bluuebunny/crossref_metadata_2025 huggingface dataset:
High-Density Horizontal Stacking of Chondrocytes via the Synergy of Biocompatible Magnetic Gelatin Nanocarriers and Internal Magnetic Navigation for Enhancing Cartilage Repair
Authors: Yang Shan-Wei , Chen Yong-Ji , Chen Ching-Jung , Liu Jen-Tsai , Yang Chin-Yi , Tsai Jen-Hao , Lu Huai-En , Chen San-Yuan , Chang Shwu-Jen
Published: February 2022

Osteoarthritis (OA) is a globally occurring articular cartilage degeneration disease that adversely affects both the physical and mental well-being of the patient, including limited mobility. One major pathological characteristic of OA is primarily related to articular cartilage defects resulting from abrasion and catabolic and proinflammatory mediators in OA joints. Although cell therapy has hitherto been regarded as a promising treatment for OA, the therapeutic effects did not meet expectations due to the outflow of implanted cells. Here, we aimed to explore the repair effect of magnetized chondrocytes using magnetic amphiphilic-gelatin nanocarrier (MAGNC) to enhance cellular anchored efficiency and cellular magnetic guidance (MG) toward the superficial zone of damaged cartilage. The results of in vitro experiments showed that magnetized chondrocytes could be rapidly guided along the magnetic force line to form cellular amassment. Furthermore, the Arg-Gly-Asp (RGD) motif of gelatin in MAGNC could integrate the interaction among cells to form cellular stacking. In addition, MAGNCs upregulated the gene expression of collagen II (Col II), aggrecan, and downregulated that of collagen I (Col I) to reduce cell dedifferentiation. In animal models, the magnetized chondrocytes can be guided into the superficial zone with the interaction between the internal magnetic field and MAGNC to form cellular stacking. In vivo results showed that the intensity of N-sulfated-glycosaminoglycans (sGAG) and Col II in the group of magnetized cells with magnetic guiding was higher than that in the other groups. Furthermore, smooth closure of OA cartilage defects was observed in the superficial zone after 8 weeks of implantation. The study revealed the significant potential of MAGNC in promoting the high-density stacking of chondrocytes into the cartilage surface and retaining the biological functions of implanted chondrocytes for OA cartilage repair.
Hybrid Printing Method of Polymer and Continuous Fiber-Reinforced Thermoplastic Composites (CFRTPCs) for Pipes through Double-Nozzle Five-Axis Printer
Authors: Zhang Haiguang , Lei Xu , Hu Qingxi , Wu Shichao , Aburaia Mohamed , Gonzalez-Gutierrez Joamin , Lammer Herfried
Published: February 2022

The most widely used 3D process, fused deposition modeling (FDM), has insufficient interlayer adhesion due to its layer-by-layer forming method. A support material is also essential for the hollow parts and cantilevers. Moreover, the polymer materials used have limited mechanical properties. These issues have restricted the application of FDM in high-performance fields. Continuous fiber-reinforced thermoplastic composites (CFRTPCs) have high mechanical properties and have recently become the focus of research in the field of 3D printing. This paper, using pipe parts as an example, proposes a hybrid of pure polymer (pure PLA used) and CFRTPC (flax fiber pre-impregnated filament) material to develop a printing method based on the outstanding mechanical properties of CFRTPC material. After studying the printing path planning algorithm, the CFRTPC filament is laid along the axial and radial directions on the surface of the polymer base to improve the printed parts’ properties. The method feasibility and algorithm accuracy are verified through the development of five-axis printing equipment with a double nozzle. Through the printed sample’s tensile, compression and bending tests, the results show that the tensile, compressive and bending properties of PLA pipe can be significantly enhanced by laying CFRTPC filament along the axial and radial directions of the pipe. To summarize, the introduction of CFRTPCs greatly improved the mechanical properties of the printed parts, and the implementation of our method provides an effective way to solve the defects of the FDM process.
Fādil Al-Samarra’ī’s Contribution to Literary and Rhetorical Exegesis of the Qur’an
Authors: Abdullah Abdul-Samad
Published: February 2022

This article explores and assesses Fādil al-Samarrā’ī’s contribution to literary and rhetorical Qur’anic exegesis, especially regarding the rhetorical inimitability of the Qur’an. The article looks at how al-Samarrā’ī approaches the Qur’anic text to reveal its miraculous expressional secrets and its rhetorical inimitability with mere Arabic linguistic tools while giving contexts high priority in his analyses and interpretations. Al-Samarrā’ī was able to reach the semantics and purposes of the Qur’an based on the Qur’anic language itself, relying on its sentence structure and order, as well as on the structures, significance, and special meanings of words (which distinguish them from their synonyms), and how all of it relates to the purposes and objectives of the Qur’an. Al-Samarrā’ī sought to use morphology, semantics, and syntax to reach the purposes of the Holy Qur’an and discover its miraculous and inimitable eloquence. To achieve this, al-Samarrā’ī relied on the rich and vast literature on the subject. Guided by the intellectual language and empirical questions of his time, his tremendous effort and contribution to the literature has helped to demystify this complex subject.
Who Speaks for Coptic Rights in Egypt Today? (2013–2021)
Authors: Tadros Mariz , Habib Akram
Published: February 2022

This paper explores the nature of Coptic struggles for religious equality in Egypt in the period between 2013 and 2021. The key research question informing this paper is: in a context where the space for civic action to demand rights for equality and religious freedom is deeply circumscribed, who fills the vacuum of mediating Coptic grievances and what are the implications for institutionalizing religious freedom and promoting the greater public good? The methodology informing this paper is a multi-scalar linking national level political analysis of the relationship between the President and the Patriarch with the relationship between the church leadership and authorities in the governorate of Minya and its implications for local level governance of sectarian violence against Copts. The paper makes three key propositions. First, the relationship between the President and the Pope cannot be assumed to be a proxy for state-church relations more widely because the positively demonstrated political will of the President has not led to the institutionalization of religious equality at different levels of governance. Second, the assumption of Bishop Makarious of a representational role in defending and promoting the rights of Copts has led to a trade off in institutional rights encroaching on the principle of ecclesiastical affairs being free from governmental meddling. The third proposition is that the political vacuum created by the elimination of mediation of rights via civil society actors has not only negatively affected opportunities for championing the institutionalization of rights at different levels but has also wielded a loss for the promotion of public good more broadly.
Assessing the Usefulness of LiDAR for Monitoring the Structure of a Montane Forest on a Subtropical Oceanic Island
Authors: Parada-Díaz Jesús , Fernández López Ángel B. , Gómez González Luis A. , del Arco Aguilar Marcelino J. , González-Mancebo Juana María
Published: February 2022

The monitoring of ecosystems and forests is an urgent requirement in the current framework of global change. It is particularly necessary on oceanic islands where their rich biodiversity is highly vulnerable, with many narrow-ranged endemic species. Quantifying and mapping forest health through key ecological variables are essential steps for management, but it will also be challenging and may require a lot of resources. Remote sensing has the potential to be a very useful tool to assess the development and conservation status of forests. We assessed the applicability of the light detection and ranging (LiDAR) on the laurel forests of La Gomera, making allometric equations for various measurements of the forest structure, linking field inventory from 2019 and 2017 LiDAR data through standard linear regressions. Decision trees and logistic regressions were also used to assess the performance of LiDAR in the recognition of young-growth and old-growth laurel forests. The obtained allometric models were a good fit in general and their predictions were in line with already known data. Likewise, decision tree and logistic regression to distinguish young-growth and old-growth forests had a similar performance in both cases, with a high to medium-high degree of accuracy. Therefore, LiDAR was revealed to be a useful tool for the monitoring of the laurel forest by the managers.
Parameter Estimation for Uniformly Accelerating Moving Target in the Forward Scatter Radar Network
Authors: Ai Xiaofeng , Zheng Yuqing , Xu Zhiming , Zhao Feng
Published: February 2022

Passive radar based on the global navigation satellite positioning system (GNSS) has become the focus of attention in the field of radar. A parameter estimation method is proposed in the forward scatter radar (FSR) network based on GNSS to extend the application scenarios. For uniformly accelerating moving targets, only the instant times when the target crosses the individual baselines are used to retrieve the target motion parameters. The target position, velocity, and acceleration information can be obtained. Firstly, the minimum network configuration is derived theoretically. Then, the effects of crossing time error, station location error, transmitting/receiving station deployment, and target height on the accuracy are analyzed through Monte Carlo simulations. Finally, the simulation results indicate that the target position estimation error is in the order of 100 m. This paper provides the fundamental theory of aerial target positioning with a GNSS-based FSR network.
FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection
Authors: Dewangan Anshuman , Pande Yash , Braun Hans-Werner , Vernon Frank , Perez Ismael , Altintas Ilkay , Cottrell Garrison W. , Nguyen Mai H.
Published: February 2022

The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high-fire-risk days, a small fire ignition can rapidly grow and become out of control. Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios. In this work, we present the Fire Ignition Library (FIgLib), a publicly available dataset of nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras deployed in Southern California. We also introduce SmokeyNet, a novel deep learning architecture using spatiotemporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines and rivals human performance. We hope that the availability of the FIgLib dataset and the SmokeyNet architecture will inspire further research into deep learning methods for wildfire smoke detection, leading to automated notification systems that reduce the time to wildfire response.
Satellite-Based Methodology for Purposes of Rescue Archaeology of Cultural Heritage Threatened by Dam Construction
Authors: Zaina Federico , Tapete Deodato
Published: February 2022

The destruction of cultural heritage caused by dams represents a major issue especially in an age of climate change and narrowly focused development policies. To counteract this phenomenon, archaeologists and cultural heritage experts have relied upon rescue archaeology practices generally limited to fieldwork methodologies, while remote sensing of satellite imagery remains under-considered. To bridge this gap, we build on a multidisciplinary collaboration exploring the potential of Synthetic Aperture Radar (SAR) and open access multispectral satellite imagery, for quantifying the archaeological evidence located within a prospective reservoir area before dam construction. Based on previous research by Marchetti (2020) claiming the necessity for ad hoc protocols to document and monitor the impact of dams on cultural heritage, we selected two complementary situations: the planned dam of Halabiyeh in Syria and the under construction Grand Ethiopian Renaissance Dam (GERD) in Ethiopia. These case studies were analyzed with state-of-the-art methodologies to develop a feasible workflow that may contribute to fostering the use of satellite imagery in operational contexts such as those represented by these particular cases, and be replicated by archaeologists in other areas. The workflow is designed to be integrated to ground-truthing methodologies into two dedicated protocols named Pre-Construction Archaeological Risk Assessment (PCARA) and Pre-Flooding Rescue Archaeological Program (PFRAP) which could eventually become a standard procedure for rescue archaeology in dams areas.
A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations
Authors: Jiang Yanan , Luo Huiyuan , Xu Qiang , Lu Zhong , Liao Lu , Li Huajin , Hao Lina
Published: February 2022

Landslide displacement prediction is crucial for the early warning of slope failure but remains a challenging task due to its spatiotemporal complexity. Although temporal dependency has been well studied and discussed, spatial dependence is relatively less explored due to its significant variations of the spatial structure of landslides. In this study, a novel graph convolutional incorporating GRU network (GC-GRU-N) is proposed and applied to landslide displacement forecasts. The model conducts attribute-augmented graph convolution (GC) operations on GNSS displacement data with weighted adjacency matrices and an attribute-augmented unit to combine features, including the displacements, the distance, and other external influence factors to capture spatial dependence. The output of multi-weight graph convolution is then applied to the gated recurrent unit (GRU) network to learn temporal dependencies. The related optimal hyper-parameters are determined by comparison experiments. When applied to two typical landslide sites in the Three Gorge Reservoir (TGR), China, GC-GRU-N outperformed the comparative models in both cases. The ablation experiment results also show that the attribute augmentation, which considers external factors of landslide displacement, can further improve the model’s prediction performance. We conclude that the GC-GRU-N model can provide robust landslide displacement forecasting with high efficiency.
SiamMixer: A Lightweight and Hardware-Friendly Visual Object-Tracking Network
Authors: Cheng Li , Zheng Xuemin , Zhao Mingxin , Dou Runjiang , Yu Shuangming , Wu Nanjian , Liu Liyuan
Published: February 2022

Siamese networks have been extensively studied in recent years. Most of the previous research focuses on improving accuracy, while merely a few recognize the necessity of reducing parameter redundancy and computation load. Even less work has been done to optimize the runtime memory cost when designing networks, making the Siamese-network-based tracker difficult to deploy on edge devices. In this paper, we present SiamMixer, a lightweight and hardware-friendly visual object-tracking network. It uses patch-by-patch inference to reduce memory use in shallow layers, where each small image region is processed individually. It merges and globally encodes feature maps in deep layers to enhance accuracy. Benefiting from these techniques, SiamMixer demonstrates a comparable accuracy to other large trackers with only 286 kB parameters and 196 kB extra memory use for feature maps. Additionally, we verify the impact of various activation functions and replace all activation functions with ReLU in SiamMixer. This reduces the cost when deploying on mobile devices.
Investigation on the Properties of Gneiss under Different Ground Stresses
Authors: Wang Rui , Wang Yuncai , Deng Xianghui , Qin Yuan , Xie Bingxin
Published: February 2022

Initial geostress has great influence on the properties of gneiss. The physical and mechanical properties of gneiss vary considerably due to different initial geostresses, which exert a huge effect on the stability of underground engineering. In order to explore the influence of initial ground stress on the properties of gneiss. Changes in the physical properties (e.g., P-wave velocity and volumetric weight), mechanical properties (e.g., compressive strength, elastic modulus, and residual strength) and failure mode of gneiss are analyzed by conducting physical and mechanical tests on gneiss in different ground stress areas. The results show that high geostress can improve the pre-peak mechanical properties of gneiss, and weaken its post-peak mechanical properties. When the initial geostress is greater, the pre-peak mechanical properties are better, and the post-peak mechanical properties are worse. The failure mode of gneiss under high ground stress is primarily brittle failure. When the initial ground stress is greater, brittleness is stronger. According to the research results of this paper, it can provide the basis for the optimization and improvement of underground engineering support in gneiss strata with high geostress. The research results have important reference value and guiding significance for underground engineering construction in high geostress gneiss areas.
HeadSLAM: Pedestrian SLAM with Head-Mounted Sensors
Authors: Hou Xinyu , Bergmann Jeroen
Published: February 2022

Research focused on human position tracking with wearable sensors has been developing rapidly in recent years, and it has shown great potential for application within healthcare, smart homes, sports, and emergency services. Pedestrian Dead Reckoning (PDR) with Inertial Measurement Units (IMUs) is one of the most promising solutions within this domain, as it does not rely on any additional infrastructure, whilst also being suitable for use in a diverse set of scenarios. However, PDR is only accurate for a limited period of time before unbounded errors, due to drift, affect the position estimate. Error correction can be difficult as there is often a lack of efficient methods for calibration. HeadSLAM, a method specifically designed for head-mounted IMUs, is proposed to improve the accuracy during longer tracking times (10 min). Research participants (n = 7) were asked to walk in both indoor and outdoor environments wearing head-mounted sensors, and the obtained HeadSLAM accuracy was subsequently compared to that of the PDR method. A significant difference (p < 0.001) in the average root-mean-squared error and absolute error was found between the two methods. HeadSLAM had a consist lower error across all scenarios and subjects in a 20 h walking dataset. The findings of this study show how the HeadSLAM algorithm can provide a more accurate long-term location service for head-mounted, low-cost sensors. The improved performance can support inexpensive applications for infrastructureless navigation.
Fault Diagnostics and Tolerance Analysis of a Microgrid System Using Hamilton–Jacobi–Isaacs Equation and Game Theoretic Estimations in Sliding Mode Observers
Authors: Shahzad Ebrahim , Khan Adnan Umar , Iqbal Muhammad , Albalawi Fahad , Khan Muhammad Attique , Saeed Ahmad , Ghoneim Sherif S. M.
Published: February 2022

This paper focuses on robustness and sensitivity analysis for sensor fault diagnosis of a voltage source converter based microgrid model. It uses robust control parameters such as minimum sensitivity parameter (H−), maximum robustness parameter (H∞), and compromised both (H−/H∞), being incorporated in the sliding mode observer theory using the game theoretic saddle point estimation achieved through convex optimization of constrained LMIs. The approach used works in a way that the mentioned robust control parameters are embedded in Hamilton–Jacobi–Isaacs-Equation (HJIE) and are also used to determine the inequality version of HJIE, which is, in terms of the Lyapunov function, faults/disturbances and augmented state/output estimation error as its variables. The stability analysis is also presented by negative definiteness of the same inequality version of HJIE, and additionally, it also gives linear matrix inequalities (LMIs), which are optimized using iterative convex optimization algorithms to give optimal sliding mode observer gains enhanced with robustness to maximal preset values of disturbances and sensitivity to minimal preset values of faults. The enhanced sliding mode observer is used to estimate states, faults, and disturbances using sliding mode observer theory. The optimality of sliding mode observer gains for sensitivity of the observer to minimal faults and robustness to maximal disturbance is a game theoretic saddle point estimation achieved through convex optimization of LMIs. The paper includes results for state estimation errors, faults’ estimation/reconstruction, fault estimation errors, and fault-tolerant-control performance for current and potential transformer faults. The considered faults and disturbances in current and potential transformers are sinusoidal nature composite of magnitude/phase/harmonics at the same time.
Re-Learning EXP3 Multi-Armed Bandit Algorithm for Enhancing the Massive IoT-LoRaWAN Network Performance
Authors: Almarzoqi Samar Adel , Yahya Ahmed , Matar Zaki , Gomaa Ibrahim
Published: February 2022

Long-Range Wide Area Network (LoRaWAN) is an open-source protocol for the standard Internet of Things (IoT) Low Power Wide Area Network (LPWAN). This work’s focal point is the LoRa Multi-Armed Bandit decentralized decision-making solution. The contribution of this paper is to study the effect of the re-learning EXP3 Multi-Armed Bandit (MAB) algorithm with previous experts’ advice on the LoRaWAN network performance. LoRa smart node has a self-managed EXP3 algorithm for choosing and updating the transmission parameters based on its observation. The best parameter choice needs previously associated distribution advice (expert) before updating different choices for confidence. The paper proposes a new approach to study the effects of combined expert distribution for each transmission parameter on the LoRaWAN network performance. The successful transmission of the packet with optimized power consumption is the pivot of this paper. The validation of the simulation result has proven that combined expert distribution improves LoRaWAN network’s performance in terms of data throughput and power consumption.
A Dynamic Imaging Simulation Method of Infrared Aero-Optical Effect Based on Continuously Varying Gaussian Superposition Model
Authors: Zhang Shuyuan , Chen Xin , Zu Yingqing , Rao Peng
Published: February 2022

Aero-optical effect correction has become a crucial issue in airborne infrared imaging. However, it is impractical to test the correction algorithm using flight tests and numerical simulation because of its high cost. This study proposes a dynamic imaging simulation method for the infrared aero-optical effect based on a continuously varying Gaussian superposition model. The influence of infrared image degradation under different high-speed aerodynamic flow fields was investigated in detail. A continuously varying Gaussian superposition model was established for flight speed, altitude, and attitude. A dynamic infrared scene simulation model was constructed. Experimental results show that the proposed method can realistically simulate actual aero-optical effects of any flight case. Moreover, it can simulate continuous frames of aerodynamically degraded infrared images. The method uses a simpler model than numerical simulation and provides more data for multitype tasks.
Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years
Authors: Civera Marco , Surace Cecilia
Published: February 2022

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well.
DFusion: Denoised TSDF Fusion of Multiple Depth Maps with Sensor Pose Noises
Authors: Niu Zhaofeng , Fujimoto Yuichiro , Kanbara Masayuki , Sawabe Taishi , Kato Hirokazu
Published: February 2022

The truncated signed distance function (TSDF) fusion is one of the key operations in the 3D reconstruction process. However, existing TSDF fusion methods usually suffer from the inevitable sensor noises. In this paper, we propose a new TSDF fusion network, named DFusion, to minimize the influences from the two most common sensor noises, i.e., depth noises and pose noises. To the best of our knowledge, this is the first depth fusion for resolving both depth noises and pose noises. DFusion consists of a fusion module, which fuses depth maps together and generates a TSDF volume, as well as the following denoising module, which takes the TSDF volume as the input and removes both depth noises and pose noises. To utilize the 3D structural information of the TSDF volume, 3D convolutional layers are used in the encoder and decoder parts of the denoising module. In addition, a specially-designed loss function is adopted to improve the fusion performance in object and surface regions. The experiments are conducted on a synthetic dataset as well as a real-scene dataset. The results prove that our method outperforms existing methods.
High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things
Authors: Sun Tianying , Feng Yuan , Li Chen , Zhang Xingzhi
Published: February 2022

Impacted by global warming, the global sea surface temperature (SST) has increased, exerting profound effects on local climate and marine ecosystems. So far, investigators have focused on the short-term forecast of a small or medium-sized area of the ocean. It is still an important challenge to obtain accurate large-scale and long-term SST predictions. In this study, we used the reanalysis data sets provided by the National Centers for Environmental Prediction based on the Internet of Things technology and temporal convolutional network (TCN) to predict the monthly SSTs of the Indian Ocean from 2014 to 2018. The results yielded two points: Firstly, the TCN model can accurately predict long-term SSTs. In this paper, we used the Pearson correlation coefficient (hereafter this will be abbreviated as “correlation”) to measure TCN model performance. The correlation coefficient between the predicted and true values was 88.23%. Secondly, compared with the CFSv2 model of the American National Oceanic and Atmospheric Administration (NOAA), the TCN model had a longer prediction time and produced better results. In short, TCN can accurately predict the long-term SST and provide a basis for studying large oceanic physical phenomena.
On-Line Foreign Object Detection Using Double DD Coils in an Inductive Wireless Power Transfer System
Authors: Prosen Nataša , Milanovič Miro , Domajnko Jure
Published: February 2022

This paper proposes an on-line method for foreign object detection in a double DD coil system. The foreign object is detected by real-time measurement of the mutual inductance between the transfer pads. Measurement of the mutual inductance between coils can be performed at the start, during initialisation, or during the wireless power transfer. The coils in the double DD coil structure can be used separately; one coil can be used for power transfer and the other can be used for the mutual inductance measurement. The mutual inductance measurement is based on the voltage measurement across the open circuit receiver coil. The measured value of mutual inductance between the transmitter and the receiver pad can be used in a control algorithm and in a foreign object detection algorithm. Additionally, a 2DDq coil structure can be used as a replacement for the double DD coil structure, which increases the power transfer density. The DD coils in the double DD coil structure can also be driven using two phase-shifted voltages, which enables better location and detection of foreign objects. The method also helps to differentiate the mutual inductance change due to the distance change from the mutual inductance change due to the presence of a foreign object.
A Wearable System for Jump Detection in Inline Figure Skating
Authors: Panfili Antonio , Spanò Alvise , Cortesi Agostino
Published: February 2022

This article presents the design and experimental evaluation of a non-invasive wearable sensor system that can be used to acquire crucial information about athletes’ performance during inline figure skating training. By combining distance and time-of-flight sensors and gyroscopes, the system is able to detect when jumps are performed and provides a live view of the data (e.g., the number and height of jumps) through a graphical user interface. The main novelty of our approach lies in the way in which the optical sensors are orientated. Typically, the sensors are orientated horizontally and positioned in pairs on the ground, where they measure the time interval between the moment the athlete leaves the ground and the moment they land. In our system, an optical sensor is placed under each foot and is vertically orientated so as to constantly measure the distance from the ground. In addition, a gyroscope sensor is placed on the athlete’s back, which provides information on the direction and angular momentum of the movement. By combining this data, the system provides the accurate detection of various jumps and technical elements without any constraints on the training ground. In this paper, the system is also compared to similar platforms in the literature, although there are no other specific systems that are available for inline figure skating. The results of the experimental evaluation, which was performed by high profile athletes, confirm its effectiveness in correctly detecting jumps, especially considering its compromise between precision and the overall cost of the equipment.
ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features
Authors: Mathunjwa Bhekumuzi M. , Lin Yin-Tsong , Lin Chien-Hung , Abbod Maysam F. , Sadrawi Muammar , Shieh Jiann-Shing
Published: February 2022

In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were detected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhythmia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.
Real-Time Foot Tracking and Gait Evaluation with Geometric Modeling
Authors: Foo Ming Jeat , Chang Jen-Shuan , Ang Wei Tech
Published: February 2022

Gait evaluation is important in gait rehabilitation and assistance to monitor patient’s balance status and assess recovery performance. Recent technologies leverage on vision-based systems with high portability and low operational complexity. In this paper, we propose a new vision-based foot tracking algorithm specially catering to overground gait assistive devices, which often have limited view of the users. The algorithm models the foot and the shank of the user using simple geometry. Through cost optimization, it then aligns the models to the point cloud, showing the back view of the user’s lower limbs. The system outputs the poses of the feet, which are used to compute the spatial-temporal gait parameters. Seven healthy young subjects are recruited to perform overground and treadmill walking trials. The results of the algorithm are compared with the motion capture system and a third-party gait analysis software. The algorithm has a fitting rotational and translational errors of less than 20 degrees and 33 mm, respectively, for 0.4 m/s walking speed. The gait detection F1 score achieves more than 96.8%. The step length and step width errors are around 35 mm, while the cycle time error is less than 38 ms. The proposed algorithm provides a fast, contactless, portable, and cost-effective gait evaluation method without requiring the user to wear any customized footwear.
Built Heritage Repurposing and Communities Engagement: Symbiosis, Enabling Processes, Key Challenges
Authors: Viola Serena
Published: February 2022

The paper debates the results achieved in the Sanità district of Naples within the funded research project Play_ACT by the Department of Architecture, University of Naples Federico II. The research investigates community engagement processes in built heritage design for repurposing. Based on a mixed deductive and inductive approach, this paper frames the built heritage in the following ways: (a) a record of historical development and way of life, a linking capital between community and context; (b) the enabler of community engagement, representative of creative skills and work of humankind; and (c) a driving force for growth. The paper analyses several community-driven repurposing practices experienced within European programs. Supported by the ISO 37104: 2019 and UNI 11151-1: 2005 standards, the paper focuses on the replicability of community engagement in built heritage repurposing processes, outlining the commonalities in terms of strategies, actions, and actors involved in: (1) discovery; (2) negotiation; and (3) sharing. Focusing on the Sanità district of Naples, the research experiences the sustainability of community-led repurposing, observing long-lasting spillover effects on the physical, economic, cultural, and social subsystems. Finally, with the support of a heritage case study, Cristallini 73, future key challenges for a durable and inclusive community engagement are outlined: A activation of collaborative networks; B promotion of inclusive dynamics of long-life learning; and C creation of cultural and creative industries.
Analysis of the Temporal and Spatial Distribution of Extreme Climate Indices in Central China
Authors: Li Yan , Zhao Junfang , Miao Rui , Huang Yan , Fan Xiaoqing , Liu Xiaoqing , Wang Xueqi , Wang Ye , Shen Yuyang
Published: February 2022

Using the daily precipitation and temperature data of 101 meteorological stations in four provinces of central China (Henan, Hubei, Hunan, Jiangxi) from 1988 to 2017, we analyzed the temporal and spatial dynamics and periodicity of nine extreme climate indices in central China, using the predefined methods for analyzing extreme climate events, such as a M-K test, a linear trend analysis, and a wavelet analysis. The extreme climate characteristics and changes in central China in the past 30 years were revealed. The results showed that the CSDI was significantly reduced linearly at a rate of −0.19 d/10a, and the WSDI and TXx increased significantly at rates of 0.25 d/10a and 0.30℃/10a, respectively. The CDD decreased significantly at a rate of −1.67 d/10a. The duration of extreme low-temperature and drought events in central China showed a gradual shortening, while the duration of extreme high-temperature events and the high-temperature values increased. The results of the abrupt climate change test showed that some extreme climate indices in central China had significant abrupt climate changes after 2000. Analyzing the cyclicality of each index, it was determined that the extreme climate index in central China had a significant cyclical change every 2–4 years, and the change was more notable after 2000. Analyzing the spatial distribution of the extreme climate indices, it was determined that Jiangxi had the longest duration of all high-temperature events, and was the largest and longest of events of extreme precipitation. It was also determined that the Jiangxi region was at greater risk of extreme climate events in central China. The results of this study can provide a scientific basis for climate change trends, local disaster prevention, and mitigation management in central China.
Characterizing the Payback and Profitability for Automated Heavy Duty Vehicle Platooning
Authors: Sujan Vivek , Jones Perry T. , Siekmann Adam
Published: February 2022

Heavy duty vehicle platooning under highway operating conditions has been projected to provide significant fuel economy gains based on aerodynamic drag improvements of the platooning vehicles. Realizing these benefits and the economic viability under real-world operating conditions presents several challenges. The objective of this paper (the third as part of a series) is to analytically quantify the payback and profitability of heavy-duty vehicles platooning across the U.S. Interstate highway system. In this paper, a rigorous assessment of several factors that influence the platooning system payback for an end-user as well as the revenue potential for suppliers who may be utilizing an equipment lease model dependent on end-user savings, is presented. In this assessment key interactions explored include market adoption rates, platooning velocities, platoon-able daily mileage, platooning likelihood, variations in baseline powertrain fuel economy (diesel or electric), price of fuel (diesel or electricity), platooning fuel economy benefits, price of the added technology, and the impact of natural platooning due to traffic interactions. Further, the paper explores the economic impact of higher levels of vehicle automation for the trailing vehicles in the platoon, where extending the driver Hours of Service (HoS) may provide additional financial benefits. While the approach makes use of a limited fidelity vehicle analytical model for longitudinal dynamics and operations economics, the narrative provides application decision personnel with a mechanism and well-defined set of impact factors to consider as part of their architectural selection process.
Roles of Economic Development Level and Other Human System Factors in COVID-19 Spread in the Early Stage of the Pandemic
Authors: Fan Peilei , Chen Jiquan , Sarker Tanni
Published: February 2022

We identified four distinct clusters of 151 countries based on COVID-19 prevalence rate from 1 February 2020 to 29 May 2021 by performing nonparametric K-means cluster analysis (KmL). We forecasted future development of the clusters by using a nonlinear 3-parameter logistic (3PL) model, and found that peak points of development are the latest for Cluster I and earliest for Cluster IV. Based on partial least squares structural equation modeling (PLS-SEM) for the first twenty weeks after 1 February 2020, we found that the prevalence rate of COVID-19 has been significantly influenced by major elements of human systems. Better health infrastructure, more restriction of human mobility, higher urban population density, and less urban environmental degradation are associated with lower levels of prevalence rate (PR) of COVID-19. The most striking discovery of this study is that economic development hindered the control of COVID-19 spread among countries in the early stage of the pandemic. Highlights: While richer countries have advantages in health and other urban infrastructures that may alleviate the prevalence rate of COVID-19, the combination of high economic development level and low restriction on human mobility has led to faster spread of the virus in the first 20 weeks after 1 February 2020.
Marketing Management in the Hotel Industry: A Systematic Literature Review by Using Text Mining
Authors: Horng Jeou-Shyan , Liu Chih-Hsing , Chou Sheng-Fang , Yu Tai-Yi , Hu Da-Chian
Published: February 2022

The current research is a systematic review of the literature on hotel marketing management that provides evidence that hotel marketing management contributes to organizational performance and satisfaction, and provides insights into how hotel operators can successfully implement it. This systematic review of the literature is based on the analysis of 417 papers via a text-mining methodology. Through cluster analysis, we divided the literature on hotel marketing management into four clusters, namely, literature regarding marketing reviews and frameworks, marketing strategies, services, and customers. These results pointed to some potential directions for future research in each cluster. This research can benefit researchers studying the current topics in the hotel marketing management field and help them recognize potential research areas. Additionally, it enables hoteliers to understand the benefits and processes of hotel marketing and defines the key elements of implementing a successful marketing campaign.
Managing the Tension between Trust and Confidentiality in Mobile Supply Chains
Authors: Ghondaghsaz Nassim , Chokparova Zarina , Engesser Sven , Urbas Leon
Published: February 2022

This research investigates the tension between trust and confidentiality in Mobile Supply Chains (MSCs), where a production asset that is owned by one of the partners is outsourced to another partner of the supply chain for the production of goods, chemicals, or pharmaceuticals. The novelty of the MSC concept is to be found in its innovative and sustainable approach to production and operation processes in supply chains. Implementation of the MSC model could, however, raise trust and confidentiality concerns. The interplay of trust and confidentiality, or preservation of information privacy, between partners plays a central role in the supply chains, particularly because they are genuinely dependent on each other. Qualitative data analysis was used, in which semi-structured interviews with the experts from the chemical and pharmaceutical industries in Germany were conducted to investigate the tension between trust and confidentiality, and important factors affecting it. The results of the study present four different integration scenarios, namely, the low-quality, conservative, grey-box, and innovation scenarios, which consider different levels of trust, confidentiality and information sharing. Subsequently, the tension between trust and confidentiality is analyzed within these scenarios, and three effective strategies which encourage partners to balance the tension between trust and confidentiality are proposed. The study indicates that the balance between trust and confidentiality can be maintained in some scenarios when critical factors such as transparency, trust negotiation, and a reward-sharing system are present.
Research on the Industrial Structure Upgrading Effect of the Employment Mobility of Graduates from China’s “Double First-Class” Colleges and Universities
Authors: Zhao Yilin , He Feng , Feng Ying
Published: February 2022

This paper develops a panel data fixed effect model to analyze the impact of the employment mobility of “Double First-class” college graduates on the rationalization of the industrial structure, advanced industrial structure, high-tech industrial structure upgrading, and industrial structure upgrading of the producer service industry. The results show that the mobility of graduates from “Double First-class” colleges and universities has a positive effect on upgrading the industrial structure of various provinces. The difference is that the mobility of “Double First-class” college graduates has a greater impact on the upgrading of the industrial structure of high-end producer services, but has a weaker impact on the structural upgrading of high-tech industries. There is a mismatch between the employment needs of “Double First-class” college graduates and the jobs provided by high-tech industries. The mobility of graduates from “Double First-class” colleges and universities has obvious differences in the promotion of industrial structure upgrading in different regions. Therefore, this paper proposes to increase investment in high-level human capital to promote the free flow of labor and other production factors across industries, sectors, and regions.
A Critical Review of Power Take-Off Wave Energy Technology Leading to the Conceptual Design of a Novel Wave-Plus-Photon Energy Harvester for Island/Coastal Communities’ Energy Needs
Authors: Prasad Kushal A. , Chand Aneesh A. , Kumar Nallapaneni Manoj , Narayan Sumesh , Mamun Kabir A.
Published: February 2022

As the global interest in renewable energy generation continues, the need to develop new and innovative solutions is being explored every day throughout the world by researchers and innovators. Hybrid renewable energy innovations are gaining progressive interest not only because of the threat of climate change but also due to the technological advancements seen in renewables. Ocean waves have immense potential as a renewable energy source, and related technologies have advanced continuously over the past few decades. In response, this paper extensively studies wave energy converters (WECs) based on the power take-off (PTO) technique, and presents a novel hybrid wave-plus-photon energy (HWPE) harvester called Wavevoltaics, based on wave and solar energy capture systems for coastal communities’ power needs, in line with decarbonization measures. The HWPE harvester uses a simple rack-and-pinion mechanism in combination with solar cell technology to convert the wave energy into usable electrical energy in a water column structural design. This novel HWPE device can be used to provide power for lighting and gadgets for coastal communities that rely heavily on fossil fuels for their lighting and electrical needs. Later in the paper, the challenges faced in hybrid wave energy development are presented.