bluuebunny/crossref_metadata_2025 huggingface dataset:
Retrospective Analysis of Functional Pain among Professional Climbers
Authors: Jakub Matuska , Marta Jokiel , Przemysław Domaszewski , Mariusz Konieczny , Paweł Pakosz , Tomasz Dybek , Daria Wotzka , Elżbieta Skorupska
Published: March 2022

Climbing became one of the official Olympic sports in 2020. The nociplastic pain mechanism is indicated as important in professional sports. Functional pain, which has not been examined in climbers until now, can be an example of nociplastic pain. This study aimed to determine functional pain locations in climbers according to gender and dominant climbing style. Climbers (n = 183) and healthy subjects (n = 160) completed an online survey focused on functional pain occurrence in the head, spine, and upper limbs. The logistic regression showed that climbing predisposes one to functional pain at: Gleno-humeral joint (odds ratio (OR): 3.06; area under the curve (AUC): 0.635), elbow (OR: 2.86; AUC: 0.625), fingers (OR: 7.74; AUC: 0.733), all (p < 0.05). Among the climbers, the female gender predisposed one to pain at: GHJ (OR: 3.34; AUC: 0.638), thoracic spine (OR: 1.95; AUC: 0.580), and lumbosacral spine (OR: 1.96; AUC: 0.578), all (p < 0.05). Climbing predisposes one to functional pain development in the upper limb. While the male climbers mainly suffered from finger functional pain, the female climbers reported functional pain in the GHJ and the thoracic and lumbosacral spine. Further studies on functional pain occurrence are recommended.
Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms
Authors: Zhao Siguang , Wang Mingnian , Yi Wenhao , Yang Di , Tong Jianjun
Published: March 2022

The quality evaluation of the surrounding rock is the cornerstone of tunnel design and construction. Previous studies have confirmed the existence of a relationship between drilling parameters and the quality of surrounding rock. The application of drilling parameters to the intelligent classification of surrounding rock has the natural advantages of automatic information collection, real-time analysis, and no extra work. In this work, we attempt to establish the intelligent surrounding rock classification model and software system driven by drilling parameters. We collected 912 samples containing four drilling parameters (penetration velocity, hammer pressure, rotation pressure, and feed pressure) and three surrounding rock (grade-III, grade-IV, and grade-V). Based on the python machine learning toolkit (Scikit-learn), 10 types of supervised machine learning algorithms were used to train the intelligent surrounding rock classification model with the model parameter selection technology of grid search cross validation. The results show that the average accuracy is 0.82, which proves the feasibility of this method. Finally, the tunnel surrounding rock intelligent classification system was established based on three models with better comprehensive performance among them. The classification accuracy of the system was 0.87 in the tunnel test section, which indicates that the system has good generalization performance and practical value.
Correlation of Gravity and Magnetic Field Changes Preceding Strong Earthquakes in Yunnan Province
Authors: Liu Dong , Li Jiancheng , Ni Zhe , Zhao Yufei , Zheng Qiuyue , Du Bin
Published: March 2022

The annual variation trend of the gravity and lithospheric magnetic field for adjacent periods are analyzed by using the observation of rover gravity and geomagnetic fields in Yunnan from 2011 to 2021, which tend to be consistent every year during the seismogenic process of a strong earthquake. Thus, this study normalizes the annual value of the adjacent periods for the gravity and lithospheric magnetic field. The normalized values are converted into two classifications that can be compared within [−1,1]. In Yunnan Province, a grid of 0.1° × 0.1° was used to compare the data correlation between the variation of gravity and the variation in the lithospheric magnetic field at the same location. The results are as follows. First, the variation trend of the gravity field and total magnetic field tend to be synchronous year to year in strong earthquake years. The range of consistency increases gradually with the approach of the earthquake year reaching its maximum one year before the earthquake. Throughout the region, the overlap number of normalized annual variations in gravity and magnetic field reaches its maximum, and the peak difference of kernel density curve reaches its minimum. Second, the correlation coefficient of the annual variation in the gravity and magnetic field increases year to year during the development of a strong earthquake within a smaller region surrounding the event. The maximum appears one year before the earthquake, and after the earthquake, the correlation decreases. The analysis of gravity and magnetic fusion characteristics can be employed for the prediction of strong earthquakes.
Improvement of the Standard Chimney Electrostatic Precipitator by Dividing the Flue Gas Stream into a Larger Number of Pipes
Authors: Trnka Juraj , Jandačka Jozef , Holubčík Michal
Published: March 2022

Combustion of biomass-based solid fuels is becoming increasingly popular, especially in small heat sources. A major problem in the combustion of biomass is the increased production of emissions and especially the solid component of PM particles. Currently, the most used solution to this problem is the application of electrostatic chimney separators, which innovations are discussed in our article. Two models of electrostatic precipitators were constructed in this work. The aim of this work was to compare the use of a standard single-pipe chimney electrostatic precipitator with a newer four-pipe variant. Eight measurements were performed on both devices with and without the use of an electrostatic precipitator, on the basis of which the separation efficiency was evaluated for both variants. The results of the measurements showed the initial value of the average PM production in the one-pipe variant decreased from 1012 to 416 mg.m−3 when using the separator, while in the use of the four-pipe variant it decreased from the starting value 342 to only 152 mg.m−3. These results show that the improvement of the classic single-pipe separator by increasing the number of tubes significantly reduced the production of PM emissions and increased the separation efficiency from 66 to 85%.
Active Tendon Control of Stay Cable by a Giant Magnetostrictive Actuator Considering Time-Delay
Authors: Wang Wenxi , Yang Zhilin , Hu Renkang , Hua Xugang , Wang Xiuyong
Published: March 2022

Active control of a stay cable through changing its tension is an effective way to lower the vibration amplitude. In this paper, active tendon control of a stay cable using a giant magnetostrictive actuator (GMA), which can generate axial deformation in a short period (normally less than 10 ms) under magnetic fields, is studied considering the time-delay. The bilinear controlled state equation of the small-sag cable is established by employing the Lagrange formulation. The linearization method for the bilinear controlled state equation is proposed and validated by numerical simulations. A GMA is designed and manufactured to actively control a stay cable model and the relationship between the output force and the input voltage is obtained by experiments. Based on the phase-shifting method, a multi-mode control algorithm with the time-delay compensation is proposed, and the time-delay compensation matrix based on the state feedback control algorithm is obtained. Based on a 1:20 scaled cable model, simulations and experiments are conducted to investigate the control performance of the proposed method under different external loads including the free vibration, harmonic excitation and random excitation. The results show that the tendon control of cable vibration by a GMA may be invalid and even amplify the vibration amplitude when the compensation of time-delay is not considered. Moreover, the excellent control performance can be achieved by using the proposed time-delay compensation method.
An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application
Authors: Razmjoo Armin , Gandomi Amirhossein , Mahlooji Maral , Astiaso Garcia Davide , Mirjalili Seyedali , Rezvani Alireza , Ahmadzadeh Sahar , Memon Saim
Published: March 2022

As smart cities (SCs) emerge, the Internet of Things (IoT) is able to simplify more sophisticated and ubiquitous applications employed within these cities. In this regard, we investigate seven predominant sectors including the environment, public transport, utilities, street lighting, waste management, public safety, and smart parking that have a great effect on SC development. Our findings show that for the environment sector, cleaner air and water systems connected to IoT-driven sensors are used to detect the amount of CO2, sulfur oxides, and nitrogen to monitor air quality and to detect water leakage and pH levels. For public transport, IoT systems help traffic management and prevent train delays, for the utilities sector IoT systems are used for reducing overall bills and related costs as well as electricity consumption management. For the street-lighting sector, IoT systems are used for better control of streetlamps and saving energy associated with urban street lighting. For waste management, IoT systems for waste collection and gathering of data regarding the level of waste in the container are effective. In addition, for public safety these systems are important in order to prevent vehicle theft and smartphone loss and to enhance public safety. Finally, IoT systems are effective in reducing congestion in cities and helping drivers to find vacant parking spots using intelligent smart parking.
Searching Strategies with Low Computational Costs for Multiple-Vehicle Bike Sharing System Routing Problem
Authors: Tsushima Honami , Matsuura Takafumi , Ikeguchi Tohru
Published: March 2022

We have already proposed a multiple-vehicle bike sharing system routing problem (mBSSRP) to adjust the number of bicycles at each port using multiple vehicles in short time. However, there are many strict constraints in the mBSSRP, thus it is difficult to obtain feasible solutions of the mBSSRP for some instances. To obtain feasible solutions of the mBSSRP, we have proposed a mBSSRP with soft constraints (mBSSRP-S) that removes some constraints from mBSSRP and appends violations to an objective function as penalties, and a searching strategy that explores both the feasible and infeasible solution spaces. Numerical experiments indicated that solving mBSSRP-S to obtain feasible solutions of mBSSRP results in better performance than solving mBSSRP directly. However, mBSSRP-S includes infeasible solutions of mBSSRP, thus the neighborhood solutions and computational costs increase. In this study, we propose search strategies with low computational costs while maintaining performance. In particular, we propose two search strategies: the first one is to reduce neighborhood solutions to obtain a feasible solution in a short time before finding a feasible solution of the mBSSRP, and the second one is to change the problem to be solved (mBSSRP or mBSSRP-S) after a feasible solution is obtained and to search good near-optimal solutions in a short time. As the first search strategy, we propose two search methods for reducing the number of neighborhood solutions in the Or-opt and the CROSS-exchange and compare their performance with our previous results. From numerical experiments, we confirmed that a feasible solution can be obtained within a short time by exploring only the normal order insertion of the Or-opt and the normal order exchange of the CROSS-exchange as the neighborhood solutions. Next, as the second search strategy after a feasible solution of mBSSRP is obtained, we propose four search methods and compare their performance with our previous results. Numerical experiments show that the search method that only searches for the normal order insertion of the Or-opt and the normal order exchange of the CROSS-exchange with hard constraints after obtaining a feasible solution can obtain short tours within a short time.
Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods
Authors: Jaruenpunyasak Jermphiphut , García Seco de Herrera Alba , Duangsoithong Rakkrit
Published: March 2022

Lower-body detection can be useful in many applications, such as the detection of falling and injuries during exercises. However, it can be challenging to detect the lower-body, especially under various lighting and occlusion conditions. This paper presents a novel lower-body detection framework using proposed anthropometric ratios and compares the performance of deep learning (convolutional neural networks and OpenPose) and traditional detection methods. According to the results, the proposed framework helps to successfully detect the accurate boundaries of the lower-body under various illumination and occlusion conditions for lower-limb monitoring. The proposed framework of anthropometric ratios combined with convolutional neural networks (A-CNNs) also achieves high accuracy (90.14%), while the combination of anthropometric ratios and traditional techniques (A-Traditional) for lower-body detection shows satisfactory performance with an averaged accuracy (74.81%). Although the accuracy of OpenPose (95.82%) is higher than the A-CNNs for lower-body detection, the A-CNNs provides lower complexity than the OpenPose, which is advantageous for lower-body detection and implementation on monitoring systems.
Multigraphene Prepared by One-Pot Pyrolysis of Diatomite/Polypropylene Composites
Authors: Chen Yankun , Wang Biao
Published: March 2022

Multigraphene was prepared via a one-pot pyrolysis method using polypropylene (PP) as the carbon source and diatomite (DM) as the catalyst. The obtained graphene had 4–6 layers and a D/G intensity ratio of 0.70 and a 2D/G intensity ratio of 0.72, indicating a high degree of graphitization. When the pyrolysis temperature was higher than 850 °C under argon, the graphene yield was greatly dependent on the DM content. The highest graphene yield of 25.86% was obtained by pyrolysis of PP with 30 wt.% DM at the temperature of 1000 °C. A catalytic effect of DM and infusible cross-linking structure formation were proposed to explain the possible mechanism of graphene growth during the pyrolysis of the DM/PP composites.
Movement Compensated Driver’s Respiratory Rate Extraction
Authors: Yoo Young-Keun , Shin Hyun-Chool
Published: March 2022

In non-contact vital sign monitoring using radar, radar signal distorted by the surrounding unspecified factors is unsuitable for monitoring vital signs. In order to monitor vital signs accurately, it is essential to compensate for distortion of radar signals caused by surrounding environmental factors. In this paper, we propose a driver vital signal compensation method in driving situations, including the driver’s movements using a frequency-modulated continuous-wave (FMCW) radar. Driver’s movement is quantified from the radar signal and used to set a distortion signal compensation index to compensate for the signal distortion induced in the driving situation that the driver’s movement occurs. The experimental results show that the respiration rate estimated from the radar signal compensated through the proposed method is similar to the actual respiration rate than from the signal before calibration. These results confirm the possibility of using the proposed method in a non-statistic situation and effectiveness in estimating respiration rate reflecting human movement in monitoring vital signs using FMCW radar.
Robust Adaptive Path Following Control Strategy for Underactuated Unmanned Surface Vehicles with Model Deviation and Actuator Saturation
Authors: Fan Yunsheng , Zou Xinpeng , Wang Guofeng , Mu Dongdong
Published: March 2022

This paper shows solicitude for the path following control issues of underactuated unmanned surface vehicles subject to unknown external disturbances, deviation of vessel model parameters and actuator saturation. Initially, an improved adaptive integral line-of-sight (IALOS) guidance law is introduced to estimate the sideslip angle, which helps to promote the precision of path following. Furthermore, a finite-time convergent disturbance observer is utilized to size up time-varying disturbances and the single-parameter neural network strategy is utilized to reduce the impact of model deviation. Meanwhile, by introducing a finite-time auxiliary dynamic system to improve the impact of actuator saturation (input saturation), the higher-order tracking differentiator (TDS) is introduced into the backstepping controller for reducing the number of derivations. It is shown that all error signals of the control system, employing Lyapunov stability theory, are uniformly ultimately bounded. Finally, the validity of the put forward scheme is validated by numerical simulations.
High Definition tDCS Effect on Postural Control in Healthy Individuals: Entropy Analysis of a Crossover Clinical Trial
Authors: Favoretto Diandra B. , Bergonzoni Eduardo , Nascimento Diego Carvalho , Louzada Francisco , Lemos Tenysson W. , Batistela Rosangela A. , Moraes Renato , Leite João P. , Rimoli Brunna P. , Edwards Dylan J. , Edwards Taiza G. S.
Published: March 2022

Objective: Converging evidence supporting an effect of transcranial direct current stimulation (tDCS) on postural control and human verticality perception highlights this strategy as promising for post-stroke rehabilitation. We have previously demonstrated polarity-dependent effects of high-definition tDCS (HD-tDCS) on weight-bearing asymmetry. However, there is no investigation regarding the time-course of effects on postural control induced by HD-tDCS protocols. Thus, we performed a nonlinear time series analysis focusing on the entropy of the ground reaction force as a secondary investigation of our randomized, double-blind, placebo-controlled, crossover clinical trial. Materials and Methods: Twenty healthy right-handed young adults received the following conditions (random order, separate days); anode center HD-tDCS, cathode center HD-tDCS or sham HD-tDCS at 1, 2, and 3 mA over the right temporo-parietal junction (TPJ). Using summarized time series of transfer entropy, we evaluated the exchanging information (causal direction) between both force plates and compared the dose-response across the healthy subjects with a Generalized Linear Hierarchical/Mixed Model (GLMM). Results: We found significant variation during the dynamic information flow (p < 0.001) among the dominant bodyside (and across time). A greater force transfer entropy was observed from the right to the left side during the cathode-center HD-tDCS up to 2 mA, with a causal relationship in the information flow (equilibrium force transfer) from right to left that decreased over time. Conclusions: HD-tDCS intervention induced a dynamic influence over time on postural control entropy. Right hemisphere TPJ stimulation using cathode-center HD-tDCS can induce an asymmetry of body weight distribution towards the ipsilateral side of stimulation. These results support the clinical potential of HD-tDCS for post-stroke rehabilitation.
Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
Authors: Gao Ziwen , Li Zhiyi , Luo Jiaying , Li Xiaolin
Published: March 2022

This paper describes the construction a short-text aspect-based sentiment analysis method based on Convolutional Neural Network (CNN) and Bidirectional Gating Recurrent Unit (BiGRU). The hybrid model can fully extract text features, solve the problem of long-distance dependence on the sequence, and improve the reliability of training. This article reports empirical research conducted on the basis of literature research. The first step was to obtain the dataset and perform preprocessing, after which scikit-learn was used to perform TF-IDF calculations to obtain the feature word vector weight, obtain the aspect-level feature ontology words of the evaluated text, and manually mark the ontology of the reviewed text and the corresponding sentiment analysis polarity. In the sentiment analysis section, a hybrid model based on CNN and BiGRU (CNN + BiGRU) was constructed, which uses corpus sentences and feature words as the vector input and predicts the emotional polarity. The experimental results prove that the classification accuracy of the improved CNN + BiGRU model was improved by 12.12%, 8.37%, and 4.46% compared with the Convolutional Neural Network model (CNN), Long-Short Term Memory model (LSTM), and Convolutional Neural Network (C-LSTM) model.
Pattern Classification and Gear Design of Spatial Noncircular Gear Continuously Variable Transmission
Authors: Yu Yongquan , Lin Chao , Xu Ping
Published: March 2022

Noncircular gear and curve face gear are collectively referred to as spatial noncircular gear. Combined with the transmission characteristics of spatial noncircular gear, a new classification design method of spatial noncircular gear continuously variable transmission (CVT) pattern was proposed, including two categories named addition type and multiplication type, with a total of five subcategories. Through the combination of the transmission ratio changing mechanism and transmission selection mechanism, it can realize the CVT. The overall transmission ratio depends on the phase angle between the spatial noncircular gear pairs and is independent of the input rotation angle. Firstly, the CVT principle and transmission ratio characteristics of each pattern were analyzed. Then the tooth profile classification design of spatial noncircular gear was carried out, and the general parametric design equations of the tooth surface with different tooth profiles were obtained. Finally, through simulation, the overall transmission ratio of each pattern was compared between theory and simulation, which verified the correctness of the CVT classification design method. The advantages and disadvantages of the two categories were analyzed. Moreover, through the transmission experiments of curve face gear pairs with different tooth profiles, the transmission ratios were compared between theory and experiment. Considering the influencing factors such as machining error, assembly error and measurement error, the experimental error is within a reasonable error range, which verified the correctness of the tooth profile classification design method. This study provides a new idea for the further research and application of spatial noncircular gear CVT.
Two-Dimensional Flow Field Measurement Method for Sediment-Laden Flow Based on Optical Flow Algorithm
Authors: Tao Weiliang , Liu Yan , Ma Zhimin , Hu Wenbin
Published: March 2022

This paper proposes a novel particle image velocimetry (PIV) technique to generate an instantaneous two-dimensional velocity field for sediment-laden fluid based on the optical flow algorithm of ultrasound imaging. In this paper, an ultrasonic PIV (UIV) system is constructed by integrating a medical ultrasound instrument and an ultrasonic particle image velocimetry algorithm. The medical ultrasound instrument with a phased sensor array is used to acquire acoustic echo signals of particles in water and generate two-dimensional underwater ultrasound images. Based on the optical flow field, the instantaneous velocity of the particles in water corresponding to the pixels in the ultrasonic particle images is derived from the grayscale change between adjacent images under the L-K local constraint based on the optical flow field, and finally, the two-dimensional flow field is obtained. Through multiple sets of experiments, the proposed algorithm is verified. The experimental results are compared with those of the conventional cross-correlation algorithms. The results show that the L-K optical flow method can not only obtain the underwater velocity field accurately, but also has the advantages of good smoothness and extensive suitability, especially for the flow field measurement in sediment-laden fluid than conventional algorithms.
Improved MSRN-Based Attention Block for Mask Alignment Mark Detection in Photolithography
Authors: Park Juyong , Jeong Jongpil
Published: March 2022

Wafer chips are manufactured in the semiconductor industry through various process technologies. Photolithography is one of these processes, aligning the wafer and scanning the circuit pattern on the wafer on which the photoresist film is formed by irradiating light onto the circuit pattern drawn on the mask. As semiconductor technology is highly integrated, alignment is becoming increasingly difficult due to problems such as reduction of alignment margin, transmittance due to level stacking structure, and an increase in wafer diameter in the photolithography process. Various methods and research to reduce the misalignment problem that is directly related to the yield of production are constantly being conducted. In this paper, we use machine vision for exposure equipment to improve the image resolution quality of marks for accurate alignment. To improve image resolution quality, we propose an improved Multi-Scale Residual Network (MSRN) that combines Attention Mechanism using a Multi-Scale Residual Attention Block to improve image resolution quality. Our proposed method can extract enhanced features using two different bypass networks and attention blocks with different scale convolution filters. Experiments were used to verify this method, and the performance was improved compared with previous research.
An Effective Tangent Stiffness of Train–Track–Bridge Systems Based on Artificial Neural Network
Authors: Gu Quan , Pan Jinghao , Liu Yongdou , Fu Minhong , Zhang Jianguo
Published: March 2022

Dynamic response analysis of a train–track–bridge (TTB) system is a challenging task for researchers and engineers, partially due to the complicated nature of the wheel–rail interaction (WRI). When Newton’s method is used to solve implicit nonlinear finite element equations of a TTB system, consistent tangent stiffness (CTS) is essential to guarantee the quadratic convergence rate. However, the derivation and software implementation of CTS for the WRI element require significant efforts. Artificial neural network (ANN) can directly obtain a potentially good tangent stiffness by a trained relationship between input nodal displacement/velocity and output tangent stiffness. In this paper, the backpropagation neural-network-based tangent stiffness (BPTS) of the WRI element is derived and implemented into a general finite element software, OpenSees, and verified by dynamic response analysis of a high-speed train running on a seven span simply supported beam bridge. The accuracy and efficiency are compared between the use of BPTS and CTS. The results demonstrate that BPTS can not only save the significant efforts of deriving and software implementing CTS but also improve computational efficiency while ensuring good accuracy.
Sensitivity Analysis for Transient Thermal Problems Using the Complex-Variable Finite Element Method
Authors: Rincon-Tabares Juan-Sebastian , Velasquez-Gonzalez Juan C. , Ramirez-Tamayo Daniel , Montoya Arturo , Millwater Harry , Restrepo David
Published: March 2022

Solving transient heat transfer equations is required to understand the evolution of temperature and heat flux. This physics is highly dependent on the materials and environmental conditions. If these factors change with time and temperature, the process becomes nonlinear and numerical methods are required to predict the thermal response. Numerical tools are even more relevant when the number of parameters influencing the model is large, and it is necessary to isolate the most influential variables. In this regard, sensitivity analysis can be conducted to increase the process understanding and identify those variables. Here, we combine the complex-variable differentiation theory with the finite element formulation for transient heat transfer, allowing one to compute efficient and accurate first-order sensitivities. Although this approach takes advantage of complex algebra to calculate sensitivities, the method is implemented with real-variable solvers, facilitating the application within commercial software. We present this new methodology in a numerical example using the commercial software Abaqus. The calculation of sensitivities for the temperature and heat flux with respect to temperature-dependent material properties, boundary conditions, geometric parameters, and time are demonstrated. To highlight, the new sensitivity method showed step-size independence, mesh perturbation independence, and reduced computational time contrasting traditional sensitivity analysis methods such as finite differentiation.
A Cloud Microservices Architecture for Data Integrity Verifiability Based on Blockchain
Authors: López-Pimentel Juan Carlos , Morales-Rosales Luis Alberto , Algredo-Badillo Ignacio
Published: March 2022

The current digital age, mainly characterized by an economy based upon information technology, demands a data integrity service, even more so because organizations and companies are migrating their services over the cloud. This is not a simple task; it is cumbersome since traditional schemes in databases could be subject to modifications. However, it can be solved using blockchain technology. This paper provides a data integrity verifiability architecture for cloud systems based on blockchain. The architecture provides a mechanism to store events (as logs) within a blockchain platform from any cloud system. Users can then consult data integrity through a microservice, acting as an intermediate server that carries out a set of verification steps within the blockchain, which confirms the integrity of a previously stored log. Our architecture takes advantage of the blockchain strength concerning integrity, providing a traceability track of the stored logs. A prototype system and a case study were implemented based on the proposed architecture. Our experimental results show that the proposed decentralized architecture can be adapted to cloud existing systems that were born without blockchain technology and require a modular and scalable audit characteristic.
Optimal Modeling of an Elevator Chassis under Crash Scenario Based on Characterization and Validation of the Hyperelastic Material of Its Shock Absorber System
Authors: Giagopoulos Dimitrios , Arailopoulos Alexandros , Chatziparasidis Iraklis
Published: March 2022

A wide variety of hyperelastic rubber-like materials, exhibiting strong nonlinear stress–strain relations under large deformations, is applied in various industrial mechanical systems and engineering applications involving shock and vibration absorbers. An optimal design procedure of an elevator chassis crashing on a hyperelastic shock absorber in a fail scenario, applicable in large-scale mechanical systems or industrial structures of high importance under strong nonlinear dynamic excitation, is presented in this work. For the characterization of the hyperelastic absorber, a Mooney–Rivlin material model was adopted, and a series of in-lab compression quasi-static tests were conducted. Applying a fully parallelizable state-of-the-art stochastic model updating methodology, coupled with robust, accurate and efficient Finite Element Analysis (FEA) software, the hyperelastic behavior of the shock absorber was validated under uniaxial large deformation, in order to tune all material parameters and develop a high-fidelity FE model of the shock absorber system. Next, a series of in situ full-scale experimental trials were carried out using a test-case elevator chassis, representing the crash scenario on the buffer absorber system, after a controlled free fall. A limited number of sensors, i.e., triaxial accelerometers and strain gauges, were placed at characteristic points of the real structure of the elevator chassis recording experimental data. A discrete Finite Element (FE) model of the experimentally tested arrangement involving the elevator chassis and updated buffer absorber system along with all boundary conditions was developed and used in explicit nonlinear analysis of the crash scenario. Steel material properties and the characterized updated Mooney–Rivlin material model were assigned to the elevator chassis and buffer, respectively. A direct comparison of the numerical and experimental data validated the reliability and accuracy of the methodology applied, whereas results of the analysis were used in order to redesign and optimize a new-design elevator chassis, achieving minimum design stresses and satisfying serviceability limit states.
Occupational Risk Assessment of Wind Turbines in Bangladesh
Authors: Bepary Bijoy , Kabir Golam
Published: March 2022

Wind energy is among the foremost vital renewable energy sources in the world. With the increase in its popularity and use, the requirement for safety measures regarding this type of energy is becoming more prevalent. The development and operation requirements that come with installing and running wind turbines have many risks that need managing and mitigation. This study implemented a risk evaluation method for the transportation, construction, operation, and maintenance of wind turbines, employing the fuzzy method. Fuzzy Analytical Hierarchy Process (FAHP), a multi-criteria higher cognitive process technique, was used to determine the weights of the risk parameters evaluated with the Fine–Kinney method. After that, the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) was employed for ranking the hazard’s source. Using Occupational Health and Safety (OHS) consultants, this study was conducted in Bangladesh regarding its onshore turbines. Findings have revealed that the most prevalent hazards during transportation, construction, operation, and maintenance, respectively, are “Driving vehicles at night in dark weather conditions”, “Work in hot and humid conditions”, “Inclement weather”, and “Entering of unauthorized persons”. The results of this study can help the OHS department to track these risks and to control and minimize them.
Prediction of Carbon Emissions in China’s Power Industry Based on the Mixed-Data Sampling (MIDAS) Regression Model
Authors: Xu Xiaoxiang , Liao Mingqiu
Published: March 2022

China is currently the country with the largest carbon emissions in the world, to which, the power industry contributes the greatest share. To reduce carbon emissions, reliable and timely forecasting measures are important and necessary. By using different frequency variables, in this study, we used the mixed-data sampling (MIDAS) regression model to forecast the annual carbon emissions of China’s power industry compared with a benchmark model. It was found that the MIDAS model had a higher prediction accuracy than models such as the autoregressive distributed lag (ARDL) model. Moreover, our results showed that the MIDAS model could conduct timely nowcasting, which is useful when the data have some releasing lag. Through this prediction method, the results also demonstrated that the carbon emissions of the power industry have a significant relationship with GDP and thermal power generation, and that the value of carbon emissions would keep increasing in the years of 2021 and 2022.
Ionosphere Tomographic Model Based on Neural Network with Balance Cost and Dynamic Correction Using Multi-Constraints
Authors: Zhu Haoyu , Yu Jieqing , Dai Yuchen , Zhu Yanyu , Huang Yingqi
Published: March 2022

A Neural network (NN) is a promising tool for the tomographic inversion of the ionosphere. However, existing research has adopted an unbalanced cost function for training purposes and a preset image for constraint purposes, resulting in the output image being dominated by measurements. To address these problems, we proposed an NN-based tomographic model with a balance cost function and a dynamic correction process (BCDC) for ionosphere inversion. The cost function is composed of two balance terms corresponding to the measurements and the selected constraints, respectively. The produced image in the forward process of the NN is corrected dynamically by fitting each vertical profile with orthogonal basis functions (EOFs) and the Chapman function and then by smoothing the voxels of each layer with a moving window approach horizontally. The corrected image is then used to calculate the slant total electron content (STEC) parameter, which is further translated into the term of the cost for the vertical and horizontal constraints. Experiments were carried out to validate the BCDC method and compared with a recently developed tomographic method and the international reference ionosphere (IRI) model. Results show that the parameters derived from the BCDC model demonstrate good consistency with the observations. Comparing with the reference methods, the BCDC method performs better in the validations of vertical profiles, F2 layer peak density (NmF2), STEC parameter and vertical total electron content map. Further analysis also shows that a balance cost function is of benefit to achieve an image of better quality.
Bio-Based Solutions for Agriculture: Foliar Application of Wood Distillate Alone and in Combination with Other Plant-Derived Corroborants Results in Different Effects on Lettuce (Lactuca Sativa L.)
Authors: Fedeli Riccardo , Vannini Andrea , Guarnieri Massimo , Monaci Fabrizio , Loppi Stefano
Published: March 2022

Bio-stimulants are showing growing success and are gradually replacing synthetic fertilizers in agriculture. Wood distillate (WD), also known as wood vinegar or pyroligneous acid, is a by-product of biomass pyrolysis and is increasingly used as a bio-stimulant for crop production. This study investigated whether weekly foliar applications of 0.25% and 0.50% WD have a differential effect on the chlorophyll and sugar content as well as biomass production in lettuce (Lactuca sativa L.). Moreover, the additional beneficial effect from the addition of corroborants of plant origin such as 3% soy lecithin and 5% flavonoid-rich wood glycolic extract to WD (BF) was investigated. Moreover, the possible toxicological concern from some potentially toxic elements (PTEs), namely Cd, Cu, Fe, Pb, and Zn, which may be abundant in WD was verified. After four weeks, we found that 0.25% WD not only increases lettuce biomass, which has an economic value, but also has beneficial effects on other qualitative parameters such as sugars and total sweetness. On the other hand, the use of 0.5% WD decreased the content of soluble sugars, suggesting a hormetic-type effect. We did not find evidence of further beneficial effects from the addition to WD of plant-derived corroborants, nor of any enrichment in the content of the investigated PTEs.
First Modern Data on the Lophophore Nervous System in Adult Novocrania anomala and a Current Assessment of Brachiopod Phylogeny
Authors: Temereva Elena
Published: March 2022

Although the lophophore is regarded as the main synapomorphy of all lophophorates, the evolution of the lophophore in certain groups of lophophorates remains unclear. To date, the innervation of the lophophore has been studied with modern methods only for three brachiopod species belonging to two subphyla: Linguliformea and Rhynchonelliformea. In the third subphylum, the Craniiformea, there are data for juveniles but not for adults. In the current research, the innervation of the lophophore in Novocrania anomala adults was studied by immunocytochemistry and confocal laser scanning microscopy. In the spiral lophophore of adults of the craniiform N. anomala, each arm is innervated by six brachial nerves: main, additional main, accessory, second accessory, additional lower, and lower brachial nerves. Compared with other brachiopod species, this complex innervation of the lophophore correlates with the presence of many lophophoral muscles. The general anatomy of the lophophore nervous system and the peculiarities of the organization of the subenteric ganglion of the craniiform N. anomala have a lot in common with those of rhynchonelliforms but not with those of linguliforms. These findings are consistent with the “Calciata” hypothesis of the brachiopod phylogeny and are inconsistent with the inference that the Craniiformea and Linguliformea are closely related.
The Epstein-Barr Virus Hacks Immune Checkpoints: Evidence and Consequences for Lymphoproliferative Disorders and Cancers
Authors: Biggi Alison Felipe Bordini , Elgui de Oliveira Deilson
Published: March 2022

The Epstein-Barr Virus (EBV) is a gammaherpesvirus involved in the etiopathogenesis of a variety of human cancers, mostly of lymphoid and epithelial origin. The EBV infection participates in both cell transformation and tumor progression, also playing an important role in subverting immune responses against cancers. The homeostasis of the immune system is tightly regulated by inhibitory mechanisms affecting key immune effectors, such as T lymphocytes and NK cells. Collectively known as immune checkpoints, these mechanisms rely on a set of cellular receptors and ligands. These molecules may be candidate targets for immune checkpoints blockade—an emergent and promising modality of immunotherapy already proven to be valuable for a variety of human cancers. The EBV was lately suspected to interfere with the expression of immune checkpoint molecules, notably PD-1 and its ligands, found to be overexpressed in cases of Hodgkin lymphoma, nasopharyngeal, and gastric adenocarcinomas associated with the viral infection. Even though there is compelling evidence showing that the EBV interferes with other immune checkpoint regulators (e.g., CTLA-4, LAG-3, TIM-3, and VISTA), the published data are still scarce. Herein, we discuss the current state of the knowledge on how the EBV interferes with the activity of immune checkpoints regulators, as well as its implications considering the immune checkpoints blockade for clinical management of the EBV-associated malignancies, notably lymphomas.
Quantitative Analysis of the Interactions of Metal Complexes and Amphiphilic Systems: Calorimetric, Spectroscopic and Theoretical Aspects
Authors: Migliore Rossella , Biver Tarita , Barone Giampaolo , Sgarlata Carmelo
Published: March 2022

Metals and metal-based compounds have many implications in biological systems. They are involved in cellular functions, employed in the formation of metal-based drugs and present as pollutants in aqueous systems, with toxic effects for living organisms. Amphiphilic molecules also play important roles in the above bio-related fields as models of membranes, nanocarriers for drug delivery and bioremediating agents. Despite the interest in complex systems involving both metal species and surfactant aggregates, there is still insufficient knowledge regarding the quantitative aspects at the basis of their binding interactions, which are crucial for extensive comprehension of their behavior in solution. Only a few papers have reported quantitative analyses of the thermodynamic, kinetic, speciation and binding features of metal-based compounds and amphiphilic aggregates, and no literature review has yet addressed the quantitative study of these complexes. Here, we summarize and critically discuss the recent contributions to the quantitative investigation of the interactions of metal-based systems with assemblies made of amphiphilic molecules by calorimetric, spectrophotometric and computational techniques, emphasizing the unique picture and parameters that such an analytical approach may provide, to support a deep understanding and beneficial use of these systems for several applications.
Influence of Fetomaternal Microchimerism on Maternal NK Cell Reactivity against the Child’s Leukemic Blasts
Authors: Martin Lena-Marie , Kruchen Anne , Fehse Boris , Müller Ingo
Published: March 2022

Persistence of fetal cells in the circulation of the mother (fetal microchimerism, FM) is associated with increased survival and reduced relapse of children with leukemia receiving a haploidentical hematopoietic stem cell transplantation (hHSCT). NK cells play an important role in maternal tolerance towards the unborn child. In this study, 70 mother–child pairs were prospectively analyzed for the occurrence of FM, KIR genotype and HLA-C type. We found that occurrence and level of FM were influenced by three maternal genetic factors: presence of an HLA-C1 allele, absence of KIR2DL3 and presence of a cen-B/B motif. Furthermore, an HLA-C match between mother and child favored persistence of FM. NK cells from FM+ mothers showed a 40% higher specific degranulation against their filial leukemic blasts than NK cells from FM− mothers, suggesting the presence of educated maternal NK cells. Nevertheless, cytotoxicity of parental NK cells against filial leukemic blasts was independent of KIR genetics (haplotype, B content score, centromeric and telomeric KIR gene regions) and independent of FM, indicating that additional immune effector mechanisms contribute to the beneficial effect of persisting FM in hHSCT.
The Forgotten Brother: The Innate-like B1 Cell in Multiple Sclerosis
Authors: Halperin Saar T. , ’t Hart Bert A. , Luchicchi Antonio , Schenk Geert J.
Published: March 2022

Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system (CNS), traditionally considered a chronic autoimmune attack against the insulating myelin sheaths around axons. However, the exact etiology has not been identified and is likely multi-factorial. Recently, evidence has been accumulating that implies that autoimmune processes underlying MS may, in fact, be triggered by pathological processes initiated within the CNS. This review focuses on a relatively unexplored immune cell—the “innate-like” B1 lymphocyte. The B1 cell is a primary-natural-antibody- and anti-inflammatory-cytokine-producing cell present in the healthy brain. It has been recently shown that its frequency and function may differ between MS patients and healthy controls, but its exact involvement in the MS pathogenic process remains obscure. In this review, we propose that this enigmatic cell may play a more prominent role in MS pathology than ever imagined. We aim to shed light on the human B1 cell in health and disease, and how dysregulation in its delicate homeostatic role could impact MS. Furthermore, novel therapeutic avenues to restore B1 cells’ beneficial functions will be proposed.
Neural Network-Based Calculator for Rat Glomerular Filtration Rate
Authors: Pellicer-Valero Óscar J. , Massaro Giampiero A. , Casanova Alfredo G. , Paniagua-Sancho María , Fuentes-Calvo Isabel , Harvat Mykola , Martín-Guerrero José D. , Martínez-Salgado Carlos , López-Hernández Francisco J.
Published: March 2022

Glomerular filtration is a pivotal process of renal physiology, and its alterations are a central pathological event in acute kidney injury and chronic kidney disease. Creatinine clearance (ClCr), a standard method for glomerular filtration rate (GFR) measurement, requires a long and tedious procedure of timed (usually 24 h) urine collection. We have developed a neural network (NN)-based calculator of rat ClCr from plasma creatinine (pCr) and body weight. For this purpose, matched pCr, weight, and ClCr trios from our historical records on male Wistar rats were used. When evaluated on the training (1165 trios), validation (389), and test sets (660), the model committed an average prediction error of 0.196, 0.178, and 0.203 mL/min and had a correlation coefficient of 0.863, 0.902, and 0.856, respectively. More importantly, for all datasets, the NN seemed especially effective at comparing ClCr among groups within individual experiments, providing results that were often more congruent than those measured experimentally. ACLARA, a friendly interface for this calculator, has been made publicly available to ease and expedite experimental procedures and to enhance animal welfare in alignment with the 3Rs principles by avoiding unnecessary stressing metabolic caging for individual urine collection.