bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Fatigue Properties of Rejuvenated Asphalt Prepared by Waste Cooking Oil Pre-Desulfurized Crumb Tire Rubber
Authors: Dong Ruikun , Zhang Zhiyu , Zhou Tao , Deng Weitong , You Hong
Published: February 2023

Fatigue cracking has hitherto been a crucial constraint on the development of reclaimed asphalt pavements attributed to the performance of rejuvenated asphalt binder. Therefore, it is extremely significant to evaluate the fatigue performance of rejuvenated asphalt precisely and objectively and to improve the fatigue life of rejuvenated asphalt binders. With preceding research in our group, this paper investigated the fatigue properties of waste rubber/oil (WRO) rejuvenated asphalt and universal rejuvenated asphalt by dynamic shear rheometer test (DSR). The applicability of common fatigue life evaluation indexes and the response to internal and external influences on the fatigue performance of rejuvenated asphalt were analyzed. It is demonstrated that Np20 corresponding to the mutagenesis of phase angle is physically significant and independent of the parameters including rejuvenator type, loading mode and loading level, which was recommended as the evaluation index for fatigue life of rejuvenated asphalt in this paper. The fatigue performance of both WRO and universal rejuvenated asphalt is found to decrease with loading frequency and loading level, but the fatigue life of WRO rejuvenated asphalt is comparatively superior to the latter, particularly at high loading frequencies and levels. Influenced by waste tire crumb rubber (WTCR), increasing the proportion of WTCR can improve the fatigue life of rejuvenated asphalt. When compared to other rejuvenated asphalt, RWRO@55 rejuvenated asphalt shows better fatigue performance and its fatigue life rebounds at high loading frequency. Consequently, the recommended mastic–oil ratio is 5:5. However, when the rheological recoverability compensation is considered, the fatigue lifetime evaluation of rejuvenated asphalt will be changed significantly, and therefore the fatigue performance evaluation of rejuvenated asphalt should consider the influence of rheological recoverability to develop a comprehensive evaluation system.
Dimensional Stability of Light-Activated Urethane Dimethacrylate Denture Base Resins
Authors: Mishra Swati , Chaturvedi Saurabh , Ali Mariyam , Pandey Kaushik Kumar , Alqahtani Nasser M. , Alfarsi Mohammed A. , Addas Mohamed Khaled , Vaddamanu Sunil Kumar , Al Ahmari Nasser M. , Alqahtani Saeed M. , Yaqoob Ashfaq , Alqahtani Waleed M. S.
Published: February 2023

An accurate and dimensionally stable trial denture base is required for a successful denture. The aim of this in vitro study was to assess the dimensional stability of a light-activated urethane dimethacrylate (UDMA) visible light cure (VLC) denture base with three fabrication techniques and different curing cycles. Forty-five VLC denture base samples were divided evenly into three groups. Group A used a conventional fabrication technique with a curing cycle of 5 min. Group B used a modified fabrication technique with two 4-min curing cycles. Group C used a multi-step fabrication technique with three curing cycles (4 min, plus 4 min, plus 2 min). The samples were sectioned and observed under a stereomicroscope to measure the discrepancy between the sample and the master cast. The mean dimensional discrepancy (mm) at the molar region at mid-palate, after 24 h in Group A, B and C was 0.790 mm, 0.741 mm and 0.379 mm, respectively; at the right ridge crest, it was 0.567, 0.408 and 0.185, while at the left ridge crest it was 0.475, 0.331 and 0.125, respectively. Statistical analysis showed significantly different dimensional discrepancies among the groups at all three sites; right ridge crest (F = 93.54, p < 0.001), left ridge crest (F = 105.96, p < 0.001) and mid-palate (F = 125.53, p < 0.001). Within the limitations of this laboratory study, it can be concluded that the denture base using a multi-step fabrication technique with three curing cycles provides better adaptation than the conventional technique. The significance of the study is that clinicians should consider performing denture base fabrication using a multi-step technique to enhance adaptation and hence the stability of the dentures for patients.
Conversion of Carbonaceous Organic Impurities (Methyldichlorosilane) in Trichlorosilane Using Weakly Basic Anion-Exchange Resin as Solid Catalyst
Authors: Liu Jianhua , Zhang Miaolei , Huang Guoqiang
Published: February 2023

Trichlorosilane (SiHCl3) is an important raw material for preparing solar cells and semiconductor chips in the Siemens method. Since the boiling points of SiHCl3 and methyldichlorosilane (CH3SiHCl2) are close to each other, it is difficult to remove CH3SiHCl2 from the raw material to obtain high-purity products by traditional distillation methods. Therefore, we propose an efficient catalytic approach to convert CH3SiHCl2 to methyltrichlorosilane (CH3SiCl3) with an anion-exchange resin as the catalyst and tetrachlorosilane (SiCl4) as the Cl donor in a continuous-flow fixed bed reactor. Seven anion-exchange resins, including D201, D301, D303, 201 × 7, D301F, D315 and D380 were evaluated. The results showed that D301 resin had the best performance. Reaction conditions such as reaction temperature, reactant molar ratio and catalyst stability were investigated. The maximum conversion of CH3SiHCl2 was 60% at an optimum reaction temperature of 150 °C, n(CH3SiHCl2):n(SiCl4) = 1:3 and a liquid hourly space velocity (LHSV) of 3.0 h−1. A possible reaction mechanism is explained based on the reaction data obtained in the current work.
A Thermal-Fluid-Solid Coupling Computation Model of Initiation Pressure Using Supercritical Carbon Dioxide Fracturing
Authors: Chen Yi , Kang Zhihong , Kang Yuzhu , Chen Xiaocheng , Chen Xiaohong , Fan Qingteng , Du Yukun , Wang Jinguang
Published: February 2023

With the characteristics of low fracturing pressure, little damage to the reservoirs, and assuming the role of carbon storage, supercritical carbon dioxide (SC-CO2) fracturing is suitable for the development of unconventional oil and gas resources. Based on the tensile failure mechanism of rocks, this paper establishes a thermal-fluid-solid coupling initiation pressure model for SC-CO2 fracturing. Using this model, the changes in formation temperature and pore pressure near a wellbore caused by invasion of CO2 into the formation are analyzed, as well as the impact of these changes on the tangential stress of reservoir rocks. The field data of SC-CO2 fracturing in a sandstone gas well are used to validate the reliability of the model. The results show that SC-CO2 fracturing can significantly reduce the initiation pressure, which decreases with the increase in fracturing fluid injection rate. The minimum value of tangential stress is located at the well wall, and the direction of tangential stress caused by formation temperature and pore pressure is opposite, with the former greater than the latter. The increase in Poisson’s ratio, the increase in elastic modulus and the decrease in bottom hole temperature can reduce the initial fracturing pressure of the reservoir. The computation model established in this paper provides an effective method for understanding the reservoir fracturing mechanism under the condition of SC-CO2 invasion.
Overall Review on Recent Applications of Raman Spectroscopy Technique in Dentistry
Authors: Otel Iulian
Published: February 2023

The present paper reviews the applications of Raman spectroscopy in dentistry in the past two decades. This technique is considered a highly promising optical modality, widely used for the chemical identification and characterization of molecular structures, providing detailed information on the structural arrangement, crystal orientations, phase, and polymorphism, molecular interactions and effects of bonding, chemical surrounding environment, and stress on samples. Raman spectroscopy has been appropriate to investigate both organic and inorganic components of dental tissues since it provides accurate and precise spectral information on present minerals through the observation of the characteristic energies of their vibrational modes. This method is becoming progressively important in biomedical research, especially for non-invasiveness, non-destructiveness, high biochemical specificity, low water sensitivity, simplicity in analyzing spectral parameters, near-infrared region capability, and in vivo remote potential by means of fiber-optics. This paper will address the application of Raman spectroscopy in different fields of dentistry, found to be the most relevant and prevalent: early recognition of carious lesions; bleaching products performance; demineralizing effect from low-pH foods and acidic beverages; and efficiency of remineralization agents. Additionally, this review includes information on fiber-optic remote probe measurements. All described studies concern caries detection, enamel characterization, and assessment indicating how and to what extent Raman spectroscopy can be applied as a complementary diagnostic method.
The Relationship between the Items of the Barthel Index and Short-Term Prognosis in Terminal Cancer Patients
Authors: Okamoto Shinya , Okazaki Kazuko , Okada Masahiro , Murakami Fumiyoshi , Sugihara Hiroki , Hoshino Yoshinori , Ogawa Yuka , Banshoya Kengo , Takei Eisuke , Takeda Shuso , Sugihara Narumi
Published: February 2023

Predicting the short-term prognosis of patients with terminal cancer is important for treatment decisions and improving patients’ quality of life. Recently, it has been reported that the Barthel Index (BI) can predict short-term prognosis. This study aimed to distinguish the BI items that can more accurately predict the short-term prognosis of terminal cancer patients from among the other BI items. This study compared the accuracy of predicting the 1-, 2-, and 3-week prognosis of BI and individual BI items in 158 cancer patients who died between January 2018 and June 2020 at the Onomichi Municipal Hospital in Japan. For predicting the 1- and 2-week prognosis, the BI item “feeding” scores of 0/5 and 10 showed higher accuracies (0.766 and 0.715, respectively) than BI scores between 0–15/20–100. For predicting a 3-week prognosis, the BI item “mobility” scores of 0, 5/10, 15 and the BI item “grooming” scores between 0/5 showed higher accuracies (0.627 and 0.614, respectively) than BI scores between 0–35/40–100. BI and individual BI items may be an option for prognostic prediction in terminal cancer patients.
Some Key Issues on Pseudorange-Based Point Positioning with GPS, BDS-3, and Galileo Observations
Authors: Zhou Feng , Wang Xiaoyang
Published: February 2023

Nowadays, BDS-3 and Galileo are still developing and have global service capabilities. This study aims to provide a comprehensive analysis of pseudorange-based/single point positioning (SPP) among GPS, BDS-3, and Galileo on a global scale. First, the positioning accuracy distribution of adding IGSO and GEO to the MEO of BDS-3 is analyzed. The results show that after adding IGSO and GEO, the accuracy of 3D in the Asia-Pacific region is significantly improved. Then, the positioning accuracy of the single-system and single-frequency SPP was validated and compared. The experimental results showed that the median RMS values for the GPS, Galileo, and BDS-3 are 1.10/1.10/1.30 m and 2.57/2.69/2.71 m in the horizontal and vertical components, respectively. For the horizontal component, the GPS and Galileo had better positioning accuracy in the middle- and high-latitude regions, while BDS-3 had better positioning accuracy in the Asia-Pacific region. For the vertical component, poorer positioning accuracy could be seen near the North Pole and the equator for all three systems. Meanwhile, in comparison with the single-system and single-frequency SPP, the contribution of adding pseudorange observations from the other satellite system and frequency band was analyzed fully. Overall, the positioning accuracy can be improved to varying degrees. Due to the observation of noise amplification, the positioning errors derived from dual-frequency SPP were much noisier than those from single-frequency SPP. Moreover, the positioning performance of single-frequency SPP with the ionosphere delay corrected with CODE final (COD), rapid (COR), 1-day predicted (C1P), and 2-day predicted (C2P) global ionospheric map (GIM) products was investigated. The results showed that SPP with COD had the best positioning accuracy, SPP with COR ranked second, while C1P and C2P were comparable and slightly worse than SPP with COR. SPP with GIM products demonstrated a better positioning accuracy than that of the single- and dual-frequency SPP. The stability and variability of the inter-system biases (ISBs) derived from the single-frequency and dual-frequency SPP were compared and analyzed, demonstrating that they were stable in a short time. The differences in ISBs among different receivers with single-frequency SPP are smaller than that of dual-frequency SPP.
Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention
Authors: Weilandt Frank , Behling Robert , Goncalves Romulo , Madadi Arash , Richter Lorenz , Sanona Tiago , Spengler Daniel , Welsch Jona
Published: February 2023

In this article, we propose a deep learning-based algorithm for the classification of crop types from Sentinel-1 and Sentinel-2 time series data which is based on the celebrated transformer architecture. Crucially, we enable our algorithm to do early classification, i.e., predict crop types at arbitrary time points early in the year with a single trained model (progressive intra-season classification). Such early season predictions are of practical relevance for instance for yield forecasts or the modeling of agricultural water balances, therefore being important for the public as well as the private sector. Furthermore, we improve the mechanism of combining different data sources for the prediction task, allowing for both optical and radar data as inputs (multi-modal data fusion) without the need for temporal interpolation. We can demonstrate the effectiveness of our approach on an extensive data set from three federal states of Germany reaching an average F1 score of 0.92 using data of a complete growing season to predict the eight most important crop types and an F1 score above 0.8 when doing early classification at least one month before harvest time. In carefully chosen experiments, we can show that our model generalizes well in time and space.
DRE-Net: A Dynamic Radius-Encoding Neural Network with an Incremental Training Strategy for Interactive Segmentation of Remote Sensing Images
Authors: Yang Liangzhe , Zi Wenjie , Chen Hao , Peng Shuang
Published: February 2023

Semantic segmentation of remote sensing (RS) images, which is a fundamental research topic, classifies each pixel in an image. It plays an essential role in many downstream RS areas, such as land-cover mapping, road extraction, traffic monitoring, and so on. Recently, although deep-learning-based methods have shown their dominance in automatic semantic segmentation of RS imagery, the performance of these existing methods has relied heavily on large amounts of high-quality training data, which are usually hard to obtain in practice. Moreover, human-in-the-loop semantic segmentation of RS imagery cannot be completely replaced by automatic segmentation models, since automatic models are prone to error in some complex scenarios. To address these issues, in this paper, we propose an improved, smart, and interactive segmentation model, DRE-Net, for RS images. The proposed model facilitates humans’ performance of segmentation by simply clicking a mouse. Firstly, a dynamic radius-encoding (DRE) algorithm is designed to distinguish the purpose of each click, such as a click for the selection of a segmentation outline or for fine-tuning. Secondly, we propose an incremental training strategy to cause the proposed model not only to converge quickly, but also to obtain refined segmentation results. Finally, we conducted comprehensive experiments on the Potsdam and Vaihingen datasets and achieved 9.75% and 7.03% improvements in NoC95 compared to the state-of-the-art results, respectively. In addition, our DRE-Net can improve the convergence and generalization of a network with a fast inference speed.
A Conical Model Approach for Invariant Points of Very Long Baseline Interferometry and Satellite Laser Ranging
Authors: Bae Tae-Suk , Hong Chang-Ki
Published: February 2023

A new realization of the international terrestrial reference frame, a combination of four different space geodetic techniques, was released in 2022. Each geodetic solution should be combined carefully based on the local tie information at the co-located site. Although many approaches have been successfully applied to connect different geodetic sensors, to date, there has been no unified mathematical representation for the target motions. Herein, a unified conical model was developed to estimate the invariant points of geodetic sensors using a more robust and consistent approach. It modeled the motion of targets, in either the horizontal or vertical axis, as cones; thus, homogeneous modeling was implemented. In addition to its simplicity, the model simultaneously estimated the tilting of the vertical axis and horizontal offset. The mathematical relationship and normality of the normal vector were modeled as a Gauss–Markov model with fixed constraints. The pre-computed initial coordinates of the pillars and targets were adjusted simultaneously to calculate the correlation information for the local tie vector. The complete model was successfully applied to the co-located site, which was transformed into a global reference frame via the Helmert transformation based on the global navigation satellite system campaign. The results showed that the proposed method is more efficient in terms of the number of parameters for invariant points of geodetic sensors (only 13% compared to the 3D circle fitting type conventional approach). In addition, the reliability of the estimated solution can be increased by avoiding an ill-conditioned linear system through the conical model.
HCFPN: Hierarchical Contextual Feature-Preserved Network for Remote Sensing Scene Classification
Authors: Yuan Jingwen , Wang Shugen
Published: February 2023

Convolutional neural networks (CNNs) have made significant advances in remote sensing scene classification (RSSC) in recent years. Nevertheless, the limitations of the receptive field cause CNNs to suffer from a disadvantage in capturing contextual information. To address this issue, vision transformer (ViT), a novel model that has piqued the interest of academics, is used to extract latent contextual information in remote sensing scene classification. However, when confronted with the challenges of large-scale variations and high interclass similarity in scene classification images, the original ViT has the drawback of ignoring important local features, thereby causing the model’s performance to degrade. Consequently, we propose the hierarchical contextual feature-preserved network (HCFPN) by combining the advantages of CNNs and ViT. First, a hierarchical feature extraction module based on ResNet-34 is utilized to acquire the multilevel convolutional features and high-level semantic features. Second, a contextual feature-preserved module takes advantage of the first two multilevel features to capture abundant long-term contextual features. Then, the captured long-term contextual features are utilized for multiheaded cross-level attention computing to aggregate and explore the correlation of multilevel features. Finally, the multiheaded cross-level attention score and high-level semantic features are classified. Then, a category score average module is proposed to fuse the classification results, whereas a label smoothing approach is utilized prior to calculating the loss to produce discriminative scene representation. In addition, we conduct extensive experiments on two publicly available RSSC datasets. Our proposed HCPFN outperforms most state-of-the-art approaches.
Retrieving Vertical Cloud Radar Reflectivity from MODIS Cloud Products with CGAN: An Evaluation for Different Cloud Types and Latitudes
Authors: Wang Fengxian , Liu Yubao , Zhou Yongbo , Sun Rongfu , Duan Jing , Li Yang , Ding Qiuji , Wang Haoliang
Published: February 2023

Retrieving cloud vertical structures with satellite remote-sensing measurements is highly desirable and technically challenging. In this paper, the conditional adversarial neural network (CGAN) for retrieving the equivalent cloud radar reflectivity at 94 GHz of the Cloud Profile Radar (CPR) onboard CloudSat is extended and evaluated comprehensively for different cloud types and geographical regions. The CGAN-based retrieval model was extended with additional data samples and improved with a new normalization adjustment. The model was trained with the labeled datasets of the moderate-resolution imaging spectroradiometer (MODIS) cloud top pressure, cloud water path, cloud optical thickness, and effective particle radius data, and the CloudSat/CPR reflectivity from 2010 to 2017 over the global oceans. The test dataset, containing 24,427 cloud samples, was statistically analyzed to assess the performance of the model for eight cloud types and three latitude zones with multiple verification metrics. The results show that the CGAN model possesses good reliability for retrieving clouds with reflectivity > −25 dBZ. The model performed the best for deep convective systems, followed by nimbostratus, altostratus, and cumulus, but presented a very limited ability for stratus, cirrus, and altocumulus. The model performs better in the low and middle latitudes than in the high latitudes. This work demonstrated that the CGAN model can be used to retrieve vertical structures of deep convective clouds and nimbostratus with great confidence in the mid- and lower latitude region, laying the ground for retrieving reliable 3D cloud structures of the deep convective systems including convective storms and hurricanes from MODIS cloud products and used for predicting these storms.
SISLU-Net: Spatial Information-Assisted Spectral Information Learning Unmixing Network for Hyperspectral Images
Authors: Sun Le , Chen Ying , Li Baozhu
Published: February 2023

Spectral unmixing is among one of the major hyperspectral image analysis tasks that aims to extract basic features (endmembers) at the subpixel level and estimate their corresponding proportions (fractional abundances). Recently, the rapid development of deep learning networks has provided us with a new method to solve the problem of spectral unmixing. In this paper, we propose a spatial-information-assisted spectral information learning unmixing network (SISLU-Net) for hyperspectral images. The SISLU-Net consists of two branches. The upper branch focuses on the extraction of spectral information. The input of the upper branch is a number of pixels randomly extracted from the hyperspectral image. The data are fed into the network as a random combination of different pixel blocks each time. The random combination of batches can boost the network to learn global spectral information. Another branch focuses on learning spatial information from the entire hyperspectral image and transmitting it to the upper branch through the shared weight strategy. This allows the network to take into account the spectral information and spatial information of HSI at the same time. In addition, according to the distribution characteristics of endmembers, we employ Wing loss to solve the problem of uneven distributions of endmembers. Experimental results on one synthetic and three real hyperspectral data sets show that SISLU-Net is effective and competitive compared with several state-of-the-art unmixing algorithms in terms of the spectral angle distance (SAD) of the endmembers and the root mean square error (RMSE) of the abundances.
Prediction of Significant Wave Heights with Engineered Features from GNSS Reflectometry
Authors: Becker Jan M. , Roggenbuck Ole
Published: February 2023

Estimating reflector heights at stationary GNSS sites with interferometric reflectometry (IR) is a well-established technique in ocean remote sensing. Additionally, IR offers the possibility to estimate the significant wave height (SWH) with a linear model using the damping coefficient from an inverse modelling applied to GNSS signal-to-noise ratio (SNR) observations. Such a linear model serves as a benchmark in the present study, where an alternative approach for the estimation of both SWH and reflector height is presented that is based on kernel regression and clustering techniques. In this alternative approach, the reflector height is estimated by analyzing local extrema occurring in the interference pattern that is present in GNSS SNR observations. Various predictors are derived from clustering statistics and the estimated reflector heights. These predictors are used for the SWH determination with supervised machine learning. Linear models, bagged regression trees, and artificial neural networks are applied and respective results are compared for various predictor sets. In a second step, damping coefficients obtained from the inverse modelling mentioned above are additionally taken into account as predictors. The usability of the alternative approach is demonstrated. Compared to the benchmark, a significant improvement in terms of accuracy is found for an artificial neural network with predictors from both the alternative and the inverse modelling approach.
An Advanced Rider-Cornering-Assistance System for PTW Vehicles Developed Using ML KNN Method
Authors: Jalti Fakhreddine , Hajji Bekkay , Acri Alberto , Calì Michele
Published: February 2023

The dynamic behavior of a Powered Two-Wheeler (PTW) is much more complicated than that of a car, which is due to the strong coupling between the longitudinal and lateral dynamics produced by the large roll angles. This makes the analysis of the dynamics, and therefore the design and synthesis of the controller, particularly complex and difficult. In relation to assistance in dangerous situations, several recent manuscripts have suggested devices with limitations of cornering velocity by proposing restrictive models. However, these models can lead to repulsion by the users of PTW vehicles, significantly limiting vehicle performance. In the present work, the authors developed an Advanced Rider-cornering Assistance System (ARAS) based on the skills learned by riders running across curvilinear trajectories using Artificial Intelligence (AI) and Neural Network (NN) techniques. New algorithms that allow the value of velocity to be estimated by prediction accuracy of up to 99.06% were developed using the K-Nearest Neighbor (KNN) Machine Learning (ML) technique.
Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence
Authors: Silva R. , Guedes A. , Ribeiro D. , Vale C. , Meixedo A. , Mosleh A. , Montenegro P.
Published: February 2023

The identification of instability problems in freight trains circulation such as unbalanced loads is of particular importance for railways management companies and operators. The early detection of unbalanced loads prevents significant damages that may cause service interruptions or derailments with high financial costs. This study aims to develop a methodology capable of automatically identifying unbalanced vertical loads considering the limits proposed by the reference guidelines. The research relies on a 3D numerical simulation of the train–track dynamic response to the presence of longitudinal and transverse scenarios of unbalanced vertical loads and resorting to a virtual wayside monitoring system. This methodology is based on measured data from accelerometers and strain gauges installed on the rail and involves the following steps: (i) feature extraction, (ii) features normalization based on a latent variable method, (iii) data fusion, and (iv) feature discrimination based on an outlier and a cluster analysis. Regarding feature extraction, the performance of ARX and PCA models is compared. The results prove that the methodology is able to accurately detect and classify longitudinal and transverse unbalanced loads with a reduced number of sensors.
Development and Prospect of Smart Materials and Structures for Aerospace Sensing Systems and Applications
Authors: Wang Wenjie , Xiang Yue , Yu Jingfeng , Yang Long
Published: February 2023

The rapid development of the aviation industry has put forward higher and higher requirements for material properties, and the research on smart material structure has also received widespread attention. Smart materials (e.g., piezoelectric materials, shape memory materials, and giant magnetostrictive materials) have unique physical properties and excellent integration properties, and they perform well as sensors or actuators in the aviation industry, providing a solid material foundation for various intelligent applications in the aviation industry. As a popular smart material, piezoelectric materials have a large number of application research in structural health monitoring, energy harvest, vibration and noise control, damage control, and other fields. As a unique material with deformation ability, shape memory materials have their own outstanding performance in the field of shape control, low-shock release, vibration control, and impact absorption. At the same time, as a material to assist other structures, it also has important applications in the fields of sealing connection and structural self-healing. Giant magnetostrictive material is a representative advanced material, which has unique application advantages in guided wave monitoring, vibration control, energy harvest, and other directions. In addition, giant magnetostrictive materials themselves have high-resolution output, and there are many studies in the direction of high-precision actuators. Some smart materials are summarized and discussed in the above application directions, aiming at providing a reference for the initial development of follow-up related research.
Altering Fish Behavior by Sensing Swarm Patterns of Fish in an Artificial Aquatic Environment Using an Interactive Robotic Fish
Authors: Manawadu Udaka A. , De Zoysa Malsha , Perera J. D. H. S. , Hettiarachchi I. U. , Lambacher Stephen G. , Premachandra Chinthaka , De Silva P. Ravindra S.
Published: February 2023

Numerous studies have been conducted to prove the calming and stress-reducing effects on humans of visiting aquatic environments. As a result, many institutions have utilized fish to provide entertainment and treat patients. The most common issue in this approach is controlling the movement of fish to facilitate human interaction. This study proposed an interactive robot, a robotic fish, to alter fish swarm behaviors by performing an effective, unobstructed, yet necessary, defined set of actions to enhance human interaction. The approach incorporated a minimalistic but futuristic physical design of the robotic fish with cameras and infrared (IR) sensors, and developed a fish-detecting and swarm pattern-recognizing algorithm. The fish-detecting algorithm was implemented using background subtraction and moving average algorithms with an accuracy of 78%, while the swarm pattern detection implemented with a Convolutional Neural Network (CNN) resulted in a 77.32% accuracy rate. By effectively controlling the behavior and swimming patterns of fish through the smooth movements of the robotic fish, we evaluated the success through repeated trials. Feedback from a randomly selected unbiased group of subjects revealed that the robotic fish improved human interaction with fish by using the proposed set of maneuvers and behavior.
Vision System Measuring the Position of an Aircraft in Relation to the Runway during Landing Approach
Authors: Kordos Damian , Krzaczkowski Paweł , Rzucidło Paweł , Gomółka Zbigniew , Zesławska Ewa , Twaróg Bogusław
Published: February 2023

This paper presents a vision system that measures the position of an aircraft relative to the runway (RWY) during a landing approach. It was assumed that all the information necessary for a correct approach was based entirely on an analysis of the image of the runway and its surroundings. It was assumed that the way the algorithm works, as well as possible, should imitate the pilot’s perception of the runway. Taking into account the above and the fact that the infrastructure at each airport is different, it has been decided to use artificial neural networks with a dedicated learning process for any airport, based on the simulation environments. Such an action will enable the generation of a synthetic video sequence without the need for costly and time-consuming flights. The presented solution was tested in real flight conditions on an experimental aircraft, and the selected test results are presented in this article.
An Overview of Wearable Haptic Technologies and Their Performance in Virtual Object Exploration
Authors: van Wegen Myla , Herder Just L. , Adelsberger Rolf , Pastore-Wapp Manuela , van Wegen Erwin E. H. , Bohlhalter Stephan , Nef Tobias , Krack Paul , Vanbellingen Tim
Published: February 2023

We often interact with our environment through manual handling of objects and exploration of their properties. Object properties (OP), such as texture, stiffness, size, shape, temperature, weight, and orientation provide necessary information to successfully perform interactions. The human haptic perception system plays a key role in this. As virtual reality (VR) has been a growing field of interest with many applications, adding haptic feedback to virtual experiences is another step towards more realistic virtual interactions. However, integrating haptics in a realistic manner, requires complex technological solutions and actual user-testing in virtual environments (VEs) for verification. This review provides a comprehensive overview of recent wearable haptic devices (HDs) categorized by the OP exploration for which they have been verified in a VE. We found 13 studies which specifically addressed user-testing of wearable HDs in healthy subjects. We map and discuss the different technological solutions for different OP exploration which are useful for the design of future haptic object interactions in VR, and provide future recommendations.
Deep Learning of GNSS Acquisition
Authors: Borhani-Darian Parisa , Li Haoqing , Wu Peng , Closas Pau
Published: February 2023

Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby the CAF is fed to a data-driven classifier that outputs binary class posteriors. The class posteriors are used to compute a Bayesian hypothesis test to statistically decide the presence or absence of a GNSS signal. The versatility and computational affordability of the proposed method are addressed by splitting the CAF into smaller overlapping sections, which are fed to a bank of parallel classifiers whose probabilistic results are optimally fused to provide a so-called probability ratio map from which acquisition is decided. Additionally, the article shows how noncoherent integration schemes are enabled through optimal data fusion, with the goal of increasing the resulting classifier accuracy. The article provides simulation results showing that the proposed data-driven method outperforms current CAF maximization strategies, enabling enhanced acquisition at medium-to-high carrier-to-noise density ratios.
Massive Data Storage Solution for IoT Devices Using Blockchain Technologies
Authors: Maftei Alexandru A. , Lavric Alexandru , Petrariu Adrian I. , Popa Valentin
Published: February 2023

The Internet of Things (IoT) concept involves connecting devices to the internet and forming a network of objects that can collect information from the environment without human intervention. Although the IoT concept offers some advantages, it also has some issues that are associated with cyber security risks, such as the lack of detection of malicious wireless sensor network (WSN) nodes, lack of fault tolerance, weak authorization, and authentication of nodes, and the insecure management of received data from IoT devices. Considering the cybersecurity issues of IoT devices, there is an urgent need of finding new solutions that can increase the security level of WSNs. One issue that needs attention is the secure management and data storage for IoT devices. Most of the current solutions are based on systems that operate in a centralized manner, ecosystems that are easy to tamper with and provide no records regarding the traceability of the data collected from the sensors. In this paper, we propose an architecture based on blockchain technology for securing and managing data collected from IoT devices. By implementing blockchain technology, we provide a distributed data storage architecture, thus eliminating the need for a centralized network topology using blockchain advantages such as immutability, decentralization, distributivity, enhanced security, transparency, instant traceability, and increased efficiency through automation. From the obtained results, the proposed architecture ensures a high level of performance and can be used as a scalable, massive data storage solution for IoT devices using blockchain technologies. New WSN communication protocols can be easily enrolled in our data storage blockchain architecture without the need for retrofitting, as our system does not depend on any specific communication protocol and can be applied to any IoT application.
Machine-Learning-Based Methodology for Estimation of Shoulder Load in Wheelchair-Related Activities Using Wearables
Authors: Amrein Sabrina , Werner Charlotte , Arnet Ursina , de Vries Wiebe H. K.
Published: February 2023

There is a high prevalence of shoulder problems in manual wheelchair users (MWUs) with a spinal cord injury. How shoulder load relates to shoulder problems remains unclear. This study aimed to develop a machine-learning-based methodology to estimate the shoulder load in wheelchair-related activities of daily living using wearable sensors. Ten able-bodied participants equipped with five inertial measurement units (IMU) on their thorax, right arm, and wheelchair performed activities exemplary of daily life of MWUs. Electromyography (EMG) was recorded from the long head of the biceps and medial part of the deltoid. A neural network was trained to predict the shoulder load based on IMU and EMG data. Different cross-validation strategies, sensor setups, and model architectures were examined. The predicted shoulder load was compared to the shoulder load determined with musculoskeletal modeling. A subject-specific biLSTM model trained on a sparse sensor setup yielded the most promising results (mean correlation coefficient = 0.74 ± 0.14, relative root-mean-squared error = 8.93% ± 2.49%). The shoulder-load profiles had a mean similarity of 0.84 ± 0.10 over all activities. This study demonstrates the feasibility of using wearable sensors and neural networks to estimate the shoulder load in wheelchair-related activities of daily living.
Subwavelength Diffractive Optical Elements for Generation of Terahertz Coherent Beams with Pre-Given Polarization State
Authors: Pavelyev Vladimir , Khonina Svetlana , Degtyarev Sergey , Tukmakov Konstantin , Reshetnikov Anton , Gerasimov Vasily , Osintseva Natalya , Knyazev Boris
Published: February 2023

Coherent terahertz beams with radial polarization of the 1st, 2nd, and 3rd orders have been generated with the use of silicon subwavelength diffractive optical elements (DOEs). Silicon elements were fabricated by a technology similar to the technology used before for the fabrication of DOEs forming laser terahertz beams with pre-given mode content. The beam of the terahertz Novosibirsk Free Electron Laser was used as the illuminating beam. The experimental results are in good agreement with the results of the computer simulation.
Durian Waste Husks as an Adsorbent in Improving Soaking Water during the Retting Process of Piper nigrum L. (Pepper Berries)
Authors: Hamzah Muhammad Hazwan , Amran Ainaa Syaheera , Aili Hamzah Adila Fazliyana , Mohd Nor Mohd Zuhair , Shamsudin Rosnah , Che Man Hasfalina , Wan Razali Wan Aizuddin
Published: February 2023

The potential of raw durian husk and NaOH-modified durian husk as an adsorbent, using different doses, 0.5 g, 1.0 g, 1.5 g, and 2.0 g, is investigated to improve soaking water of pepper berries during the retting process. The surface area and the pore size of the durian husk were examined using Brunner Emmett and Teller analysis. The surface area of NaOH-modified durian husk is higher (2.33 m2/g) compared to the raw durian husk (1.51 m2/g). NaOH-modified durian husk has a higher porous structure than the raw durian husk, but both pore diameters are more than 50 nm, which is considered micropore raw material. The effect of the raw durian husk on pH, chemical oxygen demand (COD), dissolved oxygen (DO), and turbidity were compared to the NaOH-modified durian husk with different doses. The 2.0 g of NaOH-modified durian husk enhanced changes in the four parameters. The highest pH value using NaOH-modified durian husk was 6.10 ± 0.02, while turbidity and COD increased to 971.33 ± 1.15 NTU and 1984.67 ± 3.21 mg/L, respectively. The DO of NaOH-modified durian husk shows the lowest reduction to 1.49 mg/L with 2.0 g of NaOH-modified durian husk. The experimental data was best fitted with a first-order kinetic model. Durian husk treated with NaOH could be used as a potential adsorbent to enhance the soaking water for pepper berries.
Smart Thinking on Co-Creation and Engagement: Searchlight on Underground Built Heritage
Authors: Smaniotto Costa Carlos , Volzone Rolando , Ruchinskaya Tatiana , Solano Báez Maria del Carmen , Menezes Marluci , Ercan Müge Akkar , Rollandi Annalisa
Published: February 2023

This paper aims to explore public participation for activating underground built heritage (UBH). It describes and analyses practices of stakeholders’ engagement in different UBH assets, based on experiences gathered in the scope of the European COST Action ‘Underground4value’. It brings together five inspiring cases from Italy, Portugal, Spain, Switzerland and the United Kingdom, in which digital and mobile technologies were used as tools to improve community experiences in UBH. Thus, the paper discusses ‘smartness’ from the perspective of people and communities around cultural assets, where ‘smartness’ becomes a new connotation and a pathway to advance (local) knowledge and know-how. Therefore, this paper takes on the challenge to define a smart city as an ecosystem for people’s empowerment and participation, and, in particular, to explore social tools for creating new values in heritage placemaking—where sharing knowledge becomes a fundamental principle.
The eSports Medicine: Pre-Participation Screening and Injuries Management—An Update
Authors: Rossoni Agostino , Vecchiato Marco , Brugin Erica , Tranchita Eliana , Adami Paolo Emilio , Bartesaghi Manuela , Cavarretta Elena , Palermi Stefano
Published: February 2023

Recently, electronic sports (eSports) became one of the growing forms of new media due to the wide diffusion of games and online technologies. Even if there is still a debate about the definition and characterization of eSports, eAthletes train heavily, compete in tournaments, must abide by competition, association, and governing body rules, just like all other athletes. Furthermore, as in any other competitive discipline, there can be injuries. Aberrant sitting posture, repetitive movements, screen vision, prolonged playing hours, and a sedentary lifestyle can lead to several medical hazards in musculoskeletal, ophthalmology, neurological, and metabolic systems. Moreover, several cardiovascular changes occur in eAthletes. This paper aims to explore the different injuries that can occur in a professional eAthlete, suggesting how every high-level gamer could benefit from a pre-participation evaluation and a correct injury prevention strategy.
Lack of Spatial Planning as a Cause of Environmental Injustice in the Context of the Provision of Health Safety to Urban Residents Based on the Example of Warsaw
Authors: Maciejewska Alina , Ulanicka-Raczyńska Marianna
Published: February 2023

Spatial planning based on environmental justice is a key activity in the process of the provision of equal rights to live in a safe environment and possess the opportunities of using it. Irrational development of land containing historical earth surface contamination constitutes a severe threat to the health safety of residents, and it may consequently lead to slow violence. This paper’s objective is to identify districts of Warsaw where the phenomena of environmental injustice and slow violence in post-industrial areas occur and fill in the indicated knowledge gap in such issues in Poland. The aim is also to answer the question as to whether contamination of the pedosphere and changes in land use in brownfields have a considerable effect on differences related to the health safety of residents of particular districts of Warsaw. The results of analyses of correlations of soil environment risk, health safety of residents, social, and planning conditions show that, in districts with a large share of areas included in the register of historical earth surface contamination, higher-than-average soil environment risk occurs, and it is related to the transformation of brownfields. Wola is a district affected by the phenomenon of slow violence and environmental injustice. According to the research, Wola is an area of accumulation of the highest levels of soil contamination, as well as some of the least favorable indices of health safety of residents and social conditions (in the case of both, Wola takes the second position). It is also a place of dynamic, unplanned transformations of brownfields, resulting in the “discovery” of historical earth surface contamination at the stage of the investment process. As evidenced based on the example of Wola, lack of spatial planning in contaminated areas leads to the exposure of their residents to a higher soil environment risk that may result in reduced health safety and the occurrence of slow violence. Therefore, rational planning of development of land containing historical earth surface contamination, with consideration of the aspects of health safety of residents, is an instrument of provision of environmental justice in terms of access to healthy life and residential environment.
Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2
Authors: Bouchekara Houssem R. E. H. , Sha’aban Yusuf A. , Shahriar Mohammad S. , Ramli Makbul A. M. , Mas’ud Abdullahi A.
Published: February 2023

In this paper, the Wind Farm Layout Optimization/Expansion (WFLO/E) problem is formulated in a multi-objective optimization way with specific constraints. Furthermore, a new approach is proposed and tested for the variable reduction technique in the WFLO/E problem. To solve this problem, a new method based on the hybridization of the Multi-Objective Evolutionary Algorithm Based on An Enhanced Inverted Generational Distance Metric (MOEA/IGD-NS) and the Two-Archive Algorithm 2 (Two Arch2) is developed. This approach is named (MOEA/IGD-NS/TA2). The performance of the proposed approach is tested against six case studies. For each case study, a set of solutions represented by the Pareto Front (PF) is obtained and analyzed. It can be concluded from the obtained results that the designer/planner has the freedom to select several configurations based on their experience and economic and technical constraints.