Search Results (4,375)

Sigmoid functions are widely used for the description of viscoelastic material properties of asphalt mixtures. Unfortunately, there are still no known closed functions for describing connections among model parameters in the time and the frequency domains. In most cases, complicated interconversion methods are applied for the conversion of viscoelastic material properties. To solve this problem, an empirical material model with four parameters has been developed. Parameters of the model can be quickly determined in the frequency domain and can be used in an unchanged way for the description of the material behaviour of the asphalt mixture in the time domain. The new model starts from the mathematical formula of the Ramberg–Osgood material model (short form RAMBO) and its main advantage is that its parameters are totally independent. Model calculations have been performed for the determination of factors necessary for the interconversion in the time and the frequency domains, applying the approximate procedure of Ninomiya and Ferry. The analysis of data has indicated that the interconversion factors in the time and the frequency domains depend only on the slope of the new empirical model function. Consequently, there is no need for further calculations, since the RAMBO model parameters determined in the frequency domain provide an excellent characterisation of the analysed mixture in the time domain as well. The developed new empirical material model has been verified using laboratory data and exact numerical calculations. Full article

Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available. This work reports on two compact sensor system designs, both reaching the FDA accuracy benchmark. Each design commonly comprises a mid-infrared QCL for emission, a multiple attenuation total reflection prism (MATR) for data acquisition, and a computer-controlled infrared detector for data analysis. The first design translates the comb-like signals into conventional spectra, and then data-mines the resultant spectra to yield blood glucose concentrations. When a pressure actuator is employed to press the patient’s hypothenar against the MATR, the sensor accuracy is considered to reach the FDA accuracy benchmark. The second design abandons the data processing step of translating combs-to-spectra and directly data-mines the “first-hand” comb signal. Beyond increasing the measurement accuracy to the FDA accuracy benchmark, even without a pressure actuator, direct comb data-mining upgrades the sensor system with speed and data integrity, which can impact the healthcare of diabetic patients. Specifically, the sensor performance is validated with 492 glucose absorption scans in the time domain, each with 20 million datapoints measured from four subjects with glucose concentrations of 3.9–7.9 mM. The sensor data-mines 164 sets of critical singularity strengths, each comprising 4 critical singularity strengths directly from the 9840 million raw signal datapoints, and the 656 critical singularity strengths are subjected to a machine-learning regression model analysis, which yields 164 glucose concentrations. These concentrations are correlated with those measured with a standard finger-pricking glucometer. An accuracy of 99.6% is confirmed from the 164 measurements with errors not more than 15% from the reference of the standard glucometer. Full article

Location data collected from mobile devices via global positioning system often lack semantic information and can form sparse trajectories in space and time. This study investigates whether user age groups can be accurately classified solely from such sparse spatial–temporal trajectories. We propose a feature extraction method based on a Gaussian mixture model (GMM), which assigns representative points (RPs) by clustering the location data and aggregating user trajectories into these RPs. We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. In our experiments, we introduced a missing value ratio ${\theta }_{\mathrm{th}}$ to quantify trajectory sparsity and analyzed the effect of trajectory sparsity on the classification accuracy and generalizability performance of the ML models. The results indicate that GMM-based features outperform IDNN-based features in both classification accuracy and generalization performance. Notably, the RF model achieved the highest accuracy, whereas the SVC model displayed stable generalizability. As the missing value ratio ${\theta }_{\mathrm{th}}$ increases, the IDNN becomes more susceptible to overfitting, whereas the GMM-based approach preserves accuracy and robustness. These findings suggest that sparse trajectories can still offer meaningful classification performance with appropriate feature design and model selection even without semantic information. This approach holds promise for domains where large-scale, sparse trajectory data are common, including urban planning, marketing analysis, and public policy. Full article

The dual-cylinder parallel balance oil circuit is an important heavy-duty support mechanism. Driven by the automation and unmanned trend of equipment in various industries, the internal leakage analysis and corresponding fault diagnosis for this mechanism are increasingly being valued. To solve this problem, verified by numerous simulation analyses and theoretical deduction, the pressure signal in the rodless chamber during the pressure maintenance stage was used innovatively to construct the fault features of the internal leakage, which is common and low-cost to be obtained. Then, the wavelet packet decomposition was used to extract three energy features and two time-domain features. Finally, an internal leakage diagnosis was performed based on the five features extracted from the experimental data, and the accuracy and robustness of the proposed five features were verified, which indicated that the proposed fault features and diagnosis method are practical in engineering. Full article

Complaint processing is of great importance for companies because it allows them to understand customer satisfaction levels, which is crucial for business success. It allows them to show the real perceptions of users and thus visualize the problems, which are regularly processed from oral or written natural language, derived from the provision of a service. In addition, the treatment of complaints is relevant because according to the laws of each country, companies have the obligation to respond to these complaints in a specified time. The specialized literature mentions that enterprises lost USD 75 billion due to poor customer service, highlighting that companies need to know and understand customer perceptions, especially emotions, and product reviews to gain insight and learn about customer feedback because of the importance of the voice of the customer for an organization. In general, it is evident that there is a need for research related to computational language processing to handle user requests. The authors show great interest in computational techniques for the processing of this information in natural language and how this could contribute to the improvement of processes within the productive sector. This work searches in indexed journals for information related to computational methods for processing relevant data from user complaints. It is proposed to apply a systematic literature review (SLR) method combining literature review guides by Kitchenham and the PRISMA statement. The systematic process allows the extraction of consistent information, and after applying it, 27 articles were obtained from which the analysis was conducted. The results show various proposals using linguistic, statistical, machine learning, and hybrid methods. We find that most authors combine Natural Language Processing (NLP) and Machine Learning (ML) to create hybrid methods. The methods extract relevant information from complaints of the customers in natural language in various domains, such as government, medical, banks, e-commerce, public services, agriculture, customer service, environmental, and tourism, among others. This work contributes as support for the creation of new systems that can give companies a significant competitive advantage due to their ability to reduce the response time of the complaints as established by law. Full article

The analysis of continuous events for any application involves the discretization of an event into sequences with potential historical dependencies. These sequences represent time stamps or samplings of a continuous process collectively forming a time series dataset utilized for training recurrent neural networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for pattern prediction. The challenge is to ensure that the estimates from the trained models are consistent in the same input domain for different discretizations of the same or similar continuous history-dependent events. In other words, if different time stamps are used during the prediction phase after training, the model is still expected to give consistent predictions based on the knowledge it has learned. To address this, we present a novel RNN transition formula intended to produce consistent estimates in a wide range of engineering applications. The approach was validated with synthetically generated datasets in 1D, 2D, and 3D spaces, intentionally designed to exhibit high non-linearity and complexity. Furthermore, we have verified our results with real-world datasets to ensure practical applicability and robustness. These assessments show the ability of the proposed method, which involves restructuring the mathematical structure and extending conventional RNN architectures, to provide reliable and consistent estimates for complex time series data. Full article

Rotating machines are vital for ensuring reliability, safety, and operational availability across various industrial sectors. Among the faults that can affect these machines, shaft misalignment is particularly critical due to its impact on other components connected to the shaft, making it a key focus for diagnostic systems. Misalignment can lead to significant energy losses, and therefore, early detection is crucial. Vibration analysis is an effective method for identifying misalignment at an early stage, enabling corrective actions before it negatively impacts equipment efficiency and energy consumption. To improve monitoring efficiency, it is essential that the diagnostic system is not only intelligent but also capable of operating in real-time. This study proposes a methodology for diagnosing shaft misalignment faults by combining wavelet transform for feature extraction and transfer learning for fault classification. The accuracy of the proposed soft real-time solution is validated through a comparison with other time-frequency transformation techniques and transfer learning networks. The methodology also includes an experimental procedure for simulating misalignment faults using a laser measurement tool. Additionally, the study evaluates the thermal impacts and vibration signature of each type of misalignment fault through multi-sensor monitoring, highlighting the effectiveness and robustness of the approach. First, wavelet transform is used to obtain a good representation of the signal in the time-frequency domain. This step allows for the extraction of key features from multi-sensor vibration signals. Then, the transfer learning network processes these features through its different layers to identify the faults and their severity. This combination provides an intelligent decision-support tool for diagnosing misalignment faults, enabling early detection and real-time monitoring. The proposed methodology is tested on two datasets: the first is a public dataset, while the second was created in the laboratory to simulate shaft misalignment using a laser alignment tool and to demonstrate the effect of this defect on other components through thermal imaging. The evaluation is carried out using various criteria to demonstrate the effectiveness of the methodology. The results highlight the potential of implementing the proposed soft real-time solution for diagnosing shaft misalignment faults. Full article

A nuclear electromagnetic pulse (NEMP) is the fourth effect of a nuclear explosion, characterized by a strong electromagnetic field that can instantly damage electronic devices. To investigate the spatial field value distribution characteristics of the source region of low-altitude NEMPs, this study employed a finite-difference time-domain (FDTD) method based on a rotating ellipsoidal hyperbolic coordinate system. Due to intense field variations near the explosion center, non-uniform grids were employed for both spatial and temporal steps, and an OpenMP parallel algorithm was utilized to enhance computational efficiency. Analysis focused on the following two scenarios: varying angles at a constant distance and varying distances at a constant angle, considering both transverse magnetic (TM) and transverse electric (TE) waves. The results indicate that the spatial field value distribution characteristics differ between the two wave types. For TM waves, the electric and magnetic fields share the same polarity, but their waveform polarities are opposite above and below the explosion center. A TE wave is exactly the opposite. Compared with a TM wave, a TE wave has stronger peak electromagnetic fields but narrower pulse widths and lower overall energy. This research provides significant support for the development of nuclear explosion detection technology and offers theoretical foundations for the protection of surrounding environmental facilities. Full article

In the domain of fire-resistant steels, the characteristics of precipitates significantly influence material properties. This study developed a novel heat treatment protocol to concurrently achieve both interphase precipitation and random precipitation. Samples were subjected to isothermal treatments at various temperatures and durations, while techniques such as scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were employed to thoroughly analyze the coarsening behavior of the two types of precipitate and reveal their thermal stability differences. The results show that the growth and coarsening rates of interphase precipitates are substantially lower than random precipitates. Coarsening kinetics analysis reveals that the radius of random precipitates follows a 1/3 power law with time at 600 °C and 650 °C, whereas the radius of interphase precipitates adheres to a 1/6 power law at 600 °C and a 1/5 power law at 650 °C. Furthermore, interphase precipitation demonstrates excellent size uniformity, which hinders the formation of a concentration gradient, thereby reducing the coarsening rate and enhancing thermal stability. After prolonged tempering treatment, interphase precipitation maintains a higher strengthening contribution than random precipitation. This study provides novel insights and theoretical foundations for the design and development of fire-resistant steels. Full article

In recent decades, technology has undergone significant transformations, aimed at optimizing and enhancing the quality of human life. A prime example of this progress is the Internet of Things (IoT) technology. Today, the IoT is widely applied across diverse sectors, including logistics, communications, agriculture, education, and infrastructure, demonstrating its versatility and profound relevance in various domains. Agriculture has historically been a fundamental sector for meeting humanity’s basic needs, and it is indispensable for survival and development. A critical factor in this regard is climatic and meteorological conditions directly influencing agricultural productivity. Therefore, real-time monitoring and analysis of these variables becomes imperative for optimizing production and reducing vulnerability to climate change. This paper presents the development and implementation of a low-power wide-area network (LPWAN) solution using LoRaWAN (long-range wide-area network) technology, designed for real-time environmental monitoring in agricultural applications. The system consists of energy-efficient end nodes and a custom-configured gateway, designed to optimize data transmission and power consumption. The end nodes integrate advanced sensors for temperature, humidity, and pressure, ensuring accurate data collection. Full article

This study delves into rotor–stator interaction within a bidirectional bulb tubular pump under cavitation conditions. Using pressure pulsation tests on a model pump and numerical simulations performed with ANSYS CFX software, we analyzed pressure pulsation and flow field data across three distinct flow rates and multiple cavitation numbers. Both time-domain and frequency-domain analyses were conducted to examine the patterns of pressure pulsation influenced by flow rates and cavitation numbers at various monitoring locations. A numerical flow field analysis further validated the findings. The results reveal that rotor–stator interaction manifests in the vaneless spaces of the pump during cavitation. The onset of cavitation alters the amplitudes of dominant frequencies at different flow rates. Near the guide vane and impeller, the dominant frequencies shift toward the impeller frequency and guide vane frequency, respectively. Under low-flow conditions, the rotor–stator interaction effect is more conspicuous due to the deteriorated flow pattern. Pressure pulsations are more strongly influenced in the front vaneless space (FVP) than in the rear vaneless space (RVP). This difference arises because the front guide vane destabilizes rather than stabilizes the flow pattern, worsening the rotor–stator interaction. Additionally, the FVP is less affected by the impeller than the RVP, further amplifying the influence of rotor–stator interaction on pressure pulsation. These findings provide a theoretical foundation for mitigating the effects of rotor–stator interaction on the operational stability and efficiency of bidirectional bulb tubular pumps. Full article

Quantum Random Number Generators (QRNGs) have been theoretically proven to be able to generate completely unpredictable random sequences, and have important applications in many fields. However, the practical implementation of QRNG is always susceptible to the unwanted classical noise or device imperfections, which inevitably diminishes the quality of the generated random bits. It is necessary to perform the post-processing to extract the true quantum randomness contained in raw data generated by the entropy source of QRNG. In this work, a novel post-processing method for QRNG based on Zero-phase Component Analysis (ZCA) whitening is proposed and experimentally verified through both time and spectral domain analysis, which can effectively reduce auto-correlations and flatten the spectrum of the raw data, and enhance the random number generation rate of QRNG. Furthermore, the randomness extraction is performed after ZCA whitening, after which the final random bits can pass the NIST test. Full article

Due to increasing decentralized power applications, power electronics are gaining importance, also in distribution grids. Since their scope of investigation is diverse, their versatile models and their use in grid calculations are important. In this work, a three-phase grid-synchronous inverter with an LCL filter is considered. It is defined as a component of the “Extended Node Method” to make it applicable in this node-based transient grid calculation method. Because the component stucture always looks the same and the construction of the grid system of equations always follows the same, straightforward process, the model can be applied easily and several times to large network calculations. Furthermore, an approach is developed for how inverter control algorithms are interconnected with the method’s results in the time domain. This allows for the fast analysis of converter control schemes in different grid topologies. To evaluate its accuracy, the developed approach is compared to equivalent calculations with Simulink and shows very good agreement, also for steep transients. In the long term, this model is intended to bridge the gap to other DC systems like electrochemical components and to gas and heating networks with the Extended Node Method. Full article

Introduction: Artificial intelligence (AI) is transforming healthcare by enhancing diagnostic accuracy, treatment, and patient monitoring, benefiting older adults by offering personalized care plans. AI-powered tools help manage chronic conditions and maintain independence, making them a valuable asset in addressing aging challenges. Objectives: The objectives are as follows: 1. To identify and describe AI-power-based exercise programs for older adults. 2. To highlight primary evidence gaps in AI interventions for functional improvement and mobility. 3. To evaluate the quality of existing reviews on this topic. Methods: The evidence gap map (EGM) will follow the five-step method, adhering to the Campbell Collaboration guidelines and, if available at the time of reporting, PRISMA-AI standards. Guided by the Metaverse Equitable Rehabilitation Therapy framework, this study will categorize findings across domains like equity, health service integration, interoperability, governance, and humanization. The study will include systematic reviews, randomized controlled trials, and pre-and post-intervention designs. Results will be reported following PRISMA-AI guidelines. We will use AMSTAR-2 Checklist for Analyzing Systematic Reviews on AI Interventions for Improving mobility and function in Older Adults to evaluate the reliability of systematic reviews and focus on internal validity. Conclusions: This comprehensive analysis will act as a critical resource for guiding future research, refining clinical interventions, and influencing policy decisions to enhance AI-driven solutions for aging populations. The EGM aims to bridge existing evidence gaps, fostering a more informed, equitable, and effective approach to AI solutions for older adults. Full article

The simple spring structure, with detachable electrical contacts, is a very suitable solution for many applications, such as electromechanical relays and connectors. However, they are prone to exhibit instantaneous interruption faults under mechanical vibration environments. In this paper, the governing equations of the modal analysis of a beam with cantilever-Hertzian contact boundary conditions are presented. Then, the time domain analysis method and frequency domain analysis method for solving the forced vibration response are described explicitly. Next, the effect of the axial force on the modal frequency of a detailed model sourced from the practical relay is investigated by using commercial ANSYS Workbench 2021R1 software. Afterward, the harmonic response of the beam is numerically solved individually by using the transient analysis model and the harmonic analysis model in ANSYS Workbench 2021R1 software. Then, the influences of the damping coefficient and excited frequency on the contact force response are investigated. The experimental results of transient displacement and contact resistance of the beam structure agree well with the simulation outcomes. It is proven that there is a linear relationship between the stiffness coefficient and the mass coefficient, which are used for characterizing the damping of the structures in the time domain method and frequency domain methods. Full article