Search Results (1,029)

The Barcelona Metropolitan Area (AMB) has implemented various policies to reduce car use and promote more sustainable mobility. Initiatives such as superblocks, Low Emission Zones (LEZs), and the Bicivia network aim to transform the urban model in response to environmental and congestion challenges. However, the high reliance on private vehicles for intermunicipal travel, uneven infrastructure, and social resistance to certain changes remain significant issues. This study examines the evolution of mobility patterns and assesses the effectiveness of the above policies in fostering real and sustainable change. A mixed-methods approach was adopted, which combined an exploratory factor analysis (EFA) of 2011–2024 data, trend linear regression, and a comparative international analysis. The EFA identified four key structural dimensions: traditional transport infrastructure, active mobility and bus lines, public bicycles and mixed use, and transport efficiency and punctuality. The findings reveal a clear reduction in private car use and an increase in sustainable modes of transport. This indicates that there are prospects for future transformation. Nonetheless, challenges persist in intermunicipal mobility and the public acceptance of the measures. This study provides empirical and comparative evidence and emphasizes the need for integrated metropolitan governance to achieve a resilient and sustainable urban model. Full article

Transparent polyimides with excellent mechanical properties and high optical transmittance have been widely used in various optical and electrical applications. However, due to the rigidity of their aromatic structure, their flexibility is limited, making them unsuitable for applications requiring different form factors, such as flexible display cover windows. Furthermore, the refractive index of most transparent polyimides is approximately 1.57, which differs from that of the optically clear adhesives (OCAs) and window materials that have values typically around 1.5, resulting in visual distortion. This study employed 4,4′-(hexafluoroisopropylidene)diphthalic anhydride (6FDA) and 2,2′-bis(trifluoromethyl)benzidine (TFMB) as the base structure of polyimides (6T). Additionally, 1,3-bis(aminomethyl)cyclohexane (BAC) with a monocyclic structure and bis(aminomethyl)bicyclo[2,2,1]heptane (BBH) with a bicyclic structure were introduced as co-monomers or crosslinking agents to 6T. The mechanical, thermal, and optical properties of the obtained copolymers (6T-BAC and 6T-BBH series) and crosslinked polymers (6T-CL-BAC and 6T-CL-BBH series) were compared. Both the copolymer series (6T-BAC and 6T-BBH) and the crosslinked series (6T-CL-BAC and 6T-CL-BBH) exhibited improved optical properties compared to the conventional 6T, with maximum transmittance exceeding 90% and refractive indices ranging from approximately 1.53 to 1.55. Notably, the copolymer series achieved transmittance levels above 95% and exhibited lower refractive indices (~1.53), demonstrating superior optical performance relative not only to the 6T baseline but also to the crosslinked series. The alicyclic polyimides synthesized in this study exhibited mechanical flexibility, high optical transmittance, and a refractive index approaching 1.5, demonstrating their applicability for use as flexible display cover window materials. Full article

Efficient object detection is crucial to real-time monitoring applications such as autonomous driving or security systems. Modern RGB cameras can produce high-resolution images for accurate object detection. However, increased resolution results in increased network latency and power consumption. To minimise this latency, Convolutional Neural Networks (CNNs) often have a resolution limitation, requiring images to be down-sampled before inference, causing significant information loss. Event-based cameras are neuromorphic vision sensors with high temporal resolution, low power consumption, and high dynamic range, making them preferable to regular RGB cameras in many situations. This project proposes the fusion of an event-based camera with an RGB camera to mitigate the trade-off between temporal resolution and accuracy, while minimising power consumption. The cameras are calibrated to create a multi-modal stereo vision system where pixel coordinates can be projected between the event and RGB camera image planes. This calibration is used to project bounding boxes detected by clustering of events into the RGB image plane, thereby cropping each RGB frame instead of down-sampling to meet the requirements of the CNN. Using the Common Objects in Context (COCO) dataset evaluator, the average precision (AP) for the bicycle class in RGB scenes improved from 21.08 to 57.38. Additionally, AP increased across all classes from 37.93 to 46.89. To reduce system latency, a novel object detection approach is proposed where the event camera acts as a region proposal network, and a classification algorithm is run on the proposed regions. This achieved a 78% improvement over baseline. Full article

This study investigates public perceptions of sustainable mobility within university environments, which are important trip generation hubs with the potential to influence and disseminate sustainable mobility behaviors. Using sentiment analysis on 120,236 tweets from São Paulo, Rio de Janeiro, Lisbon, and Porto, tweets were classified into positive, neutral, and negative sentiments to assess perceptions across transport modes. It was hypothesized that universities would exhibit more positive sentiment toward active and public transport modes compared to perceptions of these modes within the broader city environment. Results show that active modes and public transport consistently receive higher positive sentiment rates than individual motorized modes, and, considering the analyzed contexts, universities demonstrate either similar (São Paulo) or more positive perceptions compared to the overall sentiment observed in the city (Rio de Janeiro, Lisbon, and Porto). Chi-square tests confirmed significant associations between transport mode and sentiment distribution. An exploratory analysis using topic modeling revealed that perceptions around bicycle use are linked to themes of safety, cycling infrastructure, and bike sharing. The findings highlight opportunities to promote sustainable mobility in universities by leveraging user sentiment while acknowledging limitations such as demographic bias in social media data and potential misclassification. This study advances data-driven methods to support targeted strategies for increasing active and public transport in university settings. Full article

This paper examines how artificial intelligence (AI) can be strategically deployed to enhance urban planning and environmental livability in Riyadh by generating data-driven, people-centric design interventions. Unlike previous studies that concentrate primarily on visualization, this research proposes an integrative appraisal framework that combines AI-generated design with site-specific environmental data and native vegetation typologies. This study was conducted across key jurisdictional areas including the Northern Ring Road, King Abdullah Road, Al Rabwa, Al-Malaz, Al-Suwaidi, Al-Batha, and King Fahd Road. Using AI tools, urban scenarios were developed to incorporate expanded pedestrian pathways (up to 3.5 m), dedicated bicycle lanes (up to 3.0 m), and ecologically adaptive green buffer zones featuring native drought-resistant species such as Date Palm, Acacia, and Sidr. The quantitative analysis of post-intervention outcomes revealed surface temperature reductions of 3.2–4.5 °C and significant improvements in urban esthetics, walkability, and perceived safety—measured on a five-point Likert scale with 80–100% increases in user satisfaction. Species selection was validated for ecological adaptability, minimal maintenance needs, and compatibility with Riyadh’s sandy soils. This study directly supports the Kingdom of Saudi Arabia’s Vision 2030 by demonstrating how emerging technologies like AI can drive smart, sustainable urban transformation. It aligns with Vision 2030’s urban development goals under the Quality-of-Life Program and environmental sustainability pillar, promoting healthier, more connected cities with elevated livability standards. The research not only delivers practical design recommendations for planners seeking to embed sustainability and digital innovation in Saudi urbanism but also addresses real-world constraints such as budgetary limitations and infrastructure integration. Full article

This paper explores the potential of public webcams as a source of data for transport research. Eight different open-source object detection models were tested on three publicly accessible webcams located in the city of Brunswick, Germany. Fifteen images at different lighting conditions (bright light, dusk, and night) were selected from each webcam and manually labelled with regard to the following six categories: cars, persons, bicycles, trucks, trams, and buses. The manual counts in these six categories were then compared to the number of counts found by the object detection models. The results show that public webcams constitute a useful source of data for transport research. In bright light conditions, applying out-of-the-box object detection models can yield reliable counts of cars or persons in public squares, streets, and junctions. However, the detection of cars and persons was not reliably accurate at dusk or night. Thus, different object detection models might have to be used to generate accurate counts in different lighting conditions. Furthermore, the object detection models worked less well for identifying trams, buses, bicycles, and trucks. Hence fine-tuning and adapting the models to the specific webcams might be needed to achieve satisfactory results for these four types of traffic participants. Full article

This paper proposes a general workflow for collecting and analyzing open data from Bicycle Sharing Systems (BSSs) that was developed using data from the Valenbisi system, operated in Valencia by the French company JCDecaux; however, the stages of the proposed workflow are service-independent and can be applied broadly. Cycling has become an increasingly popular mode of transportation, leading to the emergence of BSSs in modern cities. Parallel to this, Smart City solutions have been implemented using Internet of Things (IoT) technologies, such as embedded sensors and GPS-based communication systems, which have become essential to everyday life. When public transportation services or bicycle sharing systems are used, real-time information about the services is provided to customers, including vehicle tracking based on GPS technology and the availability of bikes via sensors installed at bike rental stations. The bike stations were examined from two different perspectives: first, their daily usage, and second, the types of facilities located in their surroundings. Based on these two approaches, the overlap between the clustering results was analyzed—specifically, the similarity in how stations could be grouped and the correlation between their usage and locations. To enhance the raw data retrieved from the service provider’s official API, the stations were annotated based on OpenStreetMap and Overpass API data. Data visualization was created using Tableau from Salesforce. Based on the results, an agreement of 62% was found between the results of the two different clustering approaches. Full article

This research explores how artificial intelligence (AI) can be leveraged to optimize packaging design, reduce operational costs, and enhance sustainability in e-commerce. As packaging waste and shipping inefficiencies grow alongside global online retail demand, traditional methods for determining box size, material use, and logistics planning have become economically and environmentally inadequate. Using a three-phase framework, this study integrates data-driven diagnostics, AI modeling, and real-world validation. In the first phase, a systematic analysis of current packaging inefficiencies was conducted through secondary data, benchmarking, and cost modeling. Findings revealed significant waste caused by over-packaging, suboptimal box-sizing, and poor alignment between product characteristics and logistics strategy. In the second phase, a random forest (RF) machine learning model was developed to predict optimal packaging configurations using key product features: weight, volume, and fragility. This model was supported by AI simulation tools that enabled virtual testing of material performance, space efficiency, and damage risk. Results demonstrated measurable improvements in packaging optimization, cost reduction, and emission mitigation. The third phase validated the AI framework using practical case studies—ranging from a college textbook to a fragile kitchen dish set and a high-volume children’s bicycle. The model successfully recommended right-sized packaging for each product, resulting in reduced material usage, improved shipping density, and enhanced protection. Simulated cost-saving scenarios further confirmed that smart packaging and AI-generated configurations can drive efficiency. The research concludes that AI-based packaging systems offer substantial strategic value, including cost savings, environmental benefits, and alignment with regulatory and consumer expectations—providing scalable, data-driven solutions for e-commerce enterprises such as Amazon and others. Full article

Bicycle use has been increasing in many countries for active, sustainable transportation and recreation. Bicycling can benefit an individual’s mental and physical health and contribute to a community’s well-being and desirability, and it is more environmentally sustainable than automobiles. Nonprofit organizations lead bicycle advocacy efforts in the USA, both for bicycling as recreation and as part of local transportation systems. Outride is one of the larger advocacy organizations, and it sponsors a unique grant system targeting grassroots bicycling organizations dedicated to increasing bicycling. Using the Bicycle Community Development Framework (BCDF) as a lens, this study aims to evaluate Outride’s efforts through an interpretative phenomenological approach (IPA) using semi-structured interviews to gather data regarding grant recipients’ experiences using Outride funds. Findings suggest fund recipients are increasing bicycling through programs and infrastructure development, but with more intentionality, could better support building bicycle communities. Regarding the BCDF, the recipients strongly promoted education, engineering, and equity & accessibility while fostering a sense of community, belonging, and empowerment in their participants. Full article

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

This study addresses the ’last-mile’ transportation challenges that arise in steep and narrow forest terrain by proposing a novel robotic palanquin system called roCaGo. It is inspired by the mechanical principles of two-wheel-steering and two-wheel-drive (2WS/2WD) bicycles. The roCaGo system integrates front- and rear-wheel-drive mechanisms, as well as a central suspension structure for carrying loads. Unlike conventional forestry machinery, which requires wide, well-maintained roads or permanent rail systems, the roCaGo system enables flexible, operator-assisted transport along narrow, unprepared mountain paths. A dynamic model of the system was developed to design a stabilization control strategy, enabling roCaGo to maintain transport stability and assist the operator during navigation. Numerical simulations and preliminary physical experiments demonstrate its effectiveness in challenging forest environments. Furthermore, the applicability of roCaGo has been extended to include use as a mobile third-person viewpoint platform to support the remote operation of existing forestry equipment; specifically the LV800crawler vehicle equipped with a front-mounted mulcher. Field tests involving LiDAR sensors mounted on roCaGo were conducted to verify its ability to capture the environmental data necessary for non-line-of-sight teleoperation. The results show that roCaGo is a promising solution for improving labor efficiency and ensuring operator safety in forest logistics and remote-controlled forestry operations. Full article

Background and Clinical Significance: Traumatic brain injury (TBI) represents a major public health concern due to its profound neurological, psychological, and socioeconomic consequences. Effective management is essential to optimize patient outcomes and reduce healthcare burden. In cases involving extensive bone loss or complex fractures, particularly when decompressive craniectomy (DC) is considered, secondary cranial reconstruction is typically required. However, DC is associated with prolonged hospitalization, multiple surgical interventions, an increased risk of complications, and higher costs. Case Presentation: We present the case of a 59-year-old male involved in a high-energy bicycle accident, sustaining severe craniofacial trauma with multiple midface fractures, a multifragmented left cranial fracture, and a left-sided epidural hematoma with brain compression. Hematoma evacuation and immediate primary reconstruction of the fractured skull using autologous bone were successfully performed, avoiding the need for DC. The patient recovered under intensive care and was transferred to a neurorehabilitation center. Conclusions: Primary reconstruction of large skull fractures using autologous bone should remain the goal, whenever possible, in order to avoid additional costs, risks, and complications. Full article

Background: Dithiolopyrrolones (DTPs), such as holomycin and thiolutin, exhibit potent antibacterial activities. DTPs contain a disulfide within a unique bicyclic scaffold, which may chelate metal ions and disrupt metal-dependent cellular processes once the disulfide is reductively transformed to thiols. However, the contribution of the intrinsic redox mechanism of DTPs to their antibacterial activity remains unclear. Herein we used pyrroloformamide (Pyf) A, a DTP with a unique formyl substituent, as a prototype to study the antibacterial potential and mechanism against ESKAPE pathogens, in particular carbapenem-resistant Klebsiella pneumoniae (CRKP). Methods: The antibacterial and anti-biofilm activities of Pyf A were mainly assessed against clinical CRKP isolates. Propidium iodide staining, scanning electron microscopy, glutathione (GSH) quantification, and reactive oxygen species (ROS) analysis were utilized to infer its anti-CRKP mechanism. The synergistic antibacterial effects of Pyf A and AgNO₃ were evaluated through checkerboard and time-kill assays, as well as in vivo murine wound and catheter biofilm infection models. Results: Pyf A exhibited broad-spectrum antibacterial activity against ESKAPE pathogens with minimum inhibitory concentrations ranging from 0.25 to 4 μg/mL. It also showed potent anti-biofilm effects against CRKP. Pyf A disrupted the cell membranes of CRKP and markedly depleted intracellular GSH without triggering ROS accumulation. Pyf A and AgNO₃ showed synergistic anti-CRKP activities in vitro and in vivo, by disrupting both GSH- and thioredoxin-mediated redox homeostasis. Conclusions: Pyf A acts as a GSH-depleting agent and, when combined with AgNO₃, achieves dual-targeted disruption of bacterial thiol redox systems. This dual-targeting strategy enhances antibacterial efficacy of Pyf A and represents a promising therapeutic approach to combat CRKP infections. Full article

The field related to indices was developed by researchers for various purposes. Optimization is one of the purposes used by researchers in different situations. In this article, a generalized Sombor index is considered. This work is related to the idea of optimization in the families of bicyclic graphs, trees, and unicyclic graphs. We investigated optimal values in the stated families by means of well-known transformations. The transformations include the following: Transformation A, Transformation B, Transformation C, and Transformation D. Transformation A and Transformation B increase the value of the generalized Sombor index, while Transformation C and Transformation D are used for minimal values. Full article