bluuebunny/crossref_metadata_2025 huggingface dataset:
Expandable Graphite, Aluminum Diethylphospinate and Melamine Polyphosphate as Flame Retarding System in Glass Fiber-Reinforced PA6
Authors: Tomiak Florian , Schoeffel Angelina , Rathberger Klaus , Drummer Dietmar
Published: March 2022

A flame retardant system based on expandable graphite (EG), aluminum diethylphosphinate (AlPI) and melamine polyphosphate (MPP) was investigated in glass fiber- (GF) reinforced polyamide 6 (PA6). Burning characteristics were evaluated via cone calorimeter, limiting oxygen index (LOI) and UL-94 tests. Thermogravimetric analysis (TGA) and coupled Fourier transform infrared spectroscopy (FTIR) was used to investigate the decomposition process as well as flame retardant modes of actions. Specifically, in the cone calorimeter tests, formulations containing EG showed excellent flame retardant properties for non-reinforced and reinforced PA6. The best performance was achieved for 25 wt.% glass fiber-reinforced PA6 containing solely 20 wt.% EG, corresponding to a measured pHRR of 134 kW/m2 and a total smoke production of 1.2 m2. Higher glass fiber contents of 45 wt.% (30 vol.%) revealed a lower char volume, which was attributed to both the limited space available for expansion and the sheer-induced reduction in particle size during processing. All of the reinforced PA6 formulations only achieved V2 classifications, but this was at low filling degrees (10 wt.%) for both net EG or EG/AlPi/MPP combinations. For GF-reinforced PA6 containing EG/AlPi/MPP mixtures, a synergistic effect was found to improve the oxygen index up to 30.6%.
Solvability of the Non-Linearly Viscous Polymer Solutions Motion Model
Authors: Zvyagin Andrey
Published: March 2022

In this paper we consider the initial–boundary value problem describing the motion of weakly concentrated aqueous polymer solutions. The model involves the regularized Jaumann’s derivative in the rheological relation. Also this model is considered with non-linear viscosity. On the basis of the topological approximation approach to the study of hydrodynamics problems the existence of weak solutions is proved. Also we consider an optimal feedback control problem for this initial–boundary value problem. The existence of an optimal solution minimizing a given performance functional is proved.
Adaptive Mechanism for Designing a Personalized Cranial Implant and Its 3D Printing Using PEEK
Authors: Mian Syed Hammad , Moiduddin Khaja , Elseufy Sherif Mohammed , Alkhalefah Hisham
Published: March 2022

The rehabilitation of the skull’s bones is a difficult process that poses a challenge to the surgical team. Due to the range of design methods and the availability of materials, the main concerns are the implant design and material selection. Mirror-image reconstruction is one of the widely used implant reconstruction techniques, but it is not a feasible option in asymmetrical regions. The ideal design approach and material should result in an implant outcome that is compact, easy to fit, resilient, and provides the perfect aesthetic and functional outcomes irrespective of the location. The design technique for the making of the personalized implant must be easy to use and independent of the defect’s position on the skull. As a result, this article proposes a hybrid system that incorporates computer tomography acquisition, an adaptive design (or modeling) scheme, computational analysis, and accuracy assessment. The newly developed hybrid approach aims to obtain ideal cranial implants that are unique to each patient and defect. Polyetheretherketone (PEEK) is chosen to fabricate the implant because it is a viable alternative to titanium implants for personalized implants, and because it is simpler to use, lighter, and sturdy enough to shield the brain. The aesthetic result or the fitting accuracy is adequate, with a maximum deviation of 0.59 mm in the outside direction. The results of the biomechanical analysis demonstrate that the maximum Von Mises stress (8.15 MPa), Von Mises strain (0.002), and deformation (0.18 mm) are all extremely low, and the factor of safety is reasonably high, highlighting the implant’s load resistance potential and safety under high loading. Moreover, the time it takes to develop an implant model for any cranial defect using the proposed modeling scheme is very fast, at around one hour. This study illustrates that the utilized 3D reconstruction method and PEEK material would minimize time-consuming alterations while also improving the implant’s fit, stability, and strength.
Atomic Force Microscopy (AFM) on Biopolymers and Hydrogels for Biotechnological Applications—Possibilities and Limits
Authors: Joshi Jnanada , Homburg Sarah Vanessa , Ehrmann Andrea
Published: March 2022

Atomic force microscopy (AFM) is one of the microscopic techniques with the highest lateral resolution. It can usually be applied in air or even in liquids, enabling the investigation of a broader range of samples than scanning electron microscopy (SEM), which is mostly performed in vacuum. Since it works by following the sample surface based on the force between the scanning tip and the sample, interactions have to be taken into account, making the AFM of irregular samples complicated, but on the other hand it allows measurements of more physical parameters than pure topography. This is especially important for biopolymers and hydrogels used in tissue engineering and other biotechnological applications, where elastic properties, surface charges and other parameters influence mammalian cell adhesion and growth as well as many other effects. This review gives an overview of AFM modes relevant for the investigations of biopolymers and hydrogels and shows several examples of recent applications, focusing on the polysaccharides chitosan, alginate, carrageenan and different hydrogels, but depicting also a broader spectrum of materials on which different AFM measurements are reported in the literature.
When the Total Hip Replacement Fails: A Review on the Stress-Shielding Effect
Authors: Savio Davide , Bagno Andrea
Published: March 2022

Total hip arthroplasty is one of the most common and successful orthopedic surgeries. Sometimes, periprosthetic osteolysis occurs associated with the stress-shielding effect: it results in the reduction of bone density, where the femur is not correctly loaded, and in the formation of denser bone, where stresses are confined. This paper illustrates the stress shielding effect as a cause of the failing replacement of the hip joint. An extensive literature survey has been accomplished to describe the phenomenon and identify solutions. The latter refer to the design criteria and the choice of innovative materials/treatments for prosthetic device production. Experimental studies and numerical simulations have been reviewed. The paper includes an introduction to explain the scope; a section illustrating the causes of the stress shielding effect; a section focusing on recent attempts to redefine prosthetic device design criteria, current strategies to improve the osteointegration process, and a number of innovative biomaterials; functionally graded materials are presented in a dedicated section: they allow customizing prosthesis features with respect to the host bone. Conclusions recommend an integrated approach for the production of new prosthetic devices: the “engineering community” has to support the “medical community” to assure an effective translation of research results into clinical practice.
Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization
Authors: Shankar Rajendran , Ganesh Narayanan , Čep Robert , Narayanan Rama Chandran , Pal Subham , Kalita Kanak
Published: March 2022

The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models.
Reply to Cordeiro-Rodrigues (2022). Tutuism and the Moral Universe. Comment on “Gasser (2021). Animal Suffering, God and Lessons from the Book of Job. Religions 12: 1047”
Authors: Gasser Georg
Published: March 2022

In this reply, I aim to clarify my ideas presented in a recent paper and to address criticisms that have been raised by Luis Cordeiro-Rodrigues regarding my interpretation of (animal) suffering and God.
“As If Nothing Had Happened”: Karl Barth’s ‘Responsible’ Theology
Authors: O’Neil Michael D.
Published: March 2022

Adolf Hitler’s rise to power in early 1933 precipitated an ecclesial and theological crisis in the life of the German churches. Karl Barth responded to the crisis in his treatise Theological Existence Today, calling the German church to steadfast faithfulness in the face of increasing pressure to compromise the central commitments of its faith. This essay provides an exposition of Barth’s treatise, exploring his understanding of theological existence, and evaluating his rather infamous assertion that he would “carry on theology, and only theology, now as previously, and as if nothing had happened”. It finds that Barth called his peers to ‘responsible’ theology, the practice of which required a particular ethos and specific methodological commitments. Such responsibility was critical if the church was to retain both its integrity as the people of God, and its ministry, during this crisis.
Domestic and Family Violence: Responses and Approaches across the Australian Churches
Authors: Pepper Miriam , Powell Ruth
Published: March 2022

Domestic and family violence (DFV) is a serious and widespread problem in Australia and across the world, including in faith communities. There are calls for research to assist churches to better recognize, respond to and prevent violence. This study draws on data from the 2016 Australian National Church Life Survey (n = 883 senior local church leaders, n = 1270 churchgoers) to provide the first Australia-wide cross-denominational statistics on Christian clergy responses to DFV. Two-thirds of leaders had previously dealt with DFV situations in their ministry, primarily responding to victims of abuse by referring them to specialist support services and by counselling them. The findings suggest a particular depth of experience with DFV situations and strength of awareness of the needs of victims for safety and specialist support among Salvationist leaders. While, overall, a substantial majority of churchgoers felt that they could approach their church for help if they were experiencing DFV, just half of Catholics felt that they could do so. Future research should explore responses to DFV in specific denominations and culturally and linguistic diverse contexts in more detail and seek to understand the practices used by the large minority of clergy who are dealing with perpetrators.
Retrieval of Black Carbon Absorption Aerosol Optical Depth from AERONET Observations over the World during 2000–2018
Authors: Dehkhoda Naghmeh , Sim Juhyeon , Joo Sohee , Shin Sungkyun , Noh Youngmin
Published: March 2022

Black carbon (BC) absorption aerosol optical depth (AAODBC) defines the contribution of BC in light absorption and is retrievable using sun/sky radiometer measurements provided by Aerosol Robotic Network (AERONET) inversion products. In this study, we utilized AERONET-retrieved depolarization ratio (DPR, δp), single scattering albedo (SSA, ω), and Ångström Exponent (AE, å) of version 3 level 2.0 products as indicators to estimate the contribution of BC to the absorbing fractions of AOD. We applied our methodology to the AERONET sites, including North and South America, Europe, East Asia, Africa, India, and the Middle East, during 2000–2018. The long-term AAODBC showed a downward tendency over Sao Paulo (−0.001 year−1), Thessaloniki (−0.0004 year−1), Beijing (−0.001 year−1), Seoul (−0.0015 year−1), and Cape Verde (−0.0009 year−1) with the highest values over the populous sites. This declining tendency in AAODBC can be attributable to the successful emission control policies over these sites, particularly in Europe, America, and China. The AAODBC at the Beijing, Sao Paulo, Mexico City, and the Indian sites showed a clear seasonality indicating the notable role of residential heating in BC emissions over these sites during winter. We found a higher correlation between AAODBC and fine mode AOD at 440 nm at all sites except for Beijing. High pollution episodes, BC emission from different sources, and aggregation properties seem to be the main drivers of higher AAODBC correlation with coarse particles over Beijing.
A Proposed Satellite-Based Crop Insurance System for Smallholder Maize Farming
Authors: Masiza Wonga , Chirima Johannes George , Hamandawana Hamisai , Kalumba Ahmed Mukalazi , Magagula Hezekiel Bheki
Published: March 2022

Crop farming in Sub-Saharan Africa is constantly confronted by extreme weather events. Researchers have been striving to develop different tools that can be used to reduce the impacts of adverse weather on agriculture. Index-based crop insurance (IBCI) has emerged to be one of the tools that could potentially hedge farmers against weather-related risks. However, IBCI is still constrained by poor product design and basis risk. This study complements the efforts to improve IBCI design by evaluating the performances of the Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) in estimating rainfall at different spatial scales over the maize-growing season in a smallholder farming area in South Africa. Results show that CHIRPS outperforms TAMSAT and produces better results at 20-day and monthly time steps. The study then uses CHIRPS and a crop water requirements (CWR) model to derive IBCI thresholds and an IBCI payout model. Results of CWR modeling show that this proposed IBCI system can cover the development, mid-season, and late-season stages of maize growth in the study area. The study then uses this information to calculate the weight, trigger, exit, and tick for each of these growth stages. Although this approach is premised on the prevailing conditions in the study area, it can be applied in other areas with different growing conditions to improve IBCI design.
Unsupervised Remote Sensing Image Super-Resolution Guided by Visible Images
Authors: Zhang Zili , Tian Yan , Li Jianxiang , Xu Yiping
Published: March 2022

Remote sensing images are widely used in many applications. However, due to being limited by the sensors, it is difficult to obtain high-resolution (HR) images from remote sensing images. In this paper, we propose a novel unsupervised cross-domain super-resolution method devoted to reconstructing a low-resolution (LR) remote sensing image guided by an unpaired HR visible natural image. Therefore, an unsupervised visible image-guided remote sensing image super-resolution network (UVRSR) is built. The network is divided into two learnable branches: a visible image-guided branch (VIG) and a remote sensing image-guided branch (RIG). As HR visible images can provide rich textures and sufficient high-frequency information, the purpose of VIG is to treat them as targets and make full use of their advantages in reconstruction. Specially, we first use a CycleGAN to drag the LR visible natural images to the remote sensing domain; then, we apply an SR network to upscale these simulated remote sensing domain LR images. However, the domain gap between SR remote sensing images and HR visible targets is massive. To enforce domain consistency, we propose a novel domain-ruled discriminator in the reconstruction. Furthermore, inspired by the zero-shot super-resolution network (ZSSR) to explore the internal information of remote sensing images, we add a remote sensing domain inner study to train the SR network in RIG. Sufficient experimental works show UVRSR can achieve superior results with state-of-the-art unpaired and remote sensing SR methods on several challenging remote sensing image datasets.
A Supervoxel-Based Random Forest Method for Robust and Effective Airborne LiDAR Point Cloud Classification
Authors: Liao Lingfeng , Tang Shengjun , Liao Jianghai , Li Xiaoming , Wang Weixi , Li Yaxin , Guo Renzhong
Published: March 2022

As an essential part of point cloud processing, autonomous classification is conventionally used in various multifaceted scenes and non-regular point distributions. State-of-the-art point cloud classification methods mostly process raw point clouds, using a single point as the basic unit and calculating point cloud features by searching local neighbors via the k-neighborhood method. Such methods tend to be computationally inefficient and have difficulty obtaining accurate feature descriptions due to inappropriate neighborhood selection. In this paper, we propose a robust and effective point cloud classification approach that integrates point cloud supervoxels and their locally convex connected patches into a random forest classifier, which effectively improves the point cloud feature calculation accuracy and reduces the computational cost. Considering the different types of point cloud feature descriptions, we divide features into three categories (point-based, eigen-based, and grid-based) and accordingly design three distinct feature calculation strategies to improve feature reliability. Two International Society of Photogrammetry and Remote Sensing benchmark tests show that the proposed method achieves state-of-the-art performance, with average F1-scores of 89.16 and 83.58, respectively. The successful classification of point clouds with great variation in elevation also demonstrates the reliability of the proposed method in challenging scenes.
Giant Planet Atmospheres: Dynamics and Variability from UV to Near-IR Hubble and Adaptive Optics Imaging
Authors: Simon Amy A. , Wong Michael H. , Sromovsky Lawrence A. , Fletcher Leigh N. , Fry Patrick M.
Published: March 2022

Each of the giant planets, Jupiter, Saturn, Uranus, and Neptune, has been observed by at least one robotic spacecraft mission. However, these missions are infrequent; Uranus and Neptune have only had a single flyby by Voyager 2. The Hubble Space Telescope, particularly the Wide Field Camera 3 (WFC3) and Advanced Camera for Surveys (ACS) instruments, and large ground-based telescopes with adaptive optics systems have enabled high-spatial-resolution imaging at a higher cadence, and over a longer time, than can be achieved with targeted missions to these worlds. These facilities offer a powerful combination of high spatial resolution, often <0.05”, and broad wavelength coverage, from the ultraviolet through the near infrared, resulting in compelling studies of the clouds, winds, and atmospheric vertical structure. This coverage allows comparisons of atmospheric properties between the planets, as well as in different regions across each planet. Temporal variations in winds, cloud structure, and color over timescales of days to years have been measured for all four planets. With several decades of data already obtained, we can now begin to investigate seasonal influences on dynamics and aerosol properties, despite orbital periods ranging from 12 to 165 years. Future facilities will enable even greater spatial resolution and, combined with our existing long record of data, will continue to advance our understanding of atmospheric evolution on the giant planets.
Using Augmented and Virtual Reality (AR/VR) to Support Safe Navigation on Inland and Coastal Water Zones
Authors: Templin Tomasz , Popielarczyk Dariusz , Gryszko Marcin
Published: March 2022

The aim of this research is to propose a new solution to assist sailors in safe navigation on inland shallow waters by using Augmented and Virtual Reality. Despite continuous progress in the methodology of displaying bathymetric data and 3D models of the bottoms, there is still a lack of solutions promoting these data and their widespread use. Most existing products present navigation content on 2D/3D maps onscreen. Augmented Reality (AR) technology revolutionises the way digital content is displayed. This paper presents the solution for the use of AR on inland and coastal waterways to increase the safety of sailing and other activities on the water (diving, fishing, etc.). The real-time capability of AR in the proposed mobile application also allows other users to be observed on the water in limited visibility and even at night. The architecture and the prototype Mobile Augmented Reality (MAR) applications are presented. The required AR, including the preparation methodology supported by the Virtual Reality Geographic Information System (VRGIS), is also shown. The prototype’s performance has been validated in water navigation, specifically for exemplary lakes of Warmia and Mazury in Poland. The performed tests showed the great usefulness of AR in the field of content presentation during the navigation process.
Mapping Canopy Cover in African Dry Forests from the Combined Use of Sentinel-1 and Sentinel-2 Data: Application to Tanzania for the Year 2018
Authors: Verhegghen Astrid , Kuzelova Klara , Syrris Vasileios , Eva Hugh , Achard Frédéric
Published: March 2022

High-resolution Earth observation data is routinely used to monitor tropical forests. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. In this study, we demonstrate the potential of combining Sentinel-1 (S1) SAR and Sentinel-2 (S2) optical sensors in order to map the tree cover in East Africa. The overall methodology consists of: (i) the generation of S1 and S2 layers, (ii) the collection of an expert-based training/validation dataset and (iii) the classification of the satellite data. Three different classification workflows, together with different approaches to incorporating the spatial information to train the classifiers, are explored. Two types of maps were derived from these mapping approaches over Tanzania: (i) binary tree cover–no tree cover (TC/NTC) maps, and (ii) maps of the canopy cover classes. The overall accuracy of the maps is >95% for the TC/NTC maps and >85% for the forest types maps. Considering the neighboring pixels for training the classification improved the mapping of the areas that are covered by 1–10% tree cover. The study relied on open data and publicly available tools and can be integrated into national monitoring systems.
Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model
Authors: Ye Zhangxi , Wei Jiahao , Lin Yuwei , Guo Qian , Zhang Jian , Zhang Houxi , Deng Hui , Yang Kaijie
Published: March 2022

Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown. In this study, we propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U2-Net, with a deeply nested structure. Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and spectral transformation) to enlarge the data set to increase the generalization ability of the model. Secondly, four typical subareas (A–D) in the study area were selected to evaluate the performance of the U2-Net model in olive crown extraction in different scenarios, and the U2-Net model was compared with three current mainstream deep learning models (i.e., HRNet, U-Net, and DeepLabv3+) in remote sensing image segmentation effect. The results showed that the U2-Net model achieved high accuracy in the extraction of tree crown numbers in the four subareas with a mean of intersection over union (IoU), overall accuracy (OA), and F1-Score of 92.27%, 95.19%, and 95.95%, respectively. Compared with the other three models, the IoU, OA, and F1-Score of the U2-Net model increased by 14.03–23.97 percentage points, 7.57–12.85 percentage points, and 8.15–14.78 percentage points, respectively. In addition, the U2-Net model had a high consistency between the predicted and measured area of the olive crown, and compared with the other three deep learning models, it had a lower error rate with a root mean squared error (RMSE) of 4.78, magnitude of relative error (MRE) of 14.27%, and a coefficient of determination (R2) higher than 0.93 in all four subareas, suggesting that the U2-Net model extracted the best crown profile integrity and was most consistent with the actual situation. This study indicates that the method combining UVA RGB images with the U2-Net model can provide a highly accurate and robust extraction result for olive tree crowns and is helpful in the dynamic monitoring and management of orchard trees.
Retrieving the Infected Area of Pine Wilt Disease-Disturbed Pine Forests from Medium-Resolution Satellite Images Using the Stochastic Radiative Transfer Theory
Authors: Li Xiaoyao , Tong Tong , Luo Tao , Wang Jingxu , Rao Yueming , Li Linyuan , Jin Decai , Wu Dewei , Huang Huaguo
Published: March 2022

Pine wilt disease (PWD) is a global destructive threat to forests which has been widely spread and has caused severe tree mortality all over the world. It is important to establish an effective method for forest managers to detect the infected area in a large region. Remote sensing is a feasible tool to detect PWD, but the traditional empirical methods lack the ability to explain the signals and can hardly be extended to large scales. The studies using physically-based models either ignore the within-canopy heterogeneity or rely too much on prior knowledge. In this study, we propose an approach to retrieve PWD infected areas from medium-resolution satellite images of two phases based on the simulations of an extended stochastic radiative transfer model for forests infected by pests (SRTP). A small amount of prior knowledge was used, and a change of background soil was considered in this approach. The performance was evaluated in different study sites. The inversion method performs best in the three-dimensional model LESS simulation sample plots (R2 = 0.88, RMSE = 0.059), and the inversion accuracy decreases in the real forest sample plots. For Jiangxi masson pine stand with large coverage and serious damage, R2 = 0.57, RMSE = 0.074; and for Shandong black pine stand with sparse and a small number of single plant damage, R2 = 0.48, RMSE = 0.063. This study indicates that the SRTP model is more feasible for pest damage inversion over different regions compared with empirical methods. The stochastic radiative transfer theory provides a potential approach for future monitoring of terrestrial vegetation parameters.
Photoacoustic-MR Image Registration Based on a Co-Sparse Analysis Model to Compensate for Brain Shift
Authors: Farnia Parastoo , Makkiabadi Bahador , Alimohamadi Maysam , Najafzadeh Ebrahim , Basij Maryam , Yan Yan , Mehrmohammadi Mohammad , Ahmadian Alireza
Published: March 2022

Brain shift is an important obstacle to the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging to update the image-guided surgery systems. However, due to the innate limitations of the current imaging modalities, accurate brain shift compensation continues to be a challenging task. In this study, the application of intra-operative photoacoustic imaging and registration of the intra-operative photoacoustic with pre-operative MR images are proposed to compensate for brain deformation. Finding a satisfactory registration method is challenging due to the unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for photoacoustic-MR image registration, which can capture the interdependency of the two modalities. The proposed algorithm works based on the minimization of mapping transform via a pair of analysis operators that are learned by the alternating direction method of multipliers. The method was evaluated using an experimental phantom and ex vivo data obtained from a mouse brain. The results of the phantom data show about 63% improvement in target registration error in comparison with the commonly used normalized mutual information method. The results proved that intra-operative photoacoustic images could become a promising tool when the brain shift invalidates pre-operative MRI.
Development of a Novel Railway Positioning System Using RFID Technology
Authors: Olaby Osama , Hamadache Moussa , Soper David , Winship Phil , Dixon Roger
Published: March 2022

Currently, a number of positioning systems are in use to locate trains on the railway network; but these generally have limited precision. Thus, this paper focuses on testing and validating the suitability of radio frequency identification (RFID) technology, for aligning vehicles to switch and crossing (S&C) positions on the railway network. This offers the possibility of accurately knowing the position of vehicles equipped with monitoring equipment, such as the network rail track recording vehicle (TRV), and aligning the data with reference to the locations of the S&C (and ideally to key elements within a particular S&C). The concept is to install two tags, one on the switch-toe sleeper and the second on the crossing-nose sleeper, with an RFID reader that will be installed underneath the vehicle. Thus, the key features of the S&C, the switch toe and crossing nose, will be considered as a definitive reference point for the inspection vehicle’s position. As a monitoring vehicle passes over a piece of S&C, the proposed positioning system will provide information about this S&C’s ID, which is stored inside the RFID tags and will indicate the S&C’s GPS coordinates. As part of the research in this paper, more than 400 tests have been performed to investigate two different RFID technologies, passive and semi-passive, tested in a variety of conditions: including different passage speeds, different distances between the RFID reader and the tags, and varied strength signal transmitted between the reader and the tags. Based on lab testing and analysis of the recorded data, it is concluded that passive RFID technology is the most suitable of the two technologies. The conclusions find that the proposed RFID-based solution can offer a more precise positioning solution to be a reference point for the train location within the network.
A Study of the Detection of SARS-CoV-2 ORF1ab Gene by the Use of Electrochemiluminescent Biosensor Based on Dual-Probe Hybridization
Authors: Jiang Chunying , Mu Xihui , Liu Shuai , Liu Zhiwei , Du Bin , Wang Jiang , Xu Jianjie
Published: March 2022

To satisfy the need to develop highly sensitive methods for detecting the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) and further enhance detection efficiency and capability, a new method was created for detecting SARS-CoV-2 of the open reading frames 1ab (ORF1ab) target gene by a electrochemiluminescence (ECL) biosensor based on dual-probe hybridization through the use of a detection model of “magnetic capture probes—targeted nucleic acids—Ru(bpy)32+ labeled signal probes”. The detection model used magnetic particles coupled with a biotin-labeled complementary nucleic acid sequence of the SARS-CoV-2 ORF1ab target gene as the magnetic capture probes and Ru(bpy)32+ labeled amino modified another complementary nucleic acid sequence as the signal probes, which combined the advantages of the highly specific dual-probe hybridization and highly sensitive ECL biosensor technology. In the range of 0.1 fM~10 µM, the method made possible rapid and sensitive detection of the ORF1ab gene of the SARS-CoV-2 within 30 min, and the limit of detection (LOD) was 0.1 fM. The method can also meet the analytical requirements for simulated samples such as saliva and urine with the definite advantages of a simple operation without nucleic acid amplification, high sensitivity, reasonable reproducibility, and anti-interference solid abilities, expounding a new way for efficient and sensitive detection of SARS-CoV-2.
Integrated Resource Management for Fog Networks
Authors: Yang Jui-Pin , Su Hui-Kai
Published: March 2022

In this paper, we consider integrated resource management for fog networks inclusive of intelligent energy perception, service level agreement (SLA) planning and replication-based hotspot offload (RHO). In the beginning, we propose an intelligent energy perception scheme which dynamically classifies the fog nodes into a hot set, a warm set or a cold set, based on their load conditions. The fog nodes in the hot set are responsible for a quality of service (QoS) guarantee and the fog nodes in the cold set are maintained at a low-energy state to save energy consumption. Moreover, the fog nodes in the warm set are used to balance the QoS guarantee and energy consumption. Secondly, we propose an SLA mapping scheme which effectively identifies the SLA elements with the same semantics. Finally, we propose a replication-based load-balancing scheme, namely RHO. The RHO can leverage the skewed access pattern caused by the hotspot services. In addition, it greatly reduces communication overheads because the load conditions are updated only when the load variations exceed a specific threshold. Finally, we use computer simulations to compare the performance of the RHO with other schemes under a variety of load conditions. In a word, we propose a comprehensive and feasible solution that contributes to the integrated resource management of fog networks.
Multi-Scale Attention 3D Convolutional Network for Multimodal Gesture Recognition
Authors: Chen Huizhou , Li Yunan , Fang Huijuan , Xin Wentian , Lu Zixiang , Miao Qiguang
Published: March 2022

Gesture recognition is an important direction in computer vision research. Information from the hands is crucial in this task. However, current methods consistently achieve attention on hand regions based on estimated keypoints, which will significantly increase both time and complexity, and may lose position information of the hand due to wrong keypoint estimations. Moreover, for dynamic gesture recognition, it is not enough to consider only the attention in the spatial dimension. This paper proposes a multi-scale attention 3D convolutional network for gesture recognition, with a fusion of multimodal data. The proposed network achieves attention mechanisms both locally and globally. The local attention leverages the hand information extracted by the hand detector to focus on the hand region, and reduces the interference of gesture-irrelevant factors. Global attention is achieved in both the human-posture context and the channel context through a dual spatiotemporal attention module. Furthermore, to make full use of the differences between different modalities of data, we designed a multimodal fusion scheme to fuse the features of RGB and depth data. The proposed method is evaluated using the Chalearn LAP Isolated Gesture Dataset and the Briareo Dataset. Experiments on these two datasets prove the effectiveness of our network and show it outperforms many state-of-the-art methods.
Method to Minimize the Errors of AI: Quantifying and Exploiting Uncertainty of Deep Learning in Brain Tumor Segmentation
Authors: Lee Joohyun , Shin Dongmyung , Oh Se-Hong , Kim Haejin
Published: March 2022

Despite the unprecedented success of deep learning in various fields, it has been recognized that clinical diagnosis requires extra caution when applying recent deep learning techniques because false prediction can result in severe consequences. In this study, we proposed a reliable deep learning framework that could minimize incorrect segmentation by quantifying and exploiting uncertainty measures. The proposed framework demonstrated the effectiveness of a public dataset: Multimodal Brain Tumor Segmentation Challenge 2018. By using this framework, segmentation performances, particularly for small lesions, were improved. Since the segmentation of small lesions is difficult but also clinically significant, this framework could be effectively applied to the medical imaging field.
A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems
Authors: Liu Shihong , Du Shichang , Xi Lifeng , Shao Yiping , Huang Delin
Published: March 2022

Production system modeling (PSM) for quality propagation involves mapping the principles between components and systems. While most existing studies focus on the steady-state analysis, the transient quality analysis remains largely unexplored. It is of significance to fully understand quality propagation, especially during transients, to shorten product changeover time, decrease quality loss, and improve quality. In this paper, a novel analytical PSM approach is established based on the Markov model, to explore product quality propagation for transient analysis of serial multi-stage production systems. The cascade property for quality propagation among correlated sequential stages was investigated, taking into account both the status of the current stage and the quality of the outputs from upstream stages. Closed-form formulae to evaluate transient quality performances of multi-stage systems were formulated, including the dynamics of system quality, settling time, and quality loss. An iterative procedure utilizing the aggregation technique is presented to approximate transient quality performance with computational efficiency and high accuracy. Moreover, system theoretic properties of quality measures were analyzed and the quality bottleneck identification method was investigated. In the case study, the modeling error was 0.36% and the calculation could clearly track system dynamics; quality bottleneck was identified to decrease the quality loss and facilitate continuous improvement. The experimental results illustrate the applicability of the proposed PSM approach.
An Unsupervised Tunnel Damage Identification Method Based on Convolutional Variational Auto-Encoder and Wavelet Packet Analysis
Authors: Zhang Yonglai , Xie Xiongyao , Li Hongqiao , Zhou Biao
Published: March 2022

Finding a low-cost and highly efficient method for identifying subway tunnel damage can greatly reduce catastrophic accidents. At present, tunnel health monitoring is mainly based on the observation of apparent diseases and vibration monitoring, which is combined with a manual inspection to perceive the tunnel health status. However, these methods have disadvantages such as high cost, short working time, and low identification efficiency. Thus, in this study, a tunnel damage identification algorithm based on the vibration response of in-service train and WPE-CVAE is proposed, which can automatically identify tunnel damage and give the damage location. The method is an unsupervised novelty detection that requires only sufficient normal data on healthy structure for training. This study introduces the theory and implementation process of this method in detail. Through laboratory model tests, the damage of the void behind the tunnel wall is designed to verify the performance of the algorithm. In the test case, the proposed method achieves the damage identification performance with a 96.25% recall rate, 86.75% hit rate, and 91.5% accuracy. Furthermore, compared with the other unsupervised methods, the method performance and noise immunity are better than others, so it has a certain practical value.
Development of an Integrated Virtual Reality System with Wearable Sensors for Ergonomic Evaluation of Human–Robot Cooperative Workplaces
Authors: Caporaso Teodorico , Grazioso Stanislao , Di Gironimo Giuseppe
Published: March 2022

This work proposes a novel virtual reality system which makes use of wearable sensors for testing and validation of cooperative workplaces from the ergonomic point of view. The main objective is to show, in real time, the ergonomic evaluation based on a muscular activity analysis within the immersive virtual environment. The system comprises the following key elements: a robotic simulator for modeling the robot and the working environment; virtual reality devices for human immersion and interaction within the simulated environment; five surface electromyographic sensors; and one uniaxial accelerometer for measuring the human ergonomic status. The methodology comprises the following steps: firstly, the virtual environment is constructed with an associated immersive tutorial for the worker; secondly, an ergonomic toolbox is developed for muscular analysis. This analysis involves multiple ergonomic outputs: root mean square for each muscle, a global electromyographic score, and a synthetic index. They are all visualized in the immersive environment during the execution of the task. To test this methodology, experimental trials are conducted on a real use case in a human–robot cooperative workplace typical of the automotive industry. The results showed that the methodology can effectively be applied in the analysis of human–robot interaction, to endow the workers with self–awareness with respect to their physical conditions.
Sensing System for Plegic or Paretic Hands Self-Training Motivation
Authors: Zubrycki Igor , Prączko-Pawlak Ewa , Dominik Ilona
Published: March 2022

Patients after stroke with paretic or plegic hands require frequent exercises to promote neuroplasticity and to improve hand joint mobilization. Available devices for hand exercising are intended for persons with some level of hand control or provide continuous passive motion with limited patient involvement. Patients can benefit from self-exercising where they use the other hand to exercise the plegic or paretic one. However, post-stroke neuropsychological complications, apathy, and cognitive impairments such as forgetfulness make regular self-exercising difficult. This paper describes Przypominajka v2—a system intended to support self-exercising, remind about it, and motivate patients. We propose a glove-based device with an on-device machine-learning-based exercise scoring, a tablet-based interface, and a web-based application for therapists. The feasibility of on-device inference and the accuracy of correct exercise classification was evaluated on four healthy participants. Whole system use was described in a case study with a patient with a paretic hand. The anomaly classification has an accuracy of 91.3% and f1 value of 91.6% but achieves poorer results for new users (78% and 81%). The case study showed that patients had a positive reaction to exercising with Przypominajka, but there were issues relating to sensor glove: ease of putting on and clarity of instructions. The paper presents a new way in which sensor systems can support the rehabilitation of after-stroke patients with an on-device machine-learning-based classification that can accurately score and contribute to patient motivation.
Analog-Domain Suppression of Strong Interference Using Hybrid Antenna Array
Authors: Wu Kai , Zhang J. Andrew , Huang Xiaojing , Guo Y. Jay , Nguyen Diep N. , Kekirigoda Asanka , Hui Kin-Ping
Published: March 2022

The proliferation of wireless applications, the ever-increasing spectrum crowdedness, as well as cell densification makes the issue of interference increasingly severe in many emerging wireless applications. Most interference management/mitigation methods in the literature are problem-specific and require some cooperation/coordination between different radio frequency systems. Aiming to seek a more versatile solution to counteracting strong interference, we resort to the hybrid array of analog subarrays and suppress interference in the analog domain so as to greatly reduce the required quantization bits of the analog-to-digital converters and their power consumption. To this end, we design a real-time algorithm to steer nulls towards the interference directions and maintain flat in non-interference directions, solely using constant-modulus phase shifters. To ensure sufficient null depth for interference suppression, we also develop a two-stage method for accurately estimating interference directions. The proposed solution can be applicable to most (if not all) wireless systems as neither training/reference signal nor cooperation/coordination is required. Extensive simulations show that more than 65 dB of suppression can be achieved for 3 spatially resolvable interference signals yet with random directions.
Microservice Security Framework for IoT by Mimic Defense Mechanism
Authors: Ying Fei , Zhao Shengjie , Deng Hao
Published: March 2022

Containers and microservices have become the most popular method for hosting IoT applications in cloud servers. However, one major security issue of this method is that if a container image contains software with security vulnerabilities, the associated microservices also become vulnerable at run-time. Existing works attempted to reduce this risk with vulnerability-scanning tools. They, however, demand an up-to-date database and may not work with unpublished vulnerabilities. In this paper, we propose a novel system to strengthen container security from unknown attack using the mimic defense framework. Specifically, we constructed a resource pool with variant images and observe the inconsistency in execution results, from which we can identify potential vulnerabilities. To avoid continuous attack, we created a graph-based scheduling strategy to maximize the randomness and heterogeneity of the images used to replace the current images. We implemented a prototype using Kubernetes. Experimental results show that our framework makes hackers have to send 54.9% more random requests to complete the attack and increases the defence success rate by around 8.16% over the baseline framework to avoid the continuous unknown attacks.
Underwater Sound Source Localization Based on Passive Time-Reversal Mirror and Ray Theory
Authors: Liu Kuan-Wen , Huang Ching-Jer , Too Gee-Pinn , Shen Zong-You , Sun Yung-Da
Published: March 2022

This study investigates the performance of a passive time-reversal mirror (TRM) combined with acoustic ray theory in localizing underwater sound sources with high frequencies (3–7 kHz). The TRM was installed on a floating buoy and comprised four hydrophones. The ray-tracing code BELLHOP was used to determine the transfer function between a sound source and a field point. The transfer function in the frequency domain obtained from BELLHOP was transformed into the time domain. The pressure field was then obtained by taking the convolution of the transfer function in the time domain with the time-reversed signals that were received by the hydrophones in the TRM. The location with the maximum pressure value was designated as the location of the source. The performance of the proposed methodology for source localization was tested in a towing tank and in the ocean. The aforementioned tests revealed that even when the distances between a source and the TRM were up to 1600 m, the distance deviations between estimated and actual source locations were mostly less than 2 m. Errors originated mainly from inaccurate depth estimation, and the literature indicates that they can be reduced by increasing the number of TRM elements and their apertures.