Search Results (220)

The increasing concern for road safety has driven the development of advanced driver behavior analysis systems. This study presents a comprehensive review of various techniques to detect unsafe driving behaviors, with a particular emphasis on using smartphone sensors. By leveraging data from accelerometers, gyroscopes, and GPS, these methods allow for the detection of aggressive driving patterns, which may result from factors such as driver distraction or drowsiness. Modern sensor technology plays a crucial role in real-time monitoring and has significant potential to enhance vehicle safety systems. A Long Short-Term Memory (LSTM) network combined with a Conv1D layer was trained to analyze driving patterns using a sliding window technique. As technology continues evolving, its application in driver behavior analysis holds great promise for reducing traffic accidents and improving driving habits. Furthermore, the ability to gather and analyze large amounts of data from drivers in various conditions opens new opportunities for more personalized and adaptive safety solutions. This research offers insights into the future direction of driver monitoring systems and the growing impact of mobile and sensor-based solutions in transportation safety. Full article

This paper investigates the growing problem of abandoned maritime containers and the lack of effective reverse logistics to manage them: Background: The research highlights the significant environmental impact and economic burdens caused by the imbalance of container inflow and outflow, which leads to the accumulation of containers in storage yards; Methods: The study used the Delphi Method, gathering insights from a panel of experts in container transport and maintenance. The goal was to identify key challenges and potential solutions for improving container reverse logistics in Portugal; Results: The results confirm the urgent need for efficient reverse logistics strategies to address the container imbalance. The experts reached over 60% consensus on the importance of developing logistics systems and improving communication between ports. Implementing these strategies would not only reduce economic costs but also significantly lower environmental pollution; Conclusions: The paper concludes that a strategic shift toward effective reverse logistics is essential for enhancing the sustainability and operational efficiency of the maritime transport sector. Full article

Smart cities and urban planning have succeeded in gathering the attention of researchers worldwide, especially in the last decade, as a result of a series of technological, social and economic developments that have shaped the need for evolution from the traditional way in which the cities were viewed. Technology has been incorporated in many sectors associated with smart cities, such as communications, transportation, energy, and water, resulting in increasing people’s quality of life and satisfying the needs of a society in continuous change. Furthermore, with the rise in machine learning (ML) and artificial intelligence (AI), as well as Geographic Information Systems (GIS), the applications of big data analytics in the context of smart cities and urban planning have diversified, covering a wide range of applications starting with traffic management, environmental monitoring, public safety, and adjusting power distribution based on consumption patterns. In this context, the present paper brings to the fore the papers written in the 2015–2024 period and indexed in Clarivate Analytics’ Web of Science Core Collection and analyzes them from a bibliometric point of view. As a result, an annual growth rate of 10.72% has been observed, showing an increased interest from the scientific community in this area. Through the use of specific bibliometric analyses, key themes, trends, prominent authors and institutions, preferred journals, and collaboration networks among authors, data are extracted and discussed in depth. Thematic maps and topic discovery through Latent Dirichlet Allocation (LDA) and doubled by a BERTopic analysis, n-gram analysis, factorial analysis, and a review of the most cited papers complete the picture on the research carried on in the last decade in this area. The importance of big data analytics in the area of urban planning and smart cities is underlined, resulting in an increase in their ability to enhance urban living by providing personalized and efficient solutions to everyday life situations. Full article

Vehicular ad hoc networks (VANETs) are a critical aspect of intelligent transportation systems, improving safety and comfort for drivers. These networks enhance the driving experience by offering timely information vital for safety and comfort. Yet, VANETs come with their own set of challenges concerning security, privacy, and design reliability. Traditionally, vehicle authentication occurs every time a vehicle enters the domain of the roadside unit (RSU). In our study, we suggest that authentication should take place only when a vehicle has not covered a set distance, increasing system efficiency. The rise of the Internet of Things (IoT) has seen an upsurge in the use of IoT devices across various fields, including smart cities, healthcare, and vehicular IoT. These devices, while gathering environmental data and networking, often face reliability issues without a trusted intermediary. Our study delves deep into implementing Teegraph in VANETs to enhance authentication. Given the integral role of VANETs in Intelligent Transportation Systems and their inherent challenges, we turn to Hashgraph—an alternative to blockchain. Hashgraph offers a decentralized, secure, and trustworthy database. We introduce an efficient authentication system, which triggers only when a vehicle has not traversed a set distance, optimizing system efficiency. Moreover, we shed light on the indispensable role Hashgraph can occupy in the rapidly expanding IoT landscape. Lastly, we present Teegraph, a novel Hashgraph-based technology, as a superior alternative to blockchain, ensuring a streamlined, scalable authentication solution. Our approach leverages the logical key hierarchy (LKH) and packet update keys to ensure data privacy and integrity in vehicular networks. Full article

Large-scale events, such as festivals and public gatherings, pose serious problems in terms of traffic congestion, slow transaction processing, and security risks to transportation planning. This study proposes a blockchain-based solution for enhancing the efficiency and security of intelligent transport systems (ITS) by utilizing state channels and rollups. Throughput is optimized, enabling transaction speeds of 800 to 3500 transactions per second (TPS) and delays of 5 to 1.5 s. Prevent data tampering, strengthen security, and enhance data integrity from 89% to 99.999%, as well as encryption efficacy from 90% to 98%. Furthermore, our system reduces congestion, optimizes vehicle movement, and shares real-time, secure data with stakeholders. Practical applications include fast and safe road toll payments, faster public transit ticketing, improved emergency response coordination, and enhanced urban mobility. The decentralized blockchain helps maintain trust among users, transportation authorities, and event organizers. Our approach extends beyond large-scale events and proposes a path toward ubiquitous, Artificial Intelligence (AI)-driven decision-making in a broader urban transit network, informing future operations in dynamic traffic optimization. This study demonstrates the potential of blockchain to create more intelligent, more secure, and scalable transportation systems, which will help reduce urban mobility inefficiencies and contribute to the development of resilient smart cities. Full article

Traffic congestion poses a significant challenge to modern urban environments, impacting both driver satisfaction and road safety. This paper investigates the effectiveness of a smart traffic light system (STL), a solution developed under the Intelligent Transportation System (ITS) initiative by the Ministry of Works Malaysia, to address these issues in Malaysia. The system integrates a network of sensors, AI-enabled cameras, and Automatic Number Plate Recognition (ANPR) technology to gather real-time data on traffic volume and vehicle classification at congested intersections. This data is utilized to dynamically adjust traffic light timings, prioritizing traffic flow on heavily congested roads while maintaining safety standards. To evaluate the system’s performance, a comprehensive study was conducted at a selected intersection. Traffic patterns were automatically analyzed using camera systems, and the performance of the STL was compared to that of traditional traffic signal systems. The average travel time from the start to the end intersection was measured and compared. Preliminary findings indicate that the STL significantly reduces travel times and improves overall traffic flow at the intersection, with average travel time reductions ranging from 7.1% to 28.6%, depending on site-specific factors. While further research is necessary to quantify the full extent of the system’s impact, these initial results demonstrate the promising potential of STL technology to enhance urban mobility and more efficient and safer roadways by moving beyond traditional traffic signal functionalities. Full article

The transport of intensity equation enables quantitative phase imaging from only two axially displaced intensity images, facilitating the characterization of low-contrast samples like cells and microorganisms. However, the rapid selection of the correct defocused planes, crucial for real-time phase imaging of dynamic events, remains challenging. Additionally, the different images are normally acquired sequentially, further limiting phase-reconstruction speed. Here, we report on a system that addresses these issues and enables user-tuned defocusing with snapshot phase retrieval. Our approach is based on combining multi-color pulsed illumination with acousto-optic defocusing for microsecond-scale chromatic aberration control. By illuminating each plane with a different color and using a color camera, the information to reconstruct a phase map can be gathered in a single acquisition. We detail the fundamentals of our method, characterize its performance, and demonstrate live phase imaging of a freely moving microorganism at speeds of 150 phase reconstructions per second, limited only by the camera’s frame rate. 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

Zn is a trace element necessary for the functioning of about 300 enzymes. It plays a biochemical, structural, and regulatory role. It participates in the immune response, proper functioning of the endocrine system, and regulation of gene expression. Its deficiencies are most often caused by the mismatch between dietary intake and the body’s needs. Bioavailability of zinc depends on interactions with other food components. Phytates negatively affect this element’s absorption, whereas proteins, peptides, and amino acids increase its bioavailability. It has been proven that organic forms of zinc are better absorbed than inorganic compounds, like zinc oxide and sulfate. Amino acid combinations with zinc can use amino acid transporters in the absorption process. Estimation of Zn bioavailability and bioaccessibility are based on in vivo and in vitro studies, each having their advantages and disadvantages. The current review aims to gather and summarize recent research on the dietary role of Zn, especially data on bioavailability from food substances promoting/inhibiting absorption, and the latest methods for determining the level of bioavailability of this nutrient. Full article

With the acceleration of the oilfield development process during the high water content period, the contradiction between the increase in energy consumption and the decrease in the energy efficiency of the gathering and transportation system has become increasingly obvious. This paper develops a grey relational analysis model using a combination of AHP and EWM. Based on the characteristics of light oil production, a four-level evaluation indicator system is developed. Based on game theory, AHP can provide subjective weights, the EWM can provide objective weights, and subjective and objective combinations are used for a more reasonable assignment. Concurrently, the 0.05 distinguishing coefficient and the ideal reference values are selected as the GRA reference sequence to evaluate the energy consumption of the gathering and transportation system as a whole and each subsystem. The analysis of a light oil block indicates significant room for improvement in the energy efficiency correlation across the system. Taking the central processing station as an example, the grey relational degree of electricity consumption per unit of injected water is measured at 0.12, marking it as the weakest link in the system. This study supports efficiency enhancement by identifying energy consumption bottlenecks within the system. Full article

High-Speed Rail (HSR) has long been proposed as a transformative infrastructure project for Australia; yet, despite multiple feasibility studies and significant government expenditure, it remains unrealized. This study investigates the key barriers preventing HSR implementation. To achieve this, a novel mixed-methods approach that triangulates a comprehensive literature review, in-depth expert interviews, and broad stakeholder survey was employed. The Analytic Hierarchy Process (AHP) was used to quantify the relative importance of the identified barriers. Simultaneously, qualitative insights were gathered through interviews with industry leaders, government officials, and infrastructure experts. This dual approach provided a comprehensive understanding of the challenges. The findings highlight the importance of external factors. These include political uncertainty, financial constraints, and systemic logistical challenges, which go beyond technical feasibility. Based on these insights, this research identifies critical early-stage risks and contributes to a re-evaluation of HSR not solely as a transport solution but also as a vital tool for regional development. Refining cost and time estimation methodologies using reference class forecasting, fostering proactive political engagement to secure bipartisan support, enhancing private sector collaboration through early contractor involvement and risk-sharing mechanisms, and developing a national upskilling framework to address workforce shortages were also key findings. The study has garnered industry recognition and support, with experts acknowledging its contribution to the ongoing discourse on HSR implementation in Australia. Full article

In the current paper, an analysis of magnetohydrodynamic Williamson nanofluid boundary layer flow is presented, with multiple slips in a porous medium, using a newly designed human-brain-inspired Ricker wavelet neural network solver. The solver employs a hybrid approach that combines genetic algorithms, serving as a global search method, with sequential quadratic programming, which functions as a local optimization technique. The heat and mass transportation effects are examined through a stretchable surface with radiation, thermal, and velocity slip effects. The primary flow equations, originally expressed as partial differential equations (PDEs), are changed into a dimensionless nonlinear system of ordinary differential equations (ODEs) via similarity transformations. These ODEs are then numerically solved with the proposed computational approach. The current study has significant applications in a variety of practical engineering and industrial scenarios, including thermal energy systems, biomedical cooling devices, and enhanced oil recovery techniques, where the control and optimization of heat and mass transport in complex fluid environments are essential. The numerical outcomes gathered through the designed scheme are compared with reference results acquired through Adam’s numerical method in terms of graphs and tables of absolute errors. The rapid convergence, effectiveness, and stability of the suggested solver are analyzed using various statistical and performance operators. Full article

The expansion of Urban Air Mobility (UAM) has led to diverse aircraft designs, with piloted systems expected to evolve into remotely piloted and automated operations. Future advancements in Intelligent Transportation Systems (ITSs) will further improve automation capabilities, promising significant benefits to the environment and overall efficiency of UAM aircraft. However, UAM aircraft face unique operational conditions that need to be accounted for when assessing safety risks, such as lower operating altitudes and hazards present in urban settings, thus leading to a potential increased risk of collisions with foreign objects, particularly birds and drones. This paper reviews historical safety data with an aim to better assess the potential risks of UAM aircraft. A survey was conducted to gather quantitative and qualitative insights from subject matter experts, reinforcing findings from existing studies. The results highlight the need for a comprehensive risk assessment framework to guide design improvements and regulatory strategies, ensuring safer UAM operations. Full article

The quantification of methane concentrations in air is essential for the quantification of methane emissions, which in turn is necessary to determine absolute emissions and the efficacy of emission mitigation strategies. These are essential if countries are to meet climate goals. Large-scale deployment of methane analyzers across millions of emission sites is prohibitively expensive, and lower-cost instrumentation has been recently developed as an alternative. Currently, it is unclear how cheaper instrumentation will affect measurement resolution or accuracy. To test this, the Wireless Autonomous Transportable Methane Emission Reporting System (WATCH₄ERS) has been developed, comprising four commercially available sensing technologies: metal oxide (MOx,), Non-dispersion Infrared (NDIR), integrated infrared (INIR), and tunable diode laser absorption spectrometer (TDLAS). WATCHERS is the accumulated knowledge of several long-term methane measurement projects at Colorado State University’s Methane Emission Technology Evaluation Center (METEC), and this study describes the integration of these sensors into a single unit and reports initial instrument response to calibration procedures and controlled release experiments. Specifically, this paper aims to describe the development of the WATCH₄ERS unit, report initial sensor responses, and describe future research goals. Meanwhile, future work will use data gathered by multiple WATCH₄ERS units to 1. better understand the cost–benefit balance of methane sensors, and 2. identify how decreasing instrumentation costs could increase deployment coverage and therefore inform large-scale methane monitoring strategies. Both calibration and response experiments indicate the INIR has little practical use for measuring methane concentrations less than 500 ppm. The MOx sensor is shown to have a logarithmic response to methane concentration change between background and 600 ppm but it is strongly suggested that passively sampling MOx sensors cannot respond fast enough to report concentrations that change in a sub-minute time frame. The NDIR sensor reported a linear change to methane concentration between background and 600 ppm, although there was a noticeable lag in reporting changing concentration, especially at higher values, and individual peaks could be observed throughout the experiment even when the plumes were released 5 s apart. The TDLAS sensor reported all changes in concentration but remains prohibitively expensive. Our findings suggest that each sensor technology could be optimized by either operational design or deployment location to quantify methane emissions. The WATCH₄ERS units will be deployed in real-world environments to investigate the utility of each in the future. Full article

The flow management of the gas–liquid mixture module is crucial for the transmission efficiency of crude oil-and-natural gas-gathering and transportation systems. The concurrent flow of high-viscosity crude oil and natural gas in gas–liquid mixing is investigated numerically by adopting an improved volume of fluid (VOF) model programmed with the OpenFOAM v2012 software package. Over a wide range of superficial velocities for the oil, from 0.166 to 5.529 m/s, and natural gas, from 0.138 to 27.645 m/s, a variety of flow regimes of bubble flow, plug flow, slug flow, and annular flow are encountered successively, which are essentially consistent with the Brill and Mandhane flow regime identification criteria. The results show that the oil volume fraction, fluid velocity, and bubble slip velocity together affect the growth of bubbles in the pipeline at a low gas velocity. In the case of slug flow, the phenomenon of liquid film plugging is noticeable, and the flow is very unstable, which should be avoided as much as possible. Nonetheless, it is commended that stable plug flow and annular flow with a high oil transportation efficiency and minimal power consumption are friendly working conditions. Full article