bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study Protocol on the Effectiveness of Radial Shockwave Therapy on Myofascial Pain Syndrome: A Mixed Methods Study That Combines a Randomised Control Trial and Semi-Structured Interviews
Authors: Ogbeivor Collins , AlMubarak Huda , Akomolafe Tola , Alkahtani Hamad , AlMugizel Hussain , Aldosari Hala , Aldhwayan Nouf
Published: March 2023

Background: Myofascial pain syndrome (MPS) is a common, costly and often persistent musculoskeletal problem. Radial shockwave (RSW) is one of the most common treatments for MFS. However, there is very low-level evidence to support its short-term benefit, due to poor methodological qualities. Furthermore, previous studies have not considered the experiences of patients regarding this intervention. This study will investigate the effectiveness of RSW compared to a sham (placebo) for patients with MPS and establish the experiences of patients receiving the treatment. Methods: A mixed methods study of a pragmatic randomised controlled trial and semi-structured-interviews that will involve 120 potential participants with MPS is used. The intervention group will receive six sessions of RSW: 1.5 bars, 2000 pulses, frequency 15 Hz. The control group will receive an identical treatment except that they will receive a no-energy shock of 0.3 bar. Results: The outcome measures are a numeric pain scale, neck disability index (NDI), pressure pain threshold (PPT) and SF-12 questionnaires at 4 and 8 weeks’ follow-up between the two groups. Conclusion: The expectation is that this study will add to the body of knowledge required to make effective treatment choices on RSW in the management MFS.
Quantifying the Variation in Reflectance Spectra of Metrosideros polymorpha Canopies across Environmental Gradients
Authors: Seeley Megan M. , Martin Roberta E. , Vaughn Nicholas R. , Thompson David R. , Dai Jie , Asner Gregory P.
Published: March 2023

Imaging spectroscopy is a burgeoning tool for understanding ecosystem functioning on large spatial scales, yet the application of this technology to assess intra-specific trait variation across environmental gradients has been poorly tested. Selection of specific genotypes via environmental filtering plays an important role in driving trait variation and thus functional diversity across space and time, but the relative contributions of intra-specific trait variation and species turnover are still unclear. To address this issue, we quantified the variation in reflectance spectra within and between six uniform stands of Metrosideros polymorpha across elevation and soil substrate age gradients on Hawai‘i Island. Airborne imaging spectroscopy and light detection and ranging (LiDAR) data were merged to capture and isolate sunlit portions of canopies at the six M. polymorpha-dominated sites. Both intra-site and inter-site spectral variations were quantified using several analyses. A support vector machine (SVM) model revealed that each site was spectrally distinct, while Euclidean distances between site centroids in principal components (PC) space indicated that elevation and soil substrate age drive the separation of canopy spectra between sites. Coefficients of variation among spectra, as well as the intrinsic spectral dimensionality of the data, demonstrated the hierarchical effect of soil substrate age, followed by elevation, in determining intra-site variation. Assessments based on leaf trait data estimated from canopy reflectance resulted in similar patterns of separation among sites in the PC space and distinction among sites in the SVM model. Using a highly polymorphic species, we demonstrated that canopy reflectance follows known ecological principles of community turnover and thus how spectral remote sensing addresses forest community assembly on large spatial scales.
Estimating Effects of Natural and Anthropogenic Activities on Trophic Level of Inland Water: Analysis of Poyang Lake Basin, China, with Landsat-8 Observations
Authors: Li Jianzhong , Zheng Zhubin , Liu Ge , Chen Na , Lei Shaohua , Du Chao , Xu Jie , Li Yuan , Zhang Runfei , Huang Chao
Published: March 2023

The intensification of anthropogenic activities has led to the infiltration of enormous quantities of pollutants into rivers and lakes, resulting in significant deterioration in water quality and a more prominent occurrence of eutrophication. Poyang Lake, the largest freshwater lake in China, is facing a severe challenge related to eutrophication, which seriously threatens the delivery of the ecosystem service and the safety of drinking water. To address this challenge, Landsat-8 Operational Land Imager (OLI) data for the Poyang Lake Basin (PLB) from May 2013 to December 2020 were used. Since inland water bodies with complex optical characteristics, we developed a semi-analytical algorithm to assess the trophic state of the water based on two cruise field measurements in 2016 and 2019. Combining the semi-analytical trophic level index (TLI) with an atmospheric correction model is the most suitable model for OLI images of the PLB, this model was then applied to Landsat-8 time series observations. The trends of the trophic state of water bodies in PLB were revealed, and the annual, quarterly and monthly percentages of eutrophic water bodies were calculated. Natural and anthropogenic factors were then used to explain the changes in the trophic state of the PLB waters. The main findings are as follows: (1) From the 8-year observation results, it can be seen that the variation of trophic level of water in PLB showed obviously spatial and temporal variations, characterized by higher in the north than in the south and higher in winter than in summer. (2) Temperature promoted the growth of harmful algae and plays an essential role in affecting changes in the trophic level of the water. (3) Changes in the trophic level of water bodies in PLB were mainly affected by human activities. The results of spatial and temporal variation of the trophic level of water and the driving factors in PLB can extend our knowledge of water quality degradation and provide essential references for relevant policy-making institutions.
A New Strategy for Individual Tree Detection and Segmentation from Leaf-on and Leaf-off UAV-LiDAR Point Clouds Based on Automatic Detection of Seed Points
Authors: Pu Yihan , Xu Dandan , Wang Haobin , Li Xin , Xu Xia
Published: March 2023

Accurate and efficient estimation of forest volume or biomass is critical for carbon cycles, forest management, and the timber industry. Individual tree detection and segmentation (ITDS) is the first and key step to ensure the accurate extraction of detailed forest structure parameters from LiDAR (light detection and ranging). However, ITDS is still a challenge to achieve using UAV-LiDAR (LiDAR from Unmanned Aerial Vehicles) in broadleaved forests due to the irregular and overlapped canopies. We developed an efficient and accurate ITDS framework for broadleaved forests based on UAV-LiDAR point clouds. It involves ITD (individual tree detection) from point clouds taken during the leaf-off season, initial ITS (individual tree segmentation) based on the seed points from ITD, and improvement of initial ITS through a refining process. The results indicate that this new proposed strategy efficiently provides accurate results for ITDS. We show the following: (1) point-cloud-based ITD methods, especially the Mean Shift, perform better for seed point selection than CHM-based (Canopy Height Model) ITD methods on the point clouds from leaf-off seasons; (2) seed points significantly improved the accuracy and efficiency of ITS algorithms; (3) the refining process using DBSCAN (density-based spatial clustering of applications with noise) and kNN (k-Nearest Neighbor classifier) classification significantly reduced edge errors in ITS results. Our study developed a novel ITDS strategy for UAV-LiDAR point clouds that demonstrates proficiency in dense deciduous broadleaved forests, and this proposed ITDS framework could be applied to single-phase point clouds instead of the multi-temporal LiDAR data in the future if the point clouds have detailed tree trunk points.
CH4 Fluxes Derived from Assimilation of TROPOMI XCH4 in CarbonTracker Europe-CH4: Evaluation of Seasonality and Spatial Distribution in the Northern High Latitudes
Authors: Tsuruta Aki , Kivimäki Ella , Lindqvist Hannakaisa , Karppinen Tomi , Backman Leif , Hakkarainen Janne , Schneising Oliver , Buchwitz Michael , Lan Xin , Kivi Rigel , Chen Huilin , Buschmann Matthias , Herkommer Benedikt , Notholt Justus , Roehl Coleen , Té Yao , Wunch Debra , Tamminen Johanna , Aalto Tuula
Published: March 2023

Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane (XCH4) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH4 fluxes using the CarbonTracker Europe-CH4 (CTE-CH4) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research’s (SRON) operational and University of Bremen’s Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where CH4 emission maximum is later, when the SRON’s operational data are assimilated. The TROPOMI inversions show higher CH4 emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates.
Estimation of Leaf Area Index in a Typical Northern Tropical Secondary Monsoon Rainforest by Different Indirect Methods
Authors: Xie Xiansheng , Yang Yuanzheng , Li Wuzheng , Liao Nanyan , Pan Weihu , Su Hongxin
Published: March 2023

The leaf area index (LAI) is a crucial indicator for quantifying forest productivity and community ecological processes. Satellite remote sensing can achieve large-scale LAI monitoring, but it needs to be calibrated and validated according to the in situ measurements on the ground. In this study, we attempted to use different indirect methods to measure LAI in a tropical secondary forest. These methods included the LAI-2200 plant canopy analyzer (LAI-2200), Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS) (using single-station and multi-station measurements, respectively). Additionally, we tried to correct the measured LAI by obtaining indicators of woody components and clumping effects. The results showed that the LAI of this forest was large, with estimated values of 5.27 ± 1.16, 3.69 ± 0.74, 5.86 ± 1.09, 4.93 ± 1.33, and 3.87 ± 0.89 for LAI-2200, DHP, TRAC, TLS multi-station, and TLS single-station, respectively. There was a significant correlation between the different methods. LAI-2200 was significantly correlated with all other methods (p < 0.01), with the strongest correlation with DHP (r = 0.684). TRAC was significantly correlated with TLS single-station (p < 0.01, r = 0.283). TLS multi-station was significantly correlated with TLS single-station (p < 0.05, r = 0.266). With the multi-station measurement method, TLS could maximize the compensation for measurement bias due to the shadowing effects. In general, the clumping index of this forest was 0.94 ± 0.05, the woody-to-total area ratio was 3.23 ± 2.22%, and the total correction coefficient was 1.03 ± 0.07. After correction, the LAI estimates for all methods were slightly higher than before, but there was no significant difference among them. Based on the performance assessment of existing ground-based methods, we hope to enhance the inter-calibration between methods to improve their estimation accuracy under complex forest conditions and advance the validation of remote sensing inversion of the LAI. Moreover, this study also provided a practical reference to promote the application of LiDAR technology in tropical forests.
Domain Hybrid Day-Ahead Solar Radiation Forecasting Scheme
Authors: Park Jinwoong , Park Sungwoo , Shim Jonghwa , Hwang Eenjun
Published: March 2023

Recently, energy procurement by renewable energy sources has increased. In particular, as solar power generation has a high penetration rate among them, solar radiation predictions at the site are attracting much attention for efficient operation. Various approaches have been proposed to forecast solar radiation accurately. Recently, hybrid models have been proposed to improve performance through forecasting in the frequency domain using past solar radiation. Since solar radiation data have a pattern, forecasting in the frequency domain can be effective. However, forecasting performance deteriorates on days when the weather suddenly changes. In this paper, we propose a domain hybrid forecasting model that can respond to weather changes and exhibit improved performance. The proposed model consists of two stages. In the first stage, forecasting is performed in the frequency domain using wavelet transform, complete ensemble empirical mode decomposition, and multilayer perceptron, while forecasting in the sequence domain is accomplished using light gradient boosting machine. In the second stage, a multilayer perceptron-based domain hybrid model is constructed using the forecast values of the first stage as the input. Compared with the frequency-domain model, our proposed model exhibits an improvement of up to 36.38% in the normalized root-mean-square error.
Accurate and Serialized Dense Point Cloud Reconstruction for Aerial Video Sequences
Authors: Xu Shibiao , Pan Bingbing , Zhang Jiguang , Zhang Xiaopeng
Published: March 2023

Traditional multi-view stereo (MVS) is not applicable for the point cloud reconstruction of serialized video frames. Among them, the exhausted feature extraction and matching for all the prepared frames are time-consuming, and the scope of the search requires covering all the key frames. In this paper, we propose a novel serialized reconstruction method to solve the above issues. Specifically, a joint feature descriptors-based covisibility cluster generation strategy is designed to accelerate the feature matching and improve the performance of the pose estimation. Then, a serialized structure-from-motion (SfM) and dense point cloud reconstruction framework is designed to achieve high efficiency and competitive precision reconstruction for serialized frames. To fully demonstrate the superiority of our method, we collect a public aerial sequences dataset with referable ground truth for the dense point cloud reconstruction evaluation. Through a time complexity analysis and the experimental validation in this dataset, the comprehensive performance of our algorithm is better than the other compared outstanding methods.
Cognitive CAPTCHA Password Reminder
Authors: Krzyworzeka Natalia , Ogiela Lidia , Ogiela Marek R.
Published: March 2023

In recent years, the number of personal accounts assigned to one business user has been constantly growing. There could be as many as 191 individual login credentials used by an average employee, according to a 2017 study. The most recurrent problems associated with this situation faced by users are the strength of passwords and ability to recall them. Researchers have proven that “users are aware of what constitutes a secure password but may forgo these security measures in terms of more convenient passwords, largely depending on account type”. Reusing the same password across multiple platforms or creating one with dictionary words has also been proved to be a common practice amongst many. In this paper, a novel password-reminder scheme will be presented. The goal was that the user creates a CAPTCHA-like image with a hidden meaning, that only he or she can decode. The image must be in some way related to that individual’s memory or her/his unique knowledge or experience. With this image, being presented each time during logging in, the user is asked to associate a password consisting of two or more words and a number. If the image is selected properly and strong association with a person’s visual memory has been linked to it, the chances of recalling a lengthy password he/she created should not present a problem.
Technological Solutions for Human Movement Analysis in Obese Subjects: A Systematic Review
Authors: Monfrini Riccardo , Rossetto Gianluca , Scalona Emilia , Galli Manuela , Cimolin Veronica , Lopomo Nicola Francesco
Published: March 2023

Obesity has a critical impact on musculoskeletal systems, and excessive weight directly affects the ability of subjects to realize movements. It is important to monitor the activities of obese subjects, their functional limitations, and the overall risks related to specific motor tasks. From this perspective, this systematic review identified and summarized the main technologies specifically used to acquire and quantify movements in scientific studies involving obese subjects. The search for articles was carried out on electronic databases, i.e., PubMed, Scopus, and Web of Science. We included observational studies performed on adult obese subjects whenever reporting quantitative information concerning their movement. The articles must have been written in English, published after 2010, and concerned subjects who were primarily diagnosed with obesity, thus excluding confounding diseases. Marker-based optoelectronic stereophotogrammetric systems resulted to be the most adopted solution for movement analysis focused on obesity; indeed, wearable technologies based on magneto-inertial measurement units (MIMUs) were recently adopted for analyzing obese subjects. Further, these systems are usually integrated with force platforms, so as to have information about the ground reaction forces. However, few studies specifically reported the reliability and limitations of these approaches due to soft tissue artifacts and crosstalk, which turned out to be the most relevant problems to deal with in this context. In this perspective, in spite of their inherent limitations, medical imaging techniques—such as Magnetic Resonance Imaging (MRI) and biplane radiography—should be used to improve the accuracy of biomechanical evaluations in obese people, and to systematically validate less-invasive approaches.
Development and Optimization of a Highly Sensitive Sensor to Quinine-Based Saltiness Enhancement Effect
Authors: Jing Yifei , Watanabe Kentaro , Watanabe Tatsukichi , Kimura Shunsuke , Toko Kiyoshi
Published: March 2023

The saltiness enhancement effect can be produced by adding specific substances to dietary salt (sodium chloride). This effect has been used in salt-reduced food to help people forge healthy eating habits. Therefore, it is necessary to objectively evaluate the saltiness of food based on this effect. In a previous study, sensor electrodes based on lipid/polymer membrane with Na+ ionophore have been proposed to quantify the saltiness enhanced by branched-chain amino acids (BCAAs), citric acid, and tartaric acid. In this study, we developed a new saltiness sensor with the lipid/polymer membrane to quantify the saltiness enhancement effect of quinine by replacing a lipid that caused an unexpected initial drop in the previous study with another new lipid. As a result, the concentrations of lipid and ionophore were optimized to produce an expected response. Logarithmic responses have been found on both NaCl samples and quinine-added NaCl samples. The findings indicate the usage of lipid/polymer membranes on novel taste sensors to evaluate the saltiness enhancement effect accurately.
Customer Analysis Using Machine Learning-Based Classification Algorithms for Effective Segmentation Using Recency, Frequency, Monetary, and Time
Authors: Ullah Asmat , Mohmand Muhammad Ismail , Hussain Hameed , Johar Sumaira , Khan Inayat , Ahmad Shafiq , Mahmoud Haitham A. , Huda Shamsul
Published: March 2023

Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging. The recently introduced Recency, Frequency, Monetary, and Time (RFMT) model used an agglomerative algorithm for segmentation and a dendrogram for clustering, which solved the problem. However, there is still room for a single algorithm to analyze the data’s characteristics. The proposed novel approach model RFMT analyzed Pakistan’s largest e-commerce dataset by introducing k-means, Gaussian, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) beside agglomerative algorithms for segmentation. The cluster is determined through different cluster factor analysis methods, i.e., elbow, dendrogram, silhouette, Calinsky–Harabasz, Davies–Bouldin, and Dunn index. They finally elected a stable and distinctive cluster using the state-of-the-art majority voting (mode version) technique, which resulted in three different clusters. Besides all the segmentation, i.e., product categories, year-wise, fiscal year-wise, and month-wise, the approach also includes the transaction status and seasons-wise segmentation. This segmentation will help the retailer improve customer relationships, implement good strategies, and improve targeted marketing.
Determination of Munsell Soil Colour Using Smartphones
Authors: Nodi Sadia Sabrin , Paul Manoranjan , Robinson Nathan , Wang Liang , Rehman Sabih ur
Published: March 2023

Soil colour is one of the most important factors in agriculture for monitoring soil health and determining its properties. For this purpose, Munsell soil colour charts are widely used by archaeologists, scientists, and farmers. The process of determining soil colour from the chart is subjective and error-prone. In this study, we used popular smartphones to capture soil colours from images in the Munsell Soil Colour Book (MSCB) to determine the colour digitally. These captured soil colours are then compared with the true colour determined using a commonly used sensor (Nix Pro-2). We have observed that there are colour reading discrepancies between smartphone and Nix Pro-provided readings. To address this issue, we investigated different colour models and finally introduced a colour-intensity relationship between the images captured by Nix Pro and smartphones by exploring different distance functions. Thus, the aim of this study is to determine the Munsell soil colour accurately from the MSCB by adjusting the pixel intensity of the smartphone-captured images. Without any adjustment when the accuracy of individual Munsell soil colour determination is only 9% for the top 5 predictions, the accuracy of the proposed method is 74%, which is significant.
A Review of Laser Ultrasonic Lamb Wave Damage Detection Methods for Thin-Walled Structures
Authors: Zheng Shanpu , Luo Ying , Xu Chenguang , Xu Guidong
Published: March 2023

Thin-walled structures, like aircraft skins and ship shells, are often several meters in size but only a few millimeters thick. By utilizing the laser ultrasonic Lamb wave detection method (LU-LDM), signals can be detected over long distances without physical contact. Additionally, this technology offers excellent flexibility in designing the measurement point distribution. The characteristics of LU-LDM are first analyzed in this review, specifically in terms of laser ultrasound and hardware configuration. Next, the methods are categorized based on three criteria: the quantity of collected wavefield data, the spectral domain, and the distribution of measurement points. The advantages and disadvantages of multiple methods are compared, and the suitable conditions for each method are summarized. Thirdly, we summarize four combined methods that balance detection efficiency and accuracy. Finally, several future development trends are suggested, and the current gaps and shortcomings in LU-LDM are highlighted. This review builds a comprehensive framework for LU-LDM for the first time, which is expected to serve as a technical reference for applying this technology in large, thin-walled structures.
Radar Human Activity Recognition with an Attention-Based Deep Learning Network
Authors: Huan Sha , Wu Limei , Zhang Man , Wang Zhaoyue , Yang Chao
Published: March 2023

Radar-based human activity recognition (HAR) provides a non-contact method for many scenarios, such as human–computer interaction, smart security, and advanced surveillance with privacy protection. Feeding radar-preprocessed micro-Doppler signals into a deep learning (DL) network is a promising approach for HAR. Conventional DL algorithms can achieve high performance in terms of accuracy, but the complex network structure causes difficulty for their real-time embedded application. In this study, an efficient network with an attention mechanism is proposed. This network decouples the Doppler and temporal features of radar preprocessed signals according to the feature representation of human activity in the time–frequency domain. The Doppler feature representation is obtained in sequence using the one-dimensional convolutional neural network (1D CNN) following the sliding window. Then, HAR is realized by inputting the Doppler features into the attention-mechanism-based long short-term memory (LSTM) as a time sequence. Moreover, the activity features are effectively enhanced using the averaged cancellation method, which improves the clutter suppression effect under the micro-motion conditions. Compared with the traditional moving target indicator (MTI), the recognition accuracy is improved by about 3.7%. Experiments based on two human activity datasets confirm the superiority of our method compared to traditional methods in terms of expressiveness and computational efficiency. Specifically, our method achieves an accuracy close to 96.9% on both datasets and has a more lightweight network structure compared to algorithms with similar recognition accuracy. The method proposed in this article has great potential for real-time embedded applications of HAR.
Innovative Photonic Sensors for Safety and Security, Part III: Environment, Agriculture and Soil Monitoring
Authors: Breglio Giovanni , Bernini Romeo , Berruti Gaia Maria , Bruno Francesco Antonio , Buontempo Salvatore , Campopiano Stefania , Catalano Ester , Consales Marco , Coscetta Agnese , Cutolo Antonello , Cutolo Maria Alessandra , Di Palma Pasquale , Esposito Flavio , Fienga Francesco , Giordano Michele , Iele Antonio , Iadicicco Agostino , Irace Andrea , Janneh Mohammed , Laudati Armando , Leone Marco , Maresca Luca , Marrazzo Vincenzo Romano , Minardo Aldo , Pisco Marco , Quero Giuseppe , Riccio Michele , Srivastava Anubhav , Vaiano Patrizio , Zeni Luigi , Cusano Andrea
Published: March 2023

In order to complete this set of three companion papers, in this last, we focus our attention on environmental monitoring by taking advantage of photonic technologies. After reporting on some configurations useful for high precision agriculture, we explore the problems connected with soil water content measurement and landslide early warning. Then, we concentrate on a new generation of seismic sensors useful in both terrestrial and under water contests. Finally, we discuss a number of optical fiber sensors for use in radiation environments.
Serially Connected Cantilever Beam-Based FBG Accelerometers: Design, Optimization and Testing
Authors: Reghuprasad Aarathy Ezhuthupally , Colombero Chiara , Godio Alberto
Published: March 2023

We focus on the design, optimization, fabrication, and testing of fiber Bragg grating (FBG) cantilever beam-based accelerometers to measure vibrations from active seismic sources in the external environment. These FBG accelerometers possess several advantages, such as multiplexing, immunity to electromagnetic interference, and high sensitivity. Finite Element Method (FEM) simulations, calibration, fabrication, and packaging of the simple cantilever beam-based accelerometer based on polylactic acid (PLA) are presented. The influence of the cantilever beam parameters on the natural frequency and sensitivity are discussed through FEM simulation and laboratory calibration with vibration exciter. The test results show that the optimized system has a resonance frequency of 75 Hz within a measuring range of 5–55 Hz and high sensitivity of ±433.7 pm/g. Finally, a preliminary field test is conducted to compare the packaged FBG accelerometer and standard electro-mechanical 4.5-Hz vertical geophones. Active-source (seismic sledgehammer) shots are acquired along the tested line, and both systems’ experimental results are analyzed and compared. The designed FBG accelerometers demonstrate suitability to record the seismic traces and to pick up the first arrival times. The system optimization and further implementation offer promising potential for seismic acquisitions.
Towards Automated Inspections of Tunnels: A Review of Optical Inspections and Autonomous Assessment of Concrete Tunnel Linings
Authors: Sjölander Andreas , Belloni Valeria , Ansell Anders , Nordström Erik
Published: March 2023

In recent decades, many cities have become densely populated due to increased urbanization, and the transportation infrastructure system has been heavily used. The downtime of important parts of the infrastructure, such as tunnels and bridges, seriously affects the transportation system’s efficiency. For this reason, a safe and reliable infrastructure network is necessary for the economic growth and functionality of cities. At the same time, the infrastructure is ageing in many countries, and continuous inspection and maintenance are necessary. Nowadays, detailed inspections of large infrastructure are almost exclusively performed by inspectors on site, which is both time-consuming and subject to human errors. However, the recent technological advancements in computer vision, artificial intelligence (AI), and robotics have opened up the possibilities of automated inspections. Today, semiautomatic systems such as drones and other mobile mapping systems are available to collect data and reconstruct 3D digital models of infrastructure. This significantly decreases the downtime of the infrastructure, but both damage detection and assessments of the structural condition are still manually performed, with a high impact on the efficiency and accuracy of the procedure. Ongoing research has shown that deep-learning methods, especially convolutional neural networks (CNNs) combined with other image processing techniques, can automatically detect cracks on concrete surfaces and measure their metrics (e.g., length and width). However, these techniques are still under investigation. Additionally, to use these data for automatically assessing the structure, a clear link between the metrics of the cracks and the structural condition must be established. This paper presents a review of the damage of tunnel concrete lining that is detectable with optical instruments. Thereafter, state-of-the-art autonomous tunnel inspection methods are presented with a focus on innovative mobile mapping systems for optimizing data collection. Finally, the paper presents an in-depth review of how the risk associated with cracks is assessed today in concrete tunnel lining.
Camera- and Viewpoint-Agnostic Evaluation of Axial Postural Abnormalities in People with Parkinson’s Disease through Augmented Human Pose Estimation
Authors: Aldegheri Stefano , Artusi Carlo Alberto , Camozzi Serena , Di Marco Roberto , Geroin Christian , Imbalzano Gabriele , Lopiano Leonardo , Tinazzi Michele , Bombieri Nicola
Published: March 2023

Axial postural abnormalities (aPA) are common features of Parkinson’s disease (PD) and manifest in over 20% of patients during the course of the disease. aPA form a spectrum of functional trunk misalignment, ranging from a typical Parkinsonian stooped posture to progressively greater degrees of spine deviation. Current research has not yet led to a sufficient understanding of pathophysiology and management of aPA in PD, partially due to lack of agreement on validated, user-friendly, automatic tools for measuring and analysing the differences in the degree of aPA, according to patients’ therapeutic conditions and tasks. In this context, human pose estimation (HPE) software based on deep learning could be a valid support as it automatically extrapolates spatial coordinates of the human skeleton keypoints from images or videos. Nevertheless, standard HPE platforms have two limitations that prevent their adoption in such a clinical practice. First, standard HPE keypoints are inconsistent with the keypoints needed to assess aPA (degrees and fulcrum). Second, aPA assessment either requires advanced RGB-D sensors or, when based on the processing of RGB images, they are most likely sensitive to the adopted camera and to the scene (e.g., sensor–subject distance, lighting, background–subject clothing contrast). This article presents a software that augments the human skeleton extrapolated by state-of-the-art HPE software from RGB pictures with exact bone points for posture evaluation through computer vision post-processing primitives. This article shows the software robustness and accuracy on the processing of 76 RGB images with different resolutions and sensor–subject distances from 55 PD patients with different degrees of anterior and lateral trunk flexion.
A Survey on Current-Mode Interfaces for Bio Signals and Sensors
Authors: Scarsella Massimo , Barile Gianluca , Stornelli Vincenzo , Safari Leila , Ferri Giuseppe
Published: March 2023

In this study, a review of second-generation voltage conveyor (VCII) and current conveyor (CCII) circuits for the conditioning of bio signals and sensors is presented. The CCII is the most known current-mode active block, able to overcome some of the limitations of the classical operational amplifier, which provides an output current instead of a voltage. The VCII is nothing more than the dual of the CCII, and for this reason it enjoys almost all the properties of the CCII but also provides an easy-to-read voltage as an output signal. A broad set of solutions for relevant sensors and biosensors employed in biomedical applications is considered. This ranges from the widespread resistive and capacitive electrochemical biosensors now used in glucose and cholesterol meters and in oximetry to more specific sensors such as ISFETs, SiPMs, and ultrasonic sensors, which are finding increasing applications. This paper also discusses the main benefits of this current-mode approach over the classical voltage-mode approach in the realization of readout circuits that can be used as electronic interfaces for different types of biosensors, including higher circuit simplicity, better low-noise and/or high-speed performance, and lower signal distortion and power consumption.
Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction
Authors: Klaar Anne Carolina Rodrigues , Stefenon Stefano Frizzo , Seman Laio Oriel , Mariani Viviana Cocco , Coelho Leandro dos Santos
Published: March 2023

Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise their conductivity and increase leakage current until a flashover occurs. To improve the reliability of the electrical power system, it is possible to evaluate the development of the fault in relation to the increase in leakage current and thus predict whether a shutdown may occur. This paper proposes the use of empirical wavelet transform (EWT) to reduce the influence of non-representative variations and combines the attention mechanism with a long short-term memory (LSTM) recurrent network for prediction. The Optuna framework has been applied for hyperparameter optimization, resulting in a method called optimized EWT-Seq2Seq-LSTM with attention. The proposed model had a 10.17% lower mean square error (MSE) than the standard LSTM and a 5.36% lower MSE than the model without optimization, showing that the attention mechanism and hyperparameter optimization is a promising strategy.
Supervised Machine-Learning Methodology for Industrial Robot Positional Health Using Artificial Neural Networks, Discrete Wavelet Transform, and Nonlinear Indicators
Authors: Galan-Uribe Ervin , Amezquita-Sanchez Juan P. , Morales-Velazquez Luis
Published: March 2023

Robotic systems are a fundamental part of modern industrial development. In this regard, they are required for long periods, in repetitive processes that must comply with strict tolerance ranges. Hence, the positional accuracy of the robots is critical, since degradation of this can represent a considerable loss of resources. In recent years, prognosis and health management (PHM) methodologies, based on machine and deep learning, have been applied to robots, in order to diagnose and detect faults and identify the degradation of robot positional accuracy, using external measurement systems, such as lasers and cameras; however, their implementation is complex in industrial environments. In this respect, this paper proposes a method based on discrete wavelet transform, nonlinear indices, principal component analysis, and artificial neural networks, in order to detect a positional deviation in robot joints, by analyzing the currents of the actuators. The results show that the proposed methodology allows classification of the robot positional degradation with an accuracy of 100%, using its current signals. The early detection of robot positional degradation, allows the implementation of PHM strategies on time, and prevents losses in manufacturing processes.
Yolo-Pest: An Insect Pest Object Detection Algorithm via CAC3 Module
Authors: Xiang Qiuchi , Huang Xiaoning , Huang Zhouxu , Chen Xingming , Cheng Jintao , Tang Xiaoyu
Published: March 2023

Insect pests have always been one of the main hazards affecting crop yield and quality in traditional agriculture. An accurate and timely pest detection algorithm is essential for effective pest control; however, the existing approach suffers from a sharp performance drop when it comes to the pest detection task due to the lack of learning samples and models for small pest detection. In this paper, we explore and study the improvement methods of convolutional neural network (CNN) models on the Teddy Cup pest dataset and further propose a lightweight and effective agricultural pest detection method for small target pests, named Yolo-Pest, for the pest detection task in agriculture. Specifically, we tackle the problem of feature extraction in small sample learning with the proposed CAC3 module, which is built in a stacking residual structure based on the standard BottleNeck module. By applying a ConvNext module based on the vision transformer (ViT), the proposed method achieves effective feature extraction while keeping a lightweight network. Comparative experiments prove the effectiveness of our approach. Our proposal achieves 91.9% mAP0.5 on the Teddy Cup pest dataset, which outperforms the Yolov5s model by nearly 8% in mAP0.5. It also achieves great performance on public datasets, such as IP102, with a great reduction in the number of parameters.
Influence of Soluble Salt NaCl on Cracking Characteristics and Mechanism of Loess
Authors: Wei Xin , Dong Li , Chen Xuanyi , Zhou Yunru
Published: March 2023

Under the conditions of drought, cracks are likely to appear in loess due to shrinkage, which leads to salt precipitation and accumulation on the surface of loess. Therefore, salinized lands are created in loess areas. Deep study into the influence of soluble salt content on the cracking characteristics and mechanism of loess is of great significance to engineering constructions, geological problems, and disaster prevention for salinized lands in loess regions. In this paper, free desiccation experiments were carried out on the loess samples with different NaCl concentrations (a soluble salt). A high-resolution digital camera was used to obtain the sequence images of loess during the drying process. With the advantage of digital image correlation (DIC) technology and the non-contact full-field strain measurement method, the local displacement and strain on the surface of loess samples were calculated. The microstructure and main elements distribution of loess samples were obtained by scanning electron microscopy (SEM) and energy-dispersive spectrum (EDS) methods. Finally, the influence of NaCl concentrations on cracking characteristics and mechanism of loess was analyzed. The results show that, with the increase in NaCl concentration, the evaporation rate of loess samples decreased and the residual water content increased. The NaCl content can prevent the development of desiccation cracks in loess.
Particle-Size Effect of Nanoparticles on the Thermal Performance of Solar Flat Plate Technology
Authors: Adun Humphrey , Adedeji Michael , Titus Ayomide , Mangai Joakim James , Ruwa Tonderai
Published: March 2023

One of the cleanest and most efficient solar collector systems is the flat plate collector, which has applications in hot water production, drying, among others. Flat plate collectors have improved in terms of both their structural configurations and working fluids. Several studies have verified the comparatively higher efficiency of nanofluid-based flat plate collectors, relative to that of water and other thermal oils. Additionally, the influence of several nanofluid synthesis factors, such as volume fraction, pH, type of base fluid, hybridization, surfactants, and sonification, on the performance of these collectors has been highlighted in the literature. However, the effect of nanoparticle size on collector performance has received minimal research interest, despite its significant effect on both the cost of synthesis and the thermophysical properties of nanofluids. The uncertainties regarding the effect of nanoparticle size on thermal collectors have limited their practical applications. This study numerically investigates the effect of the nanoparticle size of silver (Ag) nanofluid with nanoparticle sizes between 20 nm and 100 nm on the performance of flat plate collectors. The effect of nanoparticle size on the mean fluid temperature resulted in a maximum temperature of 45.8 °C for the Ag-100 nm. An increase of 0.25 °C for the Ag-20 nm was recorded, relative to the Ag-100 nm. In addition, the Ag-100 nm was calculated to have resulted in the highest reduction in collector size (18.30%), relative to that of water.
Post-Traumatic Stress in Children and Adolescents during the COVID-19 Pandemic: A Meta-Analysis and Intervention Approaches to Ensure Mental Health and Well-Being
Authors: Ozamiz-Etxebarria Naiara , Legorburu Fernandez Idoia , Idoiaga-Mondragon Nahia , Olaya Beatriz , Cornelius-White Jeffrey H. D. , Santabárbara Javier
Published: March 2023

Background: Since the World Health Organization (WHO) declared the COVID-19 pandemic in March 2020, many measures have been taken to prevent the spread of the virus. Consequently, many minors have been confined to their homes and have had to subsequently adapt to countless protocol changes. These factors appear to have contributed to post-traumatic stress disorder (PTSD) in many children. Materials and Methods: The authors searched Medline through PubMed and other databases for studies published from 1 December 2019 to 31 December 2021 on the prevalence of PTSD in schoolchildren. The authors used a random-effects model to calculate the pooled prevalence of PTSD. Results: A total of six studies were included in this review. Our results show a pooled prevalence of PTSD of 14% in children and adolescents. Subgroup analyses identify a significantly higher prevalence of PTSD for studies conducted in China and a higher prevalence in boys. The prevalence of PTSD appeared independent of child age or the methodological rigor of the study. Conclusions: Our results suggest that a large number of children may be suffering from PTSD (post-traumatic stress disorder). Public health measures are thus needed to improve children’s mental health during and after the pandemic, so that the suffering is mitigated to prevent long-lasting effects.
Passengers’ Intentions to Use Public Transport during the COVID-19 Pandemic: A Case Study of Bangkok and Jakarta
Authors: Hidayat Aditya Mahatidanar , Choocharukul Kasem
Published: March 2023

Several problems have arisen due to the COVID-19 pandemic, particularly in the public transportation sector. Data from 2021 show that the number of bus passengers in Bangkok has decreased by 40–60% below normal conditions, and the number of bus passengers in Jakarta has decreased by around 76% compared to June 2019. In the new normal period, public transport is required to implement strict health protocols in its operations. This paper aims to evaluate potential indicators underlying the intention to use public transport during a pandemic. The target groups are public and paratransit buses in Bangkok and Jakarta, two metropolitan areas in ASEAN that have been greatly affected by this pandemic. A total of 400 questionnaires were distributed in each city. The outcome from the structural equation model reveals a relatively similar finding in both cities. Tangibility is one of the five dimensions of service quality that is influential in both cities; this indicator affects the intention to use public transportation in Jakarta (Coeff. = 14.670, p-value < 0.001) and Bangkok (Coeff. = 1.164, p-value < 0.001). In this study, confidence (CO) is significant for the intention to use public transportation in both cities with a coefficient of 0.105 for Bangkok and 0.969 for Jakarta. Furthermore, the health measure indicator (HM) has a significant value on passenger confidence (CO) in using public transportation for both cities (Coeff. = 0.808, p-value = <0.001 in Bangkok and Coeff. = −1.413, p-value = 0.008 in Jakarta). Health measures such as wearing a mask, always washing hands and not traveling if unwell are considered to be the right policies to prevent the spread of the virus during the COVID-19 pandemic. This practical implication can become a core strategy for improving the quality of public transport services in similar cities.