On the move: Influence of animal movements on count error during drone surveys.

The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Drones (i.e., Unoccupied Aircraft Systems or UAS) provide a cost- and time-efficient remote sensing option to survey animals in various landscapes and sampling conditions. However, drone-based surveys may also introduce counting errors, especially when monitoring mobile animals. Using an agent-based model simulation approach, we evaluated the error associated with counting a single animal across various drone flight patterns under three animal movement strategies (random, directional persistence, and biased toward a resource) among five animal speeds (2, 4, 6, 8, 10 m/s). Flight patterns represented increasing spatial independence (ranging from lawnmower pattern with image overlap to systematic point counts). Simulation results indicated that flight pattern was the most important variable influencing count accuracy, followed by the type of animal movement pattern, and then animal speed. A  awnmower pattern with 0% overlap produced the most accurate count of a solitary, moving animal on a landscape (average count of 1.1 ± 0.6) regardless of the animal's movement pattern and speed. Image overlap flight patterns were more likely to result in multiple counts even when accounting for mosaicking. Based on our simulations, we recommend using a lawnmower pattern with 0% image overlap to minimize error and augment drone efficacy for animal surveys. Our work highlights the importance of understanding interactions between animal movements and drone survey design on count accuracy to inform the development of broad applications among diverse species and ecosystems.

Polymeric Vehicles for Nucleic Acid Delivery: Enhancing the Therapeutic Efficacy and Cellular Uptake.

BACKGROUND: Therapeutic gene delivery may be facilitated by the use of polymeric carriers. When combined with nucleic acids to form nanoparticles or polyplexes, a variety of polymers may shield the cargo from in vivo breakdown and clearance while also making it easier for it to enter intracellular compartments. AIM AND OBJECTIVES: Polymer synthesis design choices result in a wide variety of compounds and vehicle compositions. Depending on the application, these characteristics may be changed to provide enhanced endosomal escape, longer-lasting distribution, or stronger connection with nucleic acid cargo and cells. Here, we outline current methods for delivering genes in preclinical and clinical settings using polymers. METHODOLOGY: Significant therapeutic outcomes have previously been attained using genetic material- delivering polymer vehicles in both in-vitro and animal models. When combined with nucleic acids to form nanoparticles or polyplexes, a variety of polymers may shield the cargo from in vivo breakdown and clearance while also making it easier for it to enter intracellular compartments. Many innovative diagnoses for nucleic acids have been investigated and put through clinical assessment in the past 20 years. RESULTS: Polymer-based carriers have additional delivery issues due to their changes in method and place of biological action, as well as variances in biophysical characteristics. We cover recent custom polymeric carrier architectures that were tuned for nucleic acid payloads such genomemodifying nucleic acids, siRNA, microRNA, and plasmid DNA. CONCLUSION: In conclusion, the development of polymeric carriers for gene delivery holds promise for therapeutic applications. Through careful design and optimization, these carriers can overcome various challenges associated with nucleic acid delivery, offering new avenues for treating a wide range of diseases.

Precision estimation of winter wheat crop height and above-ground biomass using unmanned aerial vehicle imagery and oblique photoghraphy point cloud data.

Introduction: Crop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural production. However, traditional data acquisition methods suffer from drawbacks such as time-consuming, laborious and destructive sampling. Methods: The current approach to estimating AGB using unmanned aerial vehicles (UAVs) remote sensing relies solely on spectral data, resulting in low accuracy in estimation. This method fails to address the ill-posed inverse problem of mapping from two-dimensional to three-dimensional and issues related to spectral saturation. To overcome these challenges, RGB and multispectral sensors mounted on UAVs were employed to acquire spectral image data. The five-directional oblique photography technique was utilized to construct the three-dimensional point cloud for extracting crop height. Results and Discussion: This study comparatively analyzed the potential of the mean method and the Accumulated Incremental Height (AIH) method in crop height extraction. Utilizing Vegetation Indices (VIs), AIH and their feature combinations, models including Random Forest Regression (RFR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR) and Ridge Regression (RR) were constructed to estimate winter wheat AGB. The research results indicated that the AIH method performed well in crop height extraction, with minimal differences between 95% AIH and measured crop height values were observed across various growth stages of wheat, yielding R2 ranging from 0.768 to 0.784. Compared to individual features, the combination of multiple features significantly improved the model's estimate accuracy. The incorporation of AIH features helps alleviate the effects of spectral saturation. Coupling VIs with AIH features, the model's R2 increases from 0.694-0.885 with only VIs features to 0.728-0.925. In comparing the performance of five machine learning algorithms, it was discovered that models constructed based on decision trees were superior to other machine learning algorithms. Among them, the RFR algorithm performed optimally, with R2 ranging from 0.9 to 0.93. Conclusion: In conclusion, leveraging multi-source remote sensing data from UAVs with machine learning algorithms overcomes the limitations of traditional crop monitoring methods, offering a technological reference for precision agriculture management and decision-making.

A perspective review on the role of engine sound in speed perception and control: state of the art and methodological suggestions.

In this review we focus on the role of in-car sound, specifically the artificial engine sounds, on drivers' speed perception and control, a topic that has received little attention so far. Previous studies indicate that removing or reducing engine sound leads drivers to underestimate speed and, consequently, to drive faster. Furthermore, evidence suggests that specific sound frequencies could play a role in this process, highlighting the importance of in-car sound features. First, we show that the amount of research in the field is scarce and rather outdated, and that this is largely due to the fact that industrial research is subject to very few publications. Then, we examine benefits and limitations of different research paradigms used and we propose a protocol to investigate systematically the phenomenon. In particular, we argue for the benefits of a wider use of psychophysical methods in speed perception, a field that has been typically explored by means of driving simulation. Finally, we highlight some methodological and statistical limitations that might impact the interpretation of the evidence considered. Our methodological considerations could be particularly useful for researchers aiming to investigate the impact of sound on speed perception and control, as well as for those involved in the design of in-car sounds. These are particularly relevant for the design of electric vehicles, which represent a challenge but also the ideal testing ground to advance the knowledge in the field.

Prediction and interpretive of motor vehicle traffic crashes severity based on random forest optimized by meta-heuristic algorithm.

Providing accurate prediction of the severity of traffic collisions is vital to improve the efficiency of emergencies and reduce casualties, accordingly improving traffic safety and reducing traffic congestion. However, the issue of both the predictive accuracy of the model and the interpretability of predicted outcomes has remained a persistent challenge. We propose a Random Forest optimized by a Meta-heuristic algorithm prediction framework that integrates the spatiotemporal characteristics of crashes. Through predictive analysis of motor vehicle traffic crash data on interstate highways within the United States in 2020, we compared the accuracy of various ensemble models and single-classification prediction models. The results show that the Random Forest (RF) model optimized by the Crown Porcupine Optimizer (CPO) has the best prediction results, and the accuracy, recall, f1 score, and precision can reach more than 90 %. We found that factors such as Temperature and Weather are closely related to vehicle traffic crashes. Closely related indicators were analyzed interpretatively using a geographic information system (GIS) based on the characteristic importance ranking of the results. The framework enables more accurate prediction of motor vehicle traffic crashes and discovers the important factors leading to motor vehicle traffic crashes with an explanation. The study proposes that in some areas consideration should be given to adding measures such as nighttime lighting devices and nighttime fatigue driving alert devices to ensure safe driving. It offers references for policymakers to address traffic management and urban development issues.

Delayed neurological deficit due to a medially misplaced thoracic pedicle screw during adolescent idiopathic scoliosis correction: a complication 6 years in the making.

PURPOSE: Neurological deficits developing years after pedicle screw misplacement is a rare phenomenon. Here, we report level IV evidence of a previously asymptomatic medial thoracic pedicle screw resulting in paraparesis after a motor vehicle accident. METHODS: A 21-year-old male presented with acute onset of paraparesis following a motor vehicle collision. Six years prior this incident, the patient underwent a thoracolumbar fusion T4-L4 for AIS performed by an outside orthopedic surgeon. CT scan and CT myelogram illustrated decreased spinal canal diameter and cord compression from a medial T8 pedicle screw. RESULTS: Surgical removal of the misplaced pedicle screw resulted in a gradual complete recovery sustained over a period of 2 years. This case is compared to those reported in the literature review between 1981 and 2019 concerning delayed neurological deterioration related to misplaced pedicle screw. CONCLUSION: This case reports a delayed neurological deficit implicating a misplaced pedicle screw. This phenomenon remains rare since 5 cases were reported in the literature over the last 4 decades. It calls into focus the need for confirmation of safe instrumentation during the intraoperative period. It also illustrates the potential difficult decision-making in regard to asymptomatic misplaced instrumentation. LEVEL OF EVIDENCE: IV.

A discrete wild horse optimizer for capacitated vehicle routing problem.

The wild horse optimizer (WHO) is a novel metaheuristic algorithm, which has been successfully applied to solving continuous engineering problems. Considering the characteristics of the wild horse optimizer, a discrete version of the algorithm, named discrete wild horse optimizer (DWHO), is proposed to solve the capacitated vehicle routing problem (CVRP). By incorporating three local search strategies-swap operation, reverse operation, and insertion operation-along with the introduction of the largest-order-value (LOV) decoding technique, the precision and quality of the solutions have been enhanced. Experimental results conducted on 44 benchmark instances indicate that, in most test cases, the solving capability of discrete wild horse optimizer surpasses that of basic wild horse optimizer (BWHO), hybrid firefly algorithm, dynamic space reduction ant colony optimization (DSRACO), and discrete artificial ecosystem-based optimization (DAEO). The discrete wild horse optimizer provides a novel approach for solving the capacitated vehicle routing problem and also offers a new perspective for addressing other discrete problems.

Consumer preference and willingness-to-pay for formal recycling of electric vehicle batteries: A discrete choice experiment in China.

The burgeoning electric vehicle (EV) market poses a substantial challenge to battery recycling systems, yet understanding EV battery recycling behavior from the demand side remains limited. Previous studies have analyzed perceptual or attitudinal factors, neglecting the observable attributes of EV battery recycling. To this end, we proposed a discrete choice model to investigate the differences between formal and informal recycling behaviors, identifying consumer preferences and willingness to pay. By analyzing 1190 sample data collected from Chongqing, China, we find that: (1) The formal recycling market exhibits greater sensitivity to prices compared to the informal recycling market. (2) The formal recycling market favors recycling by EV battery producers, whereas the informal recycling market shows the least preference for recycling by automobile producers. (3) Door-to-door recycling services are the most effective in facilitating the transition from informal to formal recycling markets for EV batteries. (4) Capacity subsidy policies outperform one-time fixed subsidy policies in incentivizing formal recycling. (5) The formal recycling market for EV batteries necessitates "traceability to the recycling outlet", as opposed to being untraceable. (6) The high-awareness group exhibits greater sensitivity to government policies compared to those with lower environmental concerns and less knowledge of EV battery recycling.

Research on the aerodynamic characteristics of dragonfly leading edge.

Dragonflies are some of the most stable and maneuverable flying organisms. To explore the mechanism of how dragonfly leading edges enhance flight lift, this article conducts a detailed study on the leading edge veins and the microstructures on them of dragonfly wings. Observations have discovered the special leading edge vein and the regularly distributed microstructures on the leading edge vein. A biomimetic model has been established, and computational fluid dynamics (CFD) simulation analysis has been conducted on the biomimetic model. The analysis explores the effects of microstructure characteristics, distribution patterns, and positions on the aerodynamic characteristics of dragonfly gliding. The analysis shows that the leading edge structure influences the incoming flow, simultaneously promotes the formation of the leading edge vortex (LEV), and increases the lift-to-drag ratio by up to 4%. A wing prototype featuring biomimetic microstructures is subsequently fabricated and tested in wind tunnel experiments. Compared with a control group without leading edge structures, the airflow passing through the biomimetic structures is influenced by the shape and arrangement of these structures. The smoother transition of the leading edge vein's shape facilitates the flow of air. The microstructures primarily filter and accelerate the airflow. The spacing of the microstructures affects the stability of the airflow, thereby influencing aerodynamic performance. Additionally, the middle-row arrangement of microstructures is more beneficial for gliding conditions, while the upper-row arrangement is more advantageous for flapping conditions. These findings enhance our understanding of insect wings and advance micro aerial vehicle applications. RESEARCH HIGHLIGHTS: This study observed the leading-edge veins and microstructures of dragonfly wings in detail. Using a biomimetic model and computational fluid dynamics (CFD) simulations, it was found that these leading-edge structures promote the formation of leading-edge vortices (LEV), increasing the lift-to-drag ratio by up to 4%. Wind tunnel experiments demonstrated that wings with biomimetic microstructures significantly improved airflow smoothness and lift compared with control wings. Additionally, the arrangement of microstructures greatly affects airflow stability and aerodynamic performance, with middle-row arrangements being more beneficial for gliding and upper-row arrangements for flapping conditions. These findings enhance our understanding of insect wings and provide innovative guidance for designing efficient micro aerial vehicles.

A multi-objective brain storm optimization for integrated distributed flexible job shop and distribution problems.

Production and distribution are critical components of the furniture supply chain, and achieving optimal performance through their integration has become a vital focus for both the academic and business communities. Moreover, as economic globalization progresses, distributed manufacturing has become a pioneering production technique. Via leveraging a distributed flexible manufacturing system, mass flexible production at lower costs can be achieved. To this end, this study presents an integrated distributed flexible job shop and distribution problem to minimize makespan and total tardiness. In our research, a set of custom furniture orders from different customers are processed among flexible job shops and then delivered by vehicles to customers as the due date. To distinctly show the presented problem, a mixed integer mathematical programming model is created, and a multi-objective brain storm optimization method is introduced considering the problem's features. In comparison to the other three advanced methods, the superiority of the algorithm created is showcased. The findings of the experiments demonstrate that the constructed model and the introduced algorithm have remarkable competitiveness in addressing the problem being examined.

Modeling and Performance Study of Vehicle-to-Infrastructure Visible Light Communication System for Mountain Roads.

Visible light communication (VLC) is considered to be a promising technology for realizing intelligent transportation systems (ITSs) and solving traffic safety problems. Due to the complex and changing environment and the influence of weather and other aspects, there are many problems in channel modeling and performance analysis of vehicular VLC. Unlike existing studies, this study proposes a practical vehicle-to-infrastructure (V2I) VLC propagation model for a typical mountain road. The model consists of both line-of-sight (LOS) and non-line-of-sight (NLOS) links. In the proposed model, the effects of vehicle mobility and weather conditions are considered. To analyze the impact of the considered propagation characteristics on the system, closed-form expressions for several performance metrics were derived, including average path loss, received power, channel capacity, and outage probability. Furthermore, to verify the accuracy of the derived theoretical expressions, simulation results were presented and analyzed in detail. The results indicate that, considering the LOS link and when the vehicle is 50 m away from the infrastructure, the difference in channel gain between moderate fog and dense fog versus clear weather conditions is 1.8 dB and 3 dB, respectively. In addition, the maximum difference in total received optical power between dense fog conditions and clear weather conditions can reach 76.2%. Moreover, under clear weather conditions, the channel capacity when vehicles are 40 m away from infrastructure is about 98.9% lower than when they are 10 m away. Additionally, the outage probability shows a high correlation with the threshold data transmission rate. Therefore, the considered propagation characteristics have a significant impact on the performance of V2I-VLC.

A Cascaded Multi-Agent Reinforcement Learning-Based Resource Allocation for Cellular-V2X Vehicular Platooning Networks.

The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system's overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with 99% probability. The average AoI quantity is maintained within the 5-10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties.

Optimizing the greenhouse gas emissions of waste transfer and transport: An integration of life cycle assessment and vehicle routing problem.

This study presents a comprehensive analysis of greenhouse gas (GHG) emissions associated with waste transfer and transport, incorporating derived leachate treatment-a factor often overlooked in existing research. Employing an integration model of life cycle assessment and a vehicle routing problem (VRP) methods, we evaluated the GHG reduction potential of waste transfer and transport system. Two Chinese counties with different topographies and demographics were selected, yielding 80 scenarios that factored in waste source separation as well as vehicle capacity, energy sources, and routes. The functional unit (FU) is transferring and transporting 1 tonne waste and treating derived leachate. The GHG emissions varied from 12 to 39 kg CO2 equivalent per FU. Waste source separation emerged as the most impactful mitigation strategy, not only for the studied system but for an integrated waste management system. Followings are the use of larger capacity vehicles and electrification of the vehicles. These insights are instrumental for policymakers and stakeholders in optimizing waste management systems to reduce GHG emissions.

Barriers and framework conditions for the market entry of second-life lithium-ion batteries from electric vehicles.

Transition to circular economy for lithium-ion batteries used in electric vehicles requires integrating multiple stages of the value cycle. However, strategies aimed at extending the lifetime of batteries are not yet sufficiently considered within the European battery industry, particularly regarding repurposing. Using second-life lithium-ion batteries (SLBs) before subsequent recycling can offer several advantages, such as the development of sustainable business models, the reduction of emissions, and alignment with UN Sustainable Development Goals 7, 12, and 13. Using expert and problem-centred interviews along with an exploratory workshop, this study guides stakeholders in the battery sector by illustrating the necessary changes for a more holistic circular economy. Moreover, an extended political, economic, social, technological, environmental, legal, and additionally safety-related (PESSTEL) analysis approach is carried out, which has not yet been used in this context. In this process, barriers, as well as necessary institutional framework conditions and organisational requirements for a successful market entry of SLB applications are investigated. Among others, key barriers relate to the competition with first-life applications and safety concerns. SLBs require high manual labour costs for repurposing, along with expenses for expired warranties and re-certifications. Ownership structures in traditional business models often result in SLBs and their corresponding usage data staying under the control of the manufacturers. Market viability, however, requires a level playing field for both first-life and second-life operators as well as circular battery and data-sharing business models. Gathering data on the ageing performance and performing improved safety testing according to test protocols facilitates the reliable assessment of SLBs.

Economic Evaluation of a Novel Treatment of Attention-Deficit/Hyperactivity Disorder in US Motor Vehicle Drivers.

Background: Attention-deficit/hyperactivity disorder (ADHD) affects approximately 4.4% of US adults. ADHD is associated with high-risk driving behavior and costly motor vehicle accidents. DYANAVEL XR (DXR) (Tris Pharma, Inc.) is a once-daily fast-acting amphetamine developed for ADHD treatment. A randomized controlled trial showed that DXR patients were 43% less likely to crash during a driving simulation than individuals taking placebo. Study outcomes suggest a DXR crash rate similar to that of a driver without ADHD, while patients treated with the current standard of care (SOC) have a 52% higher crash risk than non-ADHD drivers. Objective: The aim was to evaluate the economic benefits attributable to improved driving abilities and avoided crashes in DXR patients compared with patients treated with the SOC or those who are untreated. Methods: A cost-impact model estimated 1-year crash-related cost outcomes for DXR-treated patients compared with SOC-treated and untreated ADHD patients. SOC was assumed to consist of a combination of short-, intermediate-, and long-acting ADHD stimulant and non-stimulant medications. DXR crash risk was assumed equivalent to the non-ADHD population risk, as supported by trial data. Crash risk for untreated and SOC-treated ADHD patients were assumed to be 99% and 52% higher than the general US population, respectively. Model outcomes included the cost impact (medication- and crash-related costs) and the number of crashes, injuries, and fatalities avoided with DXR. Results: Treatment with DXR would avoid 0.82 crashes, 0.016 injuries, and 0.036 fatalities per year compared with untreated patients, and 0.036 crashes, 0.007 injuries, and 0.0001 fatalities per year compared with SOC-treated patients. Compared with a population of 25% SOC-treated patients and 75% untreated patients, DXR use would save an average of 4581 p e r p e r s o n p e r y e a r a c r o s s a l l a g e g r o u p s w h e n p r i c e d a t 80 per month, assuming all SOC-treated and untreated patients utilized DXR. When the value of quality-of-life improvement is considered, savings increase over 7-fold. Discussion: Outcomes suggest that DXR may be an economically beneficial treatment compared with SOC for ADHD patients. Conclusions: The economic model showed that DXR is cost-saving compared with no treatment and SOC by reducing the number of motor vehicle crashes in the ADHD population.

Vehicle detection and classification using an ensemble of EfficientDet and YOLOv8.

With the rapid increase in vehicle numbers, efficient traffic management has become a critical challenge for society. Traditional methods of vehicle detection and classification often struggle with the diverse characteristics of vehicles, such as varying shapes, colors, edges, shadows, and textures. To address this, we proposed an innovative ensemble method that combines two state-of-the-art deep learning models i.e., EfficientDet and YOLOv8. The proposed work leverages data from the Forward-Looking Infrared (FLIR) dataset, which provides both thermal and RGB images. To enhance the model performance and to address the class imbalances, we applied several data augmentation techniques. Experimental results demonstrate that the proposed ensemble model achieves a mean average precision (mAP) of 95.5% on thermal images, outperforming the individual performances of EfficientDet and YOLOv8, which achieved mAPs of 92.6% and 89.4% respectively. Additionally, the ensemble model attained an average recall (AR) of 0.93 and an optimal localization recall precision (oLRP) of 0.08 on thermal images. For RGB images, the ensemble model achieved mAP of 93.1%, AR of 0.91, and oLRP of 0.10, consistently surpassing the performance of its constituent models. These findings highlight the effectiveness of proposed ensemble approach in improving vehicle detection and classification. The integration of thermal imaging further enhances detection capabilities under various lighting conditions, making the system robust for real-world applications in intelligent traffic management.

Risk of motor vehicle accidents with profound injuries in patients with ankylosing spondylitis: a nationwide, population-based cohort study.

Background: Patients with ankylosing spondylitis (AS) suffer from impaired physical activity and are prone to motor vehicle accidents (MVA), but definite instruction regarding the relationship between disease evolvement and MVA and potential risk factors is lacking. Objectives: To explore the risk factors and their impact on recorded MVA with profound injuries in AS patients with prescriptions. Design: Nationwide, population-based, matched retrospective cohort study. Methods: Using Taiwanese administrative healthcare databases, with available claims data from 2003 to 2013, we selected 30,911 newly diagnosed adult AS patients with concurrent prescriptions from 2006 to 2012 as AS patients, along with 309,110 non-AS individuals as the control group, matched in gender, age at index date and year of the index date. The risk of recorded MVA with profound injuries was compared between the two groups in terms of incidence rate ratio (IRR) and log-rank test p-value. Using Cox regression analysis, we studied associations between the risk and AS diagnosis. Results: The risk of recorded MVA with profound injuries in AS patients was significantly higher than in non-AS individuals, specifically 2 years after AS diagnosis (IRR, 2.00; 95% confidence interval (CI), 1.42-2.81). For patients with follow-up periods >2 years, the adjusted risk was positively associated with suburban residence (adjusted hazard ratio (aHR), 2.18; 95% CI, 1.55-3.06), rural residence (aHR, 1.89; 95% CI, 1.27-2.80), lower insured income (aHR, 1.35; 95% CI, 1.01-1.81) and recorded MVA with profound injuries before AS diagnosis (aHR, 6.16; 95% CI, 2.53-14.96). AS diagnosis (aHR, 1.81; 95% CI, 1.27-2.59) and frequency of ambulatory visits (aHR, 1.01; 95% CI, 1.004--1.02) were specific associated factors for them compared with those with follow-up periods ⩽2 years. Conclusion: For adult AS patients in Taiwan, factors such as AS disease evolution and frequent ambulatory visits for disease control in the second year of the disease course may significantly increase the risk of recorded MVA with profound injuries beyond 2 years after AS diagnosis.

Thermosensitive chitosan-based hydrogel: A vehicle for overcoming the limitations of nose-to-brain cell therapy.

Cell therapy is a promising strategy for treating neurological pathologies but requires invasive methods to bypass the blood-brain barrier restrictions. The nose-to-brain route has been presented as a direct and less invasive alternative to access the brain. The primary limitations of this route are low retention in the olfactory epithelium and poor cell survival in the harsh conditions of the nasal cavity. Thus, using chitosan-based hydrogel as a vehicle is proposed in this work to overcome the limitations of nose-to-brain cell administration. The hydrogel's design was driven to achieve gelification in response to body temperature and a mucosa-interacting chemical structure biocompatible with cells. The hydrogel showed a < 30 min gelation time at 37 °C and >95 % biocompatibility with 2D and 3D cultures of mesenchymal stromal cells. Additionally, the viability, stability, and migration capacity of oligodendrocyte precursor cells (OPCs) within the hydrogel were maintained in vitro for up to 72 h. After the intranasal administration of the OPCs-containing hydrogel, histological analysis showed the presence of viable cells in the nasal cavity for up to 72 h post-administration in healthy athymic mice. These results demonstrate the hydrogel's capacity to increase the residence time in the nasal cavity while providing the cells with a favorable environment for their viability. This study presents for the first time the use of thermosensitive hydrogels in nose-to-brain cell therapy, opening the possibility of increasing the delivery efficiency in future approaches in translational medicine. STATEMENT OF SIGNIFICANCE: This work highlights the potential of biomaterials, specifically hydrogels, in improving the effectiveness of cell therapy administered through the nose. The nose-to-brain route has been suggested as a non-invasive way to directly access the brain. However, delivering stem cells through this route poses a challenge since their viability must be preserved and cells can be swept away by nasal mucus. Earlier attempts at intranasal cell therapy have shown low efficiency, but still hold promise to the future. The hydrogels designed for this study can provide stem cells with a biocompatible environment and adhesion to the nasal atrium, easing the successful migration of viable cells to the brain.

Drones reduce the time to defibrillation in a highly visited non-urban area: A randomized simulation-based trial.

INTRODUCTION: Out-of-hospital cardiac arrest (OHCA) has a high global incidence and mortality rate, with early defibrillation significantly improving survival. Our aim was to assess the feasibility of autonomous drone delivery of automated external defibrillators (AED) in a non-urban area with physical barriers and compare the time to defibrillate (TTD) with bystander retrieval from a public access defibrillator (PAD) point and helicopter emergency medical services (HEMS) physician performed defibrillation. METHODS: This randomized simulation-based trial with a cross-over design included bystanders performing AED retrievals either delivered by automated drone flight or on foot from a PAD point, and simulated HEMS interventions. The primary outcome was the time to defibrillation, with secondary outcomes comparing workload, perceived physical effort, and ease of use. RESULTS: Thirty-six simulations were performed. Drone-delivered AED intervention had a significantly shorter TTD [2.2 (95 % CI 2.0-2.3) min] compared to PAD retrieval [12.4 (95 % CI 10.4-14.4) min] and HEMS [18.2 (95 % CI 17.1-19.2) min]. The self-reported physical effort on a visual analogue scale for drone-delivered AED was significantly lower versus PAD [2.5 (1 - 22) mm vs. 81 (65-99) mm, p = 0.02]. The overall mean workload measured by NASA-TLX was also significantly lower for drone delivery compared to PAD [4.3 (1.2-11.7) vs. 11.9 (5.5-14.5), p = 0.018]. CONCLUSION: The use of drones for automated AED delivery in a non-urban area with physical barriers is feasible and leads to a shorter time to defibrillation. Drone-delivered AEDs also involve a lower workload and perceived physical effort than AED retrieval on foot.

A review of research on tire burst and vehicle stability control.

Tire burst is an accidental occurrence that poses a serious threat to the driving stability and road safety of vehicles. Therefore, it is of great practical significance to investigate early warning systems for tire burst and develop stability and safety control measures after burst incidents. The development of an accurate model that can effectively represent the impact of tire burst on vehicle dynamics is crucial for the design of control systems and the development of stability control strategies. Most of the existing research on tire burst models is based on static tire tests, the effectiveness of these models still needs to be further verified. The main approach to studying the impact of burst tires on vehicle performance is to embed a burst tire model into a vehicle dynamics model. Understanding the impact of tire burst on vehicle performance is essential for identifying burst incidents and developing stability control strategies. The research on burst identification primarily focuses on early warning systems and estimating vehicle state parameters after burst incidents, while the current research on stability control strategies focuses on enabling vehicles to continue running safely after burst incidents through braking, active steering, and collaborative control. Currently, there is no comprehensive review of research on vehicle tire burst stability control. Therefore, this paper primarily reviews five aspects: (a) the causes and prevention of tire burst, (b) the impact of tire burst on vehicle performance, (c) burst identification, (d) stability control strategies for burst incidents, and (e) future prospects for tire burst research.