bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Algorithm of Corrosion Fatigue Damage Evolution of Stay Cables and Structural Mechanical Behavior with Cable Fracture
Authors: Wang Lifeng , Xiao Ziwang , Duan Changsong , Li Wei , Fu Ning
Published: April 2023

From the point of view of material multiscale analysis, the deterioration of the fatigue properties of the cable is due to the micro-damage inside the strand. To describe the damage failure process of the cable as accurately as possible, many micro damage details need to be implanted in the strand model. However, with the increase of the number of wires in the cable body, this modeling method will produce a large number of elements in the finite element model of the stay cable, which makes the calculation cannot be completed iteratively. Based on this, a time-step adaptive simulation method for corrosion fatigue damage evolution of stay cables was proposed in this paper. In this method, the corrosion fatigue damage evolution model was implanted in the local part of the strand model, and a damage variable was used to comprehensively consider the evolution behavior of distributed micro-damage in the strand, which satisfies the calculation accuracy and realizes the simulation of the whole process of fatigue deterioration of the stay cable, greatly improving the calculation accuracy and convenience. Then taking an in-service cable-stayed bridge as an example, the stress of the main beam and the local spatial stress of the main beam under the special condition of the cable damage failure were studied by combining the rod model with the local model, which provides data reference for the replacement of the cable of the cable-stayed bridge with a long construction life.
Incorporating Foreshocks in an Epidemic-like Description of Seismic Occurrence in Italy
Authors: Petrillo Giuseppe , Lippiello Eugenio
Published: April 2023

The Epidemic Type Aftershock Sequence (ETAS) model is a widely used tool for cluster analysis and forecasting, owing to its ability to accurately predict aftershock occurrences. However, its capacity to explain the increase in seismic activity prior to large earthquakes—known as foreshocks—has been called into question due to inconsistencies between simulated and experimental catalogs. To address this issue, we introduce a generalization of the ETAS model, called the Epidemic Type Aftershock Foreshock Sequence (ETAFS) model. This model has been shown to accurately describe seismicity in Southern California. In this study, we demonstrate that the ETAFS model is also effective in the Italian catalog, providing good agreement with the instrumental Italian catalogue (ISIDE) in terms of not only the number of aftershocks, but also the number of foreshocks—where the ETAS model fails. These findings suggest that foreshocks cannot be solely explained by cascades of triggered events, but can be reasonably considered as precursory phenomena reflecting the nucleation process of the main event.
Usability of Perception Sensors to Determine the Obstacles of Unmanned Ground Vehicles Operating in Off-Road Environments
Authors: Nowakowski Marek , Kurylo Jakub
Published: April 2023

This article presents the essential abilities and limitations of various sensors used for object recognition in the operation environment of unmanned ground vehicles (UGVs). The use of autonomous and unmanned vehicles for reconnaissance and logistics purposes has attracted attention in many countries. There are many different applications of mobile platforms in both civilian and military fields. Herein, we introduce a newly developed manned–unmanned high-mobility vehicle called TAERO that was designed for public roads and off-road operation. Detection for unmanned mode is required in both on-road and off-road environments, but the approach to identify drivable pathway and obstacles around a mobile platform is different in each environment. Dense vegetation and trees can affect the perception system of the vehicle, causing safety risks or even collisions. The main aim was to define the limitations of the perception system in off-road environments, as well as associated challenges and possible future directions for practical applications, to improve the performance of the UGV in all-terrain conditions. Recorded datasets were used to verify vision and laser-based sensors in practical application. The future directions of work to overcome or minimize the indicated challenges are also discussed.
Estimation of Multi-Frequency, Multi-Incidence and Multi-Polarization Backscattering Coefficients over Bare Agricultural Soil Using Statistical Algorithms
Authors: Fieuzal Rémy , Baup Frédéric
Published: April 2023

In the last decade, many SAR missions have been launched to reinforce the all-weather observation capacity of the Earth. The precise modeling of radar signals becomes crucial in order to translate them into essential biophysical parameters for the management of natural resources (water, biomass and energy). The objective of this study was to demonstrate the capabilities of two statistical algorithms (i.e., multiple linear regression (MLR) and random forest (RF)) to accurately simulate the backscattering coefficients observed over bare agricultural soil surfaces. This study was based on satellite and ground data collected on bare soil surfaces over an agricultural region located in southwestern France near Toulouse. Multi-configuration backscattering coefficients were acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, in co-(abbreviated σ0HH and σ0VV) and cross-polarization states (abbreviated σ0HV and σ0VH) and at incidence angles ranging from 24° to 53°. Models were independently calibrated and validated using a ground dataset covering a wide range of soil conditions, including the topsoil moisture (range: 2.4–35.3%), root-mean-square height (range: 0.5–7.9cm) and clay fraction (range: 9–58%). Higher-magnitude correlations (r) and lower errors (RMSE) were obtained when using RF (r values ranging from 0.69 to 0.86 and RMSE from 1.95 to 1.00 dB, depending on the considered signal configuration) compared to MLR (r values ranging from 0.58 to 0.77 and RMSE from 2.22 to 1.24 dB). Both surpass the performance presented in previous studies based on either empirical, semi-empirical or physical models. In the linear approach, the information is mainly provided by the surface moisture and the angle of incidence (especially in the case of co-polarized signals, regardless of the frequency), while the influence of roughness or texture becomes significant for cross-polarized signals in the C-band. On the contrary, all the surface descriptors contribute in the approach based on RF. In future work, the use of the RF algorithm developed in this paper should improve the estimation of soil parameters.
Optimization Design of Filament Wound Composite Pressure Vessel Based on OpenSees
Authors: Dai Binbin , Zhou Mingjue , Zhang Jianye , Li Yuebing , Jin Weiya
Published: April 2023

Composite pressure vessels have the characteristics of light weight, corrosion resistance, good sealing, and high reliability, and have been widely used in military and civilian applications. With the widespread use of composite pressure vessels today, determining how to conduct scientific research and correct analysis becomes very important. With the continuous development of computer technology and the continuous improvement of finite element algorithms, the numerical calculation method has become an important method for studying composite pressure vessels. In this paper, the finite element analysis of filament wound composite pressure vessels is carried out by means of ANSYS and OpenSees. The semi-analytical method is used to optimize the design with the help of the Python tool. The parametric language is used to design different fiber layer schemes. The optimal fiber layer scheme is obtained by Nelder-Mead optimization function optimization. The optimal angle and thickness obtained after multiple iterations are 9.96875° and 0.03325 m, respectively.
Battery-Free Pork Freshness Estimation Based on Colorimetric Sensors and Machine Learning
Authors: Kim Dong-Eon , Nando Yudi April , Chung Wan-Young
Published: April 2023

In this study, a compact smart-sensor tag is developed for estimating pork freshness. The smart sensor tag can be placed in areas where packaged meat is stored or displayed. Antennas and simulated models were developed to maximize the efficiency of radio frequency (RF) energy harvesting. The proposed smart sensor tag includes a red, green, and blue sensor that detects changes in the freshness of meat. To detect the color changes in pork stored at a perishable hot temperature in an outdoor environment, this study applies Hue, Saturation, and Value conversion using machine learning, through which the freshness can be determined with a high degree of accuracy. Validation experiments of the sensor tag performance demonstrate that meat freshness can be detected at distances up to 50 cm from the RF using only the RF energy harvesting without changing the battery source. The 1D convolutional neural network model outperforms the traditional MLP and ConvLSTM models in terms of accuracy and loss.
An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion
Authors: Li Yang , Xiao Yao , Gang Jialin , Yu Qingjun
Published: April 2023

Point cloud completion is a necessary task in real-world applications of recovering a complete geometry from missing regions of 3D objects. Furthermore, model efficiency is of vital importance in computer vision. In this paper, we present an efficient encoder–decoder network that predicts missing point clouds on the basis of incomplete point clouds. There are several advantages to this approach. First, a Mixed Attention Module (MAM) was implemented to obtain the correlational information of points. Second, the proposed Bidirectional Point Pyramid Attention Network (BiPPAN) can achieve simple and fast multiscale feature fusion to capture important features. Lastly, the designed encoder–decoder framework comprises skip connections to capture long-distance dependencies and structural information. We can conclude from the results of the experiments that the proposed network is an efficient and effective method to accomplish point cloud completion tasks.
A Study of Forest Phenology Prediction Based on GRU Models
Authors: Guan Peng , Zhu Lichen , Zheng Yili
Published: April 2023

Investigating forest phenology prediction is a key parameter for assessing the relationship between climate and environmental changes. Traditional machine learning models are not good at capturing long-term dependencies due to the problem of vanishing gradients. In contrast, the Gated Recurrent Unit (GRU) can effectively address the problem of vanishing gradients and allow the neural network to capture longer-range dependencies. In this study, an optical camera was used as experimental equipment to obtain forest images. The absolute greenness index (GEI) data of the region of interest (ROI) in the images were calculated to fit the seasonal variation curve of forest phenology. The GRU neural network model was introduced to train and analyze the GEI data, and the performance of the GRU neural network was evaluated using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) methods. Finally, the model was used to predict the trend of GEI data for the next 60 days. The results showed that: (1) In terms of training and predicting forest phenology, the GRU model was validated using histograms and autocorrelation graphs, which indicated that the distribution of predicted data was consistent with the trend of actual data, the GRU model data was feasible, and the model was stable. (2) The MSE values of the GRU model at rain-fed-CK (preset point 1), sufficient drip irrigation-DIFI (preset point 3), and sufficient furrow irrigation-BIFI (preset point 5) were 9.055 × 10−5, 12.91 × 10−5, and 8.241 × 10−5, respectively. The RMSE values were 9.516 × 10−3, 11.36 × 10−3, and 7.313 × 10−3, respectively. The MAE values were 7.174 × 10−3, 8.241 × 10−3, and 5.351 × 10−3, respectively. These results indicate that the overall performance of the GRU model was good. (3) The predicted GEI data for the next 60 days showed a trend consistent with actual changes in GEI data, as demonstrated by the GRU model. The GRU model has become the preferred method for phenological prediction due to its simple internal structure and relatively short training time. Results show that the GRU model can achieve forest phenological change prediction and can reveal in-depth insights into future forest growth and climate change, providing a theoretical basis for the application of forest phenological prediction.
Research on Amphibious Multi-Rotor UAV Out-of-Water Control Based on ADRC
Authors: Tan Liguo , Liang Shuang , Su Haoxiang , Qin Zihao , Li Liyi , Huo Jianwen
Published: April 2023

This paper presents a study on controlling the out-of-water motion of amphibious multi-rotor UAVs using a cascade control method based on the Active Disturbance Rejection Control (ADRC) algorithm. The aim is to overcome the challenges of time-varying model parameters and complex external disturbances. The research involves developing an underwater dynamic model and analyzing hydrodynamic forces to calculate theoretical inertial hydrodynamic forces and simulate viscous hydrodynamic forces. This establishes the relationship between viscous hydrodynamic forces and exit velocity. A complete air dynamic model is then established, selecting model parameters based on the center of mass position of the amphibious vehicle to enable switching from water to air. To address control algorithm instability caused by changes in model parameters, position and attitude controllers are built using the ADRC algorithm. The control effects are compared with traditional PID and sliding mode controllers (SMC) to verify the effectiveness and superiority of the proposed cascade ADRC control strategy. Experimental results show that our controller has stronger anti-interference than traditional PID and SMC controllers and can overcome control instability caused by changes in model parameters. Our research highlights the importance of using ADRC-based controllers for amphibious multi-rotor UAVs to achieve robust and stable control.
Two-Step Algorithm for License Plate Identification Using Deep Neural Networks
Authors: Kundrotas Mantas , Janutėnaitė-Bogdanienė Jūratė , Šešok Dmitrij
Published: April 2023

License plate identification remains a crucial problem in computer vision, particularly in complex environments where license plates may be confused with road signs, billboards, and other objects. This paper proposes a solution by modifying the standard car–license plate–letter detection approach into a preliminary license plate detection–precise license plate detection of the four corners where the numbers are located–license plate correction–letter identification. This way, the first algorithm identifies all potential license plates and passes them as input parameters to the next algorithm for more precise detection. The main difference between this approach and other algorithms is that it uses a relatively small image compared to the whole vehicle. Thus, a small but robust network is used to find the four corners and perform a perspective transformation. This simplifies the letter recognition task for the next algorithm, as no additional transformations are required. This solution could be useful for research focusing on this specific task. It allows to apply another compact but robust neural network, increasing the overall speed of the system. Publicly available datasets were used for training and validation. The CenterNet object detection algorithm was used as a basis with a modified Hourglass-type network. The size of the network was decreased by 40% and the average accuracy was 96.19%. Speed significantly increased, reaching 2.71 ms and 405 FPS on average.
Thermo-Mechanical Buckling and Non-Linear Free Oscillation of Functionally Graded Fiber-Reinforced Composite Laminated (FG-FRCL) Beams
Authors: Alimoradzadeh Mehdi , Heidari Habib , Tornabene Francesco , Dimitri Rossana
Published: April 2023

We investigated the thermal buckling temperature and nonlinear free vibration of functionally graded fiber-reinforced composite laminated (FG-FRCL) beams. The governing nonlinear partial differential equations were derived from the Euler–Bernoulli beam theory, accounting for the von Kármán geometrical nonlinearity. Such equations were then reduced to a single equation by neglecting the axial inertia. Thus, the Galerkin method was applied to discretize the governing nonlinear partial differential equation in the form of a nonlinear ordinary differential equation, which was then solved analytically according to the He’s variational method. Three different boundary conditions were selected, namely simply, clamped and clamped-free supports. We also investigated the effect of power-index, lay-ups, and uniform temperature rise on the nonlinear natural frequency, phase trajectory and thermal buckling of FG-FRCL beams. The results showed that FG-FRCL beams featured the highest fundamental frequency, whereas composite laminated beams were characterized by the lowest fundamental frequency. Such nonlinear frequencies increase for an increased power index and a decreased temperature. Finally, it was found that FG-FRCL beams with [0/0/0] lay-ups featured the highest nonlinear natural frequency and the highest thermal buckling temperature, followed by [0/90/0] and [90/0/90] lay-ups, while a [90/90/90] lay-up featured the lowest nonlinear natural frequency and critical buckling temperature.
Examination of the Effects of Different Frequencies on Rock Fracturing via Laboratory-Scale Variable Amplitude Fatigue Loading Experiments
Authors: Gao Shaohua , Meng Huajun , Wu Yunfeng , Wang Xuebing , Wang Yu , Wu Jihuan , Wang Peng
Published: April 2023

In this study, the characteristics of the fracture evolution of argillaceous shale under increasing-amplitude loading were investigated. The GCTS RTR-2000 test system and in-situ acoustic emission (AE) monitoring were employed to execute the tests. The following results were observed. (1) The strength, deformation, and fatigue life increased with the frequency, and the morphology of the hysteresis curve changed regularly with time. (2) The cumulative damage of the rock at the location in which the stress amplitude suddenly increased exceeded that at the fatigue loading stage. The AE count and AE energy were affected by the loading frequency. (3) The secant modulus exhibited different values for different loading frequencies; the smaller the loading frequency, the fewer loading stages the samples experienced, and the faster the secant modulus decreased. The change in Poisson’s ratio over the entire process was composed of a steady growth stage and a rapid growth stage. (4) The rock exhibited two stages of damage evolution, with rapid damage accumulation occurring at the beginning of the loading and relatively smooth damage occurring thereafter. This study developed a cumulative fatigue damage model that can adequately fit the accumulated damage during the fracturing process. The experiments revealed that variable amplitude fatigue loading at different frequencies significantly influences the damage deterioration and the failure law of the rock. The results are expected to improve the understanding of the frequency effect on the fracture behavior and help predict the lifespan of rock structures. This is of great significance to the promotion of slope management, landslide disaster prevention, and mine reuse at the West Open-pit Mine.
Cross-Correlation and Fractal Analysis in the Images Diatoms Symmetry
Authors: Pestana-Nobles Roberto , Villarreal-González Reynaldo , Galan-Freyle Nataly J. , Aranguren-Díaz Yani , Machado-Sierra Elwi , Yime-Rodríguez Eugenio , Pacheco-Londoño Leonardo C.
Published: April 2023

Diatoms are one of the main species of interest due to their possible impact on the maintenance of ecosystems, as well as their photosynthetic capacity and biotechnological applications. Reliable, easy, rapid identification of diatoms remains a highly desirable goal to avoid the difficulty for experts in the visual analysis of frustules; a method commonly used to compare the distinctive traits of diatoms, such as ornaments and the marks from images taken by optical and electronic microscopy for identification. This study proposed three simple morphometric methods based on fractal dimension measurements in conjunction with 2D cross-correlation parameters to analyze diatom images. This novel approach demonstrates the differentiation feasibility between a wide variety of diatom species with heterogeneity in their structures and shapes, which could possibly expand the application of this preprocessing process for the identification of new types of images or other biological species.
Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network
Authors: Zhai Xubin , Shen Yu
Published: April 2023

The short-term bus passenger flow prediction of each bus line in a transit network is the basis of real-time cross-line bus dispatching, which ensures the efficient utilization of bus vehicle resources. As bus passengers transfer between different lines, to increase the accuracy of prediction, we integrate graph features into the recurrent neural network (RNN) to capture the spatiotemporal dependencies in the bus network. The diffusion convolution recurrent neural network (DCRNN) architecture is adopted to forecast the future number of passengers on each bus line. The demand evolution in the bus network of Jiading, Shanghai, is investigated to demonstrate the effectiveness of the DCRNN model. Compared with classic RNN models, our proposed method has an advantage of about 5% in mean average percentage error (MAPE). The incorporation of diffusion convolution shows that the travel demand in a bus line tends to be similar to that in the closely related lines. In addition, the improvement in MAPE shows that this model outputs more accurate prediction values for low-demand bus lines. It ensures that, for real-time cross-line bus dispatching with limited vehicle resources, the low-demand bus lines are less likely to be affected to maintain a decent level of service of the whole bus network.
Identification of Tree Species in Forest Communities at Different Altitudes Based on Multi-Source Aerial Remote Sensing Data
Authors: Lin Haoran , Liu Xiaoyang , Han Zemin , Cui Hongxia , Dian Yuanyong
Published: April 2023

The accurate identification of forest tree species is important for forest resource management and investigation. Using single remote sensing data for tree species identification cannot quantify both vertical and horizontal structural characteristics of tree species, so the classification accuracy is limited. Therefore, this study explores the application value of combining airborne high-resolution multispectral imagery and LiDAR data to classify tree species in study areas of different altitudes. Three study areas with different altitudes in Muyu Town, Shennongjia Forest Area were selected. Based on the object-oriented method for image segmentation, multi-source remote sensing feature extraction was performed. The recursive feature elimination algorithm was used to filter out the feature variables that were optimal for classifying tree species in each altitude study area. Four machine learning algorithms, SVM, KNN, RF, and XGBoost, were combined to classify tree species at each altitude and evaluate the accuracy. The results show that the diversity of tree layers decreased with the altitude in the different study areas. The texture features and height features extracted from LiDAR data responded better to the forest community structure in the different study areas. Coniferous species showed better classification than broad-leaved species within the same study areas. The XGBoost classification algorithm showed the highest accuracy of 87.63% (kappa coefficient of 0.85), 88.24% (kappa coefficient of 0.86), and 84.03% (kappa coefficient of 0.81) for the three altitude study areas, respectively. The combination of multi-source remote sensing numbers with the feature filtering algorithm and the XGBoost algorithm enabled accurate forest tree species classification.
Personalised Production in the Age of Circular Additive Manufacturing
Authors: Turner Chris , Oyekan John
Published: April 2023

This research examines the opportunities provided by advances in digital manufacturing technologies for the provision of products designed to meet the needs of an individual consumer. The ability to co-create products with customers could enable mass personalisation to become a popular and fast-growing mode of production. Additive manufacturing, in both 3D and 4D printing forms, opens up new opportunities for circular economy-compliant production of such highly personalised products. Industry 4.0 has been seen by many as an agenda for the utilisation of interconnected digital technologies in industry, with a particular focus on manufacturing. Industry 5.0 seeks to address challenges that have grown in importance since the inception of Industry 4.0, such as the efficient inclusion of human worker skills in tandem with automation solutions, to address highly complex manufacturing scenarios while mitigating many of the environmental issues inherent with current manufacturing practices, while using circular economy principles. In examining the production of smart fabrics, this paper puts forward a framework for circular production of additively manufactured personalised products, co-designed with inputs from consumers.
A Machine-Learning-Based Cyberattack Detector for a Cloud-Based SDN Controller
Authors: Mozo Alberto , Karamchandani Amit , de la Cal Luis , Gómez-Canaval Sandra , Pastor Antonio , Gifre Lluis
Published: April 2023

The rapid evolution of network infrastructure through the softwarization of network elements has led to an exponential increase in the attack surface, thereby increasing the complexity of threat protection. In light of this pressing concern, European Telecommunications Standards Institute (ETSI) TeraFlowSDN (TFS), an open-source microservice-based cloud-native Software-Defined Networking (SDN) controller, integrates robust Machine-Learning components to safeguard its network and infrastructure against potential malicious actors. This work presents a comprehensive study of the integration of these Machine-Learning components in a distributed scenario to provide secure end-to-end protection against cyber threats occurring at the packet level of the telecom operator’s Virtual Private Network (VPN) services configured with that feature. To illustrate the effectiveness of this integration, a real-world emerging attack vector (the cryptomining malware attack) is used as a demonstration. Furthermore, to address the pressing challenge of energy consumption in the telecom industry, we harness the full potential of state-of-the-art Green Artificial Intelligence techniques to optimize the size and complexity of Machine-Learning models in order to reduce their energy usage while maintaining their ability to accurately detect potential cyber threats. Additionally, to enhance the integrity and security of TeraFlowSDN’s cybersecurity components, Machine-Learning models are safeguarded from sophisticated adversarial attacks that attempt to deceive them by subtly perturbing input data. To accomplish this goal, Machine-Learning models are retrained with high-quality adversarial examples generated using a Generative Adversarial Network.
Assessing Vulnerability in Flood Prone Areas Using Analytic Hierarchy Process—Group Decision Making and Geographic Information System: A Case Study in Portugal
Authors: Mourato Sandra , Fernandez Paulo , Pereira Luísa Gomes , Moreira Madalena
Published: April 2023

A flood vulnerability index was constructed by coupling Geographic Information System (GIS) mapping capabilities with an Analytic Hierarchy Process (AHP) Group Decision-Making (GDM) resulting from a paired comparison matrix of expert groups to assign weights to each of the standardised criteria. A survey was sent to 25 flood experts from government organisations, universities, research institutes, NGOs, and the private sector (56% academics and 44% non-academics). Respondents made pairwise comparisons for several criteria (population, socio-economic, buildings, and exposed elements) and sub-criteria. The group priorities were obtained by combining the Consistency Ratio (CR) and Euclidean Distance (ED) measures to assess the weight of each expert and obtain a final weight for each criterion and sub-criteria. In Portugal, 23 flood-prone areas were considered, and this work contributes with a tool to assess the flood vulnerability and consequently the flood risk. The flood vulnerability index was calculated, and the relevance of the proposed framework is demonstrated for flood-prone areas, in mainland Portugal. The results showed that in all five hydrographic regions, flood-prone areas with very high vulnerability were found, corresponding to areas with a high probability of flooding. The most vulnerable areas are Ponte de Lima in the North, Coimbra, and Pombal in the Centre; Loures in the Tagus and West Region; Setúbal and Alcácer do Sal in the Alentejo Region and Monchique in the Algarve Region. This methodology has the potential to be successfully applied to other flood-prone areas, combining the opinions of stakeholders validated by a mathematical model, which allows the vulnerability of the site to be assessed.
Ultrasound Elastography in the Evaluations of Tendon-Related Disorders—A Systematic Review
Authors: Rossetto Gianluca , Scalona Emilia , Comotti Paolo , Gatti Lorenzo , Di Maso Denise , Gobbo Massimiliano , Lopomo Nicola Francesco
Published: April 2023

Tendon-related disorders are a common condition in both sports medicine and orthopedic clinical practice. Ultrasonography, power doppler, and magnetic resonance imaging (MRI) are the most widespread technologies, but the use of ultrasound elastography—including strain elastography and shear wave elastography—has been increasing in the last years. The aim of this paper is to evaluate the use of ultrasound elastography in tendon-related disorders. Research in PubMed, Scopus, and Web of Science databases is performed, and 364 papers are exported. After the study selection process, 38 papers are included in this systematic review. The risk of bias of each paper is evaluated using the RoBANS tool. Blinding, confounding variables, and measurement of exposure are the most affected items. From the included papers, tendinopathy is the most analyzed pathology, followed by tenosynovitis and rotator cuff disease. The Achilles tendon, patellar tendon, and common extensor tendon of the hand are the most analyzed tendons. Ultrasound elastography results in being the method providing good sensitivity and specificity (up to 100% and 100%, respectively, in tendinosis of the long head of the biceps tendon, in transverse plan examination) and accuracy (up to 97.8% in Achilles tendinopathy); furthermore, this technique is able to ensure real-time feedback on tissue elasticity and appears more sensitive than B-mode alone.
Joint Stiffness Identification Based on Robot Configuration Optimization Away from Singularities
Authors: Gao Ke , Zhou Xiaoqin , Wang Rongqi , Fan Mingxu , He Xiuzhi
Published: April 2023

Recently, industrial robots are mostly used in many areas because of their high dexterity and low price. Nevertheless, the low performance of robot stiffness is the primary limiting factor in machining applications. In this paper, a new method for identifying the joint stiffness of serial robots. The method considers the coupling of the end-effector’s rotational and translational displacements. Then, a new criterion is presented for optimizing the robot configuration. Next, the influence of each joint on the criterion is analyzed, and the corresponding joint is selected to simplify the experiment. After that, the method’s robustness, the sensitivity of the number of tests, and the experimental errors are analyzed. Finally, the KUKA KR60-3 robot is used as a descriptive example.
Advances in Chronic Kidney Disease in Africa
Authors: Rayner Brian L. , Jones Erika S. W. , Davidson Bianca , Wearne Nicola
Published: April 2023

Africa, particularly sub-Sharan Africa (SSA), faces major challenges in respect to chronic kidney disease (CKD). There is a rising prevalence due to the combined effects of hypertension, diabetes, and human immunodeficiency virus (HIV) (and the interaction between them) and the effect of apolipoprotein L1 (APOL1) variants on the susceptibility to CKD. Epidemiological data on the prevalence of CKD are of low-to-medium quality, and reliable data are urgently needed for health planning. Furthermore, there are important deficiencies in creatinine-based equations in underestimating the prevalence of CKD in Africa, and evidence suggests that cystatin C based equations are more reliable. There is a changing spectrum of HIV related CKD with the greater availability of antiretroviral treatment. Major clinical trials using SGLT2 inhibitors have signalled a major advance in the treatment of CKD, especially in relation to type 2 diabetes, but the affordability, availability, and relevance to the African population is not established. The importance of the effects of hypertension in pregnancy and pregnancy related acute kidney injury on CKD and the newer concept of CKD of unknown cause (CKDu) are highlighted. Hypertension remains a dominant cause of CKD in Africa, and newer information suggests that the most appropriate treatment to control blood pressure and thus prevent CKD is the combination of either amlodipine plus a thiazide diuretic or angiotensin converting enzyme (ACE) inhibitor.
Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
Authors: Lin Shaoxiong , Zhang Wangyou , Qian Yanmin
Published: April 2023

Speech enhancement has been extensively studied and applied in the fields of automatic speech recognition (ASR), speaker recognition, etc. With the advances of deep learning, attempts to apply Deep Neural Networks (DNN) to speech enhancement have achieved remarkable results and the quality of enhanced speech has been greatly improved. In this study, we propose a two-stage model for single-channel speech enhancement. The model has two DNNs with the same architecture. In the first stage, only the first DNN is trained. In the second stage, the second DNN is trained to refine the enhanced output from the first DNN, while the first DNN is frozen. A multi-frame filter is introduced to help the second DNN reduce the distortion of the enhanced speech. Experimental results on both synthetic and real datasets show that the proposed model outperforms other enhancement models not only in terms of speech enhancement evaluation metrics and word error rate (WER), but also in its superior generalization ability. The results of the ablation experiments also demonstrate that combining the two-stage model with the multi-frame filter yields better enhancement performance and less distortion.
RiceDRA-Net: Precise Identification of Rice Leaf Diseases with Complex Backgrounds Using a Res-Attention Mechanism
Authors: Peng Jialiang , Wang Yi , Jiang Ping , Zhang Ruofan , Chen Hailin
Published: April 2023

In this study, computer vision applicable to traditional agriculture was used to achieve accurate identification of rice leaf diseases with complex backgrounds. The researchers developed the RiceDRA-Net deep residual network model and used it to identify four different rice leaf diseases. The rice leaf disease test set with a complex background was named the CBG-Dataset, and a new single background rice leaf disease test set was constructed, the SBG-Dataset, based on the original dataset. The Res-Attention module used 3 × 3 convolutional kernels and denser connections compared with other attention mechanisms to reduce information loss. The experimental results showed that RiceDRA-Net achieved a recognition accuracy of 99.71% for the SBG-Dataset test set and possessed a recognition accuracy of 97.86% on the CBG-Dataset test set. In comparison with other classical models used in the experiments, the test accuracy of RiceDRA-Net on the CBG-Dataset decreased by only 1.85% compared with that on the SBG-Dataset. This fully illustrated that RiceDRA-Net is able to accurately recognize rice leaf diseases with complex backgrounds. RiceDRA-Net was very effective in some categories and was even capable of reaching 100% precision, indicating that the proposed model is accurate and efficient in identifying rice field diseases. The evaluation results also showed that RiceDRA-Net had a good recall ability, F1 score, and confusion matrix in both cases, demonstrating its strong robustness and stability.
The Effect of Different Archwires on Initial Orthodontic Pain Perception: A Prospective Controlled Cohort Study
Authors: Bartolucci Maria Lavinia , Incerti Parenti Serena , Solidoro Livia , Tonni Ingrid , Bortolotti Francesco , Paganelli Corrado , Alessandri-Bonetti Giulio
Published: April 2023

The early stages of orthodontic treatment are frequently associated with pain that can vary in intensity and duration, representing one of the main reasons for treatment discontinuation. Whilst the use of drugs is recognised as being effective to control orthodontic pain, there are no reliable data indicating the best first archwire for efficacy and minimum discomfort. A prospective controlled cohort study was conducted to compare the intensity and the characteristics of orthodontic pain during the first 15 days of treatment with 2 archwires. Fifty subjects were enrolled and divided into two groups: one received 0.012 inch stainless steel (SS) as the first archwire; the other, a 0.014 inch super-elastic nickel–titanium (Ni-Ti) archwire. Patients compiled a visual analogue scale to measure pain intensity over 15 days, a questionnaire for pain characteristics, the Somatosensory Amplification Scale and the State-Trait Anxiety Inventory to control the psychosocial component of pain. Dental casts were digitally analysed to evaluate the initial arch length discrepancy. In the first 3 days of treatment, the mean VAS values of the SS group were significantly lower than those of the Ni-Ti group (p < 0.05). No significant differences emerged between the groups concerning pain characteristics. The 0.012 inch SS archwire could be used at the beginning of orthodontic treatment to minimise pain perception and improve compliance.
Automatic Parsing and Utilization of System Log Features in Log Analysis: A Survey
Authors: Ma Junchen , Liu Yang , Wan Hongjie , Sun Guozi
Published: April 2023

System logs are almost the only data that records system operation information, so they play an important role in anomaly analysis, intrusion detection, and situational awareness. However, it is still a challenge to obtain effective data from massive system logs. On the one hand, system logs are unstructured data, and, on the other hand, system log records cannot be directly analyzed and calculated by computers. In order to deal with these problems, current researchers digitize system logs through two key steps of log parsing and feature extraction. This paper classifies, analyzes, and summarizes the current log analysis research in terms of log parsing and feature extraction by investigating articles in recent years (including ICSE, TKDD, ICDE, IJCAI, ISSRE, ICDM, ICWS, ICSME, etc.). Finally, in combination with the existing research, the research prospects in the field are elaborated and predicted.
Generalized Super-Twisting Backstepping Sliding Mode Control for Electro-Hydraulic Servo Systems Considering the Coexistence of Matched and Mismatched Uncertainties
Authors: Cao Qianyu , Su Xiaoyu
Published: April 2023

Aiming at the problem of the coexistence of matching and mismatching uncertainties in electro-hydraulic servo systems, disturbance observers and a backstepping sliding mode controller based on the generalized super-twisting algorithm (GSTA) are proposed in this paper. First, in order to compensate for the uncertainty in the controller, two generalized super-twisting disturbance observers (GSTDOs) are constructed to effectively reduce the discontinuous gain of the controller. Then, the GSTA is introduced to optimize the backstepping sliding mode controller to obtain a better control effect. Finally, the proposed control strategy is compared and simulated on the electro-hydraulic servo system. The experimental results verify that the proposed control strategy has better tracking performance. The effect of asymptotic tracking in the presence of parameter uncertainties and disturbances is achieved, while transient tracking performance and final tracking accuracy are guaranteed in the presence of time-varying uncertain nonlinearities.
The Contribution of Near-Surface Geophysics for the Site Characterization of Seismological Stations
Authors: Alexopoulos John D. , Dilalos Spyridon , Voulgaris Nicholas , Gkosios Vasileios , Giannopoulos Ioannis-Konstantinos , Kapetanidis Vasilis , Kaviris George
Published: April 2023

The Athenet network is the network of the Seismological Laboratory of the National and Kapodistrian University of Athens. We present the geophysical investigation that has been carried out at six seismological stations of the Athenet network for their site characterization. More specifically, at the location of each seismological station, four geophysical methods have been carried out: Seismic Refraction Tomography (SRT), Multichannel Analysis of Surface Waves (MASW), the Horizontal to Vertical Spectral Ratio (HVSR) technique, and Electrical Resistivity Tomography (ERT). The applied geophysical survey provided important information regarding the site characterization at the selected seismological stations, including key parameters such as the fundamental frequency fo, the shear-wave velocity VS, the average shear-wave velocity for the upper 30 m depth (VS30), the seismic bedrock depth, the soil type, and the subsurface geology. Moreover, selected elastic moduli (Poisson’s ratio, shear, bulk, and Young moduli) have been calculated. The site characterization information contributes to the determination of the amplification factors for each site that can lead to more accurate calculation of Peak Ground Acceleration (PGA) or Peak Ground Velocity (PGV) and, therefore, trustworthy Probabilistic and Stochastic Seismic Hazard Assessments. The derived fundamental frequency for the seismological stations of VILL, LOUT, THAL, and EPID have been determined to be equal to 10.4, 2.7, 1.4, and 7.1 Hz and their amplification factors to be 1.9, 3.1, 1.7, and 2.6, respectively. For stations MDRA and ATAL, these parameters could not be determined.
The Characterization of a Fragment of a Medieval Fresco from Corbii de Piatră Cave Church
Authors: Vâlcea Adriana Elena , Grecu Dorin , Mariș Izabela , Negrea Aurelian Denis , Cimpoeșu Nicanor , Giugea Daniela , Istrate Bogdan , Munteanu Corneliu , Moga Sorin Georgian , Anghel Daniel-Constantin , Rizea Alin Daniel , Petrescu Mircea Ionuț , Abrudeanu Mărioara
Published: April 2023

The fresco of the Corbii de Piatră Cave Church, dating from the end of the 13th century and the beginning of the 14th century, is applied to the sandstone wall. The degradation of the fresco on large surfaces, with many areas of detachment, has been determined by the infiltration of meteoric water through the sandstone wall on which it is applied, as well as temperature variations, and repeated wetting/drying processes. However, there are small portions of fresco that show good adhesion to the wall. The present research, aimed at providing scientific data to restorers and historians, involves the advanced characterization of a fragment of fresco with good adhesion to the wall and is being carried out by an interdisciplinary team. The stratigraphy, microstructure, compaction defects, chemical composition, and variation of chemical composition in the fresco from the pictorial surface to the mortar-sandstone interface were determined. Correlations were established between degradation processes and wall adhesion.
Particulate Matter 2.5 Level Modulates Brachial Artery Flow-Mediated Dilation Response to Aerobic Exercise in Healthy Young Men
Authors: Kim Jin-Su , Lee Do Gyun , Hwang Moon-Hyon
Published: April 2023

Particulate matter 2.5 (PM2.5) is an environmental pollutant linked with the risk of cardiovascular disease. Aerobic exercise performed in polluted environments may have fewer benefits because of increased PM2.5 inhalation during exercise. However, the vascular responses to aerobic exercise in high PM2.5 (HPM2.5) conditions remain unknown. This study aimed to examine the acute flow-mediated dilation (FMD) response to moderate-intensity treadmill running in HPM2.5 levels compared to low PM2.5 (LPM2.5) levels in healthy young males. Treadmill running in both HPM2.5 and LPM2.5 levels was completed by nine subjects. Brachial artery FMD was measured before and after the exercise to assess vascular endothelial function. Indoor PM2.5 concentration was significantly higher in HPM2.5 than in LPM2.5 conditions (p < 0.001). Scaled FMD significantly increased after the exercise in LPM2.5 conditions but not in HPM2.5 (p = 0.03), and baseline diameter increased only in HPM2.5 conditions after the exercise (p = 0.001). Baseline diameter and peak diameter were smaller, and time to peak dilation was delayed in HPM2.5 compared to LPM2.5 in pre-exercise intervention measurements (p < 0.05). Therefore, acute PM2.5 exposure can counteract the positive effect of aerobic exercise on vascular endothelial function in young males.
Analysis of the Mechanical Performance and Durability of Adhesively Bonded Joints Used in the Milling Tool Industry
Authors: de Sousa Rafael J. F. , Gomes Pedro N. , Correia Daniel S. , Carbas Ricardo J. C. , Marques Eduardo A. S. , das Neves Paulo J. C. , Afonso Willian P. , da Silva Lucas F. M.
Published: April 2023

Epoxy adhesives, widely used in multiple structural applications, are used in the milling tool industry to replace brazing and mechanical fastening when joining the cutting bits to the tool body; though their durability is still a concern. This work aims to evaluate and characterise the effect of environmental factors associated with a tool’s life cycle on the performance of these bonded joints. A gravimetric analysis was conducted on bulk adhesive plates for water, cutting emulsion and dielectric fluid to obtain diffusion and relaxation rates. Novel real joint shear specimens were developed to enable strength testing on joints which are comparable with the final application. These specimens were immersed in fluids and subjected to thermal cycles or a corrosive finishing surface treatment to simulate the tool’s life cycle. The joint’s resistance was then benchmarked. Lastly, a dimensional variation test was carried out on tool prototypes before and after ageing, showing no significant dimensional variation which could compromise the cutting performance of the tool. Overall, though it was possible to identify a decrease in strength of around 20% in most tests, joint strength was still within the values necessary for safe operation, with a large safety factor still being retained.