Search Results (60)

This article presents a comprehensive modeling and simulation framework for a capacitive MEMS accelerometer integrated with a sigma-delta analog-to-digital converter (ADC), with a focus on applications in wearable health and motion monitoring devices. The accelerometer used in the system is connected to a smartphone equipped with dedicated software and will be used to assess the risk of falling, which is crucial for patients with balance disorders. The authors designed the accelerometer with special attention paid to the specification required in a system, where the acceleration is ±2 g and the frequency is 100 Hz. They investigated the sensor’s behavior in the DC, AC, and time domains, capturing both the mechanical response of the proof mass and the resulting changes in output capacitance due to external acceleration. A key component of the simulation is the implementation of a second-order sigma-delta modulator designed to digitize the small capacitance variations generated by the sensor. The Simulink model includes the complete signal path from analog input to quantization, filtering, decimation, and digital-to-analog reconstruction. By combining MEMS+ modeling with MATLAB-based system-level simulations, the workflow offers a fast and flexible alternative to traditional finite element methods and facilitates early-stage design optimization for MEMS sensor systems intended for real-world deployment. Full article

In an active magnetic bearing (AMB) rotor system, the mass imbalance of the rotor is inevitable due to uneven materials, machining errors, assembly errors and other factors. When the rotor rotates, the unbalanced mass generates centrifugal force at the same frequency as the rotational speed, which causes vibration and affects the smooth operation of the rotor. Aiming at the mass imbalance of AMB rotor, a new method based on an adaptive notch filter (ANF) and the influence coefficient method (ICM) is proposed. Firstly, the improved ANF is used to track the rotor displacement signal, and the amplitude and phase information of the displacement signal are calculated. Then, according to the amplitude and phase information calculated by ANF, the ICM is used to calculate the counterweight information of the rotor dynamic balance, which includes the counterweight mass and counterweight position. Finally, the dynamic balance correction of the AMB rotor is realized by adding the calculated counterweight mass to both sides of the rotor. This paper validates the feasibility of the proposed method for the dynamic balance correction of the AMB rotor through simulation and experiment. The four radial displacement unbalances of the rotor were reduced by 56.6%, 62.8%, 49.2% and 63.7%, respectively. Full article

Background: Soccer is associated with substantial injury risk, with reported between 13 to 35 injuries per 1000 player-hours of competitive play. Notably, approximately 77% of soccer-related ankle injuries are attributed to ankle sprain injuries (ASIs). ASI can lead to chronic ankle instability, obesity, and post-traumatic osteoarthritis. This study focuses on identifying factors such as gender, age, body mass index (BMI), and a history of ASIs, which contribute to the development of ASI in soccer players. Methods: A systematic literature search was conducted in October 2023 across databases, including PubMed, Web of Science, Scopus, Cochrane Library, and ProQuest, without applying any filters. Keywords included ankle, ankle joint, sprain, risk factors, etc. Data extraction was performed on the included studies, with findings standardized and analyzed using Stata Statistical Software: Release 17 to determine a weighted treatment effect. Results: Our systematic review included 26 studies. The meta-analysis revealed that a history of ankle sprain is the most significant risk factor for future ASIs. BMI emerged as a risk factor in three out of seven studies, while age and height were significant in one out of six studies each. Gender and weight were not found to significantly affect ASI occurrence. Other factors identified but not subjected to a meta-analysis due to methodological heterogeneity or insufficient studies included playing surface, joint laxity, muscle weakness, match congestion, strength asymmetries, ground reaction forces, balance maintenance, skill level, and playing position. Conclusions: This research contributes valuable insights into the prevention of ASIs in soccer, highlighting the importance of previous ankle sprains and playing surface quality. These findings assist sports professionals in developing optimal conditions and strategies for effective ankle sprain prevention. Full article

The vehicle mass is a crucial state variable for achieving safe and energy-efficient driving, as it directly impacts the vehicle’s power performance, braking efficiency, and handling stability. However, current methods frequently rely on particular operating conditions or supplementary sensors, which limits their ability to provide accurate, stable, and convenient vehicle mass estimation. Moreover, as a form of public transportation, buses are subject to stringent safety standards. The frequent variations in passenger numbers result in substantial fluctuations in vehicle mass, thereby complicating the accuracy of mass estimation. To address these challenges, this paper proposes a hybrid vehicle mass estimation algorithm that integrates Robust Forgetting Factor Recursive Least Squares (Robust FFRLS) and Extended Kalman Filter (EKF). By sequentially employing these two methods, the algorithm conducts dual-stage mass estimation and incorporates a proportional coordination factor to balance the outputs from FFRLS and EKF, thereby improving the accuracy of the estimated mass. Importantly, the proposed method does not necessitate the installation of new sensors, relying instead on data from existing CAN-bus and IMU sensors, thus addressing cost control concerns for mass-produced vehicles. The algorithm was validated through MATLAB(2022b)-TruckSim(2019.0) simulations under three loading conditions: empty, half-load, and full-load. The results demonstrate that the proposed algorithm maintains an error rate below 10% across all conditions, outperforming single-method approaches and meeting the stringent requirements for vehicle mass estimation in safety and stability functions. Future work will focus on conducting real-world tests under various driving conditions to further validate the robustness and applicability of the proposed method. Full article

This paper deals with developing a comparative study of the principal time-domain system identification methods suitable for performing an experimental modal analysis of structural systems. To this end, this work focuses first on analyzing and reviewing the mathematical background concerning the analytical methods and the computational algorithms of interest for this study. The methods considered in the paper are referred to as the AutoRegressive eXogenous (ARX) method, the State-Space ESTimation (SSEST) method, the Numerical Algorithm for Subspace State-Space System Identification (N4SID), the Eigensystem Realization Algorithm (ERA) combined with the Observer/Kalman Filter Identification (OKID) method, and the Transfer Function ESTimation (TFEST) method. Starting from the identified models estimated through the methodologies reported in the paper, a set of second-order configuration-space dynamical models of the structural system of interest can also be determined by employing an estimation method for the Mass, Stiffness, and Damping (MSD) matrices. Furthermore, in practical applications, the correct estimation of the damping matrix is severely hampered by noise that corrupts the input and output measurements. To address this problem, in this paper, the identification of the damping matrix is improved by employing the Proportional Damping Coefficient (PDC) identification method, which is based on the use of the identified set of natural frequencies and damping ratios found for the case study analyzed in the paper. This work also revisits the critical aspects and pitfalls related to using the Model Order Reduction (MOR) approach combined with the Balanced Truncation Method (BTM) to reduce the dimensions of the identified state-space models. Finally, this work analyzes the performance of all the fundamental system identification methods mentioned before when applied to the experimental modal analysis of flexible structures. This is achieved by carrying out an experimental campaign based on the use of a vibrating test rig, which serves as a demonstrative example of a typical structural system. The complete set of experimental results found in this investigation is reported in the appendix of the paper. Full article

Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85–87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs. Full article

Volatile organic compounds (VOCs) pose significant risks to human health and environmental quality, prompting stringent regulations on their emissions from various industrial processes. Among VOCs, d-limonene stands out due to its low threshold and contribution to malodorous emissions. While biofiltration presents a promising approach for VOC removal, including d-limonene, a comprehensive understanding of its performance and kinetics is lacking. This study aims to comprehensively assess the performance of a lab-scale biotrickling filter in treating gas-phase d-limonene. The experimental results indicate that the biotrickling filter efficiently removed d-limonene, achieving a critical loading rate of 19.4 g m⁻³ h⁻¹ and a maximum elimination capacity of 31.8 g m⁻³ h⁻¹ (correspondingly, up to 85% removal) at the condition of 94.2 s of EBRT. Microbial activity played a significant role in biotrickling filter performance, with a strong linear correlation being observed between CO₂ production and substrate consumption. The Michaelis–Menten model was employed to represent enzyme-catalyzed reactions, suggesting no inhibition during biotrickling filter operation. Full article

Mass spectrometry (MS) is a highly selective and sensitive analytical tool with a myriad of applications, but such techniques are typically used in laboratory settings due to the handling and preparations that are necessary. The merging of two streams of robust research, portable MS systems and next-generation ambient ionization methods, now provides the ability to perform high-performance chemical screening in an on-site and on-demand manner, with natural applications in disciplines such as forensic science, where samples of interest are typically found in field environments (i.e., traffic stops, crime scenes, etc.). Correspondingly, investigations regarding the suitability and robustness of these methodologies when they are utilized for authentic forensic evidence processing are prudent. This work reports critical insights into the role that choice of spray solvent system plays regarding analytical performance of two spray-based ambient ionization sources, paper spray ionization (PSI) and filter cone spray ionization (FCSI), when employed for evidence types containing emerging synthetic cannabinoids. The systematic characterization studies reported herein show that the applied spray solvent can dramatically affect both spectral intensity and signal duration, and in some circumstances, yield deleterious false negative responses. Overall, acetonitrile-based systems are shown to strike a balance between analyte solubility concerns and spray ionization dynamics of the novel ion sources employed on portable MS systems. Full article

The Danjiangkou Reservoir is the water source of the middle route of China’s South-to-North Water Diversion Project, encompassing the Dan Reservoir and Han Reservoir. However, little is known about the ecological functions of this important ecosystem. Based on a survey conducted in 2023 in the Dan Reservoir, a mass balance model was constructed using Ecopath with Ecosim 6.6 software to characterize its food web structure and ecosystem properties. The model consisted of 18 functional groups, including producers, consumers, and detritus, covering the entire process of energy flow in the ecosystem. The outputs indicated that the fractional trophic level of functional groups in the Dan Reservoir ecosystem ranged from 1.00 to 3.50. The ecotrophic efficiencies of the main economic fish species were all less than 0.9, and the ecotrophic efficiencies of phytoplankton and detritus were less than 0.5. There were two main food chains: the detritus food chain (39%) and the grazing food chain (61%). The total energy transfer efficiency between trophic levels was only 6.02%, and there was a significant phenomenon of energy transfer blockage between trophic levels II and V. Analysis of the overall characteristics of the ecosystem revealed that the total primary production to total biomass (67.96619), connectance index (0.274), and Finn’s cycling index (2.856) of the Dan Reservoir ecosystem all indicate that the ecosystem is immature, with low nutrient recycling efficiency and poor resistance to external disturbances. This may be related to the low proportion of silver carp and bighead carp in the reservoir and the unreasonable structure of the fish community. Our results suggest that it is necessary to scientifically adjust the structure of the fish community, enhance the proportion of filter-feeding and omnivorous fish to improve the energy flow efficiency, and promote the maturity and stability of the Dan Reservoir. Full article

In recent decades, glaciers in the southeastern Tibetan Plateau (SETP) have been rapidly melting and showing a large scale of glacier mass loss. Due to the lack of large-scale, high-resolution, and high-precision observations, knowledge on the spatial distribution of the glacier mass balance and the response to climate change is limited in this region. We propose a TanDEM-X bi-static InSAR (Interferometric Synthetic Aperture Radar) algorithm with a non-local mean filter method and difference strategy, to improve the precision of glacier surface elevation change detection. Moreover, we improved the glacier mass balance estimation algorithm with a correction method for multi-source system errors and an uncertainty evaluation method based on error propagation theory to reduce the uncertainty of estimations. We used 13 pairs of TanDEM-X bi-static InSAR images to obtain the glacier mass balance data for the entire SETP. The total area of glaciers monitored was 5821 km² and the total number of glaciers monitored was 2321; the glacier surface elevation change rate was −0.505 ± 0.005 m/yr, and the glacier mass balance estimation was −454.5 ± 13.1 mm w.eq. during 2000–2014. Additionally, we analyzed the spatial distribution of the glacier mass balance within the SETP using the sub-watershed analysis method. The results showed that the mass loss rate had a decreasing trend from the southeast to the northwest. Furthermore, the temperature change and the glacier mass loss rate showed a positive correlation from the southeast to the northwest in this region. This study greatly advances our understanding of the regularities of glacier dynamics in this region, and can provide scientific support for major national goals such as the rational utilization of surrounding water resources and construction of important transportation projects. Full article

The intensive variables, geochemical, mineralogical, and petrogenetic constraints of the Iberian peraluminous rare metal granites (RMGs), many of them unknown, are presented. The mineral chemistry of ore and gangue minerals, whole rock analyses, geothermobarometry, melt water and phosphorus contents, mass balance, and Rayleigh modeling were performed to achieve these objectives. These procedures allow us to distinguish two main contrasting granitic types: Nb-Ta-rich and Nb-Ta-poor granites. The former have lower crystallization temperatures, higher water contents, and lower emplacement pressures than Nb-Ta-poor granites. Nb-Ta-rich granites also have higher fluoride contents, strong fractionation into geochemical twins, higher Na contents, and different evolutionary trends. At the deposit scale, the fractional crystallization of micas properly explains the variation in the Ta/Nb ratio in both Nb-Ta-poor and Nb-Ta-rich RMGs, although in higher-grade granites, the variation is not as clear due to the action of fluids. Fluid phase separation processes especially occurred in the Nb-Ta rich granites, thus transporting halogens and metals that increased the grades in the top and sometimes in the core of granites. Gas-driven filter pressing processes facilitated the migration of fluid and melt near solidus melt in Nb-Ta-rich granites. The geochemical signature of the Iberian rare metal granites mainly follows the trends of two-mica granites and P-rich cordierite granites, but also of granodiorites. Full article

The mechanical ventilation systems used in houses are designed to reduce carbon dioxide emissions while minimizing the energy loss resulting from ventilation. However, the increase in indoor fine particulate (PM_2.5) concentration because of external PM_2.5 influx through the ventilation system poses a problem. Here, we analyzed the changes in indoor PM_2.5 concentration, distinguishing between cases of high and low outdoor PM_2.5 concentrations and considering the efficiency of the filters used in residential mechanical ventilation systems. When using filters with the minimum efficiency reporting value (MERV) of 10 in the ventilation system, the outdoor PM_2.5 concentration was 5 μg/m³; compared to the initial concentration, the indoor PM_2.5 concentration after 60 min decreased to 73%. When the outdoor PM_2.5 concentration was 30–40 μg/m³, the indoor PM_2.5 concentration reached 91%. However, when MERV 13 filters were used, the indoor PM_2.5 concentration consistently dropped to 73–76%, regardless of the outdoor PM_2.5 concentration. Furthermore, by comparing the established equation with the mass balance model, the error was confirmed to be within 5%, indicating a good fit. This allows for the prediction of indoor PM_2.5 under various conditions when using mechanical ventilation systems, enabling the formulation of strategies for maintaining indoor PM_2.5, as recommended by the World Health Organization. Full article

Short message services (SMS), microblogging tools, instant message apps, and commercial websites produce numerous short text messages every day. These short text messages are usually guaranteed to reach mass audience with low cost. Spammers take advantage of short texts by sending bulk malicious or unwanted messages. Short texts are difficult to classify because of their shortness, sparsity, rapidness, and informal writing. The effectiveness of the hidden Markov model (HMM) for short text classification has been illustrated in our previous study. However, the HMM has limited capability to handle new words, which are mostly generated by informal writing. In this paper, a hybrid model is proposed to address the informal writing issue by weighting new words for fast short text filtering with high accuracy. The hybrid model consists of an artificial neural network (ANN) and an HMM, which are used for new word weighting and spam filtering, respectively. The weight of a new word is calculated based on the weights of its neighbor, along with the spam and ham (i.e., not spam) probabilities of short text message predicted by the ANN. Performance evaluations on benchmark datasets, including the SMS message data maintained by University of California, Irvine; the movie reviews, and the customer reviews are conducted. The hybrid model operates at a significantly higher speed than deep learning models. The experiment results show that the proposed hybrid model outperforms other prominent machine learning algorithms, achieving a good balance between filtering throughput and accuracy. Full article

The Tibetan Plateau (TP) has the largest number of high-altitude glaciers on Earth. As a source of major rivers in Asia, this region provides fresh water to more than one billion people. Any terrestrial water storage (TWS) changes there have major societal effects in large parts of the continent. Due to the recent acceleration in global warming, part of the water environment in TP has become drastically unbalanced, with an increased risk of water disasters. We quantified secular and monthly glacier-mass-balance and TWS changes in water basins from April 2002 to December 2021 through the Gravity Recovery and Climate Experiment and its Follow-on satellite mission (GRACE/GRACE-FO). Adequate data postprocessing with destriping filters and gap filling and two regularization methods implemented in the spectral and space domain were applied. The largest glacier-mass losses were found in the Nyainqentanglha Mountains and Eastern Himalayas, with rates of −4.92 ± 1.38 Gt a⁻¹ and −4.34 ± 1.48 Gt a⁻¹, respectively. The Tien Shan region showed strong losses in its eastern and central parts. Furthermore, we found small glacier-mass increases in the Karakoram and West Kunlun. Most of the glacier mass change can be explained by snowfall changes and, in some areas, by summer rainfall created by the Indian monsoon. Major water basins in the north and south of the TP exhibited partly significant negative TWS changes. In turn, the endorheic region and the Qaidam basin in the TP, as well as the near Three Rivers source region, showed distinctly positive TWS signals related to net precipitation increase. However, the Salween River source region and the Yarlung Zangbo River basin showed decreasing trends. We suggest that our new and improved TWS-change results can be used for the maintenance of water resources and the prevention of water disasters not only in the TP, but also in surrounding Asian countries. They may also help in global change studies. Full article

A three-step data assimilation (DA) of deep learning (DL) predictions to a process-based water budget is developed and applied to produce an active, operational water balance for groundwater management. In the first step, an existing water budget model provides forward model predictions of aquifer storage from meteorological observations, estimates of pumping and diversion discharge, and estimates of recharge. A Kalman filter DA approach is the second step and generates updated storage volumes by combining a long short-term memory (LSTM) network, a DL method, and predicted “measurements” with forward model predictions. The third “correction” step uses modified recharge and pumping, adjusted to account for the difference between Kalman update storage and forward model predicted storage, in forward model re-simulation to approximate updated storage volume. Use of modified inputs in the correction provides a mass-conservative water budget framework that leverages DL predictions. LSTM predictor “measurements” primarily represent missing observations due to missing or malfunctioning equipment. Pumping and recharge inputs are uncertain and unobserved in the study region and can be adjusted without contradicting measurements. Because DL requires clean and certain data for learning, a common-sense baseline facilitates interpretation of LSTM generalization skill and accounts for feature and outcome uncertainty when sufficient target data are available. DA, in contrast to DL, provides for explicit uncertainty analysis through an observation error model, which allows the integrated approach to address uncertainty impacts from an LSTM predictor developed from limited outcome observations. Full article