Search Results (101)

Safe street crossing poses significant challenges for visually impaired pedestrians, who must rely on non-visual cues to assess crossing safety. Conventional assistive technologies often fail to provide real-time, actionable information about oncoming traffic, making independent navigation difficult, particularly in uncontrolled or vehicle-based crossing scenarios. To address these challenges, we designed and evaluated two assistive systems utilizing haptic and visual feedback, tailored for traffic signal-controlled intersections and vehicle-based crossings. The results indicate that visual feedback significantly improved decision efficiency at signalized intersections, enabling users to make faster decisions, regardless of their confidence levels. However, in vehicle-based crossings, where real-time hazard assessment is crucial, haptic feedback proved more effective, enhancing decision efficiency by enabling quicker and more intuitive judgments about approaching vehicles. Moreover, users generally preferred haptic feedback in both scenarios, citing its comfort and intuitiveness. These findings highlight the distinct challenges posed by different street-crossing environments and confirm the value of multimodal feedback systems in supporting visually impaired pedestrians. Our study provides important design insights for developing effective assistive technologies that enhance pedestrian safety and independence across varied urban settings. Full article

Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aerospace, automotive, and infrastructure industries due to their high strength-to-weight ratio. However, defect detection in CFRP remains challenging, particularly in low signal-to-noise ratio (SNR) conditions. Conventional segmentation methods often struggle with noise interference and signal variations, leading to reduced detection accuracy. In this study, we evaluate the impact of thermal image preprocessing on improving defect segmentation in CFRP laminates inspected via pulsed thermography. Polynomial approximations and first- and second-order derivatives were applied to refine thermographic signals, enhancing defect visibility and SNR. The U-Net architecture was used to assess segmentation performance on datasets with and without preprocessing. The results demonstrated that preprocessing significantly improved defect detection, achieving an Intersection over Union (IoU) of 95% and an F1-Score of 99%, outperforming approaches without preprocessing. These findings emphasize the importance of preprocessing in enhancing segmentation accuracy and reliability, highlighting its potential for advancing non-destructive testing techniques across various industries. Full article

Proteinopathies, characterized by the misfolding, aggregation, and deposition of proteins, are hallmarks of various neurodegenerative and systemic diseases. Increasingly, research has highlighted the role of protein misfolding in parasitic infections, unveiling intricate interactions between host and parasite that exacerbate disease pathology and contribute to chronic outcomes. The life cycles of parasitic protozoa, including Plasmodium, Toxoplasmosis, and Leishmania species, are complicated and involve frequent changes between host and vector environments. Their proteomes are severely stressed during these transitions, which calls for highly specialized protein quality control systems. In order to survive harsh intracellular conditions during infection, these parasites have been demonstrated to display unique adaptations in the unfolded protein response, a crucial pathway controlling endoplasmic reticulum stress. In addition to improving parasite survival, these adaptations affect host cell signaling and metabolism, which may jeopardize cellular homeostasis. By causing oxidative stress, persistent inflammation, and disturbance of cellular proteostasis, host–parasite interactions also contribute to proteinopathy. For instance, Plasmodium falciparum disrupts normal protein homeostasis and encourages the accumulation of misfolded proteins by influencing host redox systems involved in protein folding. In addition to interfering with host chaperone systems, the parasitic secretion of effector proteins exacerbates protein misfolding and aggregate formation. Autophagy, apoptosis regulation, organelle integrity, and other vital cellular processes are all disrupted by these pathological protein aggregates. Long-term misfolding and aggregation can cause irreversible tissue damage, which can worsen the clinical course of illnesses like visceral leishmaniasis, cerebral malaria, and toxoplasmosis. Treating parasite-induced proteinopathies is a potentially fruitful area of therapy. According to recent research, autophagy modulators, proteasome enhancers, and small-molecule chaperones may be repurposed to lessen these effects. Pharmacological agents that target the UPR, for example, have demonstrated the ability to decrease parasite survival while also reestablishing host protein homeostasis. Targeting the proteins secreted by parasites that disrupt host proteostasis may also offer a novel way to stop tissue damage caused by proteinopathies. In conclusion, the intersection of protein misfolding and parasitic infections represents a rapidly advancing field of research. Dissecting the molecular pathways underpinning these processes offers unprecedented opportunities for developing innovative therapies. These insights could not only transform the management of parasitic diseases but also contribute to a broader understanding of proteinopathies in infectious and non-infectious diseases alike. Full article

With the rapid development of the economy, urban road congestion has become more serious. The travel times for vehicles are becoming more uncontrollable, making it challenging to reach destinations on time. In order to find an optimal route and arrive at the destination with the shortest travel time, this paper proposes a dynamic shortest travel time path planning algorithm with an overtaking function (DSTTPP-OF) based on a vehicular ad hoc network (VANET) environment. Considering the uncertainty of driving vehicles, the target vehicle (vehicle for special tasks) is influenced by surrounding vehicles, leading to possible deadlock or congestion situations that extend travel time. Therefore, overtaking planning should be conducted through V2V communication, enabling surrounding vehicles to coordinate with the target vehicle to avoid deadlock and congestion through lane changing and overtaking. In the proposed DSTTPP-OF, vehicles may queue up at intersections, so we take into account the impact of traffic signals. We classify road segments into congested and non-congested sections, calculating travel times for each section separately. Subsequently, in front of each intersection, the improved Dijkstra algorithm is employed to find the shortest travel time path to the destination, and the overtaking function is used to prevent the target vehicle from entering a deadlocked state. The real-time traffic data essential for dynamic path planning were collected through a VANET of symmetrically deployed roadside units (RSUs) along the roadway. Finally, simulations were conducted using the SUMO simulator. Under different traffic flows, the proposed DSTTPP-OF demonstrates good performance; the target vehicle can travel smoothly without significant interruptions and experiences the fewest stops, thanks to the proposed algorithm. Compared to the shortest distance path planning (SDPP) algorithm, the travel time is reduced by approximately 36.9%, and the waiting time is reduced by about 83.2%. Compared to the dynamic minimum time path planning (DMTPP) algorithm, the travel time is reduced by around 18.2%, and the waiting time is reduced by approximately 65.6%. Full article

Traffic congestion at a single intersection can propagate and thus affect adjacent intersections as well, potentially resulting in prolonged gridlock across an entire urban area. Despite numerous research efforts aimed at developing intelligent traffic signal control systems, urban areas continue to experience traffic congestion. This paper presents a novel agent-based fuzzy traffic control system for multiple road intersections. The proposed system is designed to operate in a decentralized manner, with each intersection having its own agent (fuzzy controller) functioning concurrently. The intelligent fuzzy controller of the system can recognize emergency vehicles, assess the queue length and waiting time of vehicles, measure the distance of vehicles from intersections, and consider the cumulated waiting times of short vehicle queues. Two distinct types of agent-based intelligent fuzzy traffic control systems were implemented for comparison: one involving collaboration between an agent and its immediate neighboring agent(s) (where one intersection exchanges traffic data with its immediate neighboring intersection(s)), and the other implementing a non-collaborative agent-based intelligent fuzzy traffic control system (where the individual intersection has no direct communication). Following the experimental simulations, the results were compared with those of existing intelligent fuzzy traffic control systems that lack any module to calculate the distance of the vehicles from the intersection. The results demonstrated that the proposed agent-based system of controllers exhibited superior performance compared with the existing fuzzy controllers in terms of indicators such as average waiting time, fuel consumption, and CO₂ emissions. For instance, the proposed system reduced the average waiting time of vehicles at an intersection by 48.65% compared with the existing three-stage intelligent fuzzy traffic control system. In addition, a comparison was conducted between non-collaborating and collaborating agent-based intelligent fuzzy traffic control systems, where collaboration achieved better results than the non-collaborating system. In the simulation experiments, an interesting new feature emerged: despite any direct communication missing at multiple intersections, green waves evolved with time. This emergent feature suggests that fuzzy controllers have the potential to evolve and adapt to traffic complexity issues in urban environments when operating in an autonomous agent-based mode. This study demonstrates that agent-based fuzzy controllers can effectively communicate with one another to share traffic data and improve the overall system performance. Full article

Bearing-only multi-target localization (BOMTL) determines the positions of multiple targets by intersecting bearing lines from multiple spatial locations. However, non-ideal measurements can result in a large number of ghost targets. A β-S-dimensional assignment (β-SDA) method incorporating waveguide characteristics is proposed to address the BOMTL problem in shallow water with low detection rates. The estimated distance for the warping transformation is derived from the intersection points of the bearing lines, then the autocorrelation function of the broadband beamforming output is transformed using a warping operator to obtain the corresponding characteristic spectrum. The peaks in the characteristic spectrum correspond to the cross-correlation terms of the normal modes, with the frequencies of these peaks related to the ratio of the actual distance to the estimated distance of the sound source. The global target localization results are obtained using the proposed method, which incorporates confidence coefficients derived from the characteristic spectrum and geometric intersection information from the bearing lines. Simulation and sea trial data demonstrate that the β-SDA method effectively overcomes the limitation of pure bearing-only localization in low detection rate scenarios under a given signal-to-noise ratio (SNR), and can localize target positions without requiring precise prior environmental parameters. Full article

Myokines, bioactive peptides released by skeletal muscle, have emerged as crucial regulators of metabolic and protective pathways in peripheral tissues, particularly in combating oxidative stress and inflammation. Their plasma concentration significantly increases following exercise, offering valuable insights into the role of physical activity in preventing sarcopenia and mitigating metabolic diseases, including obesity, diabetes, and metabolic dysfunction-associated steatotic liver disease (MASLD). This review focuses on discussing the roles of specific myokines in activating intracellular signaling pathways within the liver, which confer protection against steatosis and lipid peroxidation. We detail the mechanism underlying lipid peroxidation and highlight the liver’s antioxidant defenses, such as glutathione (GSH) and glutathione peroxidase 4 (GPX4), which are pivotal in reducing ferroptosis. Furthermore, we provide an in-depth analysis of key myokines, including myostatin, brain-derived neurotrophic factor (BDNF), and irisin, among others, and their potential impact on liver function. Finally, we discuss the molecular mechanisms through which these myokines influence oxidate stress and lipid metabolism, emphasizing their capacity to modulate antioxidant responses in the liver. Finally, we underscore the therapeutic potential of exercise as a non-pharmacological intervention to enhance myokine release, thereby preventing the progression of MASD through improved hepatic antioxidant defenses. This review represents a comprehensive perspective on the intersection of exercise, myokine biology, and liver health. Full article

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making. Full article

Reducing traffic energy consumption is crucial for smart cities, and vehicle carbon emissions are a key energy indicator. Traffic signal control (TSC) is a useful method because it can affect the energy consumption of vehicles on the road by controlling the stop-and-go of vehicles at traffic intersections. However, setting traffic signals to reduce energy consumption will affect traffic efficiency and this is not in line with traffic management objectives. Current studies adopt multi-objective optimization methods with high traffic efficiency and low carbon emissions to solve this problem. However, most methods use static weights, which cannot adapt to complex and dynamic traffic states, resulting in non-optimal performance. Current energy indicators for urban transportation often fail to consider passenger fairness. This fairness is significant because the purpose of urban transportation is to serve people’s mobility needs not vehicles. Therefore, this paper proposes Multi-objective Adaptive Meta-DQN TSC (MMD-TSC), which introduces a dynamic weight adaptation mechanism to simultaneously optimize traffic efficiency and energy saving, and incorporates the per capita carbon emissions as the energy indicator. Firstly, this paper integrates traffic state data such as vehicle positions, velocities, vehicle types, and the number of passengers and incorporates fairness into the energy indicators, using per capita carbon emissions as the target for reducing energy consumption. Then, it proposes MMD-TSC with dynamic weights between energy consumption and traffic efficiency as reward functions. The MMD-TSC model includes two agents, the TSC agent and the weight agent, which are responsible for traffic signal adjustment and weight calculation, respectively. The weights are calculated by a function of traffic states. Finally, the paper describes the design of the MMD-TSC model learning algorithm and uses a SUMO (Simulation of Urban Mobility) v.1.20.0 for traffic simulation. The results show that in non-highly congested traffic states, the MMD-TSC model has higher traffic efficiency and lower energy consumption compared to static multi-objective TSC models and single-objective TSC models, and can adaptively achieve traffic management objectives. Compared with using vehicle average carbon emissions as the energy consumption indicator, using per capita carbon emissions achieves Pareto improvements in traffic efficiency and energy consumption indicators. The energy utilization efficiency of the MMD-TSC model is improved by 35% compared to the fixed-time TSC. Full article

Public transportation has been identified as a viable solution to mitigate traffic congestion. Transit signal priority (TSP) control, which is widely used at signalized intersections, has been recognized as a practical strategy to improve the efficiency and reliability of bus operations. However, traditional TSP control may fall short of efficiency and is facing several challenges of negative externalities for non-transit users and the need to handle conflicting priority requests. Recent studies have proposed the use of reinforcement learning (RL) methods to identify efficient traffic signal control (TSC). Some of these studies on RL-based TSC have incorporated the concept of max-pressure (MP), which is a maximal weight-matching algorithm to minimize queue sizes. Nevertheless, the existing RL-based TSC methods focus on private vehicles and cannot adequately distinguish between buses and private vehicles. In prior research, RL-based control has been implemented within the context of bus rapid transit (BRT) systems. This study proposes a novel RL-based TSC strategy that leverages the MP concept and extends it to incorporate TSP control. This is the first implementation of RL-based TSP control within the mixed-traffic road network. A significant innovation of this research is the introduction of the priority factor (PF), which is designed to prioritize bus movements at signalized intersections. The proposed RL-based TSP with PF control seeks to balance the competing objectives of enhancing bus operations while mitigating adverse impacts on non-transit users. To evaluate the performance of the proposed TSP method with the PF mechanism, simulations were conducted on an arterial and a grid network under dynamic traffic conditions. The simulation results demonstrated that the proposed TSP with PF not only reduces bus travel times and resolves conflicts between priority requests but also does not make a significant negative impact on passenger car operations. Furthermore, the PF can be dynamically assigned according to the number of passengers on each bus, suggesting the potential for the proposed approach to be applied in various traffic management scenarios. Full article

Stress resistance is highly associated with longer and healthier lifespans in various model organisms, including nematodes, fruit flies, and mice. However, we lack a complete understanding of stress resistance in humans; therefore, we investigated how stress resistance and longevity are interlinked in humans. Using more than 180 databases, we identified 541 human genes associated with stress resistance. The curated gene set is highly enriched with genes involved in the cellular response to stress. The Reactome analysis identified 398 biological pathways, narrowed down to 172 pathways using a medium threshold (p-value < 1 × 10⁻⁴). We further summarized these pathways into 14 pathway categories, e.g., cellular response to stimuli/stress, DNA repair, gene expression, and immune system. There were overlapping categories between stress resistance and longevity, including gene expression, signal transduction, immune system, and cellular responses to stimuli/stress. The categories include the PIP3-AKT-FOXO and mTOR pathways, known to specify lifespans in the model systems. They also include the accelerated aging syndrome genes (WRN and HGPS/LMNA), while the genes were also involved in non-overlapped categories. Notably, nuclear pore proteins are enriched among the stress-resistance pathways and overlap with diverse metabolic pathways. This study fills the knowledge gap in humans, suggesting that stress resistance is closely linked to longevity pathways but not entirely identical. While most longevity categories intersect with stress-resistance categories, some do not, particularly those related to cell proliferation and beta-cell development. We also note inconsistencies in pathway terminologies with aging hallmarks reported previously, and propose them to be more unified and integral. Full article

Background: Concerns have been raised regarding the effects of perfluoroalkyl substance (PFAS) exposure on cardiovascular diseases (CVD), but clear evidence linking PFAS exposure to CVD is lacking, and the mechanism remains unclear. Objectives: To study the association between PFASs and CVD in U.S. population, and to reveal the mechanism of PFASs’ effects on CVD. Methods: To assess the relationships between individual blood serum PFAS levels and the risk of total CVD or its subtypes, multivariable logistic regression analysis and partial least squares discriminant analysis (PLS-DA) were conducted on all participants or subgroups among 3391 adults from the National Health and Nutrition Examination Survey (NHANES). The SuperPred and GeneCards databases were utilized to identify potential targets related to PFAS and CVD, respectively. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of intersection genes were performed using Metascape. Protein interaction networks were generated, and core targets were identified with STRING. Molecular docking was achieved using Autodock Vina 1.1.2. Results: There was a positive association between Me-PFOSA-AcOH and CVD (OR = 1.28, p = 0.022), especially coronary heart disease (CHD) (OR = 1.47, p = 0.007) and heart attack (OR = 1.58, p < 0.001) after adjusting for all potential covariates. Me-PFOSA-AcOH contributed the most to distinguishing between individuals in terms of CVD and non-CVD. Significant moderating effects for Me-PFOSA-AcOH were observed in the subgroup analysis stratified by sex, ethnicity, education level, PIR, BMI, smoking status, physical activity, and hypertension (p < 0.05). The potential intersection targets were mainly enriched in CVD-related pathways, including the inflammatory response, neuroactive ligand–receptor interaction, MAPK signaling pathway, and arachidonic acid metabolism. TLR4 was identified as the core target for the effects of Me-PFOSA-AcOH on CVD. Molecular docking results revealed that the binding energy of Me-PFOSA-AcOH to the TLR4-MD-2 complex was −7.2 kcal/mol, suggesting that Me-PFOSA-AcOH binds well to the TLR4-MD-2 complex. Conclusions: Me-PFOSA-AcOH exposure was significantly associated with CVD. Network toxicology and molecular docking uncovered novel molecular targets, such as TLR4, and identified the inflammatory and metabolic mechanisms underlying Me-PFOSA-AcOH-induced CVD. Full article

Coalitional communication is a dwelling amidst non-dominant differences that requires introspective, complex communicative philosophy and practice. My concern is with differentiation in hierarchies. They are understood and shaped by colonial modernity. They are historical logics and practices of settler colonialism, enslavement, and citizenship. My perspective is feminist, decolonial critiques of modern, capitalist social systems. The analysis is grounded in communicative philosophy in intercultural contexts where folks intend justice and equality. For example, in political democracies, localized social alliances actually harm one another being hegemonic by taking routes of familiarity through structures of linguistic and practical cultural systems. Communicative projects of liberation across oppressions (with monologic and single-axis perceptions) tend to miss intersections of our raced and gendered experiences. The result is unintelligibility among us. In this state, one can sense in the body the space of the liminal—with both a communicative impasse and opening. Rather than aligning liberation and domination in the impasse, I describe the creativity of liminal space as a communicative opening. The opening is a recognition of multiplicity and a refusal to assimilate each other’s lived experiences into familiar, complex codes of habituated thought and action. Examining communication hostilities in oppressed–oppressing relations is a necessary condition for coalition. Thus, coalitional communication is a call to engage a full sense of listening to one another as relevant. Ways that decipher codes and signals of resistance come to constitute the project of creating relevant intelligibility together. Praxis as critical, dialectical, and intersectional thinking is part of this method. Full article

Commercially available crowdsourced connected vehicle (CV) trajectory data have recently been used to provide stakeholders with actionable and scalable roadway mobility infrastructure performance measures. Transportation agencies and automotive original equipment manufacturers (OEMs) share a common vision of ensuring the privacy of motorists that anonymously provide their journey information. As this market has evolved, the fleet mix has changed, and some OEMs have introduced additional fuzzification of CV data around 0.5 miles of frequently visited locations. This study compared the estimated Indiana market penetration rates (MPRs) between historic non-fuzzified CV datasets from 2020 to 2023 and a 5–11 May 2024, CV dataset with fuzzified records and a reduced fleet. At selected permanent interstate and non-interstate count stations, overall CV MPRs decreased by 0.5% and 0.3% compared to 2023, respectively. However, the trend in previous years was upward. Additionally, this paper evaluated the impact on data characteristics at freeways and intersections between the 5–11 May 2024, fuzzified CV dataset and a non-fuzzified 7–13 May 2023, CV dataset. The analysis found that the total number of GPS samples decreased 10% statewide. Of the evaluated 54,284 0.1-mile Indiana freeway, US Route, and State Route segments, the number of CV samples increased for 33.8% and decreased for 65.9%. This study also evaluated 26,291 movements at 3289 intersections and found that the number of available trajectories increased for 28.3% and decreased for 70.4%. This paper concludes that data representativeness is enough to derive most relevant mobility performance measures. However, since the change in available trajectories is not uniformly distributed among intersection movements, an unintended sample bias may be introduced when computing performance measures. This may affect signal retiming or capital investment opportunity identification algorithms. Full article

Recent advancements in brain stimulation and nanomedicine have ushered in a new era of therapeutic interventions for psychiatric and neurodegenerative disorders. This review explores the cutting-edge innovations in brain stimulation techniques, including their applications in alleviating symptoms of main neurodegenerative disorders and addiction. Deep Brain Stimulation (DBS) is an FDA-approved treatment for specific neurodegenerative disorders, including Parkinson’s Disease (PD), and is currently under evaluation for other conditions, such as Alzheimer’s Disease. This technique has facilitated significant advancements in understanding brain electrical circuitry by enabling targeted brain stimulation and providing insights into neural network function and dysfunction. In reviewing DBS studies, this review places particular emphasis on the underlying main neurotransmitter modifications and their specific brain area location, particularly focusing on the dopaminergic system, which plays a critical role in these conditions. Furthermore, this review delves into the groundbreaking developments in nanomedicine, highlighting how nanotechnology can be utilized to target aberrant signaling in neurodegenerative diseases, with a specific focus on the dopaminergic system. The discussion extends to emerging technologies such as magnetoelectric nanoparticles (MENPs), which represent a novel intersection between nanoformulation and brain stimulation approaches. These innovative technologies offer promising avenues for enhancing the precision and effectiveness of treatments by enabling the non-invasive, targeted delivery of therapeutic agents as well as on-site, on-demand stimulation. By integrating insights from recent research and technological advances, this review aims to provide a comprehensive understanding of how brain stimulation and nanomedicine can be synergistically applied to address complex neuropsychiatric and neurodegenerative disorders, paving the way for future therapeutic strategies. Full article