Search Results (94)

Electric-assist bicycles have recently become very popular. However, riding them generally requires significantly more energy, generated simultaneously by the motor and the rider, compared to much lighter traditional bicycles. Assessing the energy efficiency of electric-assist bicycles in comparison to traditional bikes allows us to determine in which cases using electric bikes is cost-effective and in which it is not. This study proposes a method for evaluating the energy efficiency of bicycles, which stands out by relying on relatively imprecise data recorded at low frequency by a commercial bike computer with accessories. The core of the method is an algorithm developed by the authors to determine the tractive force acting on the bicycle and rider, based on a minimal set of recorded data: road incline, riding speed, and the wind speed component parallel to the direction of movement. Depending on the situation, the tractive force may act as a driving force or as a braking force. Based on the calculated tractive force, the power required to maintain the recorded bicycle speed can be estimated. Full article

Electric bicycles in elevators pose serious safety hazards. Fires in the confined space make escape difficult, and recent accidents involving e-bike fires have caused casualties and property damage. To prevent e-bikes from entering elevators and improve public safety, this design employs the Nezha development board as the upper computer for visual detection. It uses deep learning algorithms to recognize hazards like e-bikes. The lower computer orchestrates elevator controls, including voice alarms, door locking, and emergency halt. The system comprises two parts: the upper computer uses the YOLOv11 model for target detection, trained on a custom e-bike image dataset. The lower computer features an elevator control circuit for coordination. The workflow covers target detection algorithm application, dataset creation, and system validation. The experiments show that the YOLOv11 demonstrates superior e-bike detection performance, achieving 96.0% detection accuracy and 92.61% mAP@0.5, outperforming YOLOv3 by 6.77% and YOLOv8 by 15.91% in mAP, significantly outperforming YOLOv3 and YOLOv8. The system accurately identifies e-bikes and triggers safety measures with good practical effectiveness, substantially enhancing elevator safety. Full article

The inherent static instability of bicycles poses significant safety risks, driving research into active stabilization systems within the broader field of autonomous vehicle control. This study proposes a Variable-Speed Control Moment Gyroscope (VSCMG) system for bicycle attitude stabilization, aiming to enhance rider safety and system endurance by addressing the high power consumption of traditional Single-Gimbal CMG (SGCMG) systems. A single-axis balance model was developed, employing a proportional–derivative (PD) controller to compute the total torque demand, combined with least-squares-based power-constrained optimization and a center-of-mass alignment algorithm to achieve stable control. Experimental validation was conducted on a simplified single-axis balancing setup, designed as an abstracted bicycle model for verification purposes, equipped with two VSCMG units. This setup demonstrated the rapid stabilization of a 15.5° tilt to near 0°, with significantly reduced steady-state power consumption compared to SGCMG systems, and an effective mitigation of external disturbances at 4000 RPM, though oscillations increased at 1500 RPM. The VSCMG system achieves a balance between stability and energy efficiency through dynamic flywheel speed adjustment, and future research can enhance disturbance rejection capabilities by varying the speed, offering a viable approach for long-endurance autonomous bicycles. Full article

Physics-assisted machine learning is a powerful framework that enhances data efficiency by integrating the strengths of conventional machine learning with physical knowledge. This paper applies this concept and focuses on the design of a driver evaluator using physics-assisted unsupervised learning, which serves as a virtual reference generator that provides different driving modes for vehicles equipped with active actuators. A strategy that applies sensitivity analysis regarding the vehicle handling performance, aiming to reduce the computational workload of the clustering algorithms, is proposed. First, a bicycle model with nonlinear Pacejka’s tire models is established for the analysis of lateral dynamics. Next, mathematical interpretations of sensitivity analysis are derived to evaluate the contribution of physical parameters to the system response and build the reduced parameters set. Then, Gaussian mixture models are fitted to a database generated with the full parameters set and another with the reduced set, respectively. Finally, step-steer and constant radius tests are performed to assess the handling performance with respect to the two validated centroids. Comparisons of lateral dynamics and understeer characteristics indicate that the proposed method can accurately distinguish driving modes in a much faster manner compared to traditional machine learning. This methodology has significant potential for practical applications with large databases and more complex systems. Full article

Camera and LiDAR data fusion has been a popular research area, especially in the field of autonomous vehicles. This study evaluates the efficiency and accuracy of different depth point extraction methods, including Point-by-Point (PbyP), Complete Region Depth Extraction (CoRDE), Central Region Depth Extraction (CeRDE), and Grid Central Region Depth Extraction (GCRDE), across object categories such as person, bicycle, car, bus, and truck, and occlusion levels ranging from 0 to 3. The approaches are assessed based on extraction time, accuracy, and root mean squared error (RMSE). Bounding box-based methods, such as PbyP and CoRDE, consistently show slower extraction times compared to segmentation mask methods, with CeRDE being the most efficient in terms of computational speed. However, segmentation mask methods, particularly CeRDE and GCRDE, offer superior accuracy, especially for complex objects like trucks and cars, where bounding box methods struggle, particularly at higher occlusion levels. In terms of RMSE, segmentation mask methods consistently outperform bounding box methods, providing more precise depth estimations, particularly for larger and more occluded objects. Overall, segmentation mask methods are preferred for applications where accuracy is critical, despite their slower processing speed, while bounding box methods are suitable for real-time applications requiring faster depth extraction. GeRDE offers a balance between speed and accuracy, making it ideal for tasks needing both efficiency and precision. Full article

Background and Objectives: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a well-established technique for evaluating myocardial perfusion and function in patients with suspected or known coronary artery disease. While conventional dual-detector SPECT scanners have limitations in spatial resolution and photon detection sensitivity, recent advancements, including full-ring solid-state cadmium zinc telluride (CZT) detectors, offer enhanced image quality and improved diagnostic accuracy. This study aimed to compare the performance of Veriton-CT, a full-ring CZT SPECT system, with GE Discovery 530c, a dedicated cardiac fixed-angle gamma camera, in myocardial perfusion imaging and their correlation with coronary angiography findings. Materials and Methods: This was a prospective study that analyzed 21 patients who underwent MPI at the Department of Nuclear Medicine, Lithuanian University of Health Sciences, Kauno Klinikos. A one-day stress–rest protocol using 99mTc-Sestamibi was employed, with stress testing performed via bicycle ergometry or pharmacological induction. MPI was first conducted using GE Discovery 530c (GE Health Care, Boston, MA, USA), followed by imaging on Veriton-CT, which included low-dose CT for attenuation correction. The summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) were analyzed and compared between both imaging modalities. Coronary angiography results were retrospectively collected, and lesion-based analysis was performed to assess the correlation between imaging results and the presence of significant coronary artery stenosis (≥35% and ≥70% narrowing). Image quality and the certainty of distinguishing the inferior myocardial wall from extracardiac structures were also evaluated by two independent researchers with differing levels of experience. Results: Among the 14 patients included in the final analysis, Veriton-CT was more likely to classify MPI scans as normal (64.3%) compared to GE Discovery 530c (28.6%). Additionally, Veriton-CT provided a better assessment of the right coronary artery (RCA) basin, showing greater agreement with coronary angiography findings than GE Discovery 530c, although the difference was not statistically significant. No significant differences in lesion overlap were observed for the left anterior descending artery (LAD) or left circumflex artery (LCx) basins. Furthermore, the image quality assessment revealed slightly better delineation of extracardiac structures using Veriton-CT (Spectrum Dynamics Medical, Caesarea, Israel), particularly when evaluated by an experienced researcher. However, no significant difference was observed when assessed by a less experienced observer. Conclusions: Our findings suggest that Veriton-CT, with its full-ring CZT detector system, may offer advantages over fixed-angle gamma cameras in improving image quality and reducing attenuation artifacts in MPI. Although the difference in correlations with coronary angiography findings was not statistically significant, Veriton-CT showed a trend toward better agreement, particularly in the RCA basin. These results indicate that full-ring SPECT imaging could improve the diagnostic accuracy of non-invasive MPI, potentially reducing the need for unnecessary invasive angiography. Further studies with larger patient cohorts are required to confirm these findings and evaluate the clinical impact of full-ring SPECT technology in myocardial perfusion imaging. Full article

The rapid advancement of computer vision technology is transforming how transportation agencies collect roadway characteristics inventory (RCI) data, yielding substantial savings in resources and time. Traditionally, capturing roadway data through image processing was seen as both difficult and error-prone. However, considering the recent improvements in computational power and image recognition techniques, there are now reliable methods to identify and map various roadway elements from multiple imagery sources. Notably, comprehensive geospatial data for pedestrian and bicycle lanes are still lacking across many state and local roadways, including those in the State of Florida, despite the essential role this information plays in optimizing traffic efficiency and reducing crashes. Developing fast, efficient methods to gather this data are essential for transportation agencies as they also support objectives like identifying outdated or obscured markings, analyzing pedestrian and bicycle lane placements relative to crosswalks, turning lanes, and school zones, and assessing crash patterns in the associated areas. This study introduces an innovative approach using deep neural network models in image processing and computer vision to detect and extract pedestrian and bicycle lane features from very high-resolution aerial imagery, with a focus on public roadways in Florida. Using YOLOv5 and MTRE-based deep learning models, this study extracts and segments bicycle and pedestrian features from high-resolution aerial images, creating a geospatial inventory of these roadway features. Detected features were post-processed and compared with ground truth data to evaluate performance. When tested against ground truth data from Leon County, Florida, the models demonstrated accuracy rates of 73% for pedestrian lanes and 89% for bicycle lanes. This initiative is vital for transportation agencies, enhancing infrastructure management by enabling timely identification of aging or obscured lane markings, which are crucial for maintaining safe transportation networks. Full article

Planning in mass-customization supply and manufacturing processes is a complex process that requires continuous planning and optimization to minimize time and cost across a wide variety of choices in large production volumes. While soft computing techniques are widely used for optimizing mass-customization products, they face scalability issues when handling large datasets and rely heavily on manually defined rules, which are prone to errors. In contrast, machine learning techniques offer an opportunity to overcome these challenges by automating rule generation and improving scalability. However, their full potential has yet to be explored. This article proposes a machine learning-based approach to address this challenge, aiming to optimize both the supply and manufacturing planning phases as a practical solution for industry planning or optimization problems. The proposed approach examines supervised machine learning and deep learning techniques for manufacturing time and cost planning in various scenarios of a large-scale real-life pilot study in the bicycle manufacturing domain. This experimentation included K-Nearest Neighbors with regression and Random Forest from the machine learning family, as well as Neural Networks and Ensembles as deep learning approaches. Additionally, Reinforcement Learning was used in scenarios where real-world data or historical experiences were unavailable. The training performance of the pilot study was evaluated using cross-validation along with two statistical analysis methods: the t-test and the Wilcoxon test. These performance evaluation efforts revealed that machine learning techniques outperform deep learning methods and the reinforcement learning approach, with K-NN combined with regression yielding the best results. The proposed approach was validated by industry experts in bicycle manufacturing. It demonstrated up to a 37% reduction in both time and cost for orders compared to traditional expert estimates. Full article

Subclavian artery pseudoaneurysms are rare but potentially life-threatening vascular injuries frequently associated with trauma such as clavicle fractures. In this paper we describe the case of a 49-year-old male who developed a post-traumatic pseudoaneurysm of the subclavian artery after a bicycle accident. The diagnosis was delayed due to non-specific symptoms and an initially missed aneurysm on computed tomography imaging. Persistent pain, swelling, and erythema in the subclavian region prompted further detailed diagnostics, which ultimately revealed the pseudoaneurysm. The patient was successfully treated with endovascular stent–graft implantation. We screened the PubMed database to identify similar cases managed exclusively through endovascular intervention. Reports of iatrogenic pseudoaneurysms and those treated with open surgery were excluded. Variables such as time to diagnosis, clinical presentation, features of pseudoaneurysms, and complications were analyzed to highlight the role of endovascular techniques as a minimally invasive and effective treatment option. These cases pose both a diagnostic and a therapeutic challenge, as early recognition of symptoms is crucial to prevent serious complications including thrombosis, neurological deficits, and even limb loss. Full article

Aerodynamics is a key factor in time-trial cycling. Over the years, various aspects have been investigated, including positioning, clothing, bicycle design, and helmet shape. The present study focuses on the development of a methodology for the aerodynamic optimization of a time-trial helmet through the implementation of a reduced-order model, alongside advanced simulation techniques, such as computational fluid dynamics, radial basis functions, mesh morphing, and response surface methodology. The implementation of a reduced-order model enhances the understanding of aerodynamic interactions compared to traditional optimization workflows reported in sports-related research, facilitating the identification of an optimal helmet shape during the design phase. The study offers practical insights for refining helmet design. Starting with a baseline teardrop profile, several morphing configurations are systematically tested, resulting in a 10% reduction in the drag force acting on the helmet. The reduced-order model also facilitates the analysis of turbulent flow patterns on the cyclist’s body, providing a detailed understanding of aerodynamic interactions. By leveraging reduced-order models and advanced simulation techniques, this study contributes to ongoing efforts to reduce the aerodynamic resistance of time-trial helmets, ultimately supporting the goal of improved athlete performance. Full article

Background: Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. Aims: To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. Objectives: IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. Materials and Methods: A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 (n = 31) with and G2 (n = 49) without post stress-induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurements, echocardiography, real time breathing at rest into PTR-TOF-MS-1000, cardio-ankle vascular index, bicycle ergometry, and immediately after performing bicycle ergometry repeating the breathing analysis into the PTR-TOF-MS-1000, and after three minutes from the end of the second breath, repeat the breath into the PTR-TOF-MS-1000, then performing CTP. LASSO regression with nested cross-validation was used to find the association between the exhaled VOCs and existence of myocardial perfusion defect. Statistical processing performed with R programming language v4.2 and Python v.3.10 [^R], STATISTICA program v.12, and IBM SPSS v.28. Results: The VOCs specificity 77.6% [95% confidence interval (CI); 0.666; 0.889], sensitivity 83.9% [95% CI; 0.692; 0.964], and diagnostic accuracy; area under the curve (AUC) 83.8% [95% CI; 0.73655857; 0.91493173]. Whereas the AUC of the bicycle ergometry 50.7% [95% CI; 0.388; 0.625], specificity 53.1% [95% CI; 0.392; 0.673], and sensitivity 48.4% [95% CI; 0.306; 0.657]. Conclusions: The VOCs analysis appear to discriminate individuals with vs. without IHD using machine learning models. Other: The exhaled breath analysis reflects the myocardiocytes metabolomic signature and related intercellular homeostasis changes and regulation perturbances. Exhaled breath analysis poses a promise result to improve the diagnostic accuracy of the physical stress tests using machine learning models. Full article

The quality of bicycle path surfaces significantly influences the comfort of cyclists. This study evaluates the effectiveness of smartphone sensor data and smart bicycle lights data in assessing the roughness of bicycle paths. The research was conducted in Hasselt, Belgium, where various bicycle path pavement types, such as asphalt, cobblestone, concrete, and paving tiles, were analyzed across selected streets. A smartphone application (Physics Toolbox Sensor Suite) and SEE.SENSE smart bicycle lights were used to collect GPS and vertical acceleration data on the bicycle paths. The Dynamic Comfort Index (DCI) and Root Mean Square (RMS) values from the data collected through the Physics Toolbox Sensor Suite were calculated to quantify the vibrational comfort experienced by cyclists. In addition, the data collected from the SEE.SENSE smart bicycle light, DCI, and RMS computed results were categorized for a statistical comparison. The findings of the statistical tests revealed no significant difference in the comfort assessment among DCI, RMS, and SEE.SENSE. The study highlights the potential of integrating smartphone sensors and smart bicycle lights for efficient, large-scale assessments of bicycle infrastructure, contributing to more informed urban planning and improved cycling conditions. It also provides a low-cost solution for the city authorities to continuously assess and monitor the quality of their cycling paths. Full article

The study evaluates the impact of behavioral changes resulting from climate awareness on energy consumption and carbon emissions in European households based on the results of a two-stage survey addressed to individuals manifesting pro-ecological attitudes. In the first stage, the author analyzed 67 pro-environmental behaviors declared by the participants, identified a set of new sustainable choices, and compared them to the conservation habits used in Rasch and Campbell’s models. The 10 most popular initiatives undertaken by over 50% of participants were selected for further analysis. The influence of these initiatives on energy consumption and CO₂ emissions was assessed. A total of 24 impact indicators were identified at the building scale. Energy and carbon savings were calculated for 500 participants from 28 European countries and compared to the results computed for the 100 households used as a reference. The main conclusions from the research concern the significance of individual decisions at the building scale in the context of their actual environmental impact calculated for a larger scale. The comparative analysis showed that the highest annual energy (2292.1 MWh) and emission (267.02 tons of CO₂) savings resulted from the car-to-bicycle (or walking) transition on short-distance trips (declared by 79%) and from the transition from non-renewable to renewable energy sources (PV panels installed by 65% of respondents). Annual energy and emission savings reached, respectively, 1300 MWh and 262.6 tons of CO₂. The research findings help explain the critical importance of transforming the built environment towards renewable energy sources and supporting pedestrian and sustainable transportation. Full article

Docked Bike Sharing Systems often experience load imbalances among bike stations, leading to uneven distribution of bicycles and to challenges in meeting users’ demand. To address the load imbalances, many docked Bike Sharing Systems employ rebalancing vehicles that actively redistribute bicycles across stations, ensuring a more equitable distribution and enhancing the availability of bikes for users. The determination of the number of rebalancing vehicles in docked Bike Sharing Systems is typically based on various criteria, such as the size of the system, the density of stations, the expected demand patterns, and the desired level of service quality. This is a determining factor, in order to increase the efficiency of customer service at a reasonable cost. To enable a cost-effective rebalancing, we have used a cluster-based approach, due to the large scale of the Bike Sharing Systems, and our model is based on Markov Chains, given their proven effectiveness in this domain. Degrees of subsystem load at station level were used for modeling purposes. Additionally, a quantization strategy around cluster load was developed, to avoid state space explosion. This allowed the computation of the probability of transitioning from one degree of system load to another. A new method was developed to determine the fleet size, based on the identified subsystem steady state, describing the rebalancing necessity. The model evaluation was performed on traffic data collected from the Citi Bike New York Bike Sharing System. Based on the evaluation results, the model transition rates were in accordance with the expected values, indicating that the rebalancing operations are efficient from the point of view of the fulfillment of on-time arrival constraints. Full article

Graham and Pollack in 1971 presented applications of eigenvalues of the distance matrix in addressing problems in data communication systems. Spectral graph theory employs tools from linear algebra to retrieve the properties of a graph from the spectrum of graph-theoretic matrices. The study of graphs with “few eigenvalues” is a contemporary problem in spectral graph theory. This paper studies graphs with few distinct distance eigenvalues. After mentioning the classification of graphs with one and two distinct distance eigenvalues, we mainly focus on graphs with three distinct distance eigenvalues. Characterizing graphs with three distinct distance eigenvalues is “highly” non-trivial. In this paper, we classify all trees whose distance matrix has precisely three distinct eigenvalues. Our proof is different from earlier existing proof of the result as our proof is extendable to other similar families such as unicyclic and bicyclic graphs. The main tools which we employ include interlacing and equitable partitions. We also list all the connected graphs on ν ≤ 6 vertices and compute their distance spectra. Importantly, all these graphs on ν ≤ 6 vertices are determined from their distance spectra. We deliver a distance cospectral pair of order 7, thus making it a distance cospectral pair of the smallest order. This paper is concluded with some future directions. Full article