Safety Of Users Research Articles

Acid-catalyzed phenolation of lignin with tea polyphenol: Enhancing uv resistance and oxidation resistance for potential applications

The rapid development of the industry has led to the destruction of the earth's ozone layer, resulting in an increasingly serious problem of excessive ultraviolet radiation. Exploring effective measures to address these problems has become a hot topic. Lignin shows promise in the design and preparation of anti-ultraviolet products due to its inherent properties. However, it is important to investigate way to enhance the reactivity of lignin and determine its application form in related products. In this study, phenolic reactions with tea polyphenols were conducted through acid-catalyzed conversion, utilizing organic solvent lignin as the primary material. The phenolic hydroxyl content of the original lignin increased significantly by 218.8 %, resulting in notable improvements in UV resistance and oxidation resistance for phenolic lignin. Additionally, micro-nanocapsule emulsions were formed using phenolic lignin particles as surfactants through ultrasonic cavitation with small-molecule sunscreens. A bio-based sunscreen was prepared with phenolated lignin micro-nanocapsules as the active ingredient, achieving an SPF 100.2 and demonstrating excellent stability. The sunscreen also exhibited strong antioxidant properties and impermeability, ensuring user safety. This research offers a current solution for improving the application of lignin in sunscreens while also broadening the potential uses of plant-based materials in advanced functional products.

Alternative Approaches for Rapid Evaluation of Frictional Resistance of Aggregates

Skid resistance of pavements plays a crucial role in ensuring the safety of road users, particularly under wet weather conditions. The standardized method for evaluating the skid resistance of aggregates, AASHTO PP103, requires using a three-wheel polishing device. That test procedure requires significant time for specimen preparation, testing, and monitoring of aggregate stockpiles. This study aims to propose alternative test methods to reduce the time required for the evaluation of skid resistance of aggregates. Two approaches were examined as alternatives to the AASHTO PP103 method. The first approach involved using a micro-deval abrasion device to polish the aggregates before evaluating the skid resistance of the aggregates using a dynamic friction tester. In the second approach, a multiple linear regression equation was proposed that uses conventional mechanical performance test results as independent variables to predict skid resistance. Twenty-one aggregate samples were collected from various sources in Texas for this study. The study results indicate that both approaches can be used as surrogate methods for AASHTO PP103 for rapid screening of the skid resistance of aggregates, which could help manage aggregate quality control and assurance.

Assessing the Role of Autonomous Vehicles in Urban Areas: A Systematic Review of Literature

Autonomous vehicles (AVs) aim to improve safety and comfort of road users while contributing to the reduction of traffic congestion, air pollution, fuel consumption, and enabling mobility and accessibility of disabled and older people. As AV technology is rapidly advancing, there is an urgent need to explore how those new mobility services will impact urban transport systems, including the users, the infrastructure, and the design of future urban areas. This paper applies a systematic review to assess the role of AVs in urban areas. It reviews 41 articles published between 2003 and 2023, and uses inductive and deductive coding approaches to identify seven themes and thirty sub-themes within the literature. The seven include: benefits, attitudes, and behaviours and user perception, climate adaptation, climate mitigation, legislation and regulations, sustainability, and infrastructure. Studies related to benefits accounted for 25% of the sample, followed by behaviours and user perception (24%) and sustainability (22%). The least amount of research has been undertaken on the role of AVs to support climate adaptation. Geographically, almost half (#22) of the papers originate within Europe, followed by America (#10) and Asia (#7). There is only limited research originating from the Global South. This systematic review sets the scene for considering how AVs in public transport can be implemented in urban areas by establishing the current state of knowledge on user attitudes, perceptions, and behaviour, the benefits of AVs, the infrastructure and legislation and regulations required for AVs, and the role AVs have in climate mitigation, adaptation, and sustainability.

DEVELOPING STRATEGIES FOR JUNIOR DESIGNERS TO MANAGE THEIR DESIGN AND EMOTIONS

Junior designers may encounter difficulties when considering customers from a distinct generation who possess dissimilar physical and physiological requirements compared to their own. The understanding of the sensory and physical sensations of design is crucial as younger designers cater to the needs of future generations of users and consumers. Understanding includes both the factual and emotional components of the users' and the audience's genuine experiences. Together, these tools help junior designers consider user safety. Techniques for simulation, have been effectively used by the design industries, that combine cognitive thinking abilities with knowledge outputs to better understand users and their needs. It is essential to explore possible approaches to help junior designers improve cognitive and affective skills (including emotional changes). The effectiveness of simulation guidelines on teaching junior designers about human emotion management abilities and limitations was examined, as well as how affective skill development was influenced.

Semantic Conversational Content Moderation

In today's digital environment, social media platforms have become an integral part of our daily lives, facilitating global communication, knowledge sharing and community building. However, these platforms are increasingly vulnerable to the spread of offensive and toxic content, including misinformation, harassment and hate speech. Such content poses a significant threat to the safety and well-being of Internet users. In response to this immediate problem, we set out to develop an AI-based conversation moderation service aimed at effectively detecting and removing offensive or semantically toxic information in real time. Our solutions strive to make the internet safer and more user-friendly, thereby promoting a more positive and inclusive online environment. Key Words: Natural Language Processing(NLP), Moderation, Semantic Analysis, Mistral.

Customized Tricycle for Patient with Disabilities

Mobility of physically disabled persons is a concerning social issue nowadays. The basic Tricycle is a three- wheeled design, pedaled by disabled persons in the side and seat in the middle for sitting arrangement. They use only one hand to steer the handle because other-hand is used to rotate the pedal. Our aim is to design a low-cost tricycle that will allows person to accelerate and steer the tricycle from a single place. The way the Bike Chair works is the back is essentially a regular bicycle, but the front turns it into a tricycle, and sort-of makes it a wheelchair and bicycle combo. The disabled passenger sits on the front two wheels, as the driver uses their front wheels to steer the bike. The passenger has buckles and strap to secure the min place and top even them from falling out of the chair during the bike-ride. Furthermore, the tricycle's compact size, maneuverability, and zero emissions make it an attractive option for reducing traffic congestion and pollution in densely populated urban areas. Future research directions include optimization of battery and motor technologies for enhanced performance and efficiency, as well as integration of smart features for improved user experience and safety.

Static Load Testing Of PCI-Bridges for Assessing Functional Suitability and Its Comparison with Dynamic Testing

To accommodate the rapid development of residential and commercial areas in the Bumi Serpong Damai (BSD) Region, Banten, the construction of the Kadisurung bridge is necessary to integrate road networks. Before being operational to ensure the safety of bridge users, load testing is conducted. Parameters that must be met in the testing include not exceeding the strain and deflection limits during operational conditions, including when maximum loading occurs. To assess the bridge's performance during operation, research was conducted on both sides of the central span (western side bridge, JSB, and eastern side bridge, JST) with static load testing carried out with gradually increasing loads up to 50% UDL (136 tons), followed by gradual load removal. At JSB, the maximum deflection at 100% UDL, estimated from the deflection at 50% UDL, was 39.92 mm, and at JST, it was 31.67 mm, while the allowable deflection was 43.3 mm. Based on the deflection parameters, the relative capacities of JST and JSB are expressed as 108.5% and 136.7% respectively, relative to their allowable capacities. This indicates that the estimated bridge capacity using static load testing parameters is 6%-10% greater than when using parameters from dynamic load testing. Thus, the bridge is deemed suitable for operation.

Evaluating the effectiveness of functional decomposition in early-stage design: development and application of problem space exploration metrics

This paper aims to explore metrics for evaluating the effectiveness of functional decomposition methods regarding problem space exploration at the early design stage. Functional decomposition involves breaking down the main purpose of a complex problem or system into a set of more manageable sub-functions, leading to a clearer understanding of the problem space and its various aspects. While various metrics have been used to evaluate functional decomposition outcomes, little literature has focused on assessing its effectiveness in problem space exploration. To address the gap, this research introduces three metrics for problem space evaluation defined by functional models: quantity of unique functions (M1), breadth and depth of the hierarchical structure (M2), and relative semantic coverage ratio of the problem space (M3). An example study is conducted to illustrate the evaluation process, comparing functional analysis with and without explicit human-centric considerations using a power screwdriver as a case product. The analysis in the example study reveals that the breadth of the hierarchical structure (part of M2) is marginally larger in the condition with explicit human-centric considerations (Condition A) compared to the condition without such considerations (Condition B). However, no significant differences are observed in terms of other metrics. The qualitative analysis based on semantic comparisons suggests that Condition A facilitates participants in generating a diverse set of functions supporting user safety requirements more effectively than Condition B. Overall, the example study demonstrates the evaluation process for each metric and discusses their nuances and limitations. By proposing these metrics, this research contributes to benchmarking and evaluating the effectiveness of different methods in promoting functional analysis in engineering design. The metrics provide valuable insights into problem space exploration, offering designers a better understanding of the efficacy of their functional decomposition methods in early design stages. This, in turn, fosters more informed decision-making and contributes to the advancement of functional analysis methodologies in engineering design practices.

A novel approach toward cyberbullying with intelligent recommendations using deep learning based blockchain solution.

Integrating healthcare into traffic accident prevention through predictive modeling holds immense potential. Decentralized Defense presents a transformative vision for combating cyberbullying, prioritizing user privacy, fostering a safer online environment, and offering valuable insights for both healthcare and predictive modeling applications. As cyberbullying proliferates in social media, a pressing need exists for a robust and innovative solution that ensures user safety in the cyberspace. This paper aims toward introducing the approach of merging Blockchain and Federated Learning (FL), to create a decentralized AI solutions for cyberbullying. It has also used Alloy Language for formal modeling of social connections using specific declarations that are defined by the novel algorithm in the paper on two different datasets on Cyberbullying and are available online. The proposed novel method uses DBN to run established relation tests amongst the features in two phases, the first is LSTM to run tests to develop established features for the DBN layer and second is that these are run on various blocks of information of the blockchain. The performance of our proposed research is compared with the previous research and are evaluated using several metrics on creating the standard benchmarks for real world applications.

Assessment of the feed additive consisting of Pediococcus pentosaceusDSM 14021 for all animal species for the renewal of its authorisation (Chr. Hansen A/S).

Following a request from the European Commission, EFSA was asked to deliver a scientific opinion on the assessment of the application for renewal of Pediococcus pentosaceus DSM 14021, a technological additive for all animal species. The applicant has provided evidence that the additive currently on the market complies with the existing conditions of authorisation. The Panel concluded that the additive remains safe for all animal species, consumers and the environment under the authorised conditions of use. Regarding user safety, the Panel considers that any exposure through skin and respiratory tract is considered a risk. The Panel cannot conclude on the eye irritation potential of the additive due to the lack of data. There is no need for assessing the efficacy of the additive in the context of the renewal of the authorisation.

Assessment of the feed additive consisting of Levilactobacillus brevisDSM 21982 for all animal species for the renewal of its authorisation (Marigot Ltd T/A Celtic Sea Minerals).

Following a request from the European Commission, EFSA was asked to deliver a scientific opinion on the assessment of the application for renewal of Levilactobacillus brevis DSM 21982 as a technological feed additive, silage additive, for all animal species. The applicant has provided evidence that the additive currently on the market complies with the existing conditions of authorisation. The Panel concluded that the additive remains safe for all animal species, consumers and the environment. Regarding user safety, the additive should be considered as a skin and respiratory sensitiser. No conclusions can be drawn on the eye irritancy potential of the additive. There is no need for assessing the efficacy of the additive in the context of the renewal of the authorisation.

OFEI: A Semi-Black-Box Android Adversarial Sample Attack Framework Against DLaaS

With the growing popularity of Android devices, Android malware is seriously threatening the safety of users. Although such threats can be detected by deep learning as a service (DLaaS), deep neural networks as the weakest part of DLaaS are often deceived by the adversarial samples elaborated by attackers. In this paper, we propose a new semi-black-box attack framework called one-feature-each-iteration (OFEI) to craft Android adversarial samples. This framework modifies as few features as possible and requires less classifier information to fool the classifier. We conduct a controlled experiment to evaluate our OFEI framework by comparing it with the benchmark methods JSMF, GenAttack and pointwise attack. The experimental results show that our OFEI has a higher misclassification rate of 98.25%. Furthermore, OFEI can extend the traditional white-box attack methods in the image field, such as fast gradient sign method (FGSM) and DeepFool, to craft adversarial samples for Android. Finally, to enhance the security of DLaaS, we use two uncertainties of the Bayesian neural network to construct the combined uncertainty, which is used to detect adversarial samples and achieves a high detection rate of 99.28%.

Predicting Cybersickness Using Machine Learning and Demographic Data in Virtual Reality

The widespread adoption of virtual reality (VR) technologies is significantly hindered by the prevalence of cybersickness, a disruptive experience causing symptoms like nausea, dizziness, and disorientation. Traditional methodologies for predicting cybersickness predominantly depend on biomedical data. While effective, these methods often require invasive data collection techniques, which can be impractical and pose privacy concerns. Furthermore, existing research integrating demographic information typically does so in conjunction with biomedical or behavioral data, not as a standalone predictive tool. Addressing this gap, we investigated machine learning techniques that exclusively use demographic data to classify and predict the likelihood of cybersickness and its severity in VR environments. This method relies on noninvasive, easily accessible demographic information like age, gender, and previous VR exposure. It offers a more user-friendly and ethically sound approach to predicting cybersickness. The study explores the potential of demographic variables as standalone predictors through comprehensive data analysis, challenging the traditional reliance on biomedical metrics. We comprehensively presented the input data and statistical analysis and later carefully selected the widely used machine learning models from different classes, including k-nearest neighbors, Naive Bayes, Logistic Regression, Random Forest, and Support Vector Machine. We evaluated their performances and presented detailed results and limitations. The research findings indicate that demographic data can be used to predict the likelihood and severity of cybersickness. This research provides critical insights into future research directions, including data collection design and optimization suggestions. It opens new avenues for personalized and inclusive VR design, potentially reducing barriers to VR adoption and enhancing user comfort and safety.

Influence of Calculation Parameters on the Slope Stability of the Historical Rasos Cemetery in Vilnius (Lithuania)

Many objects of cultural and religious importance are located on naturally or artificially shaped hills. In such cases, the stability of the slopes determines both the safety of users and the facilities themselves and thus the preservation of cultural heritage. The analysis of the slope stability requires the assessment of the soil shear strength, defined as the ratio of resistance forces to driving forces. An important issue in the slope stability analysis is the proper determination of soil mechanical parameters, including their changes due to soil moisture. This paper presents an assessment of the slope stability of the Rasos Cemetery in Vilnius (Lithuania), where, due to a partial slope sliding, some of the tombstones were destroyed by being covered with soil. An analysis of the slope stability (factor of safety) was performed for two cross-sections using six calculation schemes. In each of them, calculations were carried out for three groups of soil parameters and for four design approaches (characteristic values, DA2, DA3a, and DA3b in accordance with the geotechnical standard applicable in the European Union—Eurocode 7: Geotechnical design). For the calculation approach (DA3a) for different groups of soil parameters, the values of the slope stability factors of safety were practically below 1.0. If the value of the factor of safety is less than 1.0, the slope is unstable. In the range of values 1.0–1.3, there is a serious risk of slope instability, and only when the factor of safety value reaches above 1.3 is the slope classified as stable. The comparison of the slope stability factors obtained with different design approaches (in relation to the DA3a) shows that the most unfavorable stability factor is approximately 43% lower than the result obtained from the characteristic values, approximately 21% lower than the result obtained using the DA2 design approach, and approximately 24% lower than the result obtained using the DA3b design approach. The analytical and numerical method (GEO5) analysis showed that, regardless of the adopted calculation approach, the required slope stability factor was not obtained. The article shows that selecting the correct geological cross-section to determine the critical slope slip surface and adopting the relevant design approach is crucial for slope sustainability. Based on the analysis, a proprietary solution for the construction work was proposed, which will ensure both the full stability of the slope and the safety of all graves and consequently landslide disaster prevention.

인공지능 기반 신생혈관성 나이관련황반변성 진단보조소프트웨어 사용자 인터페이스 개선 연구

With the recent development of Software as a Medical Device (SaMD), the significance of reducing use errors in medical devices is increasing. In this study, we conducted a formative evaluation of Artificial Intelligence (AI)-based software for ophthalmic image detection and diagnosis. The software automatically displays the presence or absence of neovascular age-related macular degeneration using fundus images to assist medical personnel in making diagnostic decisions. We aimed to find ways to reduce the number of use errors in formative evaluations. For this purpose, we conducted usability testing by performing tasks based on use scenarios for usability formative evaluations with intended users in their intended use environments, and then modified the user interface to reduce use errors. The initial formative evaluation revealed errors in utilizing the capture and dragand- drop functions for uploading fundus images, checking analysis results, and perform logout procedures. To reduce use errors, we improved the function by relocating the capture icon, displaying a hand-shaped cursor during dragging, inserting a fundus position guide, and eliminating the drag-and-drop function. We have also enhanced the design to present analytics results in a more intuitive manner and added a separate logout button to reduce the risk of use error. As a result, the number of use errors in the formative evaluation decreased from six to one in the summative evaluation. The development of ophthalmic image detection and diagnosis assistant software that reflects these improvements is expected to enhance user safety, usability, and reduce use errors.

Future of E-Bikes on Public Lands: A Human Factors Field Study at Minute Man National Historical Park

Electric-assist bicycles (e-bikes) are growing in popularity, and their use has increased on public lands managed by federal, state, and local agencies in the United States. Evaluation of e-bike use on public lands is hindered by a dearth of scientific research, as noted in a prior literature review completed by the study team. This paper presents the results of a human factors field study that evaluated participant speed and behavior on an unpaved, multiuse trail to answer research questions about trail user safety and social interaction. The study concludes that e-bike riders travel approximately 1 to 2 mph faster than conventional bike riders on average, but that individuals’ speeds among conventional and e-bike riders are widely distributed and overwhelmingly overlap, including at the upper and lower extremes. The sex of the rider predicts a greater difference in speed than whether they are using a conventional or an e-bike. The study also found conventional and e-bike riders generally reduce speed in areas of potential conflict and exhibit similar precautionary behaviors when passing other trail users and when crossing trail junctions and vehicle paths. Public lands managers can use these results to inform their decisions about managing e-bike use on multiuse trails with similar characteristics.

An Experimental Phototherapy Device for Studying the Effects of Blue Light on Patients with Raynaud's Phenomenon.

Raynaud's phenomenon (RP) is a condition that causes decreased blood flow to areas perfused by small blood vessels (e.g., fingers, toes). In severe cases, ulceration, gangrene, and loss of fingers may occur. Most treatments focus on inducing vasorelaxation in affected areas by the way of pharmaceuticals. Recently, animal studies have shown that vasorelaxation can be induced by non-coherent blue light (wavelength ~ 430-460nm) through the actions of melanopsin, a photoreceptive opsin protein encoded by the OPN4 gene. To study this effect in humans, a reliable phototherapy device (PTD) is needed. We outline the construction of a PTD to be used in studying blue light effects on Raynaud's patients. Our design addresses user safety, calibration, electromagnetic compatibility/interference (EMC/EMI), and techniques for measuring physiological responses (temperature sensors, laser Doppler flow sensors, infrared thermal imaging of the hands). We tested our device to ensure (1) safe operating conditions, (2) predictable, user-controlled irradiance output levels, (3) an ability for measuring physiological responses, and (4) features necessary to enable a double-blinded crossover study for a clinical trial. We also include in the Methods an approved research protocol utilizing our device that may serve as a starting point for clinical study. We introduced a reliable PTD for studying the effects of blue light therapy for patients suffering from Raynaud's phenomenon and showed that our device is safe and reliable and includes the required measurement vectors for tracking treatment effects throughout the duration of a clinical study.

Segment beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation

Augmented Reality (AR) devices, emerging as prominent mobile interaction platforms, face challenges in user safety, particularly concerning oncoming vehicles. While some solutions leverage onboard camera arrays, these cameras often have limited field-of-view (FoV) with front or downward perspectives. Addressing this, we propose a new out-of-view semantic segmentation task and Segment Beyond View (SBV), a novel audio-visual semantic segmentation method. SBV supplements the visual modality, which miss the information beyond FoV, with the auditory information using a teacher-student distillation model (Omni2Ego). The model consists of a vision teacher utilising panoramic information, an auditory teacher with 8-channel audio, and an audio-visual student that takes views with limited FoV and binaural audio as input and produce semantic segmentation for objects outside FoV. SBV outperforms existing models in comparative evaluations and shows a consistent performance across varying FoV ranges and in monaural audio settings.

Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility

For ensuring the safety of users by protecting the privacy, the traditional privacy-preserving bandit algorithm aiming to maximize the mean reward has been widely studied in scenarios such as online ride-hailing, advertising recommendations, and personalized healthcare. However, classical bandit learning is irresponsible in such practical applications as they fail to account for risks in online decision-making and ignore external system information. This paper firstly proposes privacy protected mean-volatility utility as the objective of bandit learning and proves its responsibility, because it aims at achieving the maximum probability of utility by considering the risk. Theoretically, our proposed responsible bandit learning is expected to achieve the fastest convergence rate among current bandit algorithms and generates more statistical power than classical normality-based test. Finally, simulation studies provide supporting evidence for the theoretical results and demonstrate stronger performance when using stricter privacy budgets.

Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles.

The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional "one size fits all" approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger's personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human-Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.