bluuebunny/crossref_metadata_2025 huggingface dataset:
Current Knowledge about ActiGraph GT9X Link Activity Monitor Accuracy and Validity in Measuring Steps and Energy Expenditure: A Systematic Review
Authors: Suau Quentin , Bianchini Edoardo , Bellier Alexandre , Chardon Matthias , Milane Tracy , Hansen Clint , Vuillerme Nicolas
Published: January 2024

Over recent decades, wearable inertial sensors have become popular means to quantify physical activity and mobility. However, research assessing measurement accuracy and precision is required, especially before using device-based measures as outcomes in trials. The GT9X Link is a recent activity monitor available from ActiGraph, recognized as a “gold standard” and previously used as a criterion measure to assess the validity of various consumer-based activity monitors. However, the validity of the ActiGraph GT9X Link is not fully elucidated. A systematic review was undertaken to synthesize the current evidence for the criterion validity of the ActiGraph GT9X Link in measuring steps and energy expenditure. This review followed the PRISMA guidelines and eight studies were included with a combined sample size of 558 participants. We found that (1) the ActiGraph GT9X Link generally underestimates steps; (2) the validity and accuracy of the device in measuring steps seem to be influenced by gait speed, device placement, filtering process, and monitoring conditions; and (3) there is a lack of evidence regarding the accuracy of step counting in free-living conditions and regarding energy expenditure estimation. Given the limited number of included studies and their heterogeneity, the present review emphasizes the need for further validation studies of the ActiGraph GT9X Link in various populations and in both controlled and free-living settings.
Dynamic Japanese Sign Language Recognition Throw Hand Pose Estimation Using Effective Feature Extraction and Classification Approach
Authors: Kakizaki Manato , Miah Abu Saleh Musa , Hirooka Koki , Shin Jungpil
Published: January 2024

Japanese Sign Language (JSL) is vital for communication in Japan’s deaf and hard-of-hearing community. But probably because of the large number of patterns, 46 types, there is a mixture of static and dynamic, and the dynamic ones have been excluded in most studies. Few researchers have been working to develop a dynamic JSL alphabet, and their performance accuracy is unsatisfactory. We proposed a dynamic JSL recognition system using effective feature extraction and feature selection approaches to overcome the challenges. In the procedure, we follow the hand pose estimation, effective feature extraction, and machine learning techniques. We collected a video dataset capturing JSL gestures through standard RGB cameras and employed MediaPipe for hand pose estimation. Four types of features were proposed. The significance of these features is that the same feature generation method can be used regardless of the number of frames or whether the features are dynamic or static. We employed a Random forest (RF) based feature selection approach to select the potential feature. Finally, we fed the reduced features into the kernels-based Support Vector Machine (SVM) algorithm classification. Evaluations conducted on our proprietary newly created dynamic Japanese sign language alphabet dataset and LSA64 dynamic dataset yielded recognition accuracies of 97.20% and 98.40%, respectively. This innovative approach not only addresses the complexities of JSL but also holds the potential to bridge communication gaps, offering effective communication for the deaf and hard-of-hearing, and has broader implications for sign language recognition systems globally.
HDPose: Post-Hierarchical Diffusion with Conditioning for 3D Human Pose Estimation
Authors: Lee Donghoon , Kim Jaeho
Published: January 2024

Recently, monocular 3D human pose estimation (HPE) methods were used to accurately predict 3D pose by solving the ill-pose problem caused by 3D-2D projection. However, monocular 3D HPE still remains challenging owing to the inherent depth ambiguity and occlusions. To address this issue, previous studies have proposed diffusion model-based approaches (DDPM) that learn to reconstruct a correct 3D pose from a noisy initial 3D pose. In addition, these approaches use 2D keypoints or context encoders that encode spatial and temporal information to inform the model. However, they often fall short of achieving peak performance, or require an extended period to converge to the target pose. In this paper, we proposed HDPose, which can converge rapidly and predict 3D poses accurately. Our approach aggregated spatial and temporal information from the condition into a denoising model in a hierarchical structure. We observed that the post-hierarchical structure achieved the best performance among various condition structures. Further, we evaluated our model on the widely used Human3.6M and MPI-INF-3DHP datasets. The proposed model demonstrated competitive performance with state-of-the-art models, achieving high accuracy with faster convergence while being considerably more lightweight.
A Radar Echo Simulator for the Synthesis of Randomized Training Data Sets in the Context of AI-Based Applications
Authors: Schorlemer Jonas , Altholz Jochen , Barowski Jan , Baer Christoph , Rolfes Ilona , Schulz Christian
Published: January 2024

Supervised machine learning algorithms usually require huge labeled data sets to produce sufficiently good results. For many applications, these data sets are still not available today, and the reasons for this can be manifold. As a solution, the missing training data can be generated by fast simulators. This procedure is well studied and allows filling possible gaps in the training data, which can further improve the results of a machine learning model. For this reason, this article deals with the development of a two-dimensional electromagnetic field simulator for modeling the response of a radar sensor in an imaging system based on the synthetic aperture radar principle. The creation of completely random scenes is essential to achieve data sets with large variance. Therefore, special emphasis is placed on the development of methods that allow creating random objects, which can then be assembled into an entire scene. In the context of this contribution, we focus on humanitarian demining with regard to improvised explosive devices using a ground-penetrating radar system. This is an area where the use of trained classifiers is of great importance, but in practice, there are little to no labeled datasets for the training process. The simulation results show good agreement with the measurement results obtained in a previous contribution, demonstrating the possibility of enhancing sparse training data sets with synthetic data.
Simulation-Based Approach to the Matching of a Dielectric-Filled Circular Waveguide Aperture
Authors: Xu Songyuan , Heo Jiwon , Ahn Byoung-Kwon , Lee Chan-Soo , Ahn Bierng-Chearl
Published: January 2024

The circular waveguide aperture or open-end radiator, one of the canonical antenna elements, can be filled with a dielectric material for miniaturization. With dielectric filling, the aperture reflection increases and impedance matching is necessary. This paper presents a simple but innovative simulation-based approach to the aperture matching of a dielectric-filled circular waveguide aperture. By properly loading the aperture with two- or three-section dielectric rings, the impedance matching is possible over a wide frequency range starting slightly above the TE11-mode cutoff and continuing upward. The material for the aperture matching is the same as that filling the waveguide. The proposed matching structure is analyzed and optimized using a simulation tool for the dielectric constant εr of the filling material ranging from 1.8 to 10. For εr ≥ 5, the unmatched reflection coefficient ranges from −6.0 dB to −0.9 dB while the matched reflection coefficient is from −20.4 dB to −10.0 dB. The impedance matching has been achieved over more than an octave bandwidth.
Performance Evaluation of Computer Vision Algorithms in a Programmable Logic Controller: An Industrial Case Study
Authors: Vieira Rodrigo , Silva Dino , Ribeiro Eliseu , Perdigoto Luís , Coelho Paulo Jorge
Published: January 2024

This work evaluates the use of a programmable logic controller (PLC) from Phoenix Contact’s PLCnext ecosystem as an image processing platform. PLCnext controllers provide the functions of “classical” industrial controllers, but they are based on the Linux operating system, also allowing for the use of software tools usually associated with computers. Visual processing applications in the Python programming language using the OpenCV library are implemented in the PLC using this feature. This research is focused on evaluating the use of this PLC as an image processing platform, particularly for industrial machine vision applications. The methodology is based on comparing the PLC’s performance against a computer using standard image processing algorithms. In addition, a demonstration application based on a real-world scenario for quality control by visual inspection is presented. It is concluded that despite significant limitations in processing power, the simultaneous use of the PLC as an industrial controller and image processing platform is feasible for applications of low complexity and undemanding cycle times, providing valuable insights and benchmarks for the scientific community interested in the convergence of industrial automation and computer vision technologies.
Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images
Authors: Materka Andrzej , Jurek Jakub
Published: January 2024

Accurate geometric modeling of blood vessel lumen from 3D images is crucial for vessel quantification as part of the diagnosis, treatment, and monitoring of vascular diseases. Our method, unlike other approaches which assume a circular or elliptical vessel cross-section, employs parametric B-splines combined with image formation system equations to accurately localize the highly curved lumen boundaries. This approach avoids the need for image segmentation, which may reduce the localization accuracy due to spatial discretization. We demonstrate that the model parameters can be reliably identified by a feedforward neural network which, driven by the cross-section images, predicts the parameter values many times faster than a reference least-squares (LS) model fitting algorithm. We present and discuss two example applications, modeling the lower extremities of artery–vein complexes visualized in steady-state contrast-enhanced magnetic resonance images (MRI) and the coronary arteries pictured in computed tomography angiograms (CTA). Beyond applications in medical diagnosis, blood-flow simulation and vessel-phantom design, the method can serve as a tool for automated annotation of image datasets to train machine-learning algorithms.
Adaptive Clustering for Point Cloud
Authors: Lin Zitao , Kang Chuanli , Wu Siyi , Li Xuanhao , Cai Lei , Zhang Dan , Wang Shiwei
Published: January 2024

The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to large-scale scenes. Therefore, this paper proposes an adaptive clustering segmentation method. In this method, the threshold for clustering points within the point cloud is calculated using the characteristic parameters of adjacent points. After completing the preliminary segmentation of the point cloud, the segmentation results are further refined according to the standard deviation of the cluster points. Then, the cluster points whose number does not meet the conditions are further segmented, and, finally, scene point cloud data segmentation is realized. To test the superiority of this method, this study was based on point cloud data from a park in Guilin, Guangxi, China. The experimental results showed that this method is more practical and efficient than other methods, and it can effectively segment all ground objects and ground point cloud data in a scene. Compared with other segmentation methods that are easily affected by parameters, this method has strong robustness. In order to verify the universality of the method proposed in this paper, we test a public data set provided by ISPRS. The method achieves good segmentation results for multiple sample data, and it can distinguish noise points in a scene.
Spectral Data Processing for Field-Scale Soil Organic Carbon Monitoring
Authors: Reyes Javier , Ließ Mareike
Published: January 2024

Carbon sequestration in soils under agricultural use can contribute to climate change mitigation. Spatial–temporal soil organic carbon (SOC) monitoring requires more efficient data acquisition. This study aims to evaluate the potential of spectral on-the-go proximal measurements to serve these needs. The study was conducted as a long-term field experiment. SOC values ranged between 14 and 25 g kg−1 due to different fertilization treatments. Partial least squares regression models were built based on the spectral laboratory and field data collected with two spectrometers (site-specific and on-the-go). Correction of the field data based on the laboratory data was done by testing linear transformation, piecewise direct standardization, and external parameter orthogonalization (EPO). Different preprocessing methods were applied to extract the best possible information content from the sensor signal. The models were then thoroughly interpreted concerning spectral wavelength importance using regression coefficients and variable importance in projection scores. The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy for SOC monitoring. The use of different spectrometers under varying soil conditions revealed shifts in wavelength importance. Still, our findings on the use of on-the-go spectroscopy for spatial–temporal SOC monitoring are promising.
Contextual Patch-NetVLAD: Context-Aware Patch Feature Descriptor and Patch Matching Mechanism for Visual Place Recognition
Authors: Sun Wenyuan , Chen Wentang , Huang Runxiang , Tian Jing
Published: January 2024

The goal of visual place recognition (VPR) is to determine the location of a query image by identifying its place in a collection of image databases. Visual sensor technologies are crucial for visual place recognition as they allow for precise identification and location of query images within a database. Global descriptor-based VPR methods face the challenge of accurately capturing the local specific regions within a scene; consequently, it leads to an increasing probability of confusion during localization in such scenarios. To tackle feature extraction and feature matching challenges in VPR, we propose a modified patch-NetVLAD strategy that includes two new modules: a context-aware patch descriptor and a context-aware patch matching mechanism. Firstly, we propose a context-driven patch feature descriptor to overcome the limitations of global and local descriptors in visual place recognition. This descriptor aggregates features from each patch’s surrounding neighborhood. Secondly, we introduce a context-driven feature matching mechanism that utilizes cluster and saliency context-driven weighting rules to assign higher weights to patches that are less similar to densely populated or locally similar regions for improved localization performance. We further incorporate both of these modules into the patch-NetVLAD framework, resulting in a new approach called contextual patch-NetVLAD. Experimental results are provided to show that our proposed approach outperforms other state-of-the-art methods to achieve a Recall@10 score of 99.82 on Pittsburgh30k, 99.82 on FMDataset, and 97.68 on our benchmark dataset.
The Optimized Design of Soil-Touching Parts of a Greenhouse Humanoid Weeding Shovel Based on Strain Sensing and DEM-ADAMS Coupling Simulation
Authors: Gao Jianmin , Jin Zhipeng , Ai Anjun
Published: January 2024

To overcome the shortcomings of plowing and rotary tillage, a human-like weeding shoveling machine was designed. The machine’s various moving rods were analyzed using Matlab R2019b(9.7.0.1190202) software to determine the appropriate entry and cutting conditions, as well as non-cutting conditions. It was concluded that a θ2 of 90° was optimal for cutting the soil and that the shoveling depth was suitable for greenhouse weeding. The Adams and DEM coupled discrete element simulation system was developed for this machine and was used to analyze the rotating shaft torque and shovel bending moment. A strain measurement system based on strain gauges was designed to measure the rotating shaft torque and shovel bar bending moment. A bending moment and torque measurement system was designed to perform field measurement tests for comparison with simulation results. The simulation system’s rotating shaft had an average torque error of 6.26%, while the shovel rod’s bending moment had an average error of 5.43%. The simulation accuracy was within the acceptable error range. Table U8 (81 × 44) of the Uniform Design of the Mixing Factor Level for the Homogeneous Virtual Simulation Test includes eight levels of forward machine speed ranging from 0.1 to 0.45 m/s and four levels of output shaft speed ranging from 90 to 165 r/min. Crank lengths were set at four levels ranging from 155 to 185 mm, while shovel lengths were set at four levels ranging from 185 to 230 mm. Four types of shovel shapes were proposed, including pointed curved shovels, pointed straight shovels, straight-edged curved shovels, and straight-edged straight shovels. A mathematical model was created via a regression analysis of the results of coupled simulation tests to establish the relationship between shaft torque and shovel rod bending moment, tool advance speed, shaft speed, crank length, tool length, and tool shape. The model was used to determine the optimum working parameters.
A Smart Cane Based on 2D LiDAR and RGB-D Camera Sensor-Realizing Navigation and Obstacle Recognition
Authors: Mai Chunming , Chen Huaze , Zeng Lina , Li Zaijin , Liu Guojun , Qiao Zhongliang , Qu Yi , Li Lianhe , Li Lin
Published: January 2024

In this paper, an intelligent blind guide system based on 2D LiDAR and RGB-D camera sensing is proposed, and the system is mounted on a smart cane. The intelligent guide system relies on 2D LiDAR, an RGB-D camera, IMU, GPS, Jetson nano B01, STM32, and other hardware. The main advantage of the intelligent guide system proposed by us is that the distance between the smart cane and obstacles can be measured by 2D LiDAR based on the cartographer algorithm, thus achieving simultaneous localization and mapping (SLAM). At the same time, through the improved YOLOv5 algorithm, pedestrians, vehicles, pedestrian crosswalks, traffic lights, warning posts, stone piers, tactile paving, and other objects in front of the visually impaired can be quickly and effectively identified. Laser SLAM and improved YOLOv5 obstacle identification tests were carried out inside a teaching building on the campus of Hainan Normal University and on a pedestrian crossing on Longkun South Road in Haikou City, Hainan Province. The results show that the intelligent guide system developed by us can drive the omnidirectional wheels at the bottom of the smart cane and provide the smart cane with a self-leading blind guide function, like a “guide dog”, which can effectively guide the visually impaired to avoid obstacles and reach their predetermined destination, and can quickly and effectively identify the obstacles on the way out. The mapping and positioning accuracy of the system’s laser SLAM is 1 m ± 7 cm, and the laser SLAM speed of this system is 25~31 FPS, which can realize the short-distance obstacle avoidance and navigation function both in indoor and outdoor environments. The improved YOLOv5 helps to identify 86 types of objects. The recognition rates for pedestrian crosswalks and for vehicles are 84.6% and 71.8%, respectively; the overall recognition rate for 86 types of objects is 61.2%, and the obstacle recognition rate of the intelligent guide system is 25–26 FPS.
Internet of Vehicles (IoV)-Based Task Scheduling Approach Using Fuzzy Logic Technique in Fog Computing Enables Vehicular Ad Hoc Network (VANET)
Authors: Ehtisham Muhammad , Hassan Mahmood ul , Al-Awady Amin A. , Ali Abid , Junaid Muhammad , Khan Jahangir , Abdelrahman Ali Yahya Ali , Akram Muhammad
Published: January 2024

The intelligent transportation system (ITS) relies heavily on the vehicular ad hoc network (VANET) and the internet of vehicles (IoVs), which combine cloud and fog to improve task processing capabilities. As a cloud extension, the fog processes’ infrastructure is close to VANET, fostering an environment favorable to smart cars with IT equipment and effective task management oversight. Vehicle processing power, bandwidth, time, and high-speed mobility are all limited in VANET. It is critical to satisfy the vehicles’ requirements for minimal latency and fast reaction times while offloading duties to the fog layer. We proposed a fuzzy logic-based task scheduling system in VANET to minimize latency and improve the enhanced response time when offloading tasks in the IoV. The proposed method effectively transfers workloads to the fog computing layer while considering the constrained resources of car nodes. After choosing a suitable processing unit, the algorithm sends the job and its associated resources to the fog layer. The dataset is related to crisp values for fog computing for system utilization, latency, and task deadline time for over 5000 values. The task execution, latency, deadline of task, storage, CPU, and bandwidth utilizations are used for fuzzy set values. We proved the effectiveness of our proposed task scheduling framework via simulation tests, outperforming current algorithms in terms of task ratio by 13%, decreasing average turnaround time by 9%, minimizing makespan time by 15%, and effectively overcoming average latency time within the network parameters. The proposed technique shows better results and responses than previous techniques by scheduling the tasks toward fog layers with less response time and minimizing the overall time from task submission to completion.
Efficient FPGA Implementation of Convolutional Neural Networks and Long Short-Term Memory for Radar Emitter Signal Recognition
Authors: Wu Bin , Wu Xinyu , Li Peng , Gao Youbing , Si Jiangbo , Al-Dhahir Naofal
Published: January 2024

In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep learning algorithms and data preprocessing operations have a huge demand for computing power, which cannot meet the requirements of low power consumption and high real-time processing scenarios. Therefore, many research works have remained in the experimental stage and cannot be actually implemented. To tackle this problem, this paper proposes a resource reuse computing acceleration platform based on field programmable gate arrays (FPGA), and implements a one-dimensional (1D) convolutional neural network (CNN) and long short-term memory (LSTM) neural network (NN) model for radar emitter signal recognition, directly targeting the intermediate frequency (IF) data of radar emitter signal for classification and recognition. The implementation of the 1D-CNN-LSTM neural network on FPGA is realized by multiplexing the same systolic array to accomplish the parallel acceleration of 1D convolution and matrix vector multiplication operations. We implemented our network on Xilinx XCKU040 to evaluate the effectiveness of our proposed solution. Our experiments show that the system can achieve 7.34 giga operations per second (GOPS) data throughput with only 5.022 W power consumption when the radar emitter signal recognition rate is 96.53%, which greatly improves the energy efficiency ratio and real-time performance of the radar emitter recognition system.
Comparison of ECG Saline-Conduction Technique and ECG Wire-Based Technique for Peripherally Inserted Central Catheter Insertion: A Randomized Controlled Trial
Authors: Gullo Giuseppe , Frossard Pierre , Colin Anaïs , Qanadli Salah Dine
Published: January 2024

(1) Background: The peripherally inserted central catheter (PICC) is commonly used in medicine. The tip position was shown to be a major determinant in PICC function and related complications. Recent advances in ECG guidance might facilitate daily practice. This study aimed to compare two ECG techniques, in terms of their tip-position accuracy, puncture site layout, and signal quality; (2) Methods: This randomized open study (1:1) included 320 participants. One PICC guidance technique used ECG signal transmission with saline (ST); the other technique used a guidewire (WT). Techniques were compared by the distance between the catheter tip and the cavoatrial junction (DCAJ) on chest X-rays, insertion-point hemostasis time, and the extracorporeal catheter length between the hub and the insertion point; (3) Results: The mean DCAJs were significantly different between ST (1.36 cm, 95% CI: 1.22–1.37) and WT (1.12 cm, 95% CI: 0.98–1.25; p = 0.013) groups. When DCAJs were classified as optimal, suboptimal, or inadequate, the difference between techniques had limited clinical impact (p = 0.085). However, the hemostasis time at the puncture site was significantly better with WT (no delay in 82% of patients) compared to ST (no delay in 50% of patients; p < 0.001). Conversely, ST achieved optimal and suboptimal extracorporeal lengths significantly more frequently than WT (100% vs. 66%; p < 0.001); (4) Conclusions: ECG guidance technologies achieved significantly different tip placements, but the difference had minimal clinical impact. Nevertheless, each technique displayed an important drawback at the PICC insertion point: the extracorporeal catheter was significantly longer with WT and the hemostasis delay was significantly longer with ST.
The Feasibility and Accuracy of Holographic Navigation with Laser Crosshair Simulator Registration on a Mixed-Reality Display
Authors: Qi Ziyu , Jin Haitao , Wang Qun , Gan Zhichao , Xiong Ruochu , Zhang Shiyu , Liu Minghang , Wang Jingyue , Ding Xinyu , Chen Xiaolei , Zhang Jiashu , Nimsky Christopher , Bopp Miriam H. A.
Published: January 2024

Addressing conventional neurosurgical navigation systems’ high costs and complexity, this study explores the feasibility and accuracy of a simplified, cost-effective mixed reality navigation (MRN) system based on a laser crosshair simulator (LCS). A new automatic registration method was developed, featuring coplanar laser emitters and a recognizable target pattern. The workflow was integrated into Microsoft’s HoloLens-2 for practical application. The study assessed the system’s precision by utilizing life-sized 3D-printed head phantoms based on computed tomography (CT) or magnetic resonance imaging (MRI) data from 19 patients (female/male: 7/12, average age: 54.4 ± 18.5 years) with intracranial lesions. Six to seven CT/MRI-visible scalp markers were used as reference points per case. The LCS-MRN’s accuracy was evaluated through landmark-based and lesion-based analyses, using metrics such as target registration error (TRE) and Dice similarity coefficient (DSC). The system demonstrated immersive capabilities for observing intracranial structures across all cases. Analysis of 124 landmarks showed a TRE of 3.0 ± 0.5 mm, consistent across various surgical positions. The DSC of 0.83 ± 0.12 correlated significantly with lesion volume (Spearman rho = 0.813, p < 0.001). Therefore, the LCS-MRN system is a viable tool for neurosurgical planning, highlighting its low user dependency, cost-efficiency, and accuracy, with prospects for future clinical application enhancements.
Influence of Coal-Fired Fly Ash on Measurement Error of NO2 Electrochemical Sensors
Authors: Chen Wei , Zhou Hui , Wu Shijing , Liao Dongmei
Published: January 2024

To overcome the limitations of NO2 electrochemical sensors, including their inaccurate measurements and short working life, when used around coal-fired power plants, we investigated the influence of coal-fired fly ash deposition on the measurement error of NO2 electrochemical sensors through experimental tests. The morphological characteristics and pellet diameter distribution of coal-fired fly ash pellets were determined via scanning electron microscopy. The sedimentation velocity of coal-fired fly ash pellets in the air was determined through theoretical calculations of aerodynamics and hydrodynamics. Additionally, the effect of the deposition of coal-fired fly ash on the measurement error of NO2 electrochemical sensors was determined through experimental tests. The test results show that the minimum and maximum measurement errors of the NO2 electrochemical gas sensor were 8.015% and 30.35%, respectively, after a deposition duration of 30 days with 30 mg/m3 coal-fired fly ash. This demonstrates that coal-fired fly ash deposition is the cause of the inaccurate measurements and short working life of these sensors. Coal-fired fly ash causes a decrease in the gas diffusion area of the sensor and the diffusion coefficient, thus increasing the sensor measurement error.
Non-Immersive Virtual Reality-Based Therapy Applied in Cardiac Rehabilitation: A Systematic Review with Meta-Analysis
Authors: Peinado-Rubia Ana Belén , Verdejo-Herrero Alberto , Obrero-Gaitán Esteban , Osuna-Pérez María Catalina , Cortés-Pérez Irene , García-López Héctor
Published: January 2024

Background: The aim of this systematic review with meta-analysis was to assess the effectiveness of non-immersive virtual reality (niVR) active videogames in patients who underwent cardiac rehabilitation (CR). Methods: A systematic review with meta-analysis, according to the PRISMA guidelines and previously registered in PROSPERO (CRD42023485240), was performed through a literature search in PubMed (Medline), SCOPUS, WOS, and PEDro since inception to 21 November 2023. We included randomized controlled trials (RCTs) that assessed the effectiveness of an niVR intervention, in comparison with conventional CR and usual care, on aerobic capacity and cardiovascular endurance (physical function), anxiety, depression, and quality of life (QoL). The risk of bias in individual studies was assessed using the Cochrane risk of bias tool. Effect size was estimated using Cohen’s standardized mean difference (SMD) and its 95% confidence interval (95% CI) in a random-effects model. Results: Nine RCT that met the inclusion criteria were included in the meta-analysis. The meta-analysis showed a moderate-to-large effect favoring niVR active videogames included in CR in increasing aerobic capacity and cardiovascular endurance (SMD = 0.74; 95% CI 0.11 to 1.37; p = 0.021) and reducing anxiety (SMD = −0.66; 95% CI −1.13 to −0.2; p = 0.006). Only 4.8% of patients reported adverse events while performing niVR active videogames. Conclusions: Inclusion of niVR active videogames in CR programs is more effective than conventional CR in improving aerobic capacity and cardiovascular endurance and in reducing anxiety.
Sensor-Based Assessment of Time-of-Day-Dependent Physiological Responses and Physical Performances during a Walking Football Match in Higher-Weight Men
Authors: Hidouri Sami , Driss Tarak , Tagougui Sémah , Kammoun Noureddine , Chtourou Hamdi , Hammouda Omar
Published: January 2024

Monitoring key physiological metrics, including heart rate and heart rate variability, has been shown to be of value in exercise science, disease management, and overall health. The purpose of this study was to investigate the diurnal variation of physiological responses and physical performances using digital biomarkers as a precise measurement tool during a walking football match (WFM) in higher-weight men. Nineteen males (mean age: 42.53 ± 12.18 years; BMI: 33.31 ± 4.31 kg·m−2) were engaged in a WFM at two different times of the day. Comprehensive evaluations of physiological parameters (e.g., cardiac autonomic function, lactate, glycemia, and oxygen saturation), along with physical performance, were assessed before, during, and after the match. Overall, there was a significant interaction (time of day x WFM) for mean blood pressure (MBP) (p = 0.007) and glycemia (p = 0.039). Glycemia decreased exclusively in the evening after WFM (p = 0.001), while mean blood pressure did not significantly change. Rating of perceived exertion was significantly higher in the evening than in the morning (p = 0.04), while the heart rate recovery after 1 min (HRR60s) of the match was lower in the evening than in the morning (p = 0.048). Overall, walking football practice seems to be safe, whatever the time of day. Furthermore, HRR60, glycemia, and (MBP) values were lower in the evening compared to the morning, suggesting that evening exercise practice could be safer for individuals with higher weight. The utilization of digital biomarkers for monitoring health status during WFM has been shown to be efficient.
Analytic Continuation, Phase Unwrapping, and Retrieval of the Refractive Index of Metamaterials from S-Parameters
Authors: Angiulli Giovanni , Versaci Mario , Calcagno Salvatore , Di Barba Paolo
Published: January 2024

The heuristic homogenization approach is intensively employed to characterize electromagnetic metamaterials (MMs). The effective parameters are extracted within this framework using the Nicolson–Ross–Weir (NRW) method. Special attention must be devoted to handling this procedure because of the branch ambiguity issue affecting it, i.e., the lack of uniqueness in the evaluation of the effective refractive index neff rooted in the use of the multivalued complex logarithm to invert the Airy–Fresnel relation. Over the years, several techniques based on the phase-unwrapping approach have been introduced, but without any theoretical justification. In this paper, we aim to clarify the theoretical connection between the phase unwrapping method and the analytic continuation theory framework. Furthermore, three-phase-unwrapping approaches, which descend directly from the theory we discussed, are compared to identify which approach is best suited to reconstruct the complex refractive index of metamaterials when the NRW method is applicable.
Emotivism Trends in Digital Political Communication: The Influence on the Results of the US Presidential Elections
Authors: Casas-Mas Belén , Fernández Marcellán Martin , Robles José Manuel , Vélez Daniel
Published: January 2024

Citizens, organizations and institutions are increasingly making use of digital social networks such as Twitter as a means by which to express their position as regards political topics. However, an increasing amount of academic literature coincides, in that it highlights the emotive and expressive nature of these positions. In other words, for the most part, the political opinions that are publicized are more like backing based on support or rejection (without arguments or motives). In parallel, said expressions have a key emotional element (expressions of a positive or negative affective disposition). This article consists of an analysis that aims, on the one hand, to measure the affective disposition of US citizens as expressed on Twitter during the two most recent electoral campaigns (2016 and 2020). Similarly, we have generated a model that facilitates the measurement of the extent to which the content of the aforementioned messages demonstrates arguments or motives, or lack of. By way of the use of a database for the same Twitter accounts in both elections, we provide very consistent results that highlight the lack of argumentation of the public debate and the notably polarized profile of the affective dispositions shown by participants. We use the thesis of emotivism to give a more significant analytical weighting to this research.
Immersive Learning for Lost Architectural Heritage: Interweaving the Past and Present, Physical and Digital in the Monastery of Madre de Deus
Authors: Rafeiro Jesse , Tomé Ana , Nazário Maria
Published: January 2024

This paper presents the creation of an immersive learning experience of the lost 16th-century Monastery of Madre de Deus, now the National Tile Museum in Lisbon, Portugal. It builds upon previous virtual reconstruction research which resulted in several digital models accompanied by paradata supporting the construction of different hypotheses. Reinforced by a review of relevant literature intersecting virtual heritage dissemination, research transparency and immersive learning, this paper details an immersive experience created with Shapespark 2.9.7, an online platform designed for architectural walkthroughs but repurposed for heritage dissemination. The result is a prototype that takes place in the existing building wherein the visitor can be transported to equivalent spaces of the 16th or 17th century to gain first-hand experiences of speculative pasts. While the constraints of the Shapespark platform necessitated a counterintuitive narrative workaround, this enabled creative associations to be made between the physical and virtual and the past and present. This paper identifies various advantages and disadvantages of the platform in the context of immersive learning and the long-term virtual sustainability of lost architectural heritage.
Integrating Environmental and Economic Considerations in Charging Station Planning: An Improved Quantum Genetic Algorithm
Authors: Hu Dandan , Li Xiongkai , Liu Chen , Liu Zhi-Wei
Published: January 2024

China’s pursuit of carbon peak and carbon neutrality relies heavily on the widespread adoption of electric vehicles (EVs), necessitating the optimal location and sizing of charging stations (CSs). This study proposes a model for minimizing the overall social cost by considering CS construction and operation costs, EV user charging time costs, and associated carbon emissions costs. An improved quantum genetic algorithm, integrating a dynamic rotation angle and simulated annealing elements, addresses the optimization problem. Performance evaluation employs test functions and a case study using electric taxi trajectory data from Shenzhen. Findings reveal that higher charging power does not always yield better outcomes; appropriate power selection effectively reduces costs. Increasing the number of CSs beyond a threshold fails to significantly reduce carbon emission costs but enhances demand coverage.
Sustainable Utilization of Phosphogypsum in Multi-Solid Waste Recycled Aggregates: Environmental Impact and Economic Viability
Authors: Li Wengang , Ma Liping , Qiu Shuyue , Yin Xia , Dai Quxiu , Du Wang
Published: January 2024

This study contributes to sustainable construction practices by exploring the use of phosphogypsum, a commonly discarded byproduct, in the production of recycled aggregates. Addressing both environmental and economic aspects of sustainability, we investigate the feasibility of employing phosphogypsum as a primary raw material, in collaboration with various solid waste components, using pressure molding techniques. Our research evaluates the performance of these aggregates in terms of compressive strength, softening coefficient, and their potential to reduce environmental pollution. Findings indicate that aggregates containing 70% phosphogypsum can effectively meet the requirements for C25 concrete applications, underscoring the method’s sustainability through efficient waste material utilization, reduced environmental impact, and potential economic benefits compared to new resource extraction. This approach contributes to the understanding of sustainable resource utilization in construction, resonating with global sustainability goals, particularly the United Nations’ 2030 Agenda for Sustainable Development.
Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data
Authors: Fu Yuanfang , Cai Zhenrao , Fang Chaoyang
Published: January 2024

Rural tourism serves as a crucial means for fostering rural economic prosperity and inheriting rural culture. The assessment of the quality of rural tourism development and the identification of disparities in rural tourism development among regions have become focal points in current research. This paper utilizes tourism big data to establish a system for evaluating rural tourism popularity and proposes a method for identifying rural tourism hotspots. The study explores the spatiotemporal evolution characteristics and formation mechanisms of the cold and hot patterns of rural tourism in China during two periods (pre-pandemic and post-pandemic) and on two spatial scales (provincial and municipal levels). The research findings indicate that (1) the annual variation in rural tourism popularity exhibits a fluctuating upward trend, with significant seasonal variations on a monthly basis. (2) The spatial pattern of rural tourism popularity changes with the scale effect. At the provincial level, hotspot areas form an east–west dual-core pattern, while at the municipal level, hotspot areas demonstrate an evolution from a three-core to a four-core pattern. In the post-pandemic era, rural tourism popularity in the northwest and southwest regions is experiencing a counter-trend growth. (3) At different spatiotemporal scales, influencing factors and their impact intensities vary. At the provincial level, road density and reception capacity consistently play dominant roles, and per capita disposable income significantly influences early-stage popularity enhancement. At the municipal level, resident population and tourism resources influence are the dominant factors, and the influence of air quality and regional media attention gradually strengthens. This article provides a new perspective on quantitative research in rural tourism, offering significant guidance for the rational allocation of resources in rural tourism, regional tourism collaboration, and the sustainable development of rural tourism in the post-pandemic era.
Incorporation of Defects in Finite Elements to Model Effective Mechanical Properties of Metamaterial Cells Printed by Selective Laser Melting
Authors: Vera-Rodríguez Gonzalo , Moreno-Corrales Laura , Marín-González Iván , Barba Daniel , Montáns Francisco J. , Sanz-Gómez Miguel Ángel
Published: January 2024

Additively printed mechanical metamaterial structures optimize material, energy and waste, producing more sustainable products. Their introduction in the production workflow depends on having proper tools for accurately predicting their performance. However, the additive manufacturing process incorporates significant defects which result in an important change of the effective properties of the metamaterial cell. Finite element predictions using perfect geometries and nominal base material properties result in important errors which may require excessive uncertainty-related safety design margins. This work presents a methodology to introduce the effect of the most common defects in finite element models to compute the effective mechanical response of different metamaterials printed by Selective Laser Melting. It is shown that even at elastic infinitesimal strains, the defects produce an important change in the effective mechanical capabilities of the metamaterial, which also depend on the type of the metamaterial cell studied and on the type and magnitude of defects. With the proposed methodology, which incorporates the distribution of defects in the finite element model, the predicted mechanical properties of the metamaterial better match the experimental ones. It is shown that the initial discrepancies in the order of 100% are reduced to an order of 5%.
Smart Technologies for Sustainable Urban and Regional Development
Authors: Mehmood Rashid , Yigitcanlar Tan , Corchado Juan M.
Published: January 2024

In the dynamic landscape of contemporary living spaces, cities, and societies, the call for sustainable urban and regional development resonates with increasing urgency [...]
Lean and Green Decision Model for Lean Tools Selection
Authors: Hegedić Miro , Gudlin Mihael , Golec Matija , Tošanović Nataša
Published: January 2024

Today’s businesses face a critical need to balance sustainability, environmental responsibility, and operational excellence. Integrating lean and green methodologies holds promise for achieving this balance but presents challenges in real-sector implementation. Despite extensive research in the scientific community, a universally accepted model for integrated lean and green implementation is lacking. Limited research also explores the impact of lean tools on environmental performance indicators. This study aims to identify key economic and environmental performance indicators and investigate how they are influenced by lean tools. The primary objective is to develop a decision-making model facilitating lean tool implementation based on a company’s priorities for economic and environmental performance. The methodology involves a literature review, complemented by questionnaires and interviews with experts from academia and the real sector. The findings show that commonly used lean tools positively influence economic and environmental indicators. The decision-making model, tailored to chosen criteria priorities, was successfully validated using simulated and real company data, which makes it a potentially valuable resource for companies navigating integrated lean and green methodologies. While the obtained results show promise, it is important to highlight the limitations arising from the regional focus on Croatia and the inclusion of a subset of the manufacturing industry.
Greater Energy Independence with Sustainable Steel Production
Authors: Kiessling Sandra , Gohari Darabkhani Hamidreza , Soliman Abdel-Hamid
Published: January 2024

Global energy market price volatility and an upward trajectory of prices per unit of electricity have sent all industrial sectors and many economies to the brink of recession. Alongside the urgent need for decarbonisation of all industries, achieving a globally higher level of energy independence across all sectors seems imperative. A multi-disciplinary approach with a proposed system of CO2 emissions reduction and capture technologies has the potential for short-term emissions reduction to near-zero in the steel industry—although some of the mechanisms can be implemented across most heavy industries. The findings of this research show a CO2 emissions reduction of ~30% from 977 t of CO2 to 684 t in one single blast furnace production cycle (based on 330 tonnes of liquid iron production capacity, with the mean of 2.1–3.2 tonnes CO2/t of steel and chemical reactions emissions applied), by switching the electricity provider for operating the electric heaters to providers generating energy exclusively from renewable sources. Replacing coal with biomass and adding post-combustion capture units to the blast furnace operation, will add carbon neutrality into the process—resulting in CO2 emissions reduction to near-zero. Carbon capture from biomass utilisation (BECCS) will add the benefit of carbon-negative emissions to the cycle. Simultaneously, energy-saving and process improvement measures implementation (up to 60% efficiency increase), excess heat recovery <30% of energy savings, and retrofitting renewable energy technology resulted in an energy independence of 88%. Engineering solutions, partly subsidised in the UK, are readily available for implementation in the iron and steel manufacturing industry.