bluuebunny/crossref_metadata_2025 huggingface dataset:
Potentially Pathogenic Free-Living Amoebae Isolated from Soil Samples from Warsaw Parks and Squares
Authors: Hendiger-Rizo Edyta Beata , Chmielewska-Jeznach Magdalena , Poreda Katarzyna , Rizo Liendo Aitor , Koryszewska-Bagińska Anna , Olędzka Gabriela , Padzik Marcin
Published: October 2024

Free-living amoebae (FLA) are prevalent in diverse environments, representing various genera and species with different pathogenicity. FLA-induced infections, such as the highly fatal amoebic encephalitis, with a mortality rate of 99%, primarily affect immunocompromised individuals while others such as Acanthamoeba keratitis (AK) and cutaneous amebiasis may affect immunocompetent individuals. Despite the prevalence of FLA, there is a lack of standardized guidelines for their detection near human habitats. To date, no studies on the isolation and identification of FLA in environmental soil samples in Warsaw have been published. The aim of this study was to determine the presence of amoebae in soil samples collected from Warsaw parks and squares frequented by humans. The isolated protozoa were genotyped. Additionally, their pathogenic potential was determined through thermophilicity tests. A total of 23 soil samples were seeded on non-nutrient agar plates (NNA) at 26 °C and monitored daily for FLA presence. From the total of 23 samples, 18 were positive for FLA growth in NNA and PCR (78.2%). Acanthamoeba spp. was the most frequently isolated genus, with a total of 13 positive samples (13/18; 72.2%), and the T4 genotype being the most common. Moreover, Platyamoeba placida (3/18; 16.7%), Stenamoeba berchidia (1/18; 5.6%) and Allovahlkampfia sp. (1/18; 5.6%), also potentially pathogenic amoebae, were isolated. To our knowledge, this is the first report of FLA presence and characterization in the Warsaw area.
Phage-Based Therapy in Combination with Antibiotics: A Promising Alternative against Multidrug-Resistant Gram-Negative Pathogens
Authors: Anastassopoulou Cleo , Ferous Stefanos , Petsimeri Aikaterini , Gioula Georgia , Tsakris Athanasios
Published: October 2024

The continued rise in antimicrobial resistance poses a serious threat to public health worldwide. The use of phages that can have bactericidal activity without disrupting the normal flora represents a promising alternative treatment method. This practice has been successfully applied for decades, mainly in Eastern Europe, and has recently been used as an emergency therapy for compassionate care in the United States. Here, we provide a comprehensive review of the pre-clinical and clinical applications of phage therapy concerning three major Gram-negative pathogens: Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii. The advantages and the challenges of expanding the usage of phages as an alternative or adjunctive treatment for antimicrobial-resistant bacterial infections are discussed. We emphasize the virologic complexities of using the highly adaptable phage populations as molecular tools, along with antibiotic chemical compounds, to effectively combat rapidly coevolving pathogenic bacteria in the host microenvironment. Pre-clinical studies, isolated clinical reports and a few randomized clinical trials have shown that bacteriophages can be effective in treating multidrug-resistant bacterial infections. The ability of some phages to revert the resistance against antibiotics, and possibly also against the human complement and other phages, appears to be a great advantage of phage therapy despite the inevitable emergence of phage-resistant strains. Bacteriophages (or specific phage-derived products) can enhance antimicrobial efficacy by reducing bacterial virulence via the alteration of basic bacterial structures, primarily of the cellular wall and membrane. Although several issues remain open regarding their effective clinical application, it appears that phage-based therapeutics in combination with antibiotics can provide an effective solution to the spread of antimicrobial resistance.
Association of Antibody Responses to Helicobacter pylori Proteins with Colorectal Adenoma and Colorectal Cancer
Authors: Genua Flavia , Butt Julia , Ganesan Harsha , Waterboer Tim , Hughes David J.
Published: October 2024

Helicobacter pylori (H. pylori) has been implicated in colorectal carcinogenesis. Here, the association of immune responses to bacterial exposure with advancing stages of colorectal neoplasia was assessed by multiplex serology. Immunoglobulin (Ig) A and G antibody responses to thirteen proteins of H. pylori were measured by a Luminex-based multiplex assay in plasma from patients with colorectal cancer (CRC, n = 25), advanced adenoma (n = 82), or small polyps (n = 85) and controls (n = 100). Multivariable logistic regression was used to assess the association of bacterial seropositivity with colorectal neoplasia. The threshold for overall seropositivity required subjects to be positive for at least 4 out of the 13 tested antigens. In a cohort subset with matched data (n = 34), H. pylori seropositivity was correlated with bacterial abundance in both neoplastic and matched normal tissue. While no association was found between H. pylori seropositivity and the presence of CRC, IgA seropositivity to CagA was associated with a decreased risk of advanced adenoma (odds ratio, OR = 0.48, 95% confidence intervals, CIs: 0.24–0.96). Regarding IgG, higher antibody responses to HpaA was associated with advanced adenoma occurrence (OR = 2.46, 95% CI: 1.00–6.01), while responses to HP0395, CagA and Catalase were associated with polyp development (OR = 2.65, 95%, CI: 1.31–5.36, OR = 1.83, 95% CI: 1.01–3.32, and OR = 2.16, CI: 1.09–4.29, respectively). Positive correlations were found between H. pylori abundance in the normal mucosa and levels of both the IgA and IgG antibody response to Catalase and VacA antigens (r = 0.48, p < 0.01; r = 0.37, p = 0.04; r = 0.51, p < 0.01; and r = 0.71, p = 0.04, respectively). Conversely, H. pylori abundance was negatively correlated with levels of IgA antibody response to HpaA and with IgG antibody response to HP0231 in the diseased tissue (r = −0.34, p = 0.04 and r = −0.41, p = 0.01, respectively). The association between levels of H. pylori antigens and colorectal neoplasia risk gradually decreased with the adenoma progression, implicating the early activation of the immune response at the polyp stage. Thus, the evaluation of antibody response to certain bacterial antigens may indicate the presence of early-stage colorectal neoplasia. Further studies are needed to clarify the role H. pylori or the immune response to its antigens may have in colorectal carcinogenesis stages.
Assays for Assessing Mycobacterium avium Immunity and Evaluating the Effects of Therapeutics
Authors: Abate Getahun , Meza Krystal A. , Colbert Chase G. , Eickhoff Christopher S.
Published: October 2024

In Europe and North America, the prevalence of pulmonary nontuberculous mycobacteria (NTM) is increasing. Most pulmonary NTM infections are caused by the Mycobacterium avium complex (MAC). Sadly, the treatment of pulmonary MAC is suboptimal with failure rates ranging from 37% to 58%. Therefore, there is a need to develop new therapeutics. Developing new immunotherapies and studying their interaction with standard or new drugs requires reliable assays. Four different assays including CFSE-based flow cytometry, in vitro protection assays, IFN-γ ELISPOT, and murine infection models were optimized using a reference strain of MAC (ATCC 700898) to help with the development of immunotherapies for MAC. Expansion of proliferating and IFN-γ producing human T cells is optimal after 7 days of stimulation with MAC at a multiplicity of infection (MOI) of 0.1, achieving a stimulation index of 26.5 ± 11.6 (mean ± SE). The in vitro protection assay for MAC works best by co-culturing T cells expanded for 7 days with MAC (MOI 1)-infected autologous macrophages. Aerosol MAC infection of mice allows measurement of the effects of the BCG vaccine and clarithromycin. IFN-γ ELISPOT assays with live MAC (MOI 3) stimulation of splenocytes from mice immunized with BCG help identify differences between unimmunized mice and mice immunized with BCG. In conclusion, multiple assays are available for use to identify MAC-specific effector T cells, which will help in the development of new therapeutics or vaccines against pulmonary MAC.
Study on Thermal Chamber Expansion of VH-SAGD Process Using CO2-Inducing Effect for Heavy Oil Reservoirs
Authors: Xie Haojun , Zhang Shiming , Wu Guanghuan , Li Wei
Published: October 2024

In heavy oil thermal recovery processes, higher pressure usually leads to low dryness and expansion difficulty for the injected steam in thermal recovery processes, which will result in lower oil recovery and more carbon emissions. This paper proposed a new CO2-inducing method to accelerate the steam chamber expansion, based on a core flooding experiment and numerical simulation. First, the CO2 showed significant viscosity reduction at high pressure in the PVT test. In the core flooding experiment, the CO2 provided strong flow conductivity in porous media for the thermal flooding, as the CO2 pre-injection restrained the steam condensation. Using the CO2-inducing method, CO2 pre-injection before steam built a fast flow channel in a relatively higher permeability layer and reduced the thermal injection pressure by about 1.0~2.4 MPa. As a result, the steam overlap around the injection wells became slower and the gravity drainage process was able to heat and displace the heavy oil above the channel. Furthermore, the CO2 gas trapped at the top reduced heat loss by about 12.4%. The field numerical simulation showed that this new method improved thermal recovery by 7.5% and reduced CO2 emissions by about 18 million kg/unit for the whole process. This method changes the conventional thermal expansion direction by CO2 inducing effect and fundamentally reduces heat loss, which provides significant advantages in low-carbon EOR.
Microalgae Enhance the Resistance of Pond-Dwelling Ammonia-Oxidizing Bacteria to Light Irradiation
Authors: Lu Shimin , Li Yayuan , Yuan Zehui , Liu Xingguo , Che Xuan , Cheng Guofeng , Gu Zhaojun , Wu Fan
Published: October 2024

Pond aquaculture is an important aquacultural model worldwide in which ammonia-oxidizing bacteria (AOB) are crucial for the removal of ammonia from water. The influence of light irradiation on AOB in an aquaculture pond was studied using artificial simulation wastewater under dark/light cycles of 24 h/0 h (L0), 12 h/12 h (L12), and 0 h/24 h (L24). The ammonia oxidation rates (AORs) in groups L0, L12, and L24 were 9.88 ± 0.19 mg h−1, 6.01 ± 0.32 mg h−1, and 1.85 ± 0.09 mg h−1, respectively. Long-term exposure to light had a serious impact on the AOR and decreased the abundance of Nitrosomonas spp. and their ammonia monooxygenase genes. To determine the protective effect of microalgae on AOB, different doses of freeze-dried Chlorella spp. powder were added to the nitrifying bacteria community. The photoinhibition rate of chlorophyll a (Chla) in the groups with 300 and 1300 µg L−1 of added Chlorella were 32.85% and 28.77%, respectively, while the Chla in the 2200 µg L−1 Chlorella-added group was only 0.01%, with no significant differences (p > 0.05) in AOR between the dark/light treatment subgroups. Fluorescence in situ hybridization showed that AOB, nitrite-oxidizing bacteria, and algae coexist and grow together without free AOB in the nitrifying bacterial community. It was suggested that microalgae enhance the resistance of AOB to light irradiation in a pond through the shading effect provided by algal chlorophyll and the close symbiotic relationship between microalgae and AOB.
GC/MS Screening of Substances Released from Post-Consumer Recycled HDPE Pellets into 95% Ethanol: Reproducibility and Variation between Production Batches
Authors: Brandt Melanie , Vietzke Jens-Peter , Bankmann Dennis , Gers-Barlag Heiner
Published: October 2024

The use of post-consumer recycled (PCR) plastic materials in sensitive packaging applications, such as for cosmetic products and detergents, requires a clear understanding of the identities and quantities of chemical substances, which they may release into packed products. With many potential sources of and thus different types of potentially releasable substances, a reliable non-targeted screening method is required to assess these materials. Such a method should be readily applicable in industrial practice and provide a realistic estimation of substance release. This investigation focused on the use of gas chromatography/coupled mass spectrometry (GC/MS) to analyze substances, which recycled HDPE (rHDPE) plastic pellets release into 95% ethanol under accelerated testing conditions. The results of the repeated testing of reference samples clearly demonstrated the good reproducibility of the described methodology, with standard deviations of repeated determinations of the total released substance amounts of 6.8–8.1%. The application to several production batches of three commercial rHDPE grades additionally demonstrated that the batch-to-batch variation of substances which rHDPE materials release can be confined to less than 10% of variation of the total detectable substance amount. The described methodology is therefore seen as a pragmatic, repeatable assessment of recycled HDPE plastic batches with a view to substance release.
6-DOFs Robot Placement Based on the Multi-Criteria Procedure for Industrial Applications
Authors: Aggogeri Francesco , Pellegrini Nicola
Published: October 2024

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations.
Using Advanced InSAR Techniques and Machine Learning in Google Earth Engine (GEE) to Monitor Regional Black Soil Erosion—A Case Study of Yanshou County, Heilongjiang Province, Northeastern China
Authors: Gao Yanchen , Yang Jiahui , Chen Xiaoyu , Wang Xiangwei , Li Jinbo , Azad Nasrin , Zvomuya Francis , He Hailong
Published: October 2024

The black soil region experiences complex erosion due to natural processes and intense human activities, leading to soil degradation and adverse ecological and agricultural impacts. However, the complexities involved in quantifying regional erosion poses remarkable challenges in accurately assessing the current status of regional soil erosion for effective soil conservation. To solve this issue, we proposed a new method for monitoring soil erosion using Interferometric synthetic aperture radar (InSAR) technology and machine learning algorithms within the Google Earth Engine platform. The new method not only enables regional-scale monitoring, but also ensures high accuracy in measurement (millimeter-level). The erosion susceptibility of the study area (Yanshou County, Heilongjiang Province, Northeastern China) was also classified using random forest algorithms to refine the monitored and predicted soil erosion. The results indicate that the five-year (2016–2021) deformation in Yanshou County was −11.08 mm, with a significant mean cumulative deformation of −8.08 mm yr−1 occurring in 2017. The driving factor analysis shows that the region was subject to the compound effect of water and freeze–thaw erosion, closely related to crop phenological stages. The susceptibility analysis indicates that 73.3% of the region was susceptible to erosion, with a higher probability in river areas, at high altitudes, and on steep slopes. However, good vegetation cover can reduce the risk of soil erosion to some extent. This study offers a new perspective on monitoring regional soil erosion in the black soil region of China. The proposed method holds potential for future expansion to monitor soil erosion in a larger areas, thereby guiding the strategies development for protection of the agriculturally important black soil.
Influence of Structure from Motion Algorithm Parameters on Metrics for Individual Tree Detection Accuracy and Precision
Authors: Tinkham Wade T. , Woolsey George A.
Published: October 2024

Uncrewed aerial system (UAS) structure from motion (SfM) monitoring strategies for individual trees has rapidly expanded in the early 21st century. It has become common for studies to report accuracies for individual tree heights and DBH, along with stand density metrics. This study evaluates individual tree detection and stand basal area accuracy and precision in five ponderosa pine sites against the range of SfM parameters in the Agisoft Metashape, Pix4DMapper, and OpenDroneMap algorithms. The study is designed to frame UAS-SfM individual tree monitoring accuracy in the context of data processing and storage demands as a function of SfM algorithm parameter levels. Results show that when SfM algorithms are properly tuned, differences between software types are negligible, with Metashape providing a median F-score improvement over OpenDroneMap of 0.02 and PIX4DMapper of 0.06. However, tree extraction performance varied greatly across algorithm parameters, with the greatest extraction rates typically coming from parameters causing increased density in dense point clouds and minimal point cloud filtering. Transferring UAS-SfM forest monitoring into management will require tradeoffs between accuracy and efficiency. Our analysis shows that a one-step reduction in dense point cloud quality saves 77–86% in point cloud processing time without decreasing tree extraction (F-score) or basal area precision using Metashape and PIX4DMapper but the same parameter change for OpenDroneMap caused a ~5% loss in precision. Providing reproducible processing strategies is a vital step in successfully transferring these technologies into usage as management tools.
Deep Learning-Based Approximated Observation Sparse SAR Imaging via Complex-Valued Convolutional Neural Network
Authors: Ji Zhongyuan , Li Lingyu , Bi Hui
Published: October 2024

Sparse synthetic aperture radar (SAR) imaging has demonstrated excellent potential in image quality improvement and data compression. However, conventional observation matrix-based methods suffer from high computational overhead, which is hard to use for real data processing. The approximated observation sparse SAR imaging method relieves the computation pressure, but it needs to manually set the parameters to solve the optimization problem. Thus, several deep learning (DL) SAR imaging methods have been used for scene recovery, but many of them employ dual-path networks. To better leverage the complex-valued characteristics of echo data, in this paper, we present a novel complex-valued convolutional neural network (CNN)-based approximated observation sparse SAR imaging method, which is a single-path DL network. Firstly, we present the approximated observation-based model via the chirp-scaling algorithm (CSA). Next, we map the process of the iterative soft thresholding (IST) algorithm into the deep network form, and design the symmetric complex-valued CNN block to achieve the sparse recovery of large-scale scenes. In comparison to matched filtering (MF), the approximated observation sparse imaging method, and the existing DL SAR imaging methods, our complex-valued network model shows excellent performance in image quality improvement especially when the used data are down-sampled.
Vibration Position Detection of Robot Arm Based on Feature Extraction of 3D Lidar
Authors: Hu Jinchao , Xu Xiaobin , Cao Chenfei , Tian Zhenghong , Ma Yuanshan , Sun Xiao , Yang Jian
Published: October 2024

In the process of construction, pouring and vibrating concrete on existing reinforced structures is a necessary process. This paper presents an automatic vibration position detecting method based on the feature extraction of 3D lidar point clouds. Compared with the image-based method, this method has better anti-interference performance to light with reduced computational consumption. First, lidar scans are used to capture multiple frames of local steel bar point clouds. Then, the clouds are stitched by Normal Distribution Transform (NDT) for preliminary matching and Iterative Closest Point (ICP) for fine-matching. The Graph-Based Optimization (g2o) method further refines the precision of the 3D registration. Afterwards, the 3D point clouds are projected into a 2D image. Finally, the locations of concrete vibration points and concrete casting points are discerned through point cloud and image processing technologies. Experiments demonstrate that the proposed automatic method outperforms ICP and NDT algorithms, reducing the mean square error (MSE) by 11.5% and 11.37%, respectively. The maximum discrepancies in identifying concrete vibration points and concrete casting points are 0.059 ± 0.031 m and 0.089 ± 0.0493 m, respectively, fulfilling the requirement for concrete vibration detection.
AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI
Authors: Senadheera Isuru , Hettiarachchi Prasad , Haslam Brendon , Nawaratne Rashmika , Sheehan Jacinta , Lockwood Kylee J. , Alahakoon Damminda , Carey Leeanne M.
Published: October 2024

Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain–computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.
A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing
Authors: Yuan Tangxiao , Adjallah Kondo Hloindo , Sava Alexandre , Wang Huifen , Liu Linyan
Published: October 2024

The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue of data quality in the field raises concerns among users about the robustness of automatic decision-making models before their application. This paper addresses three main challenges relative to field data quality issues during automated real-time decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, and sensor fault-tolerant control. To address these problems, this paper proposes a risk assessment framework in the case of continuous production workshops. The framework aims to determine a method for systematically assessing data quality issues in specific scenarios. It specifies the preparation requirements, as well as assumptions such as the preparation of datasets on typical working conditions, and the risk assessment model. Within the framework, the data quality issues in real-time decision-making are transformed into data deviation problems. By employing the Monte Carlo simulation method to measure the impact of these issues on the decision risk, a direct link between sensor quality and risks is established. This framework defines specific steps to address the three challenges. A case study in the steel industry confirms the effectiveness of the framework. This proposed method offers a new approach to assessing safety and reducing the risk of real-time unmanned automatic decision-making in industrial settings.
Foot–Floor Contact Sequences: A Metric for Gait Assessment in Parkinson’s Disease after Deep Brain Stimulation
Authors: Ghislieri Marco , Agostini Valentina , Rizzi Laura , Fronda Chiara , Knaflitz Marco , Lanotte Michele
Published: October 2024

Digital gait monitoring is increasingly used to assess locomotion and fall risk. The aim of this work is to analyze the changes in the foot–floor contact sequences of Parkinson’s Disease (PD) patients in the year following the implantation of Deep Brain Stimulation (DBS). During their best-ON condition, 30 PD patients underwent gait analysis at baseline (T0), at 3 months after subthalamic nucleus DBS neurosurgery (T1), and at 12 months (T2) after subthalamic nucleus DBS neurosurgery. Thirty age-matched controls underwent gait analysis once. Each subject was equipped with bilateral foot-switches and a 5 min walk was recorded, including both straight-line and turnings. The walking speed, turning time, stride time variability, percentage of atypical gait cycles, stance, swing, and double support duration were estimated. Overall, the gait performance of PD patients improved after DBS, as also confirmed by the decrease in their UPDRS-III scores from 19.4 ± 1.8 to 10.2 ± 1.0 (T0 vs. T2) (p < 0.001). In straight-line walking, the percentages of atypical cycles of PD on the more affected side were 11.1 ± 1.5% (at T0), 3.1 ± 1.5% (at T1), and 5.1 ± 2.4% (at T2), while in controls it was 3.1 ± 1.3% (p < 0.0005). In turnings, this percentage was 13.7 ± 1.1% (at T0), 7.8 ± 1.1% (at T1), and 10.9 ± 1.8% (at T2), while in controls it was 8.1 ± 1.0% (p < 0.001). Therefore, in straight-line walking, the atypical cycles decreased by 72% at T1, and by 54% at T2 (with respect to baseline), while, in turnings, atypical cycles decreased by 43% at T1, and by 20% at T2. The percentage of atypical gait cycles proved an informative digital biomarker for quantifying PD gait changes after DBS, both in straight-line paths and turnings.
Geometric Wide-Angle Camera Calibration: A Review and Comparative Study
Authors: Huai Jianzhu , Shao Yuxin , Jozkow Grzegorz , Wang Binliang , Chen Dezhong , He Yijia , Yilmaz Alper
Published: October 2024

Wide-angle cameras are widely used in photogrammetry and autonomous systems which rely on the accurate metric measurements derived from images. To find the geometric relationship between incoming rays and image pixels, geometric camera calibration (GCC) has been actively developed. Aiming to provide practical calibration guidelines, this work surveys the existing GCC tools and evaluates the representative ones for wide-angle cameras. The survey covers the camera models, calibration targets, and algorithms used in these tools, highlighting their properties and the trends in GCC development. The evaluation compares six target-based GCC tools, namely BabelCalib, Basalt, Camodocal, Kalibr, the MATLAB calibrator, and the OpenCV-based ROS calibrator, with simulated and real data for wide-angle cameras described by four parametric projection models. These tests reveal the strengths and weaknesses of these camera models, as well as the repeatability of these GCC tools. In view of the survey and evaluation, future research directions of wide-angle GCC are also discussed.
Study on the Robust Filter Method of SINS/DVL Integrated Navigation Systems in a Complex Underwater Environment
Authors: Zhu Tianlong , Li Jian , Duan Kun , Sun Shouliang
Published: October 2024

This paper proposes an improved adaptive filtering algorithm based on the Sage–Husa adaptive Kalman filtering algorithm to address the issue of measurement noise characteristics impacting the navigation accuracy in strapdown inertial navigation system (SINS)/Doppler Velocity Log (DVL) integrated navigation systems. Addressing the non-positive definite matrix problem prevalent in traditional adaptive filtering algorithms and aiming to enhance measurement noise estimation accuracy, this method incorporates upper and lower thresholds determined by a discrimination factor. In the presence of abnormal measurement data, these thresholds are utilized to adjust the covariance of the innovation, subsequently re-estimating the system’s measurement noise through a decision factor based on the innovation. Simulation and experiment results demonstrate that the proposed improved adaptive filtering algorithm outperforms the classical Kalman filter (KF) in terms of navigation accuracy and stability. Furthermore, the filtering performance surpasses that of the Sage–Husa algorithm. The simulation results in this paper show that the relative position positioning error of the improved method is reduced by 49.44% compared with the Sage–Husa filtering method.
Dynamic Network-Level Traffic Speed and Signal Control in Connected Vehicle Environment
Authors: Yuan Zihao , Zeng Xiaoqing
Published: October 2024

The advent of connected vehicles holds significant promise for enhancing existing traffic signal and vehicle speed control methods. Despite this potential, there has been a lack of concerted efforts to address issues related to vehicle fuel consumption and emissions during travel across multiple intersections controlled by traffic signals. To bridge this gap, this research introduces a novel technique aimed at optimizing both traffic signals and vehicle speeds within transportation networks. This approach is designed to contribute to the improvement of transportation networks by simultaneously addressing issues related to fuel consumption and pollutant emissions. Simulation results vividly illustrate the pronounced the effectiveness of the proposed traffic signal and vehicle speed control methods of alleviating vehicle delay, reducing stops, lowering fuel consumption, and minimizing CO2 emissions. Notably, these benefits are particularly prominent in scenarios characterized by moderate traffic density, emphasizing the versatility and positive impact of the method across varied traffic conditions.
fBrake, a Method to Simulate the Brake Efficiency of Laden Light Passenger Vehicles in PTIs While Measuring the Braking Forces of Their Unladen Configurations
Authors: Romero-Gómez Víctor , San Román José Luis
Published: October 2024

This study introduces fBrake, a novel simulation method now designed for use in periodic technical inspections of M1 and N1 vehicle categories, addressing challenges posed by Directive 2014/45/EU. The directive mandates that braking efficiency must be measured relative to the vehicle’s maximum mass, which often results in underperformance during inspections due to vehicles typically being unladen. This discrepancy arises because the maximum braking forces are proportional to the vertical load on the wheels, causing empty vehicles to lock their wheels prematurely compared to laden ones. fBrake simulates the braking forces of unladen vehicles to reflect a laden state by employing an optimal brake-force distribution curve that aligns with the vehicle’s inherent braking behavior, whether through proportioning valves or through electronic brake distribution systems in anti-lock-braking-system-equipped vehicles. Our methodology, previously applied to heavy vehicles, involved extensive experimentation with a roller brake tester, comparing the actual braking performances of dozens of vehicles to those of their simulated counterparts using fBrake. The results demonstrate that fBrake reliably replicates the braking efficiency of laden vehicles, validating its use as an accurate and effective tool for braking system assessments in periodic inspections, irrespective of the vehicle’s load condition during the test. This approach ensures compliance with regulatory requirements while enhancing the reliability and safety of vehicle inspections.
Construction Jobsite Image Classification Using an Edge Computing Framework
Authors: Chen Gongfan , Alsharef Abdullah , Jaselskis Edward
Published: October 2024

Image classification is increasingly being utilized on construction sites to automate project monitoring, driven by advancements in reality-capture technologies and artificial intelligence (AI). Deploying real-time applications remains a challenge due to the limited computing resources available on-site, particularly on remote construction sites that have limited telecommunication support or access due to high signal attenuation within a structure. To address this issue, this research proposes an efficient edge-computing-enabled image classification framework for support of real-time construction AI applications. A lightweight binary image classifier was developed using MobileNet transfer learning, followed by a quantization process to reduce model size while maintaining accuracy. A complete edge computing hardware module, including components like Raspberry Pi, Edge TPU, and battery, was assembled, and a multimodal software module (incorporating visual, textual, and audio data) was integrated into the edge computing environment to enable an intelligent image classification system. Two practical case studies involving material classification and safety detection were deployed to demonstrate the effectiveness of the proposed framework. The results demonstrated the developed prototype successfully synchronized multimodal mechanisms and achieved zero latency in differentiating materials and identifying hazardous nails without any internet connectivity. Construction managers can leverage the developed prototype to facilitate centralized management efforts without compromising accuracy or extra investment in computing resources. This research paves the way for edge “intelligence” to be enabled for future construction job sites and promote real-time human-technology interactions without the need for high-speed internet.
Consecutive Image Acquisition without Anomalies
Authors: Mur Angel , Galaup Patrice , Dedic Etienne , Henry Dominique , Aubert Hervé
Published: October 2024

An image is a visual representation that can be used to obtain information. A camera on a moving vector (e.g., on a rover, drone, quad, etc.) may acquire images along a controlled trajectory. The maximum visual information is captured during a fixed acquisition time when consecutive images do not overlap and have no space (or gap) between them. The images acquisition is said to be anomalous when two consecutive images overlap (overlap anomaly) or have a gap between them (gap anomaly). In this article, we report a new algorithm, named OVERGAP, that remove these two types of anomalies when consecutive images are obtained from an on-board camera on a moving vector. Anomaly detection and correction use here both the Dynamic Time Warping distance and Wasserstein distance. The proposed algorithm produces consecutive, anomaly-free images with the desired size that can conveniently be used in a machine learning process (mainly Deep Learning) to create a prediction model for a feature of interest.
A Crowded Object Counting System with Self-Attention Mechanism
Authors: Lien Cheng-Chang , Wu Pei-Chen
Published: October 2024

Traditional object counting systems use object detection methods to count objects. However, when objects are small, crowded, and dense, object detection may fail, leading to inaccuracies in counting. To address this issue, we propose a crowded object counting system based on density map estimation. While most density map estimation models employ encoder–decoder or multi-branch approaches to generate feature maps at different scales for obtaining an accurate density map, improving the accuracy of crowded object counting remains a challenge. In this paper, we propose a novel model that can generate more accurate density maps, utilizing the context-aware network as the primary structure and integrating the self-attention mechanism. There are three main contributions in this paper. Firstly, the self-attention mechanism is employed to improve the accuracy of density map estimation. Secondly, the missing vehicle labels in the TRANCOS database are relabeled, ensuring that the ground truth data are more complete than the original TRANCOS database, thus enabling the proposed novel model to have higher crowded object counting accuracy. Thirdly, the parameters of the self-attention mechanism are analyzed to obtain the optimum parameter combination. The experimental results demonstrate that the accuracy of crowded object counting can reach 85.9%, 90.0%, 83.4%, and 92.6% for the TRANCOS, relabeled TRANCOS, ShanghaiTech Part A, and Part B datasets, respectively. Furthermore, the ablation study for the context-aware network with self-attention mechanism analyzes the optimum parameter combination.
Use of Low-Cost Sensors to Study Atmospheric Particulate Matter Concentrations: Limitations and Benefits Discussed through the Analysis of Three Case Studies in Palermo, Sicily
Authors: Brugnone Filippo , Randazzo Luciana , Calabrese Sergio
Published: October 2024

The paper discusses the results of the concentrations of atmospheric particulate matter, in the PM2.5 and PM10 fractions, acquired by two low-cost sensors. The research was carried out from 1 July 2023 to 30 June 2024, in Palermo, Sicily. The results obtained from two systems equipped with the same sensor model were compared. Excellent linear correlation was observed between the results, with differences in measurements falling within instrumental accuracy. Two instruments equipped with different sensors, models Novasense SDS011 and Plantower PMSA003, were placed at the same site. These were complemented by a weather station to measure meteorological parameters. Upon comparing the atmospheric particulate matter concentrations measured by the two instruments, it was observed that there was a good linear correlation for PM2.5 and a poor linear correlation for PM10. Additionally, the PMSA003 sensor appeared to consistently record higher concentrations than the SDS011 sensor. During periods influenced by natural sources and/or anthropogenic activities at the regional and/or local scale, i.e., the dispersal of Saharan sands, forest fires, and local events using fireworks, abnormal concentrations of atmospheric particulate matter were detected. Despite the inherent limitations in precision and accuracy, both low-cost instruments were able to identify periods with abnormal concentrations of atmospheric particulate matter, regardless of their source or type.
Attention-Linear Trajectory Prediction
Authors: Wang Baoyun , He Lei , Song Linwei , Niu Rui , Cheng Ming
Published: October 2024

Recently, a large number of Transformer-based solutions have emerged for the trajectory prediction task, but there are shortcomings in the effectiveness of Transformers in trajectory prediction. Specifically, while position encoding preserves some of the ordering information, the self-attention mechanism at the core of the Transformer has its alignment invariance that leads to the loss of temporal information, which is crucial for trajectory prediction. For this reason, we design a simple and efficient strategy for temporal information extraction and prediction of trajectory sequences using the self-attention mechanism and linear layers. The experimental results show that the strategy can improve the average accuracy by 15.31%, effectively combining the advantages of the linear layer and the self-attention mechanism, while compensating for the shortcomings of the Transformer. Additionally, we conducted an empirical study to explore the effectiveness of the linear layer and sparse self-attention mechanisms in trajectory prediction.
DenseFusion-DA2: End-to-End Pose-Estimation Network Based on RGB-D Sensors and Multi-Channel Attention Mechanisms
Authors: Li Hanqi , Wan Guoyang , Li Xuna , Wang Chengwen , Zhang Hong , Liu Bingyou
Published: October 2024

Notably, 6D pose estimation is a critical technology that enables robotics to perceive and interact with their operational environment. However, occlusion causes a loss of local features, which, in turn, restricts the estimation accuracy. To address these challenges, this paper proposes an end-to-end pose-estimation network based on a multi-channel attention mechanism, DA2Net. Firstly, a multi-channel attention mechanism, designated as “DA2Net”, was devised using A2-Nets as its foundation. This mechanism is constructed in two steps. In the first step, the essential characteristics are extracted from the global feature space through the second-order attention pool. In the second step, a feature map is generated by the integration of position and channel attention. Subsequently, the extracted key features are assigned to each position of the feature map, enhancing both the feature representation capacity and the overall performance. Secondly, the designed attention mechanism is introduced into both the feature fusion and pose iterative refinement networks to enhance the network’s capacity to acquire local features thus improving its overall performance. The experimental results demonstrated that the estimation accuracy of DenseFusion-DA2 on the LineMOD dataset was approximately 3.4% higher than that of DenseFusion. Furthermore, the estimation accuracy surpassed that of PoseCNN, PVNet, SSD6D, and PointFusion by 8.3%, 11.1%, 20.3%, and 23.8%, respectively. The estimation accuracy also shows a significant advantage on the Occluded LineMOD and HR-Vision datasets. This research not only presents a more efficient solution for robot perception but also introduces novel ideas and methods for technological advancements and applications in related fields.
Cross-Task Differences in Frontocentral Cortical Activations for Dynamic Balance in Neurotypical Adults
Authors: Magruder Robert D. , Kukkar Komal K. , Contreras-Vidal Jose L. , Parikh Pranav J.
Published: October 2024

Although significant progress has been made in understanding the cortical correlates underlying balance control, these studies focused on a single task, limiting the ability to generalize the findings. Different balance tasks may elicit cortical activations in the same regions but show different levels of activation because of distinct underlying mechanisms. In this study, twenty young, neurotypical adults were instructed to maintain standing balance while the standing support surface was either translated or rotated. The differences in cortical activations in the frontocentral region between these two widely used tasks were examined using electroencephalography (EEG). Additionally, the study investigated whether transcranial magnetic stimulation could modulate these cortical activations during the platform translation task. Higher delta and lower alpha relative power were found over the frontocentral region during the platform translation task when compared to the platform rotation task, suggesting greater engagement of attentional and sensory integration resources for the former. Continuous theta burst stimulation over the supplementary motor area significantly reduced delta activity in the frontocentral region but did not alter alpha activity during the platform translation task. The results provide a direct comparison of neural activations between two commonly used balance tasks and are expected to lay a strong foundation for designing neurointerventions for balance improvements with effects generalizable across multiple balance scenarios.
Uncertainty-Aware Depth Network for Visual Inertial Odometry of Mobile Robots
Authors: Song Jimin , Jo HyungGi , Jin Yongsik , Lee Sang Jun
Published: October 2024

Simultaneous localization and mapping, a critical technology for enabling the autonomous driving of vehicles and mobile robots, increasingly incorporates multi-sensor configurations. Inertial measurement units (IMUs), known for their ability to measure acceleration and angular velocity, are widely utilized for motion estimation due to their cost efficiency. However, the inherent noise in IMU measurements necessitates the integration of additional sensors to facilitate spatial understanding for mapping. Visual–inertial odometry (VIO) is a prominent approach that combines cameras with IMUs, offering high spatial resolution while maintaining cost-effectiveness. In this paper, we introduce our uncertainty-aware depth network (UD-Net), which is designed to estimate both depth and uncertainty maps. We propose a novel loss function for the training of UD-Net, and unreliable depth values are filtered out to improve VIO performance based on the uncertainty maps. Experiments were conducted on the KITTI dataset and our custom dataset acquired from various driving scenarios. Experimental results demonstrated that the proposed VIO algorithm based on UD-Net outperforms previous methods with a significant margin.