Safety Systems Research Articles

Public Support for a Safe System Approach to Reducing Motor Vehicle Crashes Deaths in the United States

Conventional road safety strategies are yielding diminishing returns. The Safe System approach is based on the premise that human errors will occur on the roadway and that the system should be designed to accommodate these errors, so they do not result in death or serious injury. Elements of the system include the roadway infrastructure, vehicle engineering, and speed management.Political support for U.S. Safe System implementation gained momentum with $5 billion in appropriated funds in the 2021 Bipartisan Infrastructure Law. The purpose of this study was to measure public support for Safe System implementation. We fielded a nationally representative survey of US adults from May 4 to June 10, 2022 (N=2,274). We measured whether respondents considered individual- or systems-oriented approaches as the most effective way to prevent traffic deaths. We also assessed support for various roadway interventions and vehicle safety technologies. Most respondents agreed that system-level changes were the most effective way to prevent traffic deaths. Endorsement for system-level changes was significantly higher among younger age groups. Support for roadway improvements and vehicle safety technologies varied according to the type of intervention that was proposed. Public support for a Safe System approach is high, particularly among younger adults, which suggests that the momentum for the movement can be sustained over the long term.

Comparative Insights into Mobile Road Safety Alert Applications: Assessing User Preferences and Financial Commitment Among Greek and European Highway Users

Transportation plays a central role in modern society, influencing both economic prosperity and quality of life. However, it also poses challenges, such as road safety, that require innovative solutions. The aim of this study is to evaluate the acceptance of mobile emergency notification applications as a road safety measure on Greek and European highways. Using a quantitative approach, an online questionnaire was distributed to 1002 Greek and foreign highway users. The study focused on identifying key factors influencing the effectiveness of these applications, with particular emphasis on users' willingness to pay for ad-free notifications. Results show that the most influential factors include the type of messages sent, willingness to pay for ad-free services, and preferred methods and frequency of receiving emergency notifications. These findings have implications for the design and implementation of more effective mobile alert systems for road safety.

A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

Evaluating efficacy and safety of a novel registration-free CT-guided needle biopsy navigation system (RC 120): A multicenter, prospective clinical trial

Evaluating efficacy and safety of a novel registration-free CT-guided needle biopsy navigation system (RC 120): A multicenter, prospective clinical trial

Evaluation of grounding grid corrosion extent based on laser-induced breakdown spectroscopy (LIBS) combined with machine learning

As one of the most widely used forms of energy, the safety and stability of power systems are crucial to modern society. Grounding grids dissipate current and reduce touch and pace voltage during lightning strikes or fault currents, ensuring the safety of personnel and equipment. However, prolonged submersion in soil causes inevitable corrosion, compromising grounding efficacy by increasing resistance and reducing current dissipation. This deterioration can result in unsafe local potential differences. This study uses Laser-Induced Breakdown Spectroscopy (LIBS) to measure corrosion degrees in grounding grids. Spectral data from samples with varying corrosion extent were collected, with outliers removed using the Local Outlier Factor (LOF) algorithm. Principal Component Analysis (PCA) reduced data dimensionality, revealing clustering in spectral data corresponding to corrosion extent. Three machine learning models were compared: Adaptive Boosting - Backpropagation Neural Network (Adaboost-BP), Support Vector Machine (SVM), and Random Forest (RF). The RF model showed the highest accuracy in predicting corrosion degree (R²=0.9845, MSE=0.0296), outperforming Adaboost-BP and SVM, especially for intermediate corrosion extent. These findings validate the effectiveness and reliability of combining LIBS with machine learning for predicting grounding grid corrosion, providing a theoretical foundation for the safe operation of power systems.

Evaluation of a Multimodal Confocal Therapeutic Focused Ultrasound Apparatus: Bridging Cavitation, Thermal Ablation, and Histotripsy in Preclinical Treatments

ObjectivesThe development of versatile and user-friendly preclinical platforms is vital for therapeutic ultrasound research. We introduce a flexible ultrasound-guided focused ultrasound (FUS) platform with two confocal therapeutic transducers, allowing thermal and mechanical modalities, and present its design and, features, with validation of potential applications in preclinical studies. MethodsThe probe's acoustic properties, energy delivery efficiency, and thermal and mechanical modalities are characterized. A computational model predicts thermal effects while optimizing treatment parameters. Ex vivo tissue samples are used to validate system performance, safety, and usability. In vivo experiments on mice with MC38 tumors are presented with immunohistochemistry (IHC) to validate treatment outcomes. ResultsElectroacoustic conversion efficiency levels were 80% and 40% for 1.1 MHz and 3.3 MHz, respectively. Confocal therapy transducers at 1.1 MHz and 3.3 MHz successfully demonstrated cavitation histotripsy and thermal treatments. At 1.1 MHz, for histotripsy, −20 MPa negative peak pressure is achieved, while at 3.3 MHz used for thermal ablation a maximum of 35 MPa is reached for positive peak pressure. Numerical analysis provides thermal treatment planning, aligning with in vitro and in vivo experiments for lesion prediction. Real-time in vivo cavitation monitoring was consistent with in vitro chemical dosimetry, ensuring treatment uniformity. ConclusionThe ultrasound platform induces thermal or mechanical lesions with precise spatial resolution, validated by IHC tissue characterization. Integrated cavitation monitoring enables real-time treatment monitoring. Coupling with thermal simulations provides optimization of thermal treatment parameters. This versatile “all-in-one” therapeutic platform supports multiple treatment modalities including cavitation, thermal ablation, and histotripsy, facilitating direct comparisons to assess their efficacy in diverse therapeutic settings.

Machine Learning-Based Precise Monitoring of Aluminium-Magnesium Alloy Dust

： Al-Mg alloys are widely used in industrial production, which can lead to occupational health issues and explosion hazards. The study focuses on applying a machine learning-enhanced Kalman filtering algorithm to detect the concentration of Al-Mg alloy dust, significantly reducing dust hazards and constructing an efficient and safe dust reduction and removal system. A machine learning-based Kalman filter algorithm is proposed for fast and accurate detection of high Al-Mg dust concentrations (200-1200 g/m³). The results show that the KFGRU approach outperforms the traditional line filter method, achieving answer times between 2.6 s and 6 s—an improvement of 62.5% over the traditional method. As far as the forecast accuracy is concerned, the KFGRU method yields a minimal curve deviation value, reaching as low as 0.097, which represents a significant improvement compared to the 0.151 of the Kalman filter algorithm, the 0.217 of the sliding average method, and the 0.177 of the median filter methods.

Experimental study on hydrogen pipeline leakage: Negative pressure wave characteristics and inline detection method

This paper presents an experimental study on a potential safety system for hydrogen fuel cells. It focuses on early leakage detection and localization. This would enable effective system response to avoid the effects of catastrophic loss of containment. Experimental results are presented from a pipeline setup wherein the leak is identified utilizing inline pressure sensors and flowmeters. The investigations involve recorded data at two leak locations at different initial pressures, mass flow rates, and leak diameters. The leak detection technique is based on analysis of the negative pressure wave (NPW) intensity and propagation duration, denoted as peak-to-peak amplitude (Δh) and oscillation period (Δτ). They are extracted from filtered pressure records' first derivative (dp/dt). Δh and Δτ increase as the initial mass flow rate and leak diameter decrease. Therefore, this approach is advantageous for detecting leaks from tiny cross-sections. Δh and Δτ measured at two leak positions at different lengths from the system inlet increase at a higher initial flow rate of 1.0 g/s, but they exhibit opposite behavior at a lower rate (0.42 g/s). Increasing the sampling rate in the dp/dt-time graphs enhances the precision of leak localization. Calculation results show an effective leakage localization with an accuracy of 15 mm (2.5%). The leak flow rate during nitrogen leakage tests is about three times that of hydrogen, and it has higher Δh (about two times at 10 bar).

Augmented reality safety warnings in roadway work zones: Evaluating the effect of modality on worker reaction times

Given the aging highway infrastructure requiring extensive rebuilding and enhancements, and the consequent rise in the number of work zones, there is an urgent need to develop advanced safety systems to protect workers. While Augmented Reality (AR) holds significant potential for delivering warnings to workers, its integration into roadway work zones remains relatively unexplored. The primary objective of this study is to improve safety measures within roadway work zones by conducting an extensive analysis of how different combinations of multimodal Augmented Reality (AR) warnings influence the reaction times of workers. This paper addresses this gap through a series of experiments that aim to replicate the distinctive conditions of roadway work zones, both in real-world and virtual reality environments. Our approach comprises three key components: an advanced AR system prototype, a VR simulation of AR functionality within the work zone environment, and the Wizard of Oz technique to synchronize user experiences across experiments. To assess reaction times, we leverage both the simple reaction time (SRT) technique and an innovative vision-based metric that utilizes real-time pose estimation. By conducting five experiments in controlled outdoor work zones and indoor VR settings, our study provides valuable information on how various multimodal AR warnings impact workers reaction times. Furthermore, our findings reveal the disparities in reaction times between VR simulations and real-world scenarios, thereby gauging VR’s capability to mirror the dynamics of roadway work zones. Furthermore, our results substantiate the potential and reliability of vision-based reaction time measurements. These insights resonate well with those derived using the SRT technique, underscoring the viability of this approach for tangible real-world uses.

Wheelchair safety system using fuzzy logic controller to avoid obstruction

Wheelchair safety system using fuzzy logic controller to avoid obstruction

Evaluating near midair collision reporting systems using aircraft surveillance data: A case study at a university airport

Introduction: A near midair collision (NMAC) is defined by the Federal Aviation Administration as an event in which the crew of an aircraft perceives a situation that could lead to a midair collision or an event in which the separation between two aircraft is less than 500 feet (Federal Aviation Administration, 2018). NMAC reports collected by safety reporting systems have long been used to study and mitigate midair collision risk. However, reports submitted are subjective and require the reporter to voluntarily provide this information. With the implementation of Automatic Dependent Surveillance-Broadcast, aircraft temporal and positional data are easily collectible. Method: Using internal safety system, NASA Aviation Safety Reporting System, and FAA Near Midair Collision System reports together with ADS-B data, the effectiveness of different safety reporting systems to collect NMAC data around a towered university airport were compared over a six-month period. Unreported events were identified by utilizing ADS-B data to calculate aircraft separation events of less than 500 feet. Results: While 10 events were reported to the internal safety system, only one was reported to the NASA ASRS, and none to the FAA NMACS within the study’s scope. Sixteen events in which aircraft were within 500 feet of each other were found using ADS-B data, with none of these events having been reported to any safety reporting system. Conclusions: The findings of this study highlight the need for increased sharing in aviation safety data and show the potential of ADS-B data as a tool in studying near midair collisions and midair collision risk. Practical Applications: NMAC safety reporting systems may be less effective than expected. The addition of other sources of data such as ADS-B may be necessary to identify and investigate NMACs and other relevant near miss safety events.

Movie recommendation and classification system using block chain

Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distributed systems. The combination guarantees the safety and scalability of the recommender system. We aim to create an authorized secure exchange device using blockchain-enabled multiparty computation by adding smart contracts to the core blockchain protocol. The recommendation structure and Blockchain technology make online shopping more convenient and private. We propose a blockchain-related recommender system using the “movielens” data. The case study includes a smart contract model that recommends movies to buyers. Initially, we tested the model on a small “movielens dataset” and extended it to a 3M movielens dataset. We developed a classifier model for movielens and proposed a Dual light graph convolutional network for movielens data classification. Our results, including ablation analysis, show that blockchain strategies and Dual light graph convolutional networks can effectively improve recommender systems’ privacy. Furthermore, the suggested blockchain technique can be stretched by similar procedures.

Identification of Potential Inhibitors of Histone Deacetylase 6 Through Virtual Screening and Molecular Dynamics Simulation Approach: Implications in Neurodegenerative Diseases

Background: Histone deacetylase 6 (HDAC6) plays a crucial role in neurological, inflammatory, and other diseases; thus, it has emerged as an important target for therapeutic intervention. To date, there are no FDA-approved HDAC6-targeting drugs, and most pipeline candidates suffer from poor target engagement, inadequate brain penetration, and low tolerability. There are a few HDAC6 clinical candidates for the treatment of mostly non-CNS cancers as their pharmacokinetic liabilities exclude them from targeting HDAC6-implicated neurological diseases, urging development to address these challenges. They also demonstrate off-target toxicity due to limited selectivity, leading to adverse effects in patients. Selective inhibitors have thus been the focus of development over the past decade, though no selective and potent HDAC6 inhibitor has yet been approved. Methods: This study involved an integrated virtual screening against HDAC6 using the DrugBank database to identify repurposed drugs capable of inhibiting HDAC6 activity. The primary assessment involved the determination of the ability of molecules to bind with HDAC6. Subsequently, interaction analyses and 500 ns molecular dynamics (MD) simulations followed by essential dynamics were carried out to study the conformational flexibility and stability of HDAC6 in the presence of the screened molecules, i.e., penfluridol and pimozide. Results: The virtual screening results pinpointed penfluridol and pimozide as potential repurposed drugs against HDAC6 based on their binding efficiency and appropriate drug profiles. The docking results indicate that penfluridol and pimozide share the same binding site as the reference inhibitor with HDAC6. The MD simulation results showed that stable protein–ligand complexes of penfluridol and pimozide with HDAC6 were formed. Additionally, MMPBSA analysis revealed favorable binding free energies for all HDAC6–ligand complexes, confirming the stability of their interactions. Conclusions: The study implies that both penfluridol and pimozide have strong and favorable binding with HDAC6, which supports the idea of repositioning these drugs for the management of neurodegenerative disorders. However, further in-depth studies are needed to explore their efficacy and safety in biological systems.

Safety and effectiveness of methylphenidate ER multi-unit pellet system in ADHD patients: An open label study

Background: Attention deficit hyperactive disorder (ADHD) is a neurodevelopmental disorder occurring in children and adults. Pharmacotherapy remains the cornerstone of ADHD treatment. Stimulants such as methylphenidate are effective and have been one of the best studied and most frequently used treatment for ADHD. However, different delivery mechanisms and devices may potentially impact patient experience and real-life outcomes.Aim: This study evaluated the effectiveness of Multiple-Unit Pellet System Delivered Extended-Release Methylphenidate (Contramyl XR) on symptom control and reported outcomes in ADHD patients, in a real-world setting.Setting: A phase IV, open label, flexible dose, prospective, observational study conducted at six sites covering five provinces of South Africa.Methods: About 119 participants with ADHD (both newly diagnosed [treatment-naïve] and methylphenidate-treated [switch-over] patients) were enrolled and initiated either on Contramyl XR or switched over from methylphenidate to Contramyl XR. Primary efficacy was assessed by Weiss Functional Impairment Rating Scale (WFIRS) over 12 weeks.Results: In all, 117 participants completed the study (treatment-naïve patients: 46% [n = 55] and switch-over patients: 54% [n = 64]). Mean change from baseline in total WFIRS (95% confidence interval) was –17.7 (–21.1, –14.3; p 0.001) at week 4 and –29.3 (–33.5, –25.2; p 0.001) at week 12. At week 12, there was significant improvement in WFIRS scores, with treatment satisfaction reported by treatment-naïve patients. Switch-over patients also demonstrated comparable effectiveness.Conclusion: Contramyl XR was found to be clinically effective either as de novo or as switch therapy. It was well tolerated, and all patients chose to continue with the treatment option.Contribution: Despite distinct and different delivery mechanism of Contramyl XR, this study provides evidence for using it as an alternate treatment option versus reference methylphenidate, in both treatment-naïve and switch-over ADHD patients. Study participants willingness to continue Contramyl XR therapy post study, further strengthens the confidence on the effectiveness of Contramyl XR in managing ADHD patients.

These aren’t the droids you’re looking for: artificial intelligence and safety in future transport systems

These aren’t the droids you’re looking for: artificial intelligence and safety in future transport systems

Specific road safety issues in LMICs: an overview and some illustrative examples

This paper gives an overview of the work of PIARC Working Group 3.1.1 ‘Specific road safety issues for LMICs’. The working group performed its activities in the period 2019–2023. We prepared two deliverables: (a) a literature review, and (b) an overview of illustrative examples. The review started from the notion that the road safety problem requires a multi-disciplinary approach. The safe system approach is generally considered as the central concept for this purpose and this starting point is also taken in this study. However new directions are needed to make the safe system concept work in LMICs. The period of the first UN Decade of Action for Road Safety has brought us a series of valuable problem analysis and overview articles prepared by a variety of institutions and institutes. As a PIARC working group we did not repeat this effort, but used it as a starting point to indicate which specific issues do require a boost in LMICs for the coming period up to 2030. We concluded that the following 12 issues deserve special attention. Strategical: (i) Sustainable Development Goals integral approach, (ii) safety culture, (iii) road safety management and leadership, (iv) building road safety expertise and science; Tactical: (v) transportation system as a whole, (vi) city design, architecture, land use, rural planning, (vii) cost effectiveness, (viii) legislation and enforcement; Operational: (ix) speed, (x) sustainable safe roads, (xi) vehicles as a safe system component, (xii) post-crash health care. As a result of our discussions and analyses we also concluded that it is worthwhile and necessary to make a distinction between the issues in terms of levels of approach. Making roads and vehicles safe may be considered as operational safety measures on a component level, whereas city design and transport system issues do represent a more tactical approach. On top of that, organisational and cultural aspects do have a more strategical nature. As in many managerial and planning tasks the distinction between strategical, tactical and operational levels of approach is needed in the road safety field. It illustrates that the Safe System concept asks for a multi-level vision, thus preventing a silo approach with a limited focus on human behaviour, safe roads or vehicle safety. A further analysis for each of the selected issues resulted in priority areas, i.e. topics that deserve special attention in the LMIC context. In a similar line of reasoning we also described illustrative examples, i.e. LMIC related before-after studies that show the perspective of certain measures on an evidence basis. Although we found some illustrative cases for each of these issues, our search for evidence based cases confirmed the findings from recent overview articles indicating the lack of LMIC related road safety research. A more widespread implementation of effective road safety measures in LMICs would require a stronger national commitment and leadership from the road safety agencies point of view in the first place. Moreover an evidence based policy asks for a strong national and regional knowledge infrastructure. Ultimately road safety policies and road safety research programs are to be considered as inseparable. A strong local knowledge infrastructure may thus become the basis for an effective national road safety policy.

The impact of the Korean safe rates system on work environment and road safety

Abstract The regulation of road transport supply chains has recently become a subject of international concern. A significant body of research shows that ensuring minimum rates of pay for truck drivers is important, not only to workers’ health and safety but also to achieving improved road safety outcomes and other policy objectives related to the viability of the road transport industry. For this study, we used an empirical analysis to quantitatively demonstrate the impact of South Korea’s Safe Rates System on factors of the work environment and driver behaviours that affect road safety. Specifically, we looked at the impact of the system on market structure, truck drivers’ monthly net income, average working time, time spent on maintenance, and the frequency of overloading, speeding, and drowsiness while driving. Our results suggest that increased rates of pay for owner truck drivers resulting from the implementation of the Safe Rates System led to a reduction in the average number of contracting steps between client companies who ship goods and the truck drivers who transport them, improvement in the level of market transparency, increased monthly income, decrease in total time worked, decrease in the frequency of overloading, speeding, and drowsiness while driving, and increase in time spent on vehicle maintenance. The level of compliance with the system by clients and transport companies also had an overall positive impact on the industry structure. By empirically demonstrating the system’s impact, our results provide statistical evidence useful to the debate on reintroduction of the Safe Rates System in Korea.

Efficacy and Safety of Automated Insulin Delivery Systems in Patients with Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis.

Automated insulin delivery (AID) systems studies are upsurging, half of which were published in the last 5 years. We aimed to evaluate the efficacy and safety of AID systems in patients with type 1 diabetes mellitus (T1DM). We searched PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov until August 31, 2023. Randomized clinical trials that compared AID systems with other insulin-based treatments in patients with T1DM were considered eligible. Studies characteristics and glycemic metrics was extracted by three researchers independently. Sixty-five trials (3,623 patients) were included. The percentage of time in range (TIR) was 11.74% (95% confidence interval [CI], 9.37 to 14.12; P<0.001) higher with AID systems compared with control treatments. Patients on AID systems had more pronounced improvement of time below range when diabetes duration was more than 20 years (??.80% vs. ??.86%, P=0.031) and baseline glycosylated hemoglobin lower than 7.5% (??.93% vs. ??.87%, P=0.033). Dual-hormone full closed-loop systems revealed a greater improvement in TIR compared with hybrid closed-loop systems (??9.64% vs. ??0.87%). Notably, glycemia risk index (GRI) (??.74; 95% CI, ??.34 to ??.14; P<0.01) was also improved with AID therapy. AID systems showed significant advantages compared to other insulin-based treatments in improving glucose control represented by TIR and GRI in patients with T1DM, with more favorable effect in euglycemia by dual-hormone full closedloop systems as well as less hypoglycemia for patients who are within target for glycemic control and have longer diabetes duration.

Analyzing Autonomous Vehicle Collision Types to Support Sustainable Transportation Systems: A Machine Learning and Association Rules Approach

The increasing presence of autonomous vehicles (AVs) in transportation, driven by advances in AI and robotics, requires a strong focus on safety in mixed-traffic environments to promote sustainable transportation systems. This study analyzes AV crashes in California using advanced machine learning to identify patterns among various crash factors. The main objective is to explore AV crash mechanisms by extracting association rules and developing a decision tree model to understand interactions between pre-crash conditions, driving states, crash types, severity, locations, and other variables. A multi-faceted approach, including statistical analysis, data mining, and machine learning, was used to model crash types. The SMOTE method addressed data imbalance, with models like CART, Apriori, RF, XGB, SHAP, and Pearson’s test applied for analysis. Findings reveal that rear-end crashes are the most common, making up over 50% of incidents. Side crashes at night are also frequent, while angular and head-on crashes tend to be more severe. The study identifies high-risk locations, such as complex unsignalized intersections, and highlights the need for improved AV sensor technology, AV–infrastructure coordination, and driver training. Technological advancements like V2V and V2I communication are suggested to significantly reduce the number and severity of specific types of crashes, thereby enhancing the overall safety and sustainability of transportation systems.

Prevention of early thrombosis in transplanted vein model by encapsulation with tirofiban microneedle drug delivery system.

Early thrombosis following CABG surgery leads to perioperative myocardial infarction, which causes difficulties for clinicians and patients. Moreover, once perioperative myocardial infarction occurs, the mortality rate is extremely high. In recent years, microneedle (MN) drug delivery systems have become a research hotspot with broad clinical application prospects. These systems are capable of achieving sustained, safe, and painless local drug release. In cardiovascular applications, MNs maximize local anticoagulant effects, inhibit endometrial hyperplasia, and reduce systemic side effects. We speculate that a MN drug delivery system can be used to target transplanted veins to inhibit their thrombosis and reduce the incidence of perioperative myocardial infarction after CABG surgery. Therefore, this study developed a hyaluronic acid MN patch loaded with tirofiban and conducted preliminary physicochemical tests. The safety, efficacy, biocompatibility, and targeting of the MN system were evaluated using in vitro and in vivo experiments using a jugular vein transplantation model. The results indicate that the MN system has excellent physical properties, safety, effectiveness, biocompatibility, and strong targeting, which can effectively inhibit early local thrombus formation. In addition, the observation of early postoperative endometrial hyperplasia activation provides a foundation for future research.&#xD.