Search Results (17)

This study presents a case-specific joining method for modular, large-scale components manufactured using Selective Laser Sintering (SLS). A T-slot joint reinforced with a pultruded carbon fiber rod was developed to enable the segmental assembly of polymer fan blades that exceed the build volume of common SLS printers. Through an iterative design process, five joint variations were investigated, focusing on the optimization of slot geometry (fillet radii and wall thickness) and the integration of carbon fiber reinforcements to create a high-strength hybrid connection. The experimental findings were validated using a non-linear finite element analysis (FEA) utilizing an iteratively calibrated Young’s modulus of 710 MPa, which accounts for the 50/50 virgin-to-reused PA2200 powder ratio employed in the study. The numerical model identified that the primary sites for crack initiation were the fillet radii of the female slot, where localized equivalent plastic strains reached critical levels of up to 84% in tension and 78% in bending. The final design achieved an average tensile strength of 27.6 MPa, exceeding the design threshold of 21.9 MPa with a safety factor of 2.5. While unreinforced joints showed a 73.4% reduction in bending strength compared to solid specimens, the addition of an 8 mm carbon rod increased performance by 238.7%, restoring over 90% of the monolithic material’s strength. Numerical results confirmed that the reinforcement assumed the primary load-bearing role, effectively mitigating stresses in the polymer matrix below the ultimate tensile strength. Failure analysis clarified that the observed audible failure originated from internal fiber breakage within the rod at stresses between 900–1050 MPa. This work demonstrates that a segmental, reinforcement-based joining method can effectively overcome size constraints in polymer additive manufacturing, providing a robust and repeatable solution for rotating components subject to complex loading conditions. Full article

This paper provides a conceptual framework for soundscape appraisal as a key outcome of the hearing process. Sound appraisal involves auditory sense-making and produces the soundscape as the perceived and understood acoustic environment. The soundscape exists in the experiential domain and involves meaning-giving. Soundscape research has reached a consensus about the relevance of two experiential dimensions—pleasure and eventfulness—which give rise to four appraisal quadrants: calm, lively/vibrant, chaotic, and boring/monotonous. Requirements for and constraints on the hearing and appraisal processes follow from the demands of living in a complex world, the specific properties of source and transmission physics, and the need for auditory events and streams of single-source information. These lead to several core features and functions of the hearing process, such as prioritizing the auditory channel (loudness), forming auditory streams (audibility, primitive auditory scene analysis), prioritizing auditory streams (audible safety, noise sensitivity), and initial meaning-giving (auditory gist and perceptual layers). Combined, this leads to a model of soundscape appraisal yielding the ISO quadrant structure. Long-term aggregated appraisals lead to a sonic climate that allows for an insightful comparison of different locations. The resulting system needs additional validation and optimization to comply in detail with human appraisal and evaluation. Full article

Forcecardiography (FCG) uses force sensors to record the mechanical vibrations induced on the chest wall by cardiac and respiratory activities. FCG is usually performed via piezoelectric lead-zirconate titanate (PZT) sensors, which simultaneously record the very slow respiratory movements of the chest, the slow infrasonic vibrations due to emptying and filling of heart chambers, the faster infrasonic vibrations due to movements of heart valves, which are usually recorded via Seismocardiography (SCG), and the audible vibrations corresponding to heart sounds, commonly recorded via Phonocardiography (PCG). However, PZT sensors are not flexible and do not adapt very well to the deformations of soft tissues on the chest. This study presents a flexible FCG sensor based on a piezoelectric polyvinylidene fluoride (PVDF) transducer. The PVDF FCG sensor was compared with a well-assessed PZT FCG sensor, as well as with an electro-resistive respiratory band (ERB), an accelerometric SCG sensor, and an electronic stethoscope for PCG. Simultaneous recordings were acquired with these sensors and an electrocardiography (ECG) monitor from a cohort of 35 healthy subjects (16 males and 19 females). The PVDF sensor signals were compared in terms of morphology with those acquired simultaneously via the PZT sensor, the SCG sensor and the electronic stethoscope. Moreover, the estimation accuracies of PVDF and PZT sensors for inter-beat intervals (IBIs) and inter-breath intervals (IBrIs) were assessed against reference ECG and ERB measurements. The results of statistical analyses confirmed that the PVDF sensor provides FCG signals with very high similarity to those acquired via PZT sensors (median cross-correlation index of 0.96 across all subjects) as well as with SCG and PCG signals (median cross-correlation indices of 0.85 and 0.80, respectively). Moreover, the PVDF sensor provides very accurate estimates of IBIs, with R² > 0.99 and Bland–Altman limits of agreement (LoA) of [−5.30; 5.00] ms, and of IBrIs, with R² > 0.96 and LoA of [−0.510; 0.513] s. The flexibility of the PVDF sensor makes it more comfortable and ideal for wearable applications. Unlike PZT, PVDF is lead-free, which increases safety and biocompatibility for prolonged skin contact. Full article

Background: The rise in work zone crashes due to distracted and aggressive driving calls for improved safety measures. While Truck-Mounted Attenuators (TMAs) have helped reduce crash severity, the increasing number of crashes involving TMAs shows the need for improved warning systems. Methods: This study proposes an AI-enabled vision system to automatically alert drivers on collision courses with TMAs, addressing the limitations of manual alert systems. The system uses multi-task learning (MTL) to detect and classify vehicles, estimate distance zones (danger, warning, and safe), and perform lane and road segmentation. MTL improves efficiency and accuracy, making it ideal for devices with limited resources. Using a Generalized Efficient Layer Aggregation Network (GELAN) backbone, the system enhances stability and performance. Additionally, an alert module triggers alarms based on speed, acceleration, and time to collision. Results: The model achieves a recall of 90.5%, an mAP of 0.792 for vehicle detection, an mIOU of 0.948 for road segmentation, an accuracy of 81.5% for lane segmentation, and 83.8% accuracy for distance classification. Conclusions: The results show the system accurately detects vehicles, classifies distances, and provides real-time alerts, reducing TMA collision risks and enhancing work zone safety. Full article

This study investigated the potential of blockchain technology to transform Electronic Health Record (EHR) administration, integrity, and security. EHRs store vital health information such as medical history, diagnosis, prescriptions, and imaging findings, which may be shared with healthcare professionals to improve patient care. The existing EHR systems have a centralized framework. These centralized systems have a single point of failure, data management, integrity, and security concerns. Blockchain technology provides a solution to these problems by delivering benefits such as safety, privacy, secrecy, and decentralization. This study presents a framework for adopting blockchain technology in EHR systems, providing a comprehensive, modular, and straightforward approach. Our proposed framework addresses the constraints of existing EHR systems by providing a platform for connected and interoperable EHRs. The proposed blockchain-based patient health records management framework demonstrates the potential to address the limitations of current centralized health records systems. It offers benefits such as data privacy and security, interoperability, audibility, decentralization, and automation through the use of smart contracts. The proposed framework is implemented in Ethereum. The evaluation, i.e., cost and performance results, show that this solution is reasonable and may be used on any blockchain network, whether it is permissioned or permissionless. Full article

The purpose of this study is to compare outcomes in patients undergoing buried and non-buried free flaps for breast reconstruction, in addition to evaluating the safety and reliability of venous flow couplers. A retrospective review was performed of all patients undergoing free flap breast reconstruction between 2013 and 2023. The primary outcomes were free flap failure, complications and the number of procedures required to complete the reconstructive journey. A total of 322 flaps were performed in 254 consecutive patients, with 47.5% (n = 153) being buried and 52.0% (n = 169) being non-buried reconstructions. The most common flap of choice being deep inferior epigastric artery perforator flaps (81.9%) followed by profunda artery perforator flaps (14.3%). There was no significant difference between the two groups in complications, including flap failure (buried 2.0% vs. non-buried 1.8% p = 0.902). There was a significant reduction in the number of procedures required to complete the reconstructive journey, with 52.2% (n = 59) of patients undergoing single-stage breast reconstruction in the buried group compared with only 25.5% (n = 36) in the non-buried group (p < 0.001). Two (0.6%) patients experienced a false negative in which the signal of the flow coupler was lost but the flap was perfused during re-exploration. No flap losses occurred without being identified in advance by a loss of audible venous flow signal. Buried free flap breast reconstruction is safe and requires fewer operations to complete patients’ reconstructive journey. Flow couplers are a safe and effective method of monitoring buried free flaps in breast reconstruction. Full article

Women that receive news that they have a malignancy of gynecologic origin can have questions about their diagnosis. These questions might be posed as voice queries to the virtual assistants Siri, Alexa, Google, and Cortana. Because our world has increasingly adopted smart phones and standalone voice query devices, this study focused on the accuracy of audible replies by the virtual assistants (VAs) Siri, Alexa, Google, and Cortana to voice queries related to gynecologic oncology. Twenty-one evaluators analyzed VA audible answers to select voice queries related to gynecologic oncology. Questions were posed in three different ways for each voice query in order to maximize the likelihood of acceptability to the VAs in a 24-question panel. For general queries that were not related to gynecologic oncology, Google provided the most correct audible replies (83.3% correct), followed by Alexa (66.7% correct), Siri (45.8% correct), and Cortana (20.8% correct). For gynecologic oncology-related queries, the accuracy of the VAs was considerably lower: Google provided the most correct audible replies (18.1%), followed by Alexa (6.5%), Siri (5.5%), and Cortana (2.3%). There was a considerable drop in the accuracy of audible replies to oral queries on topics in gynecologic oncology relative to general queries that were not related to gynecologic oncology. There is considerable room for improvement in VA performance, so that caution is advised when using VAs for medical queries in gynecologic oncology. Our specific findings related to gynecologic oncology extend the work of others with regard to the low usability of general medical information obtained from VAs, so that reliance on conversational assistants for actionable medical information represents a safety risk for patients and consumers. Full article

Wearable devices designed to improve medication adherence can emit audible and vibrating alerts or send text messages to users. However, there is little information on the validation of these technologies. The aim of this scoping review was to investigate the involvement of human volunteers in the development and evaluation of wearable devices. A literature search was conducted using six databases (MEDLINE, Embase, Scopus, CINAHL, PsycInfo, and Web of Science) up to March 2020. A total of 7087 records were identified, and nine studies were included. The wearable technologies most investigated were smartwatches (n = 3), patches (n = 3), wristbands (n = 2), and neckwear (n = 1). The studies involving human volunteers were categorized into idea validation (n = 4); prototype validation (n = 5); and product validation (n = 1). One of them involved human volunteers in idea and prototype validation. A total of 782 participants, ranging from 6 to 252, were included. Only five articles reported prior approval by a research ethics committee. Most studies revealed fragile methodological designs, a lack of a control group, a small number of volunteers, and a short follow-up time. Product validation is essential for regulatory approval and encompasses the assessment of the effectiveness, safety, and performance of a wearable device. Studies with greater methodological rigor and the involvement of human volunteers can contribute to the improvement of the process before making them available on the market. Full article

Employees involved in various occupational environments that include vibration machines and any kind of vehicles are adversely subjected to multiple source noise. Thus, the corresponding noise frequencies (and mainly the infrasound ones) present high interest, especially from the viewpoint of sustainability, due to the potential effects on human safety and health (H_S&H) in sustainable engineering projects. Moreover, the occupational safety and health (OSH) visualization (a fact of unveiling the social dimension of sustainability) of occupational workplaces (by evaluating the infrasound and audible noise frequencies generated by diesel engines) could help a safety officer to lessen crucial risk factors in the OSH field and also to protect, more efficiently, the employees by taking the most essential safety measures. This study (i) suggests a technique to determine the infrasound and audible sound frequencies produced due to vibrations of diesel engines, by using biofuels (i.e., sustainable utilization of resources), in order to evaluate potential effects on human safety and health at the workplaces of sustainable engineering projects, and (ii) it ultimately aims to contribute to the improvement of the three “sustainability pillars” (economy, social, and environmental). Therefore, it provides experimental results of the frequency of the noise (regarding the infrasound and audible spectrum) that a diesel motor generates by vibration, in the frame of using different engine rpms (850, 1150, and 2000) and a variety of biofuel mixtures (B20-D80, B40-D60, B60-D40, and B80-D20). The article shows that the fuel blend meaningfully affects the generated noise, and more particularly, the usage of biofuel blends coming from mixing diesel oil with biodiesel (a fact of the emerging environmental dimension of sustainability) can produce various noise frequencies, which are determined in the infrasound and audible spectra (~10–23 Hz). The suggested technique, by ameliorating the OSH situation, doubtless will help enterprises to achieve the finest allocation of limited financial resources (a fact corresponding to the economic dimension of sustainability), allowing financial managers to have more available budget for implementing other risk-reduction projects. Full article

The train horn sound is an active audible warning signal used for warning commuters and railway employees of the oncoming train(s), assuring a smooth operation and traffic safety, especially at barrier-free crossings. This work studies deep learning-based approaches to develop a system providing the early detection of train arrival based on the recognition of train horn sounds from the traffic soundscape. A custom dataset of train horn sounds, car horn sounds, and traffic noises is developed to conduct experiments and analysis. We propose a novel two-stream end-to-end CNN model (i.e., THD-RawNet), which combines two approaches of feature extraction from raw audio waveforms, for audio classification in train horn detection (THD). Besides a stream with a sequential one-dimensional CNN (1D-CNN) as in existing sound classification works, we propose to utilize multiple 1D-CNN branches to process raw waves in different temporal resolutions to extract an image-like representation for the 2D-CNN classification part. Our experiment results and comparative analysis have proved the effectiveness of the proposed two-stream network and the method of combining features extracted in multiple temporal resolutions. The THD-RawNet obtained better accuracies and robustness compared to those of baseline models trained on either raw audio or handcrafted features, in which at the input size of one second the network yielded an accuracy of 95.11% for testing data in normal traffic conditions and remained above a 93% accuracy for the considerable noisy condition of-10 dB SNR. The proposed THD system can be integrated into the smart railway crossing systems, private cars, and self-driving cars to improve railway transit safety. Full article

Elephants are highly intelligent animals with a huge capacity for social cognition, living in large, long-lived, related herds. In captivity, it is extremely difficult to meet all of the species’ ecological needs, as well as those required individual by individual, but improvements are continually being made. After identifying impaired welfare, one collection made the decision to relocate four female African Elephants (Loxodonta africana) to a different facility. As the worlds’ largest land mammal, many safety, welfare, and logistical considerations were undertaken. The elephants travelled in two pairs, a mother–daughter pair and an older unrelated female and a younger unrelated female with a strong social bond. As a result, there was a short gap in between transports, allowing for further habituation to transport crates and the heat of summer. The changes in both social and individual behaviours of the two females remaining when their group was temporarily reduced from four to two were investigated using one-zero sampling. The study determined the daily activities of the elephants comparing ‘before transport’ to ‘after transport’ for the two remaining elephants to establish any changes in their behaviours as a result of this disturbance. Post transport, there was an increase in both human-audible vocalisations and temporal gland secretions, and hugely decreased play behaviour was observed. The dynamic between the remaining pair was also altered with more tactile behaviours from mother to daughter seen but more submission from daughter to mother. This led to the conclusion that the elephants, although mostly unrelated and living in an ‘unnatural’ captive setting, had the same signs of stress and behavioural change as would a highly related group if separated. Full article

Wearable technologies are becoming a profitable means of monitoring a person’s health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker’s health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators’ security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators’ health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system. Full article

With the development and promotion of driverless technology, researchers are focusing on designing varied types of external interfaces to induce trust in road users towards this new technology. In this paper, we investigated the effectiveness of a multimodal external human–machine interface (eHMI) for driverless vehicles in virtual environment, focusing on a two-way road scenario. Three phases of identifying, decelerating, and parking were taken into account in the driverless vehicles to pedestrian interaction process. Twelve eHMIs are proposed, which consist of three visual features (smile, arrow and none), three audible features (human voice, warning sound and none) and two physical features (yielding and not yielding). We conducted a study to gain a more efficient and safer eHMI for driverless vehicles when they interact with pedestrians. Based on study outcomes, in the case of yielding, the interaction efficiency and pedestrian safety in multimodal eHMI design was satisfactory compared to the single-modal system. The visual modality in the eHMI of driverless vehicles has the greatest impact on pedestrian safety. In addition, the “arrow” was more intuitive to identify than the “smile” in terms of visual modality. Full article

The COVID-19 pandemic imposed various restrictions on the accessibility of conventional teaching laboratories. Enabling learning and experimenting at home became necessary to support the practical element of students’ learning. Unfortunately, it is not viable to provide or share a fully featured sensor lab to every student because of the prohibitive costs involved. Therefore, repurposing electronic devices that are common to students can bring about the sought-after practical learning experience without the hefty price tag. In distinction to the conventional lab instruments, however, consumer-grade devices are not designed for use with external sensors and/or electronic circuitry. They are not professionally maintained, do not undergo periodic safety tests, and are not calibrated. Nevertheless, nearly all modern computers, laptops, tablets or smartphones are equipped with high-quality audio inputs and outputs that can generate and record signals in the audible frequency range (20 Hz–20 kHz). Despite cutting off the direct currents completely, this range might be sufficient for working with a variety of sensors. In this presentation we look at the possibilities of making sure that such repurposing by design prevents any potential harm to the learner and to her or his personal equipment. These features seem essential for unsupervised lone experimenting and avoiding damage to expensive devices. Full article

In order to prevent electric shock accidents during the maintenance and repair of high-voltage direct current (DC) live equipment, an intelligent safety early warning system device based on non-contact electric field measurement is developed in this paper, to ensure the life safety of electrical workers and the stable operation of equipment. Firstly, the self-developed electric field sensor is used to measure the electric field strength of the space around the charged device. The STM32 microprocessor in the sensor will compare the measured electric field value with a preset safety threshold. If the safety threshold is exceeded, the early warning device issues an audible alarm. It prompts the electric power operator to exit to a safe distance. In addition, the alarm prompt symbol and the real-time waveform of the measured electric field can be seen intuitively on the mobile smart APP interface. Secondly, for devices with different voltage levels, the sensor electric field measurement range and sensitivity can be dynamically adjusted by changing the motor speed. In addition, according to the voltage level identification algorithm, this is also to achieve an adaptive safety early warning of different live equipment. Finally, field experiments were performed at a ±500 kV DC converter station and an ±800 kV DC transmission line experimental platform. The measured values of the electric field are basically consistent with the simulated values, and the safety early warning device has no false positives or false negatives during the test. The experimental results show that the design of the safety warning device in this paper meets the actual needs, and it can alert the electric workers to the specified safety distance. Full article