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Physical reconstructions are a valuable methodology for quantifying head kinematics in sports impacts. By recreating the motion of human heads observed in video using instrumented test dummies in a laboratory, physical reconstructions allow for in-depth study of real-world head impacts using well-established surrogates such as the Hybrid III crash test dummy. The purpose of this paper is to review all aspects of the physical reconstruction methodology and discuss the advantages and limitations associated with different choices in case selection, study design, test surrogate, test apparatus, text matrix, instrumentation, and data processing. Physical reconstructions require significant resources to perform and are therefore typically limited to small sample sizes and a case series or case-control study design. Their accuracy may also be limited by a lack of dummy biofidelity. The accuracy, repeatability, and sensitivity of the reconstruction process can be characterized and improved by good laboratory practices and iterative testing. Because wearable sensors have their own limitations and are not available or practical for many sports, physical reconstructions will continue to provide a useful and complementary approach to measuring head acceleration in sport for the foreseeable future., (© 2022. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Graph neural networks (GNNs) are powerful models capable of managing intricate connections in non-Euclidean data, such as social networks, physical systems, chemical structures, and communication networks. Despite their effectiveness, the large-scale and complex nature of graph data demand substantial computational resources and high performance during both training and inference stages, presenting significant challenges, particularly in the context of embedded systems. Recent studies on GNNs have investigated both software and hardware solutions to enhance computational efficiency. Earlier studies on deep neural networks (DNNs) have indicated that methods like reconfigurable hardware and quantization are beneficial in addressing these issues. Unlike DNN research, studies on efficient computational methods for GNNs are less developed and require more exploration. This survey reviews the latest developments in quantization and FPGA-based acceleration for GNNs, showcasing the capabilities of reconfigurable systems (often FPGAs) to offer customized solutions in environments marked by significant sparsity and the necessity for dynamic load management. It also emphasizes the role of quantization in reducing both computational and memory demands through the use of fixed-point arithmetic and streamlined vector formats. This paper concentrates on low-power, resource-limited devices over general hardware accelerators and reviews research applicable to embedded systems. Additionally, it provides a detailed discussion of potential research gaps, foundational knowledge, obstacles, and prospective future directions. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of the dynamic parameters of a compact class passenger car powered by LPG/DME blends. The presented results are part of the research cycle of this vehicle, the purpose of which was to check the possibility of using DME (dimethyl ether) as an additive in the fuel mixture with LPG. In the presented part of the experimental research, the acceleration times of the vehicle under specific conditions were measured. On the basis of the obtained results, the relations between the average acceleration in the tested speed ranges, the fuel composition, and the degree of engine load were developed. The results of the analysis indicate that in the examined range of changes in the DME share in the fuel, comparable or higher acceleration values were obtained for all engine load levels. This confirms the usefulness of DME as a fuel component used to power SI engines. [ABSTRACT FROM AUTHOR]

In the modern era, the demand for lightweight footbridges has grown significantly. However, this trend introduces new challenges related to the dynamic behaviour of such structures, especially under pedestrian-induced loading. This paper investigates the dynamic response of footbridge decks, focusing on the effects of vibration, pedestrian loading, and design considerations. Through a detailed analysis of a simply supported footbridge, we evaluate natural frequencies, critical ranges, and acceleration responses under varying pedestrian densities. The results emphasize the need for careful consideration of dynamic effects in the design phase to ensure pedestrian comfort and structural safety. [ABSTRACT FROM AUTHOR]

The primary objective of this study was to evaluate the performance of traffic conflict measures for real-time crash risk prediction. Drone recordings were collected from a freeway section in Nanjing, China, over a year. Twenty rear-end crashes and their associated trajectories were obtained. Vehicle trajectories preceding the crash were segmented based on different time periods to represent varying crash conditions. The Extreme Value Theory (EVT) approach combined with a block maxima sampling method was then employed to investigate the generalized extreme value (GEV) distributions of extremely risky events under non-crash and crash conditions. The prediction performance was demonstrated by the differences in GEV distributions under these two conditions. Within the proposed modeling framework, the performances of Time-to-Collision (TTC), Deceleration Rate to Avoid a Crash (DRAC), and Absolute value of Derivative of Instantaneous Acceleration (ADIA) were examined and compared. The results revealed a decreasing trend in the prediction performances as the preceding time window before a crash increased. For any given length of crash conditions, TTC consistently outperformed DRAC and ADIA. Notably, TTC's reliability in crash risk prediction became more uncertain when forecasting crashes more than 2 s in advance. This study provided the optimal thresholds for TTC and ADIA for practical application in crash early warning. The methods and results in this study have the potential to be used for crash risk assessments in autonomous vehicles., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

This paper investigates recruitment decisions of temporary work with several levels of experience, from beginners to experts (heterogeneous efficiency) to accelerate a manufacturing project with the objective of labour cost minimisation. A Mixed Integer Linear Program formulation of the problem is developed to find the optimal pool of temporary work of each experience level so as to minimise the cost subject to a desired deadline. The deadline is decreased decrementally in order to identify any potential patterns of recruitment along the reduction in the project duration. A variety of projects, efficiency levels with respect to years of experience and cost scenarios are considered in an experimental framework. Simulation results show a high sensitivity of the optimal pool of temporary work to the project type, efficiency levels and costs. A worst-case analysis suggests that using solely expert temporary workers might be the best recruiting policy to accelerate a project, whatever the desired deadline. [ABSTRACT FROM AUTHOR]

Fully homomorphic encryption (FHE) has experienced significant development and continuous breakthroughs in theory, enabling its widespread application in various fields, like outsourcing computation and secure multi-party computing, in order to preserve privacy. Nonetheless, the application of FHE is constrained by its substantial computing overhead and storage cost. Researchers have proposed practical acceleration solutions to address these issues. This paper aims to provide a comprehensive survey for systematically comparing and analyzing the strengths and weaknesses of FHE acceleration schemes, which is currently lacking in the literature. The relevant researches conducted between 2019 and 2022 are investigated. We first provide a comprehensive summary of the latest research findings on accelerating FHE, aiming to offer valuable insights for researchers interested in FHE acceleration. Secondly, we classify existing acceleration schemes from algorithmic and hardware perspectives. We also propose evaluation metrics and conduct a detailed comparison of various methods. Finally, our study presents the future research directions of FHE acceleration, and also offers both guidance and support for practical application and theoretical research in this field. [ABSTRACT FROM AUTHOR]

Introduction: Navigation satellite systems can fail to work or work incorrectly in a number of conditions: signal shadowing, electromagnetic interference, atmospheric conditions, and technical problems. All of these factors can significantly affect the localization accuracy of autonomous driving systems. This emphasizes the need for other localization technologies, such as Lidar. Methods: The use of the Kalman filter in combination with Lidar can be very effective in various applications due to the synergy of their capabilities. The Kalman filter can improve the accuracy of lidar measurements by taking into account the noise and inaccuracies present in the measurements. Results: In this paper, we propose a parallel Kalman algorithm in three- dimensional space to speed up the computational speed of Lidar localization. At the same time, the initial localization accuracy of the latter is preserved. A distinctive feature of the proposed approach is that the Kalman localization algorithm itself is parallelized, rather than the process of building a map for navigation. The proposed algorithm allows us to obtain the result 3.8 times faster without compromising the localization accuracy, which was 3% for both cases, making it effective for real-time decision-making. Discussion: The reliability of this result is confirmed by a preliminary theoretical estimate of the acceleration rate based on Ambdahl's law. Accelerating the Kalman filter with CUDA for Lidar localization can be of significant practical value, especially in real-time and in conditions where large amounts of data from Lidar sensors need to be processed. [ABSTRACT FROM AUTHOR]

Background: Although the Head Injury Criteria (HIC) has been widely applied to assess head impact injuries, it faces two outstanding problems: 1) HIC is affected strongly by the cut-off frequency when processing acceleration signals. And these cut-off frequencies are experiential and lack unified guidelines; 2) If the head was impacted on a different part, should the corresponding HIC threshold be the same? If these problems are not resolved, it could potentially lead to a critical misinterpretation of the safety assessment., Methods: Finite element method was used to reconstruct head impacts. The head model includes tissues like skull, brainstem, cerebrospinal fluid, etc. The head model was impacted in the frontal, occipital, parietal or lateral direction with different impact velocities. Acceleration signals of the head model were extracted directly from the skull and the head centroid node. To obtain a robust HIC, the filtering class of acceleration signals were analyzed carefully. Then, the relation between rigid body HIC and the centroid node HIC were studied systematically., Results: When the filtering class of rigid body acceleration and centroid node acceleration reached the cut-off frequency, the corresponding derivative of HIC tended to change smoothly. Using these cut-off frequencies, robust HICs were obtained. The rigid body HIC far exceeded that of centroid node HIC, such as 8, 9, 14 and 31 times exceeded in the frontal, occipital, parietal and lateral impact conditions, respectively. Moreover, approximate linear relations were found between the rigid body HIC and the centroid node HIC in different impact directions, respectively. From these relations, the injury thresholds of rigid body HIC of various directions were given quantitatively., Conclusions: The rational filtering class like CFC 800 and CFC 700 were given for rigid body HIC and centroid node HIC, respectively. The rigid body HIC had a significant discrepancy from the centroid node HIC. Linear relations between the rigid body HIC and centroid node HIC were found, and their slopes changed with impact directions. From these relations, we can adjust the injury thresholds reasonably if the head receives different impacts. These findings can effectively enhance the applicability of HIC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The acceleration and deceleration (AD) motions are the basic motion modes of robot astronauts moving in a space station. Controlling the locomotion of the robot astronaut is very challenging due to the strong nonlinearity of its complex multi-body dynamics in a gravity-free environment. However, after training, humans can move well in space stations by pushing the bulkhead, and the motion mechanism of humans is a good reference for robot astronauts. The contribution of this study is modeling the human AD motion in a microgravity environment and proposing a human-like control method for robot astronauts moving in space stations. Specifically, the movement and contact force data of the human body during AD motion were collected on an air-floating platform. Through human AD modeling analysis, the mechanism of human motion is discovered, and semi-sinusoidal primitives of contact forces are proposed for AD motion. Then, a dynamic guidance model of human AD motion is built to complete motion planning under contact constraints, which is used as the expected model for the AD control of robot astronauts. Benefiting from the force primitives, accurate and safe planning of human-like AD motion can be completed. The characteristics and mechanism of human AD motion have been analyzed from the perspective of optimization. Lastly, based on the proposed dynamic guidance model, the AD motion policy is mapped to the robot astronaut system via a system control method based on the equivalent mapping of dynamic responses (force, velocity and pose). Through comparative analysis with real human motion data and simulation results under different conditions, the proposed AD control method can achieve human-like motion efficiently and stably. Even when confronted with errors in the robot's contact velocities and inertia parameters, the method can significantly reduce the motion errors while ensuring stability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 ISA. Published by Elsevier Ltd. All rights reserved.)

Backpacks are common devices for carrying external posterior loads. However, relatively little is known about how these external loads affect the ability to recover from balance loss. In this exploratory investigation, 16 young adults (8 female, 8 male) performed forward and backward lean-and-release balance recovery trials, while wearing a backpack that was unloaded or loaded (at 15% of individual body weight). We quantified the effects of backpack loading on balance recovery in terms of maximum recoverable lean angles, center-of-mass kinematics, and temporal-spatial stepping characteristics. Mean values of maximum lean angles were 20° and 9° in response to forward and backward perturbations, respectively. These angles significantly decreased when wearing the additional load for only backward losses of balance. During backward losses of balance, the additional load decreased peak center-of-mass velocity and increased acceleration by ∼10 and 18% respectively, which was accompanied by ∼5% faster stepping responses and steps that were ∼9% longer, 11% higher, and had an ∼10% earlier onset. Thus, wearing a backpack decreases backward balance recovery ability and changes backward recovery stepping characteristics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Kumite is a karate sparring competition in which two players face off and perform offensive and defensive techniques. Depending on the players, there may be preliminary actions (hereinafter referred to as "pre-actions"), such as pulling the arms or legs, lowering the shoulders, etc., just before a technique is performed. Since the presence of a pre-action allows the opponent to know the timing of the technique, it is important to reduce pre-actions in order to improve the kumite. However, it is difficult for beginners and intermediate players to accurately identify their pre-actions and to improve them through practice. Therefore, this study aims to construct a practice support system that enables beginners and intermediate players to understand their pre-actions. In this paper, we focus on the forefist punch, one of kumite's punching techniques. We propose a method to estimate the presence or absence of a pre-action based on the similarity between the acceleration data of an arbitrary forefist punch and a previously prepared dataset consisting of acceleration data of the forefist punch without a pre-action. We found that the proposed method can estimate the presence or absence of a pre-action in an arbitrary forefist punch with an accuracy of 86%. We also developed KARATECH as a system to support the practice of reducing pre-actions using the proposed method. KARATECH shows the presence or absence of pre-actions through videos and graphs. The evaluation results confirmed that the group using KARATECH had a lower pre-action rate.

The pronounced increase in the number of young people struggling with mental health and wellbeing continues to puzzle researchers, politicians and practitioners. Discussions around this seem to fluctuate between individualizing understandings of the problem matter and more structural, institutional and cultural ones, calling for research exploring the role of the latter. In this paper, we present analyses of how broader societal frameworks linked to acceleration, performance and psychologization affect the wellbeing of young people. Based on a longitudinal qualitative study in Denmark with 37 young men and women between the ages of 16 and 24 struggling with wellbeing, this paper unfolds structural, cultural and institutional influences on mental health issues experienced by young people. The analyses are produced using a social-analytical lens of ‘diagnosis of the times’ to explore how young people’s subjective everyday life experiences of wellbeing and wellbeing struggles must be understood as interlinked with broader societal frameworks and expectations. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have been mainly powerful and important artificial intelligence techniques, which are exploited in various computer vision applications, such as facial point detection (FPD), owing their versatility and high performance. The role of DCNNs in this area is pivotal due to their ability to learn hierarchical features, which are essential for recognizing complex patterns. On the other hand, DCNNs have considerable computational complexity due to their topology nature. An FPGA-based SoC design is needed for CNN acceleration due to the rapid development, energy efficiency, low latency and high reconfigurability of FPGAs. Despite their performance benefits, FPGA-based SoCs also come with several limitations like complexity of design. This paper proposes a dynamic partial reconfiguration (DPR) and hybrid architecture for DCNN accelerators. We propose (i) a GPU-based software implementation for DCNN-based FPD, (ii) a CNN-based acceleration and optimization method using the high-level synthesis technique, (iii) a DPR-based hybrid architecture to improve the performance of the suggested approach. To validate our design, four scenarios are put forward. The experimental results prove that the proposed work achieves a better performance in terms of reconfigurability and execution time, hardware cost, and power consumption. [ABSTRACT FROM AUTHOR]

In this paper, the mechanical and durability properties of aerated blocks are discussed. Aerated blocks are prepared using different cementitious materials with an alkaline solution in the absence of aluminium powder. The proportions of raw materials are chosen after performing two mix trials P1 and P2, keeping the density of the blocks constant at 800 kg/m3. Each mix is further tested under six curing mechanisms, namely, normal water curing, humidity chamber curing (at 50°C and 70% relative humidity) for 6 h and 10 h, curing by oven drying at 180°C for 6 h and at 80°C for 24 h and using an accelerated curing tank (ACT) at 65°C ± 5°C for 10 ± 2 h; this is termed trial 1. In trial 2, the best curing mechanism is taken and construction and demolition wastes (CDW) are substituted at different proportions in place of fly ash in both the mix proportions. In addition, trial 3 investigates the best mix proportions by substituting glass powder at 50% and 100% with fly ash and CDW, respectively. It is observed from experiments that aerated blocks manufactured with 50% CDW and heat curing by ACT showed high mechanical and durability properties after 7 days of hardening. [ABSTRACT FROM AUTHOR]

The maintenance of fatigue damage is essential to keep steel bridges safe since the fatigue crack can lead to brittle fracture. However, real-time monitoring of fatigue crack propagation using traditional strain-based sensors is still a critical challenge due to their limited ductility and durability. In this paper, we propose a fatigue crack monitoring system by employing accelerometers due to their portability, easy replaceability, and sustainability. A corresponding blind source separation (BSS) joint convolutional neural network (CNN) classifier, namely B-CNN, is also proposed to identify fatigue crack conditions by interpreting acceleration measurements directly. We introduce the BSS as the first feature conversion layer to identify the pseudo modal properties from the highly sampled acceleration measurements before any further deep learning progress conducted by CNN. Those converted features are the sparse representation of the structural performance, where the crack conditions are hidden. By adopting BSS, the time history data can be sparse expressed, and the subsequent feature learning could be accelerated by employing a shallow CNN configuration. The proposed B-CNN classifier is further utilized for predicting crack conditions from forthcoming measurements. To examine the proposed monitoring solution, an experimental case study on a steel girder is conducted. About 1-month acceleration measurements were collected and investigated in this paper. The results obtained can demonstrate a shallow B-CNN configuration is sufficient to accurately identify cracks condition with high precision, over 93%, and proper prospection on unseen measurements. [ABSTRACT FROM AUTHOR]

In recent years, whole-body vibration (WBV) training has received an increasing interest in the sports and medical fields. However, there has been inconsistency among several studies regarding the effect of WBV training on the human body, which is partly due to the lack of the existence of guidelines for using WBV training machines. To understand the effect of WBV training on the human body and build the needed regulations, it is essential first to understand the biodynamic responses to vibration which represent how vibration is transmitted to and through the human body. The purpose of this study is to systematically review previous studies that measured biodynamic responses when using WBV training machines to highlight inconsistencies in their results and provide possible reasons for them. An extensive literature search was performed on the SCOPUS database to obtain relevant studies. One hundred and fifty-six potentially relevant studies were obtained but after further screening, 23 papers from 2007 to 2020 met inclusion criteria and were included in the study. The papers were analysed with respect to acceleration, transmissibility, interface force, and apparent mass during different vibration settings, body posture, age, and sex. Results and conflicts among studies were highlighted and possible explanations for the inconsistency were provided. [ABSTRACT FROM AUTHOR]

Convolution neural networks in deep learning can solve the problem of damage identification based on vibration acceleration. By combining multiple 1D DenseNet submodels, a new ensemble learning method is proposed to improve identification accuracy. 1D DenseNet is built using standard 1D CNN and DenseNet basic blocks, and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset sampling. When using submodels for damage identification, the voting method ideas in ensemble learning are used to vote on the results of each submodel, and then vote centrally. Finally, the cantilever damage problem simulated by ABAQUS is selected as a case study to discuss the excellent performance of the proposed method. The results show that the ensemble 1D DenseNet damage identification method outperforms any submodel in terms of accuracy. Furthermore, the submodel is visualized to demonstrate its operation mode. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Konbin is the property of Air Force Institute of Technology (Instytut Techniczny Wojsk Lotniczych) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deep learning MRI reconstruction methods are often based on Convolutional neural network (CNN) models; however, they are limited in capturing global correlations among image features due to the intrinsic locality of the convolution operation. Conversely, the recent vision transformer models (ViT) are capable of capturing global correlations by applying self-attention operations on image patches. Nevertheless, the existing transformer models for MRI reconstruction rarely leverage the physics of MRI. In this paper, we propose a novel physics-based transformer model titled, the Multi-branch Cascaded Swin Transformers (McSTRA) for robust MRI reconstruction. McSTRA combines several interconnected MRI physics-related concepts with the Swin transformers: it exploits global MRI features via the shifted window self-attention mechanism; it extracts MRI features belonging to different spectral components via a multi-branch setup; it iterates between intermediate de-aliasing and data consistency via a cascaded network with intermediate loss computations; furthermore, we propose a point spread function-guided positional embedding generation mechanism for the Swin transformers which exploit the spread of the aliasing artifacts for effective reconstruction. With the combination of all these components, McSTRA outperforms the state-of-the-art methods while demonstrating robustness in adversarial conditions such as higher accelerations, noisy data, different undersampling protocols, out-of-distribution data, and abnormalities in anatomy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

A lot has changed in the global machine of international development since its inception, but the language of technological acceleration remains ubiquitous today. In this paper, I trace one of the lineages of this new acceleration in the post-dotcom boom Silicon Valley. Informed by the technophilic culture of what Richard Barbrook and the late Andy Cameron described as Californian ideology, technological acceleration offers both a language and a model for antipoverty experiments hinging on the elusive market subject of the African entrepreneur. Drawing on the writings of three Silicon Valley evangelists who have produced a written culture of what I call poetics of acceleration, and on four years of ethnographic research in Cape Town, this paper charts the frictional interfaces between technocapitalism and African development, suggesting that these frictions, while vital in the production of new profit frontiers, are also the site of more ambivalent engagements with in-between futures that perhaps outstrip the predictable ends of these entrepreneurial market experiments. [ABSTRACT FROM AUTHOR]

This paper reviews the work done on science and technology of aircraft seat ejection with history, present, and futuristic interest. It is topic of interest that is receiving significant attention in the recent years due to importance of the pilot life saving during war, mishaps, and emergencies. In the early days, the ejection seats used the concept of compressed air and simple mechanical springs. The escape of the aircrew from high-speed aircraft is a serious drawback during World War (WW) II. The jumping out of aeronauts from high-speed aircraft is extremely difficult because of high blast wind pressure, turbulence around the exposed cockpit, and accelerations during the aircraft maneuvers. All these systems are dangerous and damage to the vertebra column of an aviator. As the speed of the aircraft is low, the pilot has to jump out with his parachute. However, as the speed of the aircraft increases, jumping out from the disabled aircraft with the parachute is merely a matter of luck and does not guarantee the aeronaut’s survival. Therefore, it is highly essential to use an ejection seat when damage is caused due to either the fighter or trainer plane. The ejection of the pilot is of utmost importance to the services. Hence, the lives of the pilots are crucial than saving military planes. Safeguarding the pilots from disabled aircraft plays a significant role in services such as Indian Air Force and Indian Navy since their introduction. Different policies and regulations are enforced to ensure the survival and safety of pilots. These are considered an essential area in the safety programs. Security and safety are always threat to the pilots. The novelty of this research paper is to bring about the historical developmental concepts to advanced concepts through science and technologyelated aircraft seat ejection. This paper is based on oration presented by the author on the occasion of National Science Day in Feb 2021. Therefore, an attempt is also made to highlight the important contributions made by various scientists, researchers in the frontier areas related to science and technology of ejection seat for military fighter aircraft. [ABSTRACT FROM AUTHOR]

A sleeper, or more generally a "bearer", moves vertically under a passing train load. The extent of this motion depends on the static and dynamic load of the train, the train speed, and the support conditions at the bearer and its neighbours. Excessive motion, typically from voiding see-sawing, low support stiffness or possibly excessive stiffness, or even too little stiffness, are all of interest to maintainers. Typically, problems arise around transition zones, switches and crossings, but plain track with poor support can also be a problem. Within the last decade, low-cost micro-electro-mechanical system (MEMS) accelerometers have been used to capture the time history of vertical motion for use in condition monitoring. Existing condition monitoring systems often overlook or sometimes even ignore the possibility of problematic data, which seem to be common in monitored locations. It is essential to understand whether such "bad" data require further attention. Three problematic sites are presented, focussing on examples where the acceleration was higher than expected or the computed displacement was not as expected. Potential causes include wheel defects, hammering of the ballast by a hanging bearer, or high acceleration at some structural resonant frequency. The present paper aims to show the challenges of using MEMS accelerometers to collect data for condition monitoring and offers insights into the sort of problematic data that may be collected from real sites. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the acceleration of a fast boat using a simulation model. Mathematical equations of ship motion dynamics with two types of propeller capabilities are developed using MATLAB and Simulink as simulation tools. The equations are extended to include the acting thrust, resistance, propeller's performance curves, and the PID governor curve for the acceleration manoeuvre. The application models the dynamic differential equations representing the vessel dynamics in one degree of freedom. MATLAB code was used to develop the ship acceleration as a multibody system. Modules of hydrodynamic forces, resistance, moments, and propeller performances were implemented to simulate the ship manoeuvring process. A comparison of the results for the boat's propulsion performance with two different propellers and the characteristics of the PID governor, which controls the fuel dose in the gas turbines, was carried out. We present a summary including a comparative analysis of the results for the boat dynamics with and without the PID governor. The results obtained here confirm significant discrepancies between the results of numerical simulations with and without the PID governor. [ABSTRACT FROM AUTHOR]

A dynamic analysis of the Makhool dam using Geo-studio software is presented in this paper for the purpose of investigating the seismic behavior of earth dams. With emphasis on the dam's height, soil characteristics, and input motion, this study examines the effect of earthquake excitation on the dam's behavior. With input from the SEEP/W program, the analysis was conducted using the QUAKE/W program. In this study, the horizontal component of motion was taken at three different values: 0.04g, 0.06g, and 0.08g. In addition to total stress in x-direction and y-direction, pore water pressure, and x-displacement, the results were presented in figures. Study findings showed that the horizontal displacement and shear strain of the dam increased with dam height, with the maximum displacement of the dam equal to 94 cm at the end of the earthquake. Besides the type of dam soil, the acceleration of the dam was also affected by the strength of the soil, since weaker soil reduced the acceleration of the dam. The pore-water pressure at the dam's base was the highest, with the horizontal movement increasing with depth. In earthquake zones, seismic evaluation is vital for dam construction, and the research provides valuable advice for designing and building earth dams. [ABSTRACT FROM AUTHOR]

In the field of structural health monitoring, vibration-based damage identification remains a formidable challenge. Key to this challenge is the establishment of a reliable association between observed vibration characteristics and the actual state of structural damage (e.g. stiffness reduction). This association not only accurately indicates the presence of damage, but also the location and severity of the damage. To solve this complex pattern identification problem, a large number of approaches, including deep learning, have emerged in recent years. In this paper, we propose a new structural damage identification method that utilizes the vibration information of the structure and a convolutional neural network based on Alex NET improvement. The method consists of calculating the acceleration response power spectral density of damaged and undamaged structures under impact loading separately, and then making a difference between the two power spectral data, and subsequently introducing these power spectral difference data into the convolutional neural network for training. The use of power spectral density analysis as a preprocessing step converts the time-domain signals into frequency-domain signals, and this conversion allows the convolutional neural network to capture and learn from the specific frequency characteristics of the data, thus facilitating the learning process of the neural network model. In this paper, the effectiveness of the method is critically evaluated through numerical simulation and experimental validation, and 3% and 5% noise are added to the numerical study to test the robustness of the method. During the convolution neural network training process, the optimal training mean squared error (MSE) is 5 × 10−6 in the case of no noise addition; the optimal training MSE is 1.3 × 10−5 in the case of noise addition. Both the results of simulations and experiments confirm the high accuracy and good robustness of the method in localizing structural damage. [ABSTRACT FROM AUTHOR]

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This paper proposes a new control scheme that is composed by the sliding mode control(SMC) with three-dimensional ceiling control to reduce the multi-dimensional vibration of the vehicle's seat. The seat suspension system is made of a kind of 3-RPS parallel mechanism, in which each limb is mainly composed by the spherical pair, spring, piston rod, damping fluid and rotary pair. Based on the single-open-chain theory, Position and Orientation Characteristics (POC) set of a 3-RPS seat mechanism was analyzed to verify its movement law. Three-dimensional seat system mathematical model was established, and the SMC and ceiling control were combined to obtain the required force. The ceiling control belongs to an ideal control method, it could provide the reference for the SMC. The comparisons between the SMC and passive seat suspension system were done when the vehicles were walking or working on E, F and G-roads. The results show that the transient amplitudes of seat acceleration outputs were reduced with the proposed control scheme. The research in this work can be extended and applied for other vehicles and improve their performance. [ABSTRACT FROM AUTHOR]

Motion in waves is a crucial issue for planing craft safety and human comfort. Practically, a boat at sea may navigate in any heading angle with respect to wave directions. In this paper, the planing craft motion in regular oblique waves in a range of heading angles is investigated using computational fluid dynamics (CFD). The simulation is performed on two prismatic hulls in which slamming occurs on one hull, and the other one is in non-slamming condition. The simulation results are validated in comparison with the experiment in the head wave condition. Findings show that as the wave direction changes from the head to the oblique, heave, pitch and acceleration rapidly decrease while roll increases. Additionally, the impact acceleration becomes a quasi-periodic, and its amplitude drops at the same time. It is observed that the aforementioned influences are much more pronounced for the craft in slamming condition. [ABSTRACT FROM AUTHOR]

Copyright of Perspectives on Culture / Perspektywy Kultury is the property of Jesuit University Ignatianum in Krakow and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which can prevent accidents. However, most cars on our roads do not have driver assessment systems. In this paper, we propose an approach for recognising driving styles and enabling drivers to reach safer and more efficient driving. The system consists of two physical sensors connected to a device node with a display and a speaker. An artificial neural network (ANN) is included in the node, which analyses the data from the sensors, and then recognises the driving style. When an abnormal driving pattern is detected, the speaker will play a warning message. The prototype was assembled and tested using an interurban road, in particular on a conventional road with three driving styles. The gathered data were used to train and validate the ANN. Results, in terms of accuracy, indicate that better accuracy is obtained when the velocity, position (latitude and longitude), time, and turning speed for the 3-axis are used, offering an average accuracy of 83%. If the classification is performed considering just two driving styles, normal and aggressive, then the accuracy reaches 92%. When the geo-information and time data are included, the main novelty of this paper, the classification accuracy is improved by 13%. [ABSTRACT FROM AUTHOR]

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In this paper, a novel railway track monitoring approach is proposed that employs acceleration responses measured on an in-service train to detect the loss of stiffness in the track sub-layers. An Artificial Neural Network (ANN) algorithm is developed that works with the energies of the train acceleration responses. A numerical model of a half-car train coupled with a track profile is employed to simulate the train vertical acceleration. The energy of acceleration signals measured from 100 traversing trains is used to train the ANN for healthy track conditions. The energy is calculated every 15 m along the track, each of which is called a slice. In the monitoring phase, the trained ANN is used to predict the energies of a set of train crossings. The predicted energies are compared with the simulated ones and represented as the prediction error. The damage is modeled by reducing the soil stiffness at the sub-ballast layer that represents hanging sleepers. A damage indicator (DI) based on the prediction error is proposed to visualize the differences in the predicted energies for different damage cases. In addition, a sensitivity analysis is performed where the impact of signal noise, slice sizes, and the presence of multiple damaged locations on the performance of the DI is assessed. [ABSTRACT FROM AUTHOR]

The structural collapse of a street lighting pole represents an aspect that is often underestimated and unpredictable, but of relevant importance for the safety of people and things. These events are complex to evaluate since several sources of damage are involved. In addition, traditional inspection methods are ineffective, do not correctly quantify the residual life of poles, and are inefficient, requiring enormous costs associated with the vastness of elements to be investigated. An advantageous alternative is to adopt a distributed type of Structural Health Monitoring (SHM) technique based on the Internet of Things (IoT). This paper proposes the design of a low-cost system, which is also easy to integrate in current infrastructures, for monitoring the structural behavior of street lighting poles in Smart Cities. At the same time, this device collects previous structural information and offers some secondary functionalities related to its application, such as meteorological information. Furthermore, this paper intends to lay the foundations for the development of a method that is able to avoid the collapse of the poles. Specifically, the implementation phase is described in the aspects concerning low-cost devices and sensors for data acquisition and transmission and the strategies of information technologies (ITs), such as Cloud/Edge approaches, for storing, processing and presenting the achieved measurements. Finally, an experimental evaluation of the metrological performance of the sensing features of this system is reported. The main results highlight that the employment of low-cost equipment and open-source software has a double implication. On one hand, they entail advantages such as limited costs and flexibility to accommodate the specific necessities of the interested user. On the other hand, the used sensors require an indispensable metrological evaluation of their performance due to encountered issues relating to calibration, reliability and uncertainty. [ABSTRACT FROM AUTHOR]

Wireless smart sensors (WSS) process field data and inform inspectors about the infrastructure health and safety. In bridge engineering, inspectors need reliable data about changes in displacements under loads to make correct decisions about repairs and replacements. Access to displacement information in the field and in real-time remains a challenge as inspectors do not see the data in real time. Displacement data from WSS in the field undergoes additional processing and is seen at a different location. If inspectors were able to see structural displacements in real-time at the locations of interest, they could conduct additional observations, creating a new, information-based, decision-making reality in the field. This paper develops a new, human-centered interface that provides inspectors with real-time access to actionable structural data during inspection and monitoring enhanced by augmented reality (AR). It summarizes and evaluates the development and validation of the new human-infrastructure interface in laboratory experiments. The experiments demonstrate that the interface that processes all calculations in the AR device accurately estimates dynamic displacements in comparison with the laser. Using this new AR interface tool, inspectors can observe and compare displacement data, share it across space and time, visualize displacements in time history, and understand structural deflection more accurately through a displacement time history visualization. [ABSTRACT FROM AUTHOR]