotive electric seats.
TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting
Authors: He Xiaxia , Zhang Wenhui , Li Xiaoyu , Zhang Xiaodan
Published: November 2024

Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data. Traditional spatial-temporal graph modeling methods often rely on fixed road network structures, failing to account for the dynamic spatial correlations that vary over time. To address this, we propose a Transformer-Enhanced Adaptive Graph Convolutional Network (TEA-GCN) that alternately learns temporal and spatial correlations in traffic data layer-by-layer. Specifically, we design an adaptive graph convolutional module to dynamically capture implicit road dependencies at different time levels and a local-global temporal attention module to simultaneously capture long-term and short-term temporal dependencies. Experimental results on two public traffic datasets demonstrate the effectiveness of the proposed model compared to other state-of-the-art traffic flow prediction methods.
A Study of the Effect of Temperature on the Capacitance Characteristics of a Metal-μhemisphere Resonant Gyroscope
Authors: Yao Xiangxian , Zhao Hui , Su Zhong , Gu Xibing , Chu Sirui
Published: November 2024

Metal-μhemispherical resonant gyros (M-μHRGs) are widely used in highly dynamic navigation systems in extreme environments due to their high accuracy and structural stability. However, the effect of temperature variations on the capacitance characteristics of M-μHRGs has not been fully investigated, which is crucial for optimizing the performance of the gyro. This study aims to systematically analyze the effect of temperature on the static and dynamic capacitances of M-μHRGs. In this study, an M-μHRG structure based on a 16-tooth metal oscillator is designed, and conducted simulation experiments using non-contact capacitance measurement method and COMSOL Multiphysics 6.2 finite element simulation software in the temperature range of 233.15 K to 343.15 K. The modeling analysis of the static capacitance takes into account the thermal expansion effect, and the results show that static capacitance remains stable across the measured temperature range, with minimal effect from temperature. The dynamic capacitance exhibits significant nonlinear variations under different temperature conditions, especially in the two end temperature intervals (below 273.15 K and above 313.15 K), where the capacitance values show local extremes and fluctuations. In order to capture this nonlinear behavior, the experimental data were smoothed and fitted using the LOESS method, revealing a complex trend of the capacitance variation with temperature. The results show that the M-μHRG has good capacitance stability in the mid-temperature range, but its dynamic performance is significantly affected at extreme temperatures. This study provides a theoretical reference for the optimal design of M-μHRGs in high- and low-temperature environments.
A Deep Cryptographic Framework for Securing the Healthcare Network from Penetration
Authors: Singh Arjun , Sharma Vijay Shankar , Basheer Shakila , Chowdhary Chiranji Lal
Published: November 2024

Ensuring the security of picture data on a network presents considerable difficulties because of the requirement for conventional embedding systems, which ultimately leads to subpar performance. It poses a risk of unauthorized data acquisition and misuse. Moreover, the previous image security-based techniques faced several challenges, including high execution times. As a result, a novel framework called Graph Convolutional-Based Twofish Security (GCbTS) was introduced to secure the images used in healthcare. The medical data are gathered from the Kaggle site and included in the proposed architecture. Preprocessing is performed on the data inserted to remove noise, and the hash 1 value is computed. Using the generated key, these separated images are put through the encryption process to encrypt what they contain. Additionally, to verify the user’s identity, the encrypted data calculates the hash 2 values contrasted alongside the hash 1 value. Following completion of the verification procedure, the data are restored to their original condition and made accessible to authorized individuals by decrypting them with the collective key. Additionally, to determine the effectiveness, the calculated results of the suggested model are connected to the operational copy, which depends on picture privacy.
Evaluating Alternative Registration Planes in Imageless, Computer-Assisted Navigation Systems for Direct Anterior Total Hip Arthroplasty
Authors: Farey John E. , Chai Yuan , Xu Joshua , Maes Vincent , Sadeghpour Ameneh , Baker Neri A. , Vigdorchik Jonathan M. , Walter William L.
Published: November 2024

(1) Background: Imageless computer navigation systems have the potential to improve the accuracy of acetabular cup position in total hip arthroplasty (THA). Popular imageless navigation methods include locating the patient in a three-dimensional space (registration method) while using a baseline to angle the acetabular cup (reference plane). This study aims to compare the accuracy of different methods for determining postoperative acetabular cup positioning in THA via the direct anterior approach. (2) Methods: Fifty-one participants were recruited. Optical and inertial sensor imageless navigation systems were used simultaneously with three combinations of registration methods and reference planes: the anterior pelvic plane (APP), the anterior superior iliac spine (ASIS) and the table tilt (TT) method. Postoperative acetabular cup position, inclination, and anteversion were assessed using CT scans. (3) Results: For inclination, the mean absolute error (MAE) was lower using the TT method (2.4° ± 1.7°) compared to the ASIS (2.8° ± 1.7°, p = 0.17) and APP method (3.7° ± 2.1°, p < 0.001). For anteversion, the MAE was significantly lower for the TT method (2.4° ± 1.8°) in contrast to the ASIS (3.9° ± 3.2°, p = 0.005) and APP method (9.1° ± 6.2°, p < 0.001). (4) Conclusion: A functional reference plane is superior to an anatomic reference plane to accurately measure intraoperative acetabular cup inclination and anteversion in THA using inertial imageless navigation systems.
Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations
Authors: Yang Deshan , Wu Jingwen , He Yixin
Published: November 2024

Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay.
Passive and Active Exoskeleton Solutions: Sensors, Actuators, Applications, and Recent Trends
Authors: Preethichandra D. M. G. , Piyathilaka Lasitha , Sul Jung-Hoon , Izhar Umer , Samarasinghe Rohan , Arachchige Sanura Dunu , de Silva Liyanage C.
Published: November 2024

Recent advancements in exoskeleton technology, both passive and active, are driven by the need to enhance human capabilities across various industries as well as the need to provide increased safety for the human worker. This review paper examines the sensors, actuators, mechanisms, design, and applications of passive and active exoskeletons, providing an in-depth analysis of various exoskeleton technologies. The main scope of this paper is to examine the recent developments in the exoskeleton developments and their applications in different fields and identify research opportunities in this field. The paper examines the exoskeletons used in various industries as well as research-level prototypes of both active and passive types. Further, it examines the commonly used sensors and actuators with their advantages and disadvantages applicable to different types of exoskeletons. Communication protocols used in different exoskeletons are also discussed with the challenges faced.
Modeling of Static Stress Identification Using Electromechanical Impedance of Embedded Piezoelectric Plate
Authors: Wang Xianfeng , Liu Hui , Liu Guoxiong , Xu Dan
Published: November 2024

Working stress is an important indicator reflecting the health status of structures. Passive-monitoring technology using the piezoelectric effect can effectively monitor the dynamic stress of structures. However, under static loads, the charge generated by the piezoelectric devices can only be preserved when the external circuit impedance is infinitely large, which means passive-monitoring techniques are unable to monitor static and quasi-static stress caused by slow-changing actions. In current studies, experimental observations have shown that the impedance characteristics of piezoelectric devices are affected by external static loads, yet the underlying mechanisms remain inadequately explained. This is because the impedance characteristics of piezoelectric devices are actually dynamic characteristics under alternating voltage. Most existing impedance analysis models are based on linear elastic dynamics. Within this framework, the impact of static stress on dynamic characteristics, including impedance characteristics, cannot be addressed. Accounting for static stress in impedance modeling is a challenging problem. In this study, the static stress applied on an embedded piezoelectric plate is abstracted as the initial stress of the piezoelectric plate. Based on nonlinear elastic dynamic governing equations, using the displacement method, an impedance analysis model of an embedded piezoelectric plate considering initial stress is established and verified through a fundamental experiment and a finite element analysis. Based on this, the explicit analytical relation between initial stress and impedance characterizations is provided, the mechanism of the effect of initial stress on the impedance characterizations is revealed, and procedures to identify static stress using impedance characterizations is proposed. Moreover, the sensitivities of the impedance characterizations in response to the initial stress are thoroughly discussed. This study mainly provides a theoretical basis for monitoring static stress using the electromechanical impedance of an embedded piezoelectric plate. And the results of the present study can help with the performance prediction and design optimization of piezoelectric-based static stress sensors.
Beyond Human and Machine: An Architecture and Methodology Guideline for Centaurian Design
Authors: Pareschi Remo
Published: November 2024

The concept of the centaur, symbolizing the fusion of human and machine intelligence, has intrigued visionaries for decades. Recent advancements in artificial intelligence have made this concept not only realizable but also actionable. This synergistic partnership between natural and artificial intelligence promises superior outcomes by leveraging the strengths of both entities. Tracing its origins back to early pioneers of human–computer interaction in the 1960s, such as J.C.R. Licklider and Douglas Engelbart, the idea initially manifested in centaur chess but faced challenges as technological advances began to overshadow human contributions. However, the resurgence of generative AI in the late 2010s, exemplified by conversational agents and text-to-image chatbots, has rekindled interest in the profound potential of human–AI collaboration. This article formalizes the centaurian model, detailing properties associated with various centaurian designs, evaluating their feasibility, and proposing a design methodology that integrates human creativity with artificial intelligence. Additionally, it compares this model with other integrative theories, such as the Theory of Extended Mind and Intellectology, providing a comprehensive analysis of its place in the landscape of human–machine interaction.
Implementing Person-Centred Lean Six Sigma to Transform Dermatology Waiting Lists: A Case Study from a Major Teaching Hospital in Dublin, Ireland
Authors: Igoe Aileen , Teeling Seán Paul , McFeely Orla , McGuirk Michelle , Manning Siobhan , Kelly Vanessa , Coetzee Heather , Cunningham Úna , Connolly Karen , Lenane Patsy
Published: November 2024

The study site, a major teaching hospital in Dublin, Ireland, addressed significant challenges within its dermatology service through a comprehensive improvement initiative using a person-centred Lean Six Sigma methodology. Initially, the hospital’s dermatology department faced excessive outpatient waiting times, with 3736 patients awaiting appointments, and 1615 waiting over 12 months. The person-centred Lean Six Sigma approach, which combines Lean techniques to reduce non-value add and Six Sigma methods to eliminate variation through a person-centred lens, was applied to overhaul the referral, triage, and scheduling processes. Key interventions included standardising triage categories, centralising the triage process, and redistributing referrals equitably among consultants. A new centralised triage system was established, leading to a more efficient allocation of appointments and better management of urgent cases. Post-implementation data showed a 40% reduction in the overall waiting list and a 60% reduction in the number of patients waiting over 12 months. The initiative significantly decreased the wait times across all urgency categories, with the most notable improvements in soon and urgent referrals. These changes were also the impetus for a follow-up design-led innovation phase, where the team worked with partners across the educational and healthcare system to enable disruptive change. The success of this project provides a scalable model for improvements in similar healthcare settings.
Phytochemical Study of the Plant Centaurea bruguieriana (DC.) Hand.-Mazz. subsp. belangeriana (DC.) Bornm. of the Family Asteraceae
Authors: Dimitriadis Kyriakos Michail , Tsiftsoglou Olga , Hadjipavlou-Litina Dimitra , Arfan Mohammad , Lazari Diamanto
Published: November 2024

The aim of this study is to isolate and identify the secondary metabolites of the aerial part of the plant Centaurea bruguieriana (DC.) Hand. -Mazz. subsp. belangeriana (DC.) Bornm. (Centaurea phyllocephala) (Asteraceae), and to study the biological activities of the extracts and isolated compounds with in vitro tests. With the use of chromatography and spectroscopy we identified three elemanolides: 8α-O-(3,4-dihydroxy-2-methylenebutanoyloxy) dehydromelitensine (1), 8α-O-(3-hydroxy-4-acetoxy-2-methylene-butanoyloxy) dehydromelitensine (2) and methyl 6α,8α,15-trihydroxyelema-1,3,11(13)-trien-12-oate (3); two germacranolides: cnicin (4) and 4′-O-acetylcnicin (5); one eudesmanolide: malacitanolide (6); five flavonoids: cirsilineol (7), eupatorine (8), 5-hydroxy, 6,7,3′,4′-tetramethoxy-flavone (9), 3,4′,5,7-tetrahydroxy-6-methoxyflavone 3-O-β-D-glucopyranoside (10) and astragalin (11); and also p-OH-benzoic acid (12) and 3-hydroxy-2-methyl-butyrolactone (13). All the isolated compounds were evaluated in silico with the use of molinspiration, while the crude extract, the organic phase B and compounds 2, 4, 5 and 6 were tested as antioxidants and anti-inflammatories for the inhibition of lipid hyperoxide and the inhibition of lipoxygenase.
Utilising H5P Simulations to Enhance Social Work Education
Authors: Jefferies Gerard
Published: November 2024

Higher education is tasked with the challenge of producing graduate-ready professionals. Thus, alternative learning and assessment activities are needed to provide students with real-life complex experiences, particularly in fields like social work. This quasi-experimental design study explored the effectiveness of H5P virtual simulation to teach assessment and direct practice skills to social work students (n = 80). Mixed-methods data based on the pre/post outcomes of skill development were analysed using descriptive and bi-variate analysis as well as thematic analysis for qualitative data. A Wilcoxon signed-rank test reveals significant outcomes in assessment and practice skills between pre- and post-measures. The qualitative findings include three key themes: (1) realism, (2) engagement, and (3) practice skills. This study provides evidence and highlights the importance of using virtual simulation to help students develop skills to manage complex real-world problems.
Exact Model of Gravitational Waves and Pure Radiation
Authors: Osetrin Konstantin E. , Epp Vladimir Y. , Filippov Altair E.
Published: November 2024

An exact non-perturbative model of a gravitational wave with pure radiation is constructed. It is shown that the presence of dust matter in this model contradicts Einstein’s field equations. The exact solution to Einstein’s equations for gravitational wave and pure radiation is obtained. The trajectories of propagation and the characteristics of radiation are found. For the considered exact model of a gravitational wave, a retarded time equation for radiation is obtained. The obtained results are used to construct an exact model of gravitational wave and pure radiation for the Bianchi type IV universe.
Gröbner–Shirshov Bases for Temperley–Lieb Algebras of Type F
Authors: Lee Jeong-Yup , Lee Dong-il
Published: November 2024

For the Temperley–Lieb algebras of type Fn with n≥4, we construct their Gröbner–Shirshov bases. Explicitly, the corresponding finite sets consisting of the standard monomials of type Fn, which are exactly the fully commutative elements of Fn, are enumerated when n = 4, 5, and 6.
Ulam Stability of Second-Order Periodic Linear Differential Equations with Periodic Damping
Authors: Li Jin , Xia Xiaofang , Zhang Chuanfang , Chen Renlian
Published: November 2024

We addressed the Ulam stability of second-order periodic linear differential equations with periodic damping. The necessary and sufficient conditions for the Ulam stability of second-order periodic linear differential equations were obtained by providing an Ulam stability theorem and an Ulam instability theorem. In particular, when the elastic coefficient remained constant at 0, a very general conclusion was obtained. These results expand on the conclusions in the relevant literature. In addition, for the situation of constant coefficients, the minimum Ulam constant is provided.
Application of Differential Equations on the Ricci Curvature of Contact CR-Warped Product Submanifolds of S2n+1(1) with Semi-Symmetric Metric Connection
Authors: Khan Meraj Ali , Ishan Amira A. , Al-Dayel Ibrahim , Masood Khalid
Published: November 2024

In this paper, we explore the uses of Obata’s differential equation in relation to the Ricci curvature of an odd-dimensional sphere that possesses a semi-symmetric metric connection. Specifically, we establish that, given certain conditions, the underlying submanifold can be identified as an isometric sphere. Additionally, we investigate the impact of specific differential equations on these submanifolds and demonstrate that, when certain geometric conditions are met, the base submanifold can be characterized as a special type of warped product.
Covariant Representation of Spin and Entanglement—A Review and Reformulation
Authors: Land Martin
Published: November 2024

A consistent theory of quantum entanglement requires that constituent single-particle states belong to the same Hilbert space, the coherent eigenstates of a complete set of operators in a given representation, defined with respect to a shared continuous parameterization. Formulating such eigenstates for a single relativistic particle with spin, and applying them to the description of many-body states, presents well-known challenges. In this paper, we review the covariant theory of relativistic spin and entanglement in a framework first proposed by Stueckelberg and developed by Horwitz, Piron, et al. This approach modifies Wigner’s method by introducing an arbitrary timelike unit vector nμ and then inducing a representation of SL(2,C), based on pμ rather than on the spacetime momentum. Generalizing this approach, we construct relativistic spin states on an extended phase space {(xμ,pμ),(ζμ,πμ)}, inducing a representation on the momentum πμ, thus providing a novel dynamical interpretation of the timelike unit vector nμ=πμ/M. Studying the unitary representations of the Poincaré group on the extended phase space allows us to define basis quantities for quantum states and develop the gauge invariant electromagnetic Hamiltonian in classical and quantum mechanics. We write plane wave solutions for free particles and construct stable singlet states, and relate these to experiments involving temporal interference, analogous to the spatial interference known from double slit experiments.
Evolution of Quantum Systems with a Discrete Energy Spectrum in an Adiabatically Varying External Field
Authors: Belousov Yury
Published: November 2024

We introduce a new approach for describing nonstationary quantum systems with a discrete energy spectrum. The essence of this approach is that we describe the evolution of a quantum system in a time-dependent basis. In a sense, this approach is similar to the description of the system in the interaction representation. However, the time dependence of the basic states of the representation is determined not by the evolution operator with a time-independent Hamiltonian but by the eigenstates of the time-dependent Hamiltonian defined at the current time. The time dependence of the basic states of the representation leads to the appearance of an additional term in the Schrödinger equation, which in the case of slowly changing parameters of the Hamiltonian can be considered as a small perturbation. The adiabatic representation is suitable in cases where it is impossible to apply the standard interaction representation. The application of the adiabatic representation is illustrated by the example of two spins connected by a magnetic dipole–dipole interaction in a slowly varying external magnetic field.
From Shock to Adaptation: Evaluating the Mechanisms of Trade Friction on Firms’ Total Factor Productivity Through the Lens of Digital Transformation
Authors: Zhang Xiyin , Zhang Xindong
Published: November 2024

Against the backdrop of frequent global trade conflicts and accelerated competition in the digital economy, we investigated how trade friction shocks affect firms’ total factor productivity (TFP), with a focus on the role of digital transformation. We employed a statistical analysis method based on the multi-period difference-in-differences model with anti-dumping and countervailing measures as quasi-natural experiments to analyze firm-level panel data for Chinese exporters from 2003 to 2016. The results indicate that trade frictions compel export firms to enhance their TFP, while digital transformation further promotes this growth. This main conclusion still holds after a series of tests such as parallel trend checks, placebo tests, and variations in primary variables. The mechanism analysis shows that, in response to trade friction shocks, firms aiming to boost productivity have strategically reallocated internal resources by increasing exports of alternative products and expanding into overseas markets rather than solely relying on innovation. Additionally, digital transformation mitigates the negative effects of trade friction on firm innovation and facilitates faster internal resource reallocation during such challenges. The heterogeneity analysis reveals that digital transformation proves more beneficial for firms that are multi-product, non-state-owned, engaged in general trade, and operating in highly competitive industries. This paper contributes to the understanding of trade friction and productivity at the micro level, offering valuable insights into digital transformation strategies for firms navigating these challenges.
Generative Models for Source Code: Fine-Tuning Techniques for Structured Pattern Learning
Authors: Franzoni Valentina , Tagliente Silvia , Milani Alfredo
Published: November 2024

This study addresses the problem of how to automatically generate source code that is not only functional, but also well-structured, readable, and maintainable. Existing generative models for source code often produce functional code, but they lack consistency in structure and adherence to coding standards, essential for integration into existing application development projects and long-term software maintenance. By training the model on specific code structures, including a dataset with Italian annotations, the proposed methodology ensures that the generated code is compliant with both the functional requirements and the pre-defined coding standards. The methodology proposed in this study applies transfer learning techniques on the DeepSeek Coder model, to refine pre-trained models to generate code that integrates additional structuring constraints. By training the model on specific code structures, including a dataset with Italian comments, the proposed methodology ensures that the generated code meets both functional requirements and coding structure. Experimental results, evaluated using the perplexity metric, demonstrate the effectiveness of the proposed approach, which impacts the goals of reducing errors, and ultimately improves software development quality.