Wide Measurement Range Research Articles

Recognizing Bearings’ Degradation Stage Using Multimodal Autoencoder to Learn Features from Different Time Series

Utilizing machine learning technologies to monitor assets’ health conditions can improve the effectiveness of maintenance activities. However, accurately recognizing the current health degradation stages of industrial assets requires a time-consuming manual feature extraction due to the wide range of observable measures (e.g., temperature, vibration) and behaviors characterizing assets’ degradation. To address this issue, feature learning technology can transform minimally processed time series into informative features, i.e., able to simplify the classification task (e.g., recognizing degradation stages) regardless of the specific machine learning classifier employed. In this work, minimally preprocessed time series of vibration and temperature of industrial bearings are exploited by an autoencoder-based architecture to extract degradation-representative features to be used for recognizing their degradation stages. Different autoencoder architectures are employed to compare their data fusion strategies. The effectiveness of the proposed approach is evaluated in terms of recognition performance and the quality of the learned features by using a publicly available real-world dataset and comparing the proposed approach against a state-of-the-art feature learning technology. We tested three different multimodal autoencoder-based feature learning approaches, i.e., shared-input autoencoder (SAE), multimodal autoencoder (MMAE), and partition-based autoencoder (PAE). All the AE-based architecture results in classification performances greater or comparable with the state-of-the-art feature learning technology, despite being trained in an unsupervised fashion. Also, the features provided via PAE correspond to the greatest performances in recognizing bearings’ degradation stage, providing high-quality features both from a classification and clustering perspective. Unsupervised feature learning methodologies based on multimodal autoencoders are capable of learning high-quality features. These result in greater degradation stages recognition performances when compared to supervised state-of-the-art feature learning technology. Also, this enables the correct representation of the expected progressive degradation of the bearing.

Improving migration forecasting for transitory foreign tourists using an Ensemble DNN-LSTM model

The tourism industry is a key component in many nations’ economies, and precise Forecasting of the number of arrivals of transient international visitors is necessary for efficient resource allocation and strategic planning. Traditional approaches for predicting time series need to catch up when capturing the intricate patterns and interdependencies in data about visitor movement. To overcome this obstacle, we have developed a model that combines Ensemble Deep Neural Networks (EDNN) with Long Short-Term Memories (LSTM) to enhance migration predictions. EDNNs learn complex patterns from historical data, whereas LSTMs model sequential relationships. We start with data from the study-based Migration Forecasting for Transitory Foreign Tourists. The data was preprocessed using Adaptive mean filter (AMF) methods. We used Linear Discriminant Analysis (LDA) to look at different configurations for extracting features. The EDNN-LSTM method had better results than its comparison in terms of a wide range of performance measures having an accuracy of 97%, an MAE of 40.75%, an MSE of 33.10%, and an RMSE of 30.10%. According to the results, the EDNN-LSTM model can represent the intricate dynamics of transitory foreign visitor movement. Consequently, this model is a useful resource for tourism stakeholders, policymakers, and enterprises.

Research on Tax Policies to Promote Agricultural Modernization Development

Agriculture constitutes a fundamental sector of national economic development, and the steady growth of agriculture-related industries has contributed significantly to job creation and increased value output in the Chinese economy. The central government has consistently prioritized the healthy and sustainable development of agriculture, implementing various policies to support agricultural enterprises in enhancing their market competitiveness. This includes a wide range of tax support measures. On the other hand, the effectiveness of policy incentives in encouraging enterprises to improve their operations is unclear, given that most agricultural enterprises in the country face challenges such as small size, outdated information, weak management capabilities and poor resistance to market risks and uncertainties. This paper aims to examine the role of tax policy in the industrialised management of the agricultural sector. This paper seeks to provide new insights into governance in this area by exploring the moderating role of tax policies on the relationship between Chinese agribusinesses and their management. The findings will provide policy makers with responsible recommendations for enhancing the effectiveness of tax policies and other agriculture-related policies.

Lessons for future clinical trials in adults with Becker muscular dystrophy: Disease progression detected by muscle magnetic resonance imaging, clinical and patient-reported outcome measures.

Because Becker muscular dystrophy (BMD) is a heterogeneous disease and only few studies have evaluated adult patients, it is currently still unclear which outcome measures should be used in future clinical trials. Muscle magnetic resonance imaging, patient-reported outcome measures and a wide range of clinical outcome measures, including motor function, muscle strength and timed-function tests, were evaluated in 21 adults with BMD at baseline and at 9 and 18 months of follow-up. Proton density fat fraction increased significantly in 10/17 thigh muscles after 9 months, and in all thigh and lower leg muscles after 18 months. The 32-item Motor Function Measurement (MFM-32) scale (-1.3%, p = 0.017), North Star Ambulatory Assessment (-1.3 points, p = 0.010) and patient-reported activity limitations scale (-0.3 logits, p = 0.018) deteriorated significantly after 9 months. The 6-min walk distance (-28.7 m, p = 0.042), 10-m walking test (-0.1 m/s, p = 0.032), time to climb four stairs test (-0.03 m/s, p = 0.028) and Biodex peak torque measurements of quadriceps (-4.6 N m, p = 0.014) and hamstrings (-5.0 N m, p = 0.019) additionally deteriorated significantly after 18 months. At this timepoint, domain 1 of the MFM-32 was the only clinical outcome measure with a large sensitivity to change (standardized response mean 1.15). It is concluded that proton density fat fraction imaging of entire thigh muscles is a sensitive outcome measure to track progressive muscle fat replacement in patients with BMD, already after 9 months of follow-up. Finally, significant changes are reported in a wide range of clinical and patient-reported outcome measures, of which the MFM-32 appeared to be the most sensitive to change in adults with BMD.

Highly Sensitive Iontronic Pressure Sensor with Side-by-Side Package Based on Alveoli and Arch Structure.

Flexible pressure sensors play a significant role in wearable devices and electronic skin. Iontronic pressure sensors with high sensitivity, wide measurement range, and high resolution can meet requirements. Based on the significant deformation characteristics of alveoli to improve compressibility, and the ability of the arch to disperse vertical pressure into horizontal thrust to increase contact area, a graded hollow ball arch (GHBA) microstructure is proposed, greatly improving sensitivity. The fabrication of GHBA ingeniously employs a double-sided structure. One side uses mold casting to create convex structures, while the other utilizes the evaporation of moisture during the curing process to form concave structures. At the same time, a novel side-by-side package structure is proposed, ensuring pressure on flexible substrate is maximally transferred to the GHBA microstructure. Within the range of 0.2Pa-300kPa, the iontronic pressure sensor achieves a maximum sensitivity of 10420.8kPa-1, pressure resolution of 0.1% under the pressure of 100kPa, and rapid response/recovery time of 40/35ms. In wearable devices, it is capable of monitoring dumbbell curl exercises and wirelessly correcting sitting positions. In electronic skin, it can non-contactly detect the location of the wind source and achieve object classification prediction when combined with the CNN model.

Compassionate care during the COVID-19 pandemic

BackgroundThere was a substantial documented call for healthcare professionals to provide compassionate care during the COVID-19 pandemic and significant criticism voiced when it was lacking. This study aimed to explore perspectives on compassionate care among healthcare professionals providing care during the COVID-19 pandemic. The study focuses on healthcare professionals who participated in a wide range of COVID-19 measures, including testing, quarantine, diagnosis, and care provision (patients with COVID-19 or patients with other illnesses and comorbid with COVID-19).MethodsA qualitative design with an interpretative phenomenological analysis approach was used. Twenty frontline healthcare professionals (15 nurses and five physicians) who had worked in COVID-19 facilities in China were interviewed individually.ResultsParticipants stated that a commitment to ‘offering oneself’ and ‘balancing the advantages/disadvantages’ in providing care during the pandemic were key to alleviate population-level suffering. On a personal level, they described a desire for obtaining ‘mutual support’ and improving ‘professional competencies’ to safeguard their physical and mental well-being. Two professional competencies were notable: coping with grief and implementing infection control across the organization. Additionally, they emphasized the importance of receiving support from the health care organization, the public, and leaders in creating an ‘environment conducive to fostering compassionate care.’ConclusionHealthcare professionals recognized the centrality of compassionate care during the pandemic which entailed a commitment to offering themselves, the balancing of advantages and disadvantages in order to find the best solution, as well as the need to safeguard themselves using professional competencies. Such findings can enrich the contemporary understanding of compassion, including when it is lacking. Support from the healthcare organization, the public, and leadership were crucial in fostering compassionate care in healthcare professionals during the pandemic and in moving the field forward in the future.

Japan’s Border Measures during Covid-19: How the Crisis Shaped ‘Others’ to Protect the ‘Stability of Self’

The Covid-19 pandemic has prompted countries to implement a wide range of specific cross-border security measures. The fear and anxiety induced by this crisis have rapidly expanded and contracted countries’ understanding of ‘self’ and ‘other’. This study examines Japan’s shift from liberal pre- Covid-19 border policies to the most stringent border closure measures among the G7 countries during the pandemic. In this study, we argue that the pandemic-induced fear increased anxiety in Japan’s public health safety identity, rapidly reshaping the conceptualization of the ‘self’ and ‘other’ in crisis scenarios. This shift aligns with Japan’s historical narrative of combating unprecedented threats to public health.

FedKNN: Secure Federated k-Nearest Neighbor Search

Nearest neighbor search is a fundamental task in various domains, such as federated learning, data mining, information retrieval, and biomedicine. With the increasing need to utilize data from different organizations while respecting privacy regulations, private data federation has emerged as a promising solution. However, it is costly to directly apply existing approaches to federated k-nearest neighbor (kNN) search with difficult-to-compute distance functions, like graph or sequence similarity. To address this challenge, we propose FedKNN, a system that supports secure federated kNN search queries with a wide range of similarity measurements. Our system is equipped with a new Distribution-Aware kNN (DANN) algorithm to minimize unnecessary local computations while protecting data privacy. We further develop DANN*, a secure version of DANN that satisfies differential obliviousness. Extensive evaluations show that FedKNN outperforms state-of-the-art solutions, achieving up to 4.8× improvement on federated graph kNN search and up to 2.7× improvement on federated sequence kNN search. Additionally, our approach offers a trade-off between privacy and efficiency, providing strong privacy guarantees with minimal overhead.

A Compensator Microelectromechanical Acceleration Transducer with a Piezoelectric Sensing Element and Optical Reading

A Compensator Microelectromechanical Acceleration Transducer with a Piezoelectric Sensing Element and Optical Reading

Real-time temperature monitoring and alarm system for biological samples storage based on FPGA

In the field of biosensing, high-quality biological samples are a prerequisite for precise analysis and detection. Strictly controlled storage temperature is crucial to ensure the stability of biological samples and achieve effective detection. Different biological samples require different temperature ranges for storage. Therefore, it is essential to design a temperature monitoring and alarm system capable of alarming based on different needs. This work presents a real-time temperature monitoring and alarm system based on the DS18B20 temperature sensor and FPGA chip. The system with simple structures has a wide temperature measurement range, an adjustable temperature alarm range, and a timely alarm function for various scenarios monitoring. This work demonstrates the temperature monitoring and alarm system for simple, accurate, and low-cost modes of temperature monitoring. The real-time temperature monitoring and alarm system will provide an effective means to strictly control the storage temperature of different biological samples, ensuring the stability and effectiveness of biological samples and the accuracy and sensitivity of biosensing and analysis.

A novel capacitive sensor featuring surface microstructure and hydrogels for measuring full pressure with high-sensitivity

Compared with positive pressure measurement, which is widely used in many fields, negative pressure measurement is often ignored. However, realizing full pressure measurement, that is, positive and negative pressure measurement, with high sensitivity and wide measurement range, is one of the most urgent development directions and key challenges faced by flexible pressure sensors. To overcome this challenging problem, a highly sensitive full-pressure capacitive sensor with surface microstructure and hydrogels is introduced in this paper. The sensing mechanism of the sensor is implemented by detecting the capacitance change caused by the change in the contact area between the hydrogel and the electrode due to the internal and external pressure difference. The PVA/KOH basic gel material is used to resist shear deformation, and the uniform microstructure on the gel surface is achieved by the template method. How to maximize the sensitivity of positive and negative pressure measurements is discussed by carefully evaluating the design parameters of the sensor. The fabricated sensor has a wide measurement range (-100–250 kPa) with high sensitivity (S = 15.6 nF/kPa) in the negative pressure region and (S = 5.2 nF/kPa) in the positive pressure region. In addition, the sensor exhibits fast response, cycle stability, and low measurement limit, which further enhances its performance advantages. In order to validate the application potential of the sensor, the paper conducts experimental verification using the relationship between wind pressure measurement results and the angle of contact with the sensor, as well as the relationship between vehicle speed and wind pressure magnitude. The results show that the designed sensor has potential application prospects in full pressure measurement.

Bifactor models of psychopathology using multi-informant and multi-instrument dimensional measures in the ABCD study.

Due to limitations of categorical definitions of mental illness, there is a need for quantitative empirical investigations of the dimensional structure of psychopathology. Using exploratory bifactor methods, this study investigated a comprehensive and representative structure of psychopathology in children to better understand how psychotic-like experiences (PLEs), autism spectrum disorder (ASD) symptoms, impulsivity, and sensitivity to reward and punishment, may be integrated into extant general factor models of psychopathology. We used seven child-report and three parent-report instruments capturing diverse mental health symptoms in 11,185 children aged 9-10 from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study. We built on previous modeling frameworks by conducting both split sample and full sample factor analytic approaches that harnessed recent methodological advances in bifactor exploratory structural equation modeling (B-ESEM) to examine a wide range of psychopathology measures not previously integrated into a single analysis. Validity of psychopathology dimensions was examined by investigating associations with sex, age, cognition, imaging measures, and medical service usage. All four factor analytic models showed excellent fit and similar structure within informant. PLEs loaded most highly onto a general psychopathology factor, suggesting that they may reflect non-specific risk for mental illness. ASD symptoms loaded separately from attention/hyperactivity symptoms. Symptoms of impulsivity and sensitivity to reward and punishment loaded onto specific factors, distinct from externalizing and internalizing factors. All identified factors were associated with clinically relevant risk factors, providing preliminary evidence for their construct validity. By integrating diverse child-report and parent-report psychopathology measures for children in the ABCD sample, we deliver data on the quantitative structure of psychopathology for an exceptionally large set of measurements and discuss implications for the field.

Leukemia detection using Artificial Neural Networks in Images of Human Blood Sample

This article presents a preliminary report that uses minuscule images of blood tests to develop a diagnosis of leukemia. Examining through images is crucial since illnesses can be recognized and examined at an earlier stage using the images. The framework will be centered on leukemia and white blood cell illness. In fact, even the hematologist has trouble organizing the leukemic cells, and manually arranging the platelets takes a long time and is quite loose. In this way, early detection of leukemia recurrence allows the patient to receive the appropriate treatment. In order to address this problem, the framework will make use of the capabilities in small images and examine surface, geometry, shading, and quantifiable investigation modifications. These features' variations will be utilized as the classifier input. has transformed the use of images K proposes that (NN) and agglomeration. Examining a wide range of failure measures and increasing the intricacy of every system, the findings are examined. Utilizing feedforward (NN), image division is accomplished with less noise and a very sluggish conjunction rate. K-means agglomeration and (ANN) are intentionally used in this analysis to create a collection of processes that will work together to produce a much better presentation in (IS). An analysis has been conducted to determine the best rule for (IS).

Study on the co-phasing simulation and experimental of segmented mirror based on broadband light

At present, the co-phasing detection algorithms for segmented mirrors struggle to balance the measurement range and accuracy. This paper proposes a coarse co-phasing measurement method using the incoherent properties of a broadband (400–800 nm) light source combined with the incoherence of two semicircular diffraction patterns of visible light as a template to detect the piston error, and a narrowband algorithm for fine co-phasing measurement. The diversity factors between template patterns and actual diffraction patterns can be calculated by the cross-correlation algorithm. This proposed method addresses long measurement time caused by low target flow rate and is characterized by wide measurement range, high precision and high energy utilization. The method is theoretically researched and simulated and an experimental system which was constructed to verify the effectiveness of this method achieves 15 nm detection accuracy, and the measurement range using the proposed method can match the measurement range of the actuator.

Beta-Lactam Resistance and Phenotypic Detection of Extended-Spectrum Beta-Lactamase in Entero bacteriaceae Isolated from Community-Acquired Urinary Tract Infections

Introduction: Urinary tract infections can affect all individuals, regardless of gender and age, occupying a prominent place in nephrological pathology. The biggest problem is that a pathology with a wide range of antibiotics and other hygiene measures that can remedy it, remains so frequent with sometimes serious complications that can compromise the vital prognosis.
 Objectives: To determine antibiogram profile of enterobacteria in urinary infections.
 Methodology: This is a cross-sectional study with an analytical aim of enterobacteria isolated from urinary infections from samples from patients of all ages from January 1, 2020 to October 21, 2022.
 Results: the isolated enterobacteria strains presented high rates of resistance, i.e. 92.61% for Ampicillin, 47.94% for cefadroxil, 45.14% for cefuroxime and 46.46% for cefotaxime, 72.86% for amoxicillin and 40.44% for amoxicillin + clavulanic acid. Qualitative detection of extended spectrum Beta Lactamase was generally evaluated at 24.8% with peaks for Citrobacter sp, Klebsiella sp and E coli. For the latter, cross-resistance to quinolones was evaluated in proportions ranging from 49.45% to 85.51%.
 Conclusion: This study shows that the level of resistance of enterobacteria to beta-lactams is very high. We have observed co-resistance between beta-lactams and quinolones, antibiotics commonly used against Gram-Negative bacteria. This observation requires an improvement in the antibiotic management policy.

Developing the experimental skills of pre-service physics teachers

In this paper we present the focus, objectives, content and implementation of the elective course Selected Demonstration Experiments designed for physics teaching students. We map students’ personal experiences with the implementation of physics experiments in lower secondary school and high school, as well as their knowledge of the possibilities of demonstrating physical phenomena through experiments with simple tools. The continuing lack of experimentally oriented activities in physics education naturally has a negative impact on the experimental skills of physics teaching students. In this context, the prospective teacher should be able to carry out a wide range of physical experiments, demonstrations, experiments and measurements, interpret them appropriately and involve them in active perception. The lack of experimental equipment in schools for individual (or small group) student work can be at least partially overcome by the use of commonly available simple tools (plastic containers, stationery household tools, toys). Their availability is a strong motivation for individual observations, investigations and measurements at home. In the school laboratory, the teacher can easily provide such tools for frontal experiments, parallel work by groups of students or project work following observations at home. We consider and present a model of blended learning in an educational laboratory environment. In this paper we present selected ideas for physics observations and experiments with simple tools in the form of engaging video demonstrations. We have included the creation of our own educational video demonstration as a student semester project in the course presented. We present the success of achieving our learning objectives through student assessment.

Dual excitation self-balancing capacitance measurement method based on frequency adjustment

The double-ended excitation self-balancing bridge excitation method is a capacitance measurement method with rather a high measurement accuracy currently. However, the reference impedance of the electric bridge limits the traditional self-balancing excitation measurement method, making it difficult to achieve high accuracy and a wide range of capacitance sensor measurements. In the proposed paper, frequency adjustment method is introduced to enable impedance matching between the measured capacitance and the reference resistance. The measurement range is broadened as well as maintaining the accuracy of the capacitance measurement. The measurement circuit is composed of a reference resistor, a bridge in series with the measured capacitance and an operational amplifier follower circuit. First, we roughly measure the input impedance of the operational amplifier and the capacitive reactance of the capacitance under test using a two-step excitation method. Then, the best excitation frequency is calculated to match the reference resistor according to the capacitive reactance under test, and the input impedance of the operational amplifier is re-estimated with the best frequency. Finally, we complete the accurate measurement of the capacitive reactance under test through the use of dual excitation. The experimental results demonstrate the method’s high accuracy measurement within the range of 1.6 pF–16 nF, with the reference resistance set at 1 MΩ and the excitation frequency adjusted between 10 and 100 kHz. Additionally, dual excitation stability testing verifies relative standard deviations in the range of 0.01%–0.12%. Experiments on filter capsule sensing detection show that the proposed frequency adjustment measurement method can perform highly sensitive capacitive sensing measurements under the influence of parasitic effects.

Research on the Optimization of Multibeam Bathymetry Survey Line Layout Based on Simulated Annealing Algorithm

Multibeam bathymetry systems are widely used in water depth measurement, and their principle is based on the propagation characteristics of sound waves in water. The system has multiple advantages, such as high resolution, high accuracy, wide measurement range, automated mapping, and high efficiency. This study is based on the propagation characteristics of sound waves in water and utilizes a multibeam bathymetry system for water depth measurement, focusing on key issues such as coverage width, survey path, and seawater depth. Based on a two-dimensional plane model, the direction of the emitted beams from the measurement vessel and the relationship with seafloor slope angles are considered. The key parameter calculation model of the multibeam bathymetry system is established using the sine theorem and cosine theorem. To enhance the analysis accuracy, the calculation model is extended to three dimensions, incorporating more factors that have a significant impact on the measurement, such as the slope angle along the survey lines and changes in water depth. In practical applications, the survey lines are set to run in a north-south direction, and a greedy algorithm is used to ensure complete coverage of the measurements. Taking into account the complex seafloor topography, the fitted lines are used to represent the seafloor slopes. The multi-objective programming model is transformed into an unconstrained single-objective programming problem for further optimization. By solving the problem using simulated annealing algorithm, the optimized solution provides specific values for the spacing between adjacent survey lines, total track length, missed coverage rate of the surveyed area, and duplicate measurement coverage, enabling the optimal utilization of multibeam bathymetry systems in oceanic surveys.

MEASUREMENT OF TRANSPORTED GAS CONSUMPTION: ANALYSIS OF MEASUREMENT METHODS

One of the key aspects in the development of the economy of the Republic of Azerbaijan is the export of natural gas to foreign countries. The relevance of the issue of measuring consumption increases over time with the expansion of gasification operations in the country and the growth of gas exports to the international market. Therefore, there is a demand for the development of new projects for the improvement of the used metering systems in accordance with modern standards, their reconstruction with more efficient methods, and the discovery and application of new measuring devices and methods. During such reconstruction activities, the main focus is naturally on creating optimal and cost-effective information measurement systems. The measurement process is a precision-demanding operation, and various nuances affect the reliability of the result. Metrology is the scientific discipline that implements measurement processes. The key stage for its accuracy is the proper organization of the measurement device and methods in accordance with the characteristics of these devices. Additionally, the expertise of the personnel involved in the measurement process significantly influences the reliability of the measurement result. Failure to adhere to these processes negatively impacts the reliability of the result. In Azerbaijan, the widely used method of consumption measurement is the orifice metering method (ISO 5167-1), but this method has both advantages and disadvantages for large-diameter flow meters. These include a limited range of measured flow velocities, fouling and contamination of the diaphragm edge during operation, the complexity and high labor intensity of regular technical maintenance, high costs of spare parts, and the construction and installation of measuring devices. These problems lead to inaccuracies in gas consumption measurement, causing time and material losses during the process. Improving the accuracy of gas measurement in Azerbaijan can significantly increase financial gains. Therefore, alongside traditional methods, there is an interest in developing new methods for measuring volume flow with high accuracy and user convenience, as well as lower initial and operating costs. One of the promising areas for the development of modern flow measurement technology is the use of optoelectronics. Among them, the most significant interest lies in the use of Laser Doppler interferometry methods. These methods have been proven to be the most effective for studying physically dispersed environments (such as gas and liquid). The advantages of Laser Doppler anemometry, based on these methods, such as high accuracy and speed, a wide measurement range, resistance to noise, the possibility of simulated calibration, controllability of characteristics, and non-changeability according to the surrounding environment, make them highly favorable. The high accuracy of flow meters based on Laser Doppler anemometry allows them to be used both as working tools and as metrological support means for the verification of flow meters. Keywords: Gas consumption measurement, Measurement systems, Measurement methods, Orifice metering method, Laser Doppler.

Social capital and the effectiveness of land use policies: Evidence from rural China

This paper investigates how three forms of social capital, namely, social norms, social network and trust, influences the effectiveness of land use policies. Both long- and short-term policy outcomes are considered in the proposed analytical framework. Benefiting from a comprehensive household survey dataset covering 17 provinces in China, we adopt multiple measurements for each social capital form and policy outcome in our empirical investigation. We use a specific rural land use policy (i.e. reform to confirm, register and certify rural land rights) as a natural experiment to estimate the effect of social capital. By revealing the complex relationships amongst various forms of social capital and a wide range of policy outcome measurements, our empirical findings confirm the validity, reliability and tractability of the proposed analytical framework. Policy implications are also derived regarding how to utilise social capital to improve the effectiveness of land use policies.