bluuebunny/crossref_metadata_2025 huggingface dataset:
A Stream Function Smoothing Method for the Design of MRI Gradient Coils on Non-Developable Surfaces
Authors: Yang Bohan , Ren Hao , Zuo Tongxing , Liu Zhenyu
Published: September 2023

Insert gradient coils with similar imaging body shapes typically have smaller dimensions and higher spatial efficiency. This often allows the gradient coils the achievement of stronger and faster gradient fields. Thus, improving existing methods to make them applicable to the design of MRI gradient coils on complex surfaces has also become a challenge. This article proposes an algorithm that smooths the implicitly expressed stream function based on the intrinsic surface Laplace–Beltrami operator. This algorithm can be used to simplify the design procedure of MRI gradient coils on non-developable surfaces. The following steps are performed by the proposed algorithm: an initial design of the stream function configuration, extraction of the surface mesh, discretization of the surface smoothing operator, and a smoothing of the contour lines. To evaluate the quality of the smoothed streamline configuration, several technical parameter metrics—including magnetic field accuracy, coil power consumption, theoretical minimum wire spacing, and the maximum curvature of the contour lines—were evaluated. The proposed method was successfully validated in a design gradient coil on both developable and non-developable surfaces. All examples evolved from an initial value with a locally non-smooth and complex topological configuration to a smooth result while maintaining high magnetic field accuracy.
Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models
Authors: Asiri Abdullah A. , Shaf Ahmad , Ali Tariq , Pasha Muhammad Ahmad , Aamir Muhammad , Irfan Muhammad , Alqahtani Saeed , Alghamdi Ahmad Joman , Alghamdi Ali H. , Alshamrani Abdullah Fahad A. , Alelyani Magbool , Alamri Sultan
Published: September 2023

This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.
A Novel 3D Reversible Data Hiding Scheme Based on Integer–Reversible Krawtchouk Transform for IoMT
Authors: Yamni Mohamed , Daoui Achraf , Pławiak Paweł , Mao Haokun , Alfarraj Osama , El-Latif Ahmed A. Abd
Published: September 2023

To avoid rounding errors associated with the limited representation of significant digits when applying the floating-point Krawtchouk transform in image processing, we present an integer and reversible version of the Krawtchouk transform (IRKT). This proposed IRKT generates integer-valued coefficients within the Krawtchouk domain, seamlessly aligning with the integer representation commonly utilized in lossless image applications. Building upon the IRKT, we introduce a novel 3D reversible data hiding (RDH) algorithm designed for the secure storage and transmission of extensive medical data within the IoMT (Internet of Medical Things) sector. Through the utilization of the IRKT-based 3D RDH method, a substantial amount of additional data can be embedded into 3D carrier medical images without augmenting their original size or compromising information integrity upon data extraction. Extensive experimental evaluations substantiate the effectiveness of the proposed algorithm, particularly regarding its high embedding capacity, imperceptibility, and resilience against statistical attacks. The integration of this proposed algorithm into the IoMT sector furnishes enhanced security measures for the safeguarded storage and transmission of massive medical data, thereby addressing the limitations of conventional 2D RDH algorithms for medical images.
Road Descriptors for Fast Global Localization on Rural Roads Using OpenStreetMap
Authors: Ninan Stephen , Rathinam Sivakumar
Published: September 2023

Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot’s understanding of a given scene. For autonomous vehicles (AVs), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach, however, is less suited for rural communities that are sparsely connected and cover large areas. Topological maps such as OpenStreetMap have proven to be a useful alternative in these situations. However, vehicle localization using these maps is non-trivial, particularly for the global localization task, where the map spans large areas. To deal with this challenge, we propose road descriptors along with an initialization technique for localization that allows for fast global pose estimation. We test our algorithms on (real world) maps and benchmark them against other map-based localization as well as SLAM algorithms. Our results show that the proposed method can narrow down the pose to within 50 cm of the ground truth significantly faster than the state-of-the-art methods.
Low-Cost ZnO Spray-Coated Optical Fiber Sensor for Detecting VOC Biomarkers of Diabetes
Authors: Swargiary Kankan , Jitpratak Pannathorn , Pathak Akhilesh Kumar , Viphavakit Charusluk
Published: September 2023

A non-invasive optical fiber sensor for detecting volatile organic compounds (VOCs) as biomarkers of diabetes is proposed and experimentally demonstrated. It offers a low-cost and straightforward fabrication approach by implementing a one-step spray coating of a ZnO colloidal solution on a glass optical fiber. The structure of the optical fiber sensor is based on a single-mode fiber–coreless silica fiber–single-mode fiber (SMF-CSF-SMF) structure, where the CSF is the sensor region spliced between two SMFs. The ZnO layer of a higher refractive index coated over the sensing region improves the light interaction with the surrounding medium, leading to sensitivity enhancement. The optical properties, morphology, and elemental composition of the ZnO layer were analyzed. The sensing mechanism of the developed sensor is based on a wavelength interrogation technique showing wavelength shifts when the sensor is exposed to various VOC vapor concentration levels. Various concentrations of the three VOCs (including acetone, isopropanol, and ethanol) ranging from 20% to 100% were tested and analyzed. The sensor noticeably shows a significant response towards acetone vapor, with a better sensitivity of 0.162 nm/% vapor than for isopropanol (0.082 nm/% vapor) and ethanol (0.075 nm/% vapor) vapors. The high sensitivity and selectivity towards acetone, a common biomarker for diabetes, offers the potential for further development of this sensor as a smart healthcare system for monitoring diabetic conditions.
Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC
Authors: Zhang Yong , Liu Kangting , Gao Feng , Zhao Fengkui
Published: September 2023

Path planning and tracking control is an essential part of autonomous vehicle research. In terms of path planning, the artificial potential field (APF) algorithm has attracted much attention due to its completeness. However, it has many limitations, such as local minima, unreachable targets, and inadequate safety. This study proposes an improved APF algorithm that addresses these issues. Firstly, a repulsion field action area is designed to consider the velocity of the nearest obstacle. Secondly, a road repulsion field is introduced to ensure the safety of the vehicle while driving. Thirdly, the distance factor between the target point and the virtual sub-target point is established to facilitate smooth driving and parking. Fourthly, a velocity repulsion field is created to avoid collisions. Finally, these repulsive fields are merged to derive a new formula, which facilitates the planning of a route that aligns with the structured road. After path planning, a cubic B-spline path optimization method is proposed to optimize the path obtained using the improved APF algorithm. In terms of path tracking, an improved sliding mode controller is designed. This controller integrates lateral and heading errors, improves the sliding mode function, and enhances the accuracy of path tracking. The MATLAB platform is used to verify the effectiveness of the improved APF algorithm. The results demonstrate that it effectively plans a path that considers car kinematics, resulting in smaller and more continuous heading angles and curvatures compared with general APF planning. In a tracking control experiment conducted on the Carsim–Simulink platform, the lateral error of the vehicle is controlled within 0.06 m at both high and low speeds, and the yaw angle error is controlled within 0.3 rad. These results validate the traceability of the improved APF method proposed in this study and the high tracking accuracy of the controller.
CAT-CBAM-Net: An Automatic Scoring Method for Sow Body Condition Based on CNN and Transformer
Authors: Xue Hongxiang , Sun Yuwen , Chen Jinxin , Tian Haonan , Liu Zihao , Shen Mingxia , Liu Longshen
Published: September 2023

Sow body condition scoring has been confirmed as a vital procedure in sow management. A timely and accurate assessment of the body condition of a sow is conducive to determining nutritional supply, and it takes on critical significance in enhancing sow reproductive performance. Manual sow body condition scoring methods have been extensively employed in large-scale sow farms, which are time-consuming and labor-intensive. To address the above-mentioned problem, a dual neural network-based automatic scoring method was developed in this study for sow body condition. The developed method aims to enhance the ability to capture local features and global information in sow images by combining CNN and transformer networks. Moreover, it introduces a CBAM module to help the network pay more attention to crucial feature channels while suppressing attention to irrelevant channels. To tackle the problem of imbalanced categories and mislabeling of body condition data, the original loss function was substituted with the optimized focal loss function. As indicated by the model test, the sow body condition classification achieved an average precision of 91.06%, the average recall rate was 91.58%, and the average F1 score reached 91.31%. The comprehensive comparative experimental results suggested that the proposed method yielded optimal performance on this dataset. The method developed in this study is capable of achieving automatic scoring of sow body condition, and it shows broad and promising applications.
OTE-SLAM: An Object Tracking Enhanced Visual SLAM System for Dynamic Environments
Authors: Chang Yimeng , Hu Jun , Xu Shiyou
Published: September 2023

With the rapid development of autonomous driving and robotics applications in recent years, visual Simultaneous Localization and Mapping (SLAM) has become a hot research topic. The majority of visual SLAM systems relies on the assumption of scene rigidity, which may not always hold true in real applications. In dynamic environments, SLAM systems, without accounting for dynamic objects, will easily fail to estimate the camera pose. Some existing methods attempt to address this issue by simply excluding the dynamic features lying in moving objects. But this may lead to a shortage of features for tracking. To tackle this problem, we propose OTE-SLAM, an object tracking enhanced visual SLAM system, which not only tracks the camera motion, but also tracks the movement of dynamic objects. Furthermore, we perform joint optimization of both the camera pose and object 3D position, enabling a mutual benefit between visual SLAM and object tracking. The results of experiences demonstrate that the proposed approach improves the accuracy of the SLAM system in challenging dynamic environments. The improvements include a maximum reduction in both absolute trajectory error and relative trajectory error by 22% and 33%, respectively.
Micro-Doppler Signature Detection and Recognition of UAVs Based on OMP Algorithm
Authors: Fan Shiqi , Wu Ziyan , Xu Wenqiang , Zhu Jiabao , Tu Gangyi
Published: September 2023

With the proliferation of unmanned aerial vehicles (UAVs) in both commercial and military use, the public is paying increasing attention to UAV identification and regulation. The micro-Doppler characteristics of a UAV can reflect its structure and motion information, which provides an important reference for UAV recognition. The low flight altitude and small radar cross-section (RCS) of UAVs make the cancellation of strong ground clutter become a key problem in extracting the weak micro-Doppler signals. In this paper, a clutter suppression method based on an orthogonal matching pursuit (OMP) algorithm is proposed, which is used to process echo signals obtained by a linear frequency modulated continuous wave (LFMCW) radar. The focus of this method is on the idea of sparse representation, which establishes a complete set of environmental clutter dictionaries to effectively suppress clutter in the received echo signals of a hovering UAV. The processed signals are analyzed in the time–frequency domain. According to the flicker phenomenon of UAV rotor blades and related micro-Doppler characteristics, the feature parameters of unknown UAVs can be estimated. Compared with traditional signal processing methods, the method based on OMP algorithm shows advantages in having a low signal-to-noise ratio (−10 dB). Field experiments indicate that this approach can effectively reduce clutter power (−15 dB) and successfully extract micro-Doppler signals for identifying different UAVs.
Delamination Detection and Localization in Vibrating Composite Plates and Shells Using the Inverse Finite Element Method
Authors: Ganjdoust Faraz , Kefal Adnan , Tessler Alexander
Published: September 2023

Delamination damage is one of the most critical damage modes of composite materials. It takes place through the thickness of the laminated composites and does not show subtle surface effects. In the present study, a delamination detection approach based on equivalent von Mises strains is demonstrated for vibrating laminated (i.e., unidirectional fabric) composite plates. In this context, the governing relations of the inverse finite element method were recast according to the refined zigzag theory. Using the in situ strain measurements obtained from the surface and through the thickness of the composite shell, the inverse analysis was performed, and the strain field of the composite shell was reconstructed. The implementation of the proposed methodology is demonstrated for two numerical case studies associated with the harmonic and random vibrations of composite shells. The findings of this study show that the present damage detection method is capable of real-time monitoring of damage and providing information about the exact location, shape, and extent of the delamination damage in the vibrating composite plate. Finally, the robustness of the proposed method in response to resonance and extreme load variations is shown.
Captive Animal Behavior Study by Video Analysis
Authors: Rotaru Florin , Bejinariu Silviu-Ioan , Costin Hariton-Nicolae , Luca Ramona , Niţă Cristina Diana
Published: September 2023

Three video analysis-based applications for the study of captive animal behavior are presented. The aim of the first one is to provide certain parameters to assess drug efficiency by analyzing the movement of a rat. The scene is a three-chamber plastic box. First, the rat can move only in the middle room. The rat’s head pose is the first parameter needed. Secondly, the rodent could walk in all three compartments. The entry number in each area and visit duration are the other indicators used in the final evaluation. The second application is related to a neuroscience experiment. Besides the electroencephalographic (EEG) signals yielded by a radio frequency link from a headset mounted on a monkey, the head placement is a useful source of information for reliable analysis, as well as its orientation. Finally, a fusion method to construct the displacement of a panda bear in a cage and the corresponding motion analysis to recognize its stress states are shown. The arena is a zoological garden that imitates the native environment of a panda bear. This surrounding is monitored by means of four video cameras. We have applied the following stages: (a) panda detection for every video camera; (b) panda path construction from all routes; and (c) panda way filtering and analysis.
EgoActive: Integrated Wireless Wearable Sensors for Capturing Infant Egocentric Auditory–Visual Statistics and Autonomic Nervous System Function ‘in the Wild’
Authors: Geangu Elena , Smith William A. P. , Mason Harry T. , Martinez-Cedillo Astrid Priscilla , Hunter David , Knight Marina I. , Liang Haipeng , del Carmen Garcia de Soria Bazan Maria , Tse Zion Tsz Ho , Rowland Thomas , Corpuz Dom , Hunter Josh , Singh Nishant , Vuong Quoc C. , Abdelgayed Mona Ragab Sayed , Mullineaux David R. , Smith Stephen , Muller Bruce R.
Published: September 2023

There have been sustained efforts toward using naturalistic methods in developmental science to measure infant behaviors in the real world from an egocentric perspective because statistical regularities in the environment can shape and be shaped by the developing infant. However, there is no user-friendly and unobtrusive technology to densely and reliably sample life in the wild. To address this gap, we present the design, implementation and validation of the EgoActive platform, which addresses limitations of existing wearable technologies for developmental research. EgoActive records the active infants’ egocentric perspective of the world via a miniature wireless head-mounted camera concurrently with their physiological responses to this input via a lightweight, wireless ECG/acceleration sensor. We also provide software tools to facilitate data analyses. Our validation studies showed that the cameras and body sensors performed well. Families also reported that the platform was comfortable, easy to use and operate, and did not interfere with daily activities. The synchronized multimodal data from the EgoActive platform can help tease apart complex processes that are important for child development to further our understanding of areas ranging from executive function to emotion processing and social learning.
Investigating the Joint Amplitude and Phase Imaging of Stained Samples in Automatic Diagnosis
Authors: Hassini Houda , Dorizzi Bernadette , Thellier Marc , Klossa Jacques , Gottesman Yaneck
Published: September 2023

The diagnosis of many diseases relies, at least on first intention, on an analysis of blood smears acquired with a microscope. However, image quality is often insufficient for the automation of such processing. A promising improvement concerns the acquisition of enriched information on samples. In particular, Quantitative Phase Imaging (QPI) techniques, which allow the digitization of the phase in complement to the intensity, are attracting growing interest. Such imaging allows the exploration of transparent objects not visible in the intensity image using the phase image only. Another direction proposes using stained images to reveal some characteristics of the cells in the intensity image; in this case, the phase information is not exploited. In this paper, we question the interest of using the bi-modal information brought by intensity and phase in a QPI acquisition when the samples are stained. We consider the problem of detecting parasitized red blood cells for diagnosing malaria from stained blood smears using a Deep Neural Network (DNN). Fourier Ptychographic Microscopy (FPM) is used as the computational microscopy framework to produce QPI images. We show that the bi-modal information enhances the detection performance by 4% compared to the intensity image only when the convolution in the DNN is implemented through a complex-based formalism. This proves that the DNN can benefit from the bi-modal enhanced information. We conjecture that these results should extend to other applications processed through QPI acquisition.
Fast and Uncooled Semiconducting Ca-Doped Y-Ba-Cu-O Thin Film-Based Thermal Sensors for Infrared
Authors: Dégardin Annick , Alamarguy David , Brézard Oudot Aurore , Beldi Samir , Chaumont Christine , Boussaha Faouzi , Cheneau Antoine , Kreisler Alain
Published: September 2023

YBa2Cu3O6+x (YBCO) cuprates are semiconductive when oxygen depleted (x < 0.5). They can be used for uncooled thermal detection in the near-infrared: (i) low temperature deposition on silicon substrates, leading to an amorphous phase (a-YBCO); (ii) pyroelectric properties exploited in thermal detectors offering both low noise and fast response above 1 MHz. However, a-YBCO films exhibit a small direct current (DC) electrical conductivity, with strong non-linearity of current–voltage plots. Calcium doping is well known for improving the transport properties of oxygen-rich YBCO films (x > 0.7). In this paper, we consider the performances of pyroelectric detectors made from calcium-doped (10 at. %) and undoped a-YBCO films. First, the surface microstructure, composition, and DC electrical properties of a-Y0.9Ca0.1Ba2Cu3O6+x films were investigated; then devices were tested at 850 nm wavelength and results were analyzed with an analytical model. A lower DC conductivity was measured for the calcium-doped material, which exhibited a slightly rougher surface, with copper-rich precipitates. The calcium-doped device exhibited a higher specific detectivity (D*=7.5×107 cm·Hz/W at 100 kHz) than the undoped device. Moreover, a shorter thermal time constant (<8 ns) was inferred as compared to the undoped device and commercially available pyroelectric sensors, thus paving the way to significant improvements for fast infrared imaging applications.
An H-GrabCut Image Segmentation Algorithm for Indoor Pedestrian Background Removal
Authors: Huang Xuchao , Wang Shigang , Gao Xueshan , Luo Dingji , Xu Weiye , Pang Huiqing , Zhou Ming
Published: September 2023

In the context of predicting pedestrian trajectories for indoor mobile robots, it is crucial to accurately measure the distance between indoor pedestrians and robots. This study aims to address this requirement by extracting pedestrians as regions of interest and mitigating issues related to inaccurate depth camera distance measurements and illumination conditions. To tackle these challenges, we focus on an improved version of the H-GrabCut image segmentation algorithm, which involves four steps for segmenting indoor pedestrians. Firstly, we leverage the YOLO-V5 object recognition algorithm to construct detection nodes. Next, we propose an enhanced BIL-MSRCR algorithm to enhance the edge details of pedestrians. Finally, we optimize the clustering features of the GrabCut algorithm by incorporating two-dimensional entropy, UV component distance, and LBP texture feature values. The experimental results demonstrate that our algorithm achieves a segmentation accuracy of 97.13% in both the INRIA dataset and real-world tests, outperforming alternative methods in terms of sensitivity, missegmentation rate, and intersection-over-union metrics. These experiments confirm the feasibility and practicality of our approach. The aforementioned findings will be utilized in the preliminary processing of indoor mobile robot pedestrian trajectory prediction and enable path planning based on the predicted results.
Individual Locating of Soccer Players from a Single Moving View
Authors: Maglo Adrien , Orcesi Astrid , Denize Julien , Pham Quoc Cuong
Published: September 2023

Positional data in team sports is key in evaluating the players’ individual and collective performances. When the sole source of data is a broadcast-like video of the game, an efficient video tracking method is required to generate this data. This article describes a framework that extracts individual soccer player positions on the field. It is based on two main components. As in broadcast-like videos of team sport games, the camera view moves to follow the action and a sport field registration method estimates the homography between the pitch and the frame space. Our method estimates the positions of key points sampled on the pitch thanks to an encoder–decoder architecture. The attention mechanisms of the encoder, based on a vision transformer, captures characteristic pitch features globally in the frames. A multiple person tracker generates tracklets in the frame space by associating, with bipartite matching, the player detections between the current and the previous frames thanks to Intersection-Over-Union and distance criteria. Tracklets are then iteratively merged with appearance criteria thanks to a re-identification model. This model is fine-tuned in a self-supervised way on the player thumbnails of the video sample to specifically recognize the fine identification details of each player. The player positions in the frames projected by the homographies allow the obtaining of the real position of the players on the pitch at every moment of the video. We experimentally evaluate our sport field registration method and our 2D player tracker on public datasets. We demonstrate that they both outperform previous works for most metrics. Our 2D player tracker was also awarded first place at the SoccerNet tracking challenge in 2022 and 2023.
Detecting Cyber Attacks In-Vehicle Diagnostics Using an Intelligent Multistage Framework
Authors: Awaad Tasneem A. , El-Kharashi Mohamed Watheq , Taher Mohamed , Tawfik Ayman
Published: September 2023

The advanced technology of vehicles makes them vulnerable to external exploitation. The current trend of research is to impose security measures to protect vehicles from different aspects. One of the main problems that counter Intrusion Detection Systems (IDSs) is the necessity to have a low false acceptance rate (FA) with high detection accuracy without major changes in the vehicle network infrastructure. Furthermore, the location of IDSs can be controversial due to the limitations and concerns of Electronic Control Units (ECUs). Thus, we propose a novel framework of multistage to detect abnormality in vehicle diagnostic data based on specifications of diagnostics and stacking ensemble for various machine learning models. The proposed framework is verified against the KIA SOUL and Seat Leon 2018 datasets. Our IDS is evaluated against point anomaly attacks and period anomaly attacks that have not been used in its training. The results show the superiority of the framework and its robustness with high accuracy of 99.21%, a low false acceptance rate of 0.003%, and a good detection rate (DR) of 99.63% for Seat Leon 2018, and an accuracy of 99.22%, a low false acceptance rate of 0.005%, and good detection rate of 98.59% for KIA SOUL.
Maximize Lifetime of Wireless Rechargeable Sensor Networks with Mobile Energy-Limited Charging Device
Authors: Liu Guoqing , Chen Yaqian , Jiao Wanguo
Published: September 2023

Mobile charging devices (MCDs) have been regarded as a promising way to solve the energy shortage of wireless sensor networks. Due to ignoring some important factors, such as redundant sensor nodes, there is still room to improve network lifetimes. We propose a charging strategy for wireless sensor networks with one energy-limited MCD. To give the best support for sensor nodes which need charging the most, an algorithm is proposed to find the minimum sensor nodes which keep the coverage and connectivity of the network and have the least energy requirements. Then, the goal of maximizing network lifetime is changed into how to utilize the limited energy of the MCD to guarantee the minimum sensor nodes work as long as possible. If the MCD has enough energy for all sensor nodes, the charging algorithm is designed to minimize the outage time of the network and maximize charging efficiency. Otherwise, if the energy capacity is larger than the least energy requirement, the charging target minimizes the outage time of the minimum sensor node; otherwise the charging problem becomes maximizing the lifetime of minimum sensor nodes, which has lower complexity. The results of simulation experiments confirm that our scheme prolongs network lifetime and improves charging efficiency.
Research on Soil Moisture Estimation of Multiple-Track-GNSS Dual-Frequency Combination Observations Considering the Detection and Correction of Phase Outliers
Authors: Zhang Xudong , Ren Chao , Liang Yueji , Liang Jieyu , Yin Anchao , Wei Zhenkui
Published: September 2023

Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful separation of the reflection component from the direct component. In contrast, the presence of carrier phase and pseudorange multipath errors enables soil moisture retrieval without the requirement for separating the direct component of the signal. To acquire high-quality combined multipath errors and diversify GNSS-IR data sources, this study establishes the dual-frequency pseudorange combination (DFPC) and dual-frequency carrier phase combination (L4) that exclude geometrical factors, ionospheric delay, and tropospheric delay. Simultaneously, we propose two methods for estimating soil moisture: the DFPC method and the L4 method. Initially, the equal-weight least squares method is employed to calculate the initial delay phase. Subsequently, anomalous delay phases are detected and corrected through a combination of the minimum covariance determinant robust estimation (MCD) and the moving average filter (MAF). Finally, we utilize the multivariate linear regression (MLR) and extreme learning machine (ELM) to construct multi-satellite linear regression models (MSLRs) and multi-satellite nonlinear regression models (MSNRs) for soil moisture prediction, and compare the accuracy of each model. To validate the feasibility of these methods, data from site P031 of the Plate Boundary Observatory (PBO) H2O project are utilized. Experimental results demonstrate that combining MCD and MAF can effectively detect and correct outliers, yielding single-satellite delay phase sequences with a high quality. This improvement contributes to varying degrees of enhanced correlation between the single-satellite delay phase and soil moisture. When fusing the corrected delay phases from multiple satellite orbits using the DFPC method for soil moisture estimation, the correlations between the true soil moisture values and the predicted values obtained through MLR and ELM reach 0.81 and 0.88, respectively, while the correlations of the L4 method can reach 0.84 and 0.90, respectively. These findings indicate a substantial achievement in high-precision soil moisture estimation within a small satellite-elevation angle range.
Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients
Authors: Oei Chien Wei , Ng Eddie Yin Kwee , Ng Matthew Hok Shan , Tan Ru-San , Chan Yam Meng , Chan Lai Gwen , Acharya Udyavara Rajendra
Published: September 2023

Post-stroke depression and anxiety, collectively known as post-stroke adverse mental outcome (PSAMO) are common sequelae of stroke. About 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with PSAMO have poorer health outcomes with higher mortality and greater functional disability. In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shapley additive explanations (SHAP), an explainable AI (XAI) method, was applied to interpret the model. The best performing ML algorithm was gradient-boosted tree, which attained 74.7% binary classification accuracy. Feature importance calculated by SHAP produced a list of ranked important features that contributed to the prediction, which were consistent with findings of prior clinical studies. Some of these factors were modifiable, and potentially amenable to intervention at early stages of stroke to reduce the incidence of PSAMO.
Quantitative Dynamic Allodynograph—A Standardized Measure for Testing Dynamic Mechanical Allodynia in Chronic Limb Pain
Authors: Turgeman Dahan Noy , Vatine Jean-Jacques , Weissman-Fogel Irit , Karpin Hana , Shmuely Sharon , Bar-Shalita Tami
Published: September 2023

Background: Dynamic mechanical allodynia (DMA) is both a symptom and a central sensitization sign, yet no standardized method for quantifying the DMA area has been reported. This study aimed to establish psychometric properties for Quantitative Dynamic Allodynography (QDA), a newly developed protocol measuring the DMA area as a percentage of the body surface. Methods: Seventy-eight patients aged 18–65 diagnosed with chronic complex regional pain syndrome (CRPS) participated in this study. Test–retest reliability was conducted twice, one week apart (N = 20), and inter-rater (N = 3) reliability was conducted on 10 participants. Disease severity (CRPS Severity Score, CSS), pain intensity (VAS), and quality of life (SF-36) measures were utilized to test construct validity. Results: High inter-rater reliability (intraclass correlation coefficient (ICC) = 0.96, p < 0.001) and test–retest reliability (r = 0.98, p < 0.001) were found. Furthermore, the QDA score was found to be correlated with the CSS (r = 0.47, p < 0.001), VAS (r = 0.37, p < 0.001), and the SF-36 physical health total (r = −0.47, p < 0.001) scores. Conclusion: The QDA is the first developed reliable and valid protocol for measuring DMA in a clinical setting and may be used as a diagnostic and prognostic measure in clinics and in research, advancing the pain precision medicine approach.
PIPTO: Precise Inertial-Based Pipeline for Threshold-Based Fall Detection Using Three-Axis Accelerometers
Authors: Moutsis Stavros N. , Tsintotas Konstantinos A. , Gasteratos Antonios
Published: September 2023

After traffic-related incidents, falls are the second cause of human death, presenting the highest percentage among the elderly. Aiming to address this problem, the research community has developed methods built upon different sensors, such as wearable, ambiance, or hybrid, and various techniques, such as those that are machine learning- and heuristic based. Concerning the models used in the former case, they classify the input data between fall and no fall, and specific data dimensions are required. Yet, when algorithms that adopt heuristic techniques, mainly using thresholds, are combined with the previous models, they reduce the computational cost. To this end, this article presents a pipeline for detecting falls through a threshold-based technique over the data provided by a three-axis accelerometer. This way, we propose a low-complexity system that can be adopted from any acceleration sensor that receives information at different frequencies. Moreover, the input lengths can differ, while we achieve to detect multiple falls in a time series of sum vector magnitudes, providing the specific time range of the fall. As evaluated on several datasets, our pipeline reaches high performance results at 90.40% and 91.56% sensitivity on MMsys and KFall, respectively, while the generated specificity is 93.96% and 85.90%. Lastly, aiming to facilitate the research community, our framework, entitled PIPTO (drawing inspiration from the Greek verb “πι´πτω”, signifying “to fall”), is open sourced in Python and C.
An Experimental Investigation of the Influence of High-Frequency Standing Sound Waves on Depth Filtration Using Coarse-Pored Media
Authors: Hasenkrug Jonas , Schweitzer Rouven
Published: September 2023

When a suspension passes through a high-frequency standing sound wave, the particles it contains are manipulated by acoustic forces. In a one-dimensional sound field, these forces lead to a planar arrangement of the particles and the formation of agglomerates. It is known that the combination of these forces and depth filtration can be utilized to significantly increase the filter efficiency of coarse-pored media. So far, this concept has only been used in microfluidics. In this paper, we present the results of a scaled-up filtration channel to test the viability of the industrial application of acoustically assisted filtration systems for the removal of microparticles. The influences of acoustic power input, flow rate, and the porosity of the filter media are investigated. In addition to verifying the scalability, a significant decrease in the large particle fraction in the outflow of the channel was observed when a high-power sound field is applied. Furthermore, the formed agglomerates tend to rise to the fluid surface. The floating particles mostly consist of a large particle fraction.
Comparison of Ultra-Short Race Pace and High-Intensity Interval Training in Age Group Competitive Swimmers
Authors: Papadimitriou Konstantinos , Kabasakalis Athanasios , Papadopoulos Anastasios , Mavridis Georgios , Tsalis Georgios
Published: September 2023

The aim of this study was tο examine the acute responses to an Ultra-Short Race Pace Training (USRPT) and a High-Intensity Interval Training (HIIT), both oriented for the event of 100 m freestyle. Eighteen national-level swimmers (8 boys, 10 girls) aged 13.5 ± 0.1 years, with 8.0 ± 0.5 years of experience participated in the study. All participants completed a USRPT and a HIIT protocol consisting of 2 × 10 × 25 m (USRPT1 & USRPT2) and 5 × 50 m. Significantly higher swimming velocity (SV) were achieved in USRPT compared to HIIT (p < 0.001), while significantly lower distance per stroke (DPS) and stroke index (SI) were obtained (p = 0.007 and p < 0.001). Also, significantly lower blood lactate and glucose (BL & BG) concentrations were found after USRPT (p ≤ 0.001 and p = 0.037). Heart rate (HR) and rate of perceived exertion (RPE) were significantly lower after USRPT than HIIT (p < 0.001 and p = 0.015). According to the results, an USRPT swimming set consisting of 20 × 25 m at a 100 m pace seems to induce more specific responses in kinematic characteristics, biomarkers, HR and RPE compared to a 5 × 50 m HIIT set.
ESIB’s Antecedents: An Analytic Hierarchy Process Application in the Manufacturing Industry in Albania
Authors: Malaj Ardita , Zaim Selim , Bayyurt Nizamettin , Tarim Merve
Published: September 2023

This study examined factors that might motivate employees to engage in social innovation. The objectives of this study were to identify the relative importance of factors that impact Employee Social Intrapreneurial Behavior (ESIB) in the manufacturing sector in Albania using an AHP approach and to select the best practice that can improve Employee Social Intrapreneurial Behavior (ESIB) on a company basis in the same sector by merging a linguistic method with the AHP approach. A questionnaire was designed to collect expert viewpoints. A standardized AHP scale ranging from 1 to 9 was used in the questionnaire. Fifteen managers and experts from four important manufacturing companies in Albania (Everest Shpk., Kamëz, Albania; Lufra Shpk., Lushnje, Albania; Ajka Shpk., Lushnje, Albania and F&M Shpk., Tirane, Albania) were contacted, and eleven of them expressed an interest in engaging in our study by ranking the importance of various criteria and sub-criteria. The findings suggest that the sub-components of the internal factor, such as superior relationship quality and expected image gains, have a combined importance of 50% in the local weights in the Albanian context. The two sub-components mentioned above remain significant in the analysis of global weights, accounting for 41% of global influence. These findings confirm Hofstede’s evaluation of the Albanian culture, where the power distance dimension ranks first, accounting for 90% of the variance and thus classifying the society as very hierarchical. In such a cultural context, people accept and try to please their leaders. Furthermore, high uncertainty avoidance determines individual motivation for financial gains in an unsecure economic environment. We proposed and examined two programs that businesses could implement to boost employee participation in innovative behaviors, and the results suggest that organizations should implement a program called the Workshop. The core idea of this program is to provide monthly workshops focusing on improving humorous situations that may have positive effects on ESIB. Due to the small number of participants in this study, a future study might want to examine the viewpoints of employees as well as other stakeholders, such as NGOs or legal institutions.
Spatial-Temporal Evolution Patterns and Obstacle Factors of Urban–Rural “Economy–Society–Ecology” Coordination in the Yangtze River Delta
Authors: Chen Hao , Hua Yingying , Xu Yaying
Published: September 2023

Background: In the context of sustainable development, urban–rural integration is not solely focused on high economic growth but has been expanded to a wider range of social and ecological fields. Objectives: To analyze the spatial and temporal evolution of the coupling coordination level of urban–rural economic, social and ecological integration subsystems in the Yangtze River Delta and to explore its main obstacles, in order to provide countermeasures to promote the sustainable development of urban and rural areas. Methods: Panel data of 16 cities in the core area of the Yangtze River Delta from 2005 to 2020 were selected, and the entropy method was used to calculate the comprehensive development level of each subsystem, based on which the coupling coordination degree was measured by the coupling coordination model, and the obstacle degree of each indicator was obtained by the obstacle degree model. Outcomes: The urban–rural integration level kept a steady rise, and the “economy–society–ecology” coordination level transformed from borderline imbalances to moderate coordination. The high-value areas presented an initial “Π” shape distribution that later turned into a “>” shape, while the backward areas were primarily located at both ends of the north and south. The key obstacle factors included per capita GDP, population urbanization rate, urban and rural basic pension insurance coverage, faculty–student ratio in urban and rural primary and middle schools, per capita park green land, road network density and fertilizer application per unit area of sown. Recommendations: The systematic coordination of urban–rural integration should be promoted according to local conditions, with emphasis on broadening urban–rural communication channels, promoting the equalization of urban–rural public services and establishing the urban–rural collaborative mechanism for environmental maintenance.
Enhancing Sustainable Transportation: AI-Driven Bike Demand Forecasting in Smart Cities
Authors: Subramanian Malliga , Cho Jaehyuk , Veerappampalayam Easwaramoorthy Sathishkumar , Murugesan Akash , Chinnasamy Ramya
Published: September 2023

Due to global ecological restrictions, cities, particularly urban transportation, must choose ecological solutions. Sustainable bike-sharing systems (BSS) have become an important element in the worldwide transportation infrastructure as an alternative to fossil-fuel-powered cars in metropolitan areas. Nevertheless, the placement of docks, which are the parking areas for bikes, depends on accessibility to bike paths, population density, difficulty in bike mobility, commuting cost, the spread of docks, and route imbalance. The purpose of this study is to compare the performance of various time series and machine learning algorithms for predicting bike demand using a two-year historical log from the Capital Bikeshare system in Washington, DC, USA. Specifically, the algorithms tested are LSTM, GRU, RF, ARIMA, and SARIMA, and their performance is then measured using the MSE, MAE, and RMSE metrics. The study found GRU performed the best, with RF also producing reasonably accurate predictions. ARIMA and SARIMA models produced less accurate predictions, likely due to their assumptions of linearity and stationarity in the data. In summary, this research offers significant insights into the efficacy of diverse algorithms in forecasting bike demand, thereby contributing to future research in the field.
Analysing the Sustainability of the Production of Solid Recovered Fuel from Screening Waste
Authors: De la Torre Bayo Juan Jesús , Zamorano Toro Montserrat , Ruiz Luz Marina , Torres Rojo Juan Carlos , Martín Pascual Jaime
Published: September 2023

The development in wastewater management has caused a shift towards a circular model that prioritises energy generation and waste reduction. Traditional unitary processes in wastewater treatment, such as screening, only allow for landfill disposal without energy recovery. However, producing solid recovered fuel (SRF) from waste screening may be a possibility. The economic and environmental viability of this alternative, as a fundamental requirement for its implementation at industrial level, was assessed through a multi-scenario analysis using Monte Carlo simulation. The cost and benefit streams were determined based on the financial net present value (NPVf) and the social net present value (NPVs), including monetised CO2 emissions generated. The results showed that waste drying costs were found to be the most significant ones, with thermal drying being more financially advantageous than solar drying. The densification of SRF raises the costs by 7.88 to 8.48%, but its use as fuel would likely be profitable due to the economic benefits it provides. Current landfill disposal practices, which have an NPVs of −1052.60 EUR/t, are not a feasible, particularly when compared to the other SRF production scenarios, with maximum NPVs of −53.91 EUR/t. SRF production without densification using solar drying is the most acceptable scenario with the lowest NPVs (38.39 EUR/t).