bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent Advances in Application of Polyoxometalates in Lignocellulose Pretreatment and Transformation
Authors: Deng Haoyu , Xu Wenbiao , Zhang Dan , Li Xiangyu , Shi Junyou
Published: May 2023

Lignocellulose, composed of cellulose, hemicellulose, and lignin, holds immense promise as a renewable resource for the production of sustainable chemicals and fuels. Unlocking the full potential of lignocellulose requires efficient pretreatment strategies. In this comprehensive review, efforts were taken to survey the latest developments in polyoxometalates (POMs)-assisted pretreatment and conversion of lignocellulosic biomass. An outstanding finding highlighted in this review is that the deformation of the cellulose structure from I to II accompanied by the removal of xylan/lignin through the synergistic effect of ionic liquids (ILs) and POMs resulted in a significant increase in glucose yield and improved cellulose digestibility. Furthermore, successful integration of POMs with deep eutectic solvents (DES) or γ-valerolactone/water (GVL/water) systems has demonstrated efficient lignin removal, opening avenues for advanced biomass utilization. This review not only presents the key findings and novel approaches in POMs-based pretreatment but also addresses the current challenges and prospects for large-scale industrial implementation. By offering a comprehensive assessment of the progress in this field, this review serves as a valuable resource for researchers and industry professionals aiming to harness the potential of lignocellulosic biomass for sustainable chemical and fuel production.
Electrospinning and Electrospraying: Emerging Techniques for Probiotic Stabilization and Application
Authors: Feng Kun , Huangfu Lulu , Liu Chuanduo , Bonfili Laura , Xiang Qisen , Wu Hong , Bai Yanhong
Published: May 2023

Probiotics are beneficial for human health. However, they are vulnerable to adverse effects during processing, storage, and passage through the gastrointestinal tract, thus reducing their viability. The exploration of strategies for probiotic stabilization is essential for application and function. Electrospinning and electrospraying, two electrohydrodynamic techniques with simple, mild, and versatile characteristics, have recently attracted increased interest for encapsulating and immobilizing probiotics to improve their survivability under harsh conditions and promoting high-viability delivery in the gastrointestinal tract. This review begins with a more detailed classification of electrospinning and electrospraying, especially dry electrospraying and wet electrospraying. The feasibility of electrospinning and electrospraying in the construction of probiotic carriers, as well as the efficacy of various formulations on the stabilization and colonic delivery of probiotics, are then discussed. Meanwhile, the current application of electrospun and electrosprayed probiotic formulations is introduced. Finally, the existing limitations and future opportunities for electrohydrodynamic techniques in probiotic stabilization are proposed and analyzed. This work comprehensively explains how electrospinning and electrospraying are used to stabilize probiotics, which may aid in their development in probiotic therapy and nutrition.
Numerical and Experimental Analysis of the Mode I Interlaminar Fracture Toughness in Multidirectional 3D-Printed Thermoplastic Composites Reinforced with Continuous Carbon Fiber
Authors: Santos Jonnathan D. , Guerrero José M. , Blanco Norbert , Fajardo Jorge I. , Paltán César A.
Published: May 2023

It is well known that the use of continuous reinforcing fibers can largely improve the typical low in-plane mechanical properties of 3D-printed parts. However, there is very limited research on the characterization of the interlaminar fracture toughness of 3D-printed composites. In this study, we investigated the feasibility of determining the mode I interlaminar fracture toughness of 3D-printed cFRP composites with multidirectional interfaces. First, elastic calculations and different FE simulations of Double Cantilever Beam (DCB) specimens (using cohesive elements for the delamination, in addition to an intralaminar ply failure criterion) were carried out to choose the best interface orientations and laminate configurations. The objective was to ensure a smooth and stable propagation of the interlaminar crack, while preventing asymmetrical delamination growth and plane migration, also known as crack jumping. Then, the best three specimen configurations were manufactured and tested experimentally to validate the simulation methodology. The experimental results confirmed that, with the appropriate stacking sequence for the specimen arms, it is possible to characterize the interlaminar fracture toughness in multidirectional 3D-printed composites under mode I. The experimental results also show that both initiation and propagation values of the mode I fracture toughness depend on the interface angles, although a clear tendency could not be established.
On Modelling Sea State Bias of Jason-2 Altimeter Data Based on Significant Wave Heights and Wind Speeds
Authors: Guo Jinyun , Zhang Huiying , Li Zhen , Zhu Chengcheng , Liu Xin
Published: May 2023

Altimeter data processing is very important to improve the quality of sea surface height (SSH) measurements. Sea state bias (SSB) correction is a relatively uncertain error correction due to the lack of a clear theoretical model. At present, the commonly used methods for SSB correction are polynomial models (parametric models) and non-parametric models. The non-parametric model usually was constructed by collinear data. However, the amount of collinear data was enormous, and it contained redundant information. In this study, the non-parametric regression estimation model was optimized by using the parameter replacement method of ascending and descending tracks based on the crossover data. In this method, significant wave heights from the Jason-2 altimeter data during cycles 200–301 and wind speed from the ERA5 reanalysis data were used. The non-parametric regression estimation model of Jason-2 was constructed by combining it with local linear regression, Epanechnikov kernel function and local window width. At the same time, based on the significant wave height and wind speed at the crossover points, the SSB polynomial model containing six parameters was constructed by using the Taylor series expansion, and the model was optimized. By comparing polynomial model construction with different parameters, the optimized model was obtained. The SSH of the crossover points and the tide gauge records were used to validate these results derived from two models and GDR. Compared with the crossover discrepancies of SSH corrected by the polynomial model, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 7.9%. Compared with the crossover discrepancies of SSH corrected by the conventional non-parametric model from GDR, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 4.1%. This shows that the precision of the SSHs derived by after the SSB correction, as calculated by the non-parametric regression estimation model, was better than that of the polynomial model and the SSB correction from GDR. Using the Jason-2 altimeter data, the along-track geoid gradient and the sea level change rate of the global ocean were determined by using two models to correct the SSB. By comparing the results of the two models, the accuracy of the geoid gradient along the orbit that was obtained by the non-parametric regression estimation model was better than that of the polynomial model and GDR. The global average sea level change rate after the non-parametric regression estimation model correction was 3.47 ± 0.09 mm/y, which was the closest to the average sea level change rate that has been published in the international literature within this field.
An Approach Integrating Multi-Source Data with LandTrendr Algorithm for Refining Forest Recovery Detection
Authors: Li Mei , Zuo Shudi , Su Ying , Zheng Xiaoman , Wang Weibing , Chen Kaichao , Ren Yin
Published: May 2023

Disturbances to forests are getting worse with climate change and urbanization. Assessing the functionality of forest ecosystems is challenging because it requires not only a large amount of input data but also comprehensive estimation indicator methods. The object of the evaluation index of forest ecosystem restoration relies on the ecosystem function instead of the area. To develop the appropriate index with ecological implications, we built the hybrid assessment approach including ecosystem structure-function-habitat representatives. It was based on the Normalized Burn Ratio (NBR) spectral indicator and combined with the local forest management inventory (LFMI), Landsat, Light Detection and Ranging (LiDAR) data. The results of the visual interpretation of Google Earth’s historical imagery showed that the total accuracy of the hybrid approach was 0.94. The output of the hybrid model increased as the biodiversity index value increased. Furthermore, to solve the multi-source data availability problem, the random forest model (R2 = 0.78, RMSE = 0.14) with 0.77 total accuracy was built to generate an annual recovery index. A random forest model based on tree age is provided to simplify the hybrid approach while extending the results on time series. The recovery index obtained by the random forest model could facilitate monitoring the forest recovery rate of cold spots. The regional ecological recovery time could be predicted. These two results could provide a scientific basis for forest managers to make more effective forest restoration plans. From the perspective of space, it could ensure that the areas with slow recovery would be allocated enough restoration resources. From the perspective of time, the implementation period of the closed forest policy could also be estimated.
Microphysical Characteristics of Raindrop Size Distribution and Implications for Dual-Polarization Radar Quantitative Precipitation Estimations in the Tianshan Mountains, China
Authors: Zeng Yong , Li Jiangang , Yang Lianmei , Li Haoyang , Li Xiaomeng , Tong Zepeng , Jiang Yufei , Liu Jing , Zhang Jinru , Zhou Yushu
Published: May 2023

In order to improve the understanding of the microphysical characteristics of raindrop size distribution (DSD) under different rainfall rates (R) classes, and broaden the knowledge of the impact of radar wavelengths and R classes on the QPE of dual-polarization radars in the Tianshan Mountains, a typical arid area in China, we investigated the microphysical characteristics of DSD across R classes and dual-polarimetric radar QPE relationships across radar wavelengths and R classes, based on the DSD data from a PARSIVEL2 disdrometer at Zhaosu in the Tianshan Mountains during the summers of 2020 and 2021. As the R class increased, the DSD became wider and flatter. The mean value of the mass-weighted mean diameters (Dm) increased, while the mean value of logarithm normalized intercept parameters (log10 Nw) decreased after increasing from C1 to C3, as the R class increased. The largest contributions to R and the radar reflectivity factor from large raindrops (diameter > 3 mm) accounted for approximately 50% and 97%, respectively, while 84% of the total raindrops were small raindrops (diameter < 1 mm). Dual-polarization radars—horizontal polarization reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp)—were retrieved based on the DSD data using the T-matrix scattering method. The DSD-based polarimetric radar QPE relations of a single-parameter (R(Zh), R(Kdp)), and double-parameters (R(Zh,Zdr), R(Kdp,Zdr)) on the S-, C-, and X-bands were derived and evaluated. Overall, the performance of the R(Kdp) (R(Kdp,Zdr)) scheme was better than that of R(Zh) (R(Zh,Zdr)) for the QPE in the three bands. Furthermore, we have for the first time confirmed and quantified the performance differences in the QPE relationship of dual-polarization radars under different schemes, radar wavelengths, and R classes in typical arid areas of China. Therefore, selecting an appropriate dual-polarization radar band and QPE scheme for different R classes is necessary to improve the QPE ability compared with an independent scheme under all R classes.
Sensitivity Assessment of Land Desertification in China Based on Multi-Source Remote Sensing
Authors: Ren Yu , Liu Xiangjun , Zhang Bo , Chen Xidong
Published: May 2023

Desertification, a current serious global environmental problem, has caused ecosystems and the environment to degrade. The total area of desertified land is about 1.72 million km2 in China, which is extensively affected by desertification. Estimating land desertification risks is the top priority for the sustainable development of arid and semi-arid lands in China. In this study, the Mediterranean Desertification and Land Use (MEDALUS) model was used to assess the sensitivity of land desertification in China. Based on multi-source remote sensing data, this study integrated natural and human factors, calculated the land desertification sensitivity index by overlaying four indicators (soil quality, vegetation quality, climate quality, and management quality), and explored the driving forces of desertification using a principal component and correlation analysis. It was found that the spatial distribution of desertification sensitivity areas in China shows a distribution pattern of gradually decreasing from northwest to southeast, and the areas with very high and high desertification sensitivities were about 620,629 km2 and 2,384,410 km2, respectively, which accounts for about 31.84% of the total area of the country. The very high and high desertification sensitivity areas were mainly concentrated in the desert region of northwest China. The principal component and correlation analysis of the sub-indicators in the MEDALUS model indicated that erosion protection, drought resistance, and land use were the main drivers of desertification in China. Furthermore, the aridity index, soil pH, plant coverage, soil texture, precipitation, soil depth, and evapotranspiration were the secondary drivers of desertification in China. Moreover, the desertification sensitivity caused by drought resistance, erosion protection, and land use was higher in the North China Plain region and Guanzhong Basin. The results of the quantitative analysis of the driving forces of desertification based on mathematical statistical methods in this study provide a reference for a comprehensive strategy to combat desertification in China and offer new ideas for the assessment of desertification sensitivity at macroscopic scales.
Accelerated Restoration of Vegetation in Wuwei in the Arid Region of Northwestern China since 2000 Driven by the Interaction between Climate and Human Beings
Authors: Li Xin , Yang Liqin
Published: May 2023

The Wuwei area in the arid region of northwestern China is impacted by the harsh natural environment and human activities, and the problem of ecological degradation is severe there. In order to ensure the sustainable development of the regional social economy, it is necessary to monitor the changes in vegetation in Wuwei and its corresponding nonlinear relationships with climate change and human activities. In this study, the inter-annual and spatial–temporal evolution characteristics of vegetation in Wuwei from 1982 to 2015 have been analyzed based on non-parametric statistical methods. The analysis revealed that the areas of vegetation restoration and degradation accounted for 77 and 23% of the total area of the research area, respectively. From 1982 to 1999, vegetation degradation became extremely serious (14.4%) and was primarily concentrated in Gulang County and the high-altitude areas in the southwest. Since the ecological restoration project was implemented in 2000, there have been prominent results in vegetation restoration. The geographically and temporally weighted regression model shows that each climate factor has contributed to the vegetation restoration in the Wuwei area during the last 34 years, with their contributions ranked as precipitation (71.2%), PET (43.9%), solar radiation (34.8%), temperature (33.1%), and wind speed (31%). An analysis of the land-use data with 30 m resolution performed in this study revealed that the conversion area among land cover from 1985 to 2015 accounts for 14.9% of the total area. In it, the conversion area from non-ecological land to ecological land accounts for 5.7% of the total area. The farmland, grassland, and woodland areas have increased by 20.1, 20.6, and 8.5%, respectively, indicating that human activities such as agricultural intensification and ecological restoration projects have played a crucial role in vegetation restoration.
A Novel Device-Free Positioning Method Based on Wi-Fi CSI with NLOS Detection and Bayes Classification
Authors: Zheng Xingyu , Chen Ruizhi , Chen Liang , Wang Lei , Yu Yue , Zhang Zhenbing , Li Wei , Pei Yu , Wu Dewen , Ruan Yanlin
Published: May 2023

Device-free wireless localization based on Wi-Fi channel state information (CSI) is an emerging technique that could estimate users’ indoor locations without invading their privacy or requiring special equipment. It deduces the position of a person by analyzing the influence on the CSI of Wi-Fi signals. When pedestrians block the signals between the transceivers, the non-line-of-sight (NLOS) transmission occurs. It should be noted that NLOS has been a significant factor restricting the device-free positioning accuracy due to signal reduction and abnormalities during multipath propagation. For this problem, we analyzed the NLOS effect in an indoor environment and found that the position error in the LOS condition is different from the NLOS condition. Then, two empirical models, namely, a CSI passive positioning model and a CSI NLOS/LOS detection model, have been derived empirically with extensive study, which can obtain better robustness identified results in the case of NLOS and LOS conditions. An algorithm called SVM-NB (Support Vector Machine-Naive Bayes) is proposed to integrate the SVM NLOS detection model with the Naive Bayes fingerprint method to narrow the matching area and improve position accuracy. The NLOS identification precision is better than 97%. The proposed method achieves localization accuracy of 0.82 and 0.73 m in laboratory and corridor scenes, respectively. Compared to the Bayes method, our tests showed that the positioning accuracy of the NLOS condition is improved by 28.7% and that of the LOS condition by 26.2%.
Application of Multitemporal Change Detection in Radar Satellite Imagery Using REACTIV-Based Method for Geospatial Intelligence
Authors: Slesinski Jakub , Wierzbicki Damian , Kedzierski Michal
Published: May 2023

Constant monitoring of airports and aviation bases has become one of the priorities in today’s strategic security. It results in the necessity to develop the potential of satellite Earth observation systems and to intensify the efforts to develop the technologies of processing SAR data, in particular in the aspect of detecting changes. The aim of this work is to develop a new algorithm based on the modified core REACTIV in the multitemporal detection of changes in radar satellite imagery. For the purposes of the research works, the new algorithm implemented in the Google Earth Engine environment has been transformed so that it would meet the requirements posed by imagery intelligence. The assessment of the potential of the developed methodology was performed based on the analysis of the three main aspects of change detection: analysis of infrastructural changes, analysis of military activity, and impact effect evaluation. The proposed methodology enables automated detection of changes in multitemporal series of radar imagery. Apart from merely detecting the changes, the method also allows for the expansion of the change analysis result by adding another dimension: the determination of the time of the change.
Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing
Authors: Aslan Ayse , El-Raoui Hanane , Hanson Jack , Vasantha Gokula , Quigley John , Corney Jonathan , Sherlock Andrew
Published: May 2023

This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers’ actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others.
Center of Mass Estimation Using a Force Platform and Inertial Sensors for Balance Evaluation in Quiet Standing
Authors: Sonobe Motomichi , Inoue Yoshio
Published: May 2023

Accurate estimation of the center of mass is necessary for evaluating balance control during quiet standing. However, no practical center of mass estimation method exists because of problems with estimation accuracy and theoretical validity in previous studies that used force platforms or inertial sensors. This study aimed to develop a method for estimating the center of mass displacement and velocity based on equations of motion describing the standing human body. This method uses a force platform under the feet and an inertial sensor on the head and is applicable when the support surface moves horizontally. We compared the center of mass estimation accuracy of the proposed method with those of other methods in previous studies using estimates from the optical motion capture system as the true value. The results indicate that the present method has high accuracy in quiet standing, ankle motion, hip motion, and support surface swaying in anteroposterior and mediolateral directions. The present method could help researchers and clinicians to develop more accurate and effective balance evaluation methods.
Graph Neural Network-Based Efficient Subgraph Embedding Method for Link Prediction in Mobile Edge Computing
Authors: Deng Xiaolong , Sun Jufeng , Lu Junwen
Published: May 2023

Link prediction is critical to completing the missing links in a network or to predicting the generation of new links according to current network structure information, which is vital for analyzing the evolution of a network, such as the logical architecture construction of MEC (mobile edge computing) routing links of a 5G/6G access network. Link prediction can provide throughput guidance for MEC and select appropriate c nodes through the MEC routing links of 5G/6G access networks. Traditional link prediction algorithms are always based on node similarity, which needs predefined similarity functions, is highly hypothetical and can only be applied to specific network structures without generality. To solve this problem, this paper proposes a new efficient link prediction algorithm PLAS (predicting links by analysis subgraph) and its GNN (graph neural network) version PLGAT (predicting links by graph attention networks) based on the target node pair subgraph. In order to automatically learn the graph structure characteristics, the algorithm first extracts the h-hop subgraph of the target node pair, and then predicts whether the target node pair will be linked according to the subgraph. Experiments on eleven real datasets show that our proposed link prediction algorithm is suitable for various network structures and is superior to other link prediction algorithms, especially in some 5G MEC Access networks datasets with higher AUC (area under curve) values.
Optical Remote Sensing in Provisioning of Ecosystem-Functions Analysis—Review
Authors: Vyvlečka Pavel , Pechanec Vilém
Published: May 2023

Keeping natural ecosystems and their functions in the proper condition is necessary. One of the best contactless monitoring methods is remote sensing, especially optical remote sensing, which is used for vegetation applications. In addition to satellite data, data from ground sensors are necessary for validation or training in ecosystem-function quantification. This article focuses on the ecosystem functions associated with aboveground-biomass production and storage. The study contains an overview of the remote-sensing methods used for ecosystem-function monitoring, especially methods for detecting primary variables linked to ecosystem functions. The related studies are summarized in multiple tables. Most studies use freely available Sentinel-2 or Landsat imagery, with Sentinel-2 mostly producing better results at larger scales and in areas with vegetation. The spatial resolution is a key factor that plays a significant role in the accuracy with which ecosystem functions are quantified. However, factors such as spectral bands, algorithm selection, and validation data are also important. In general, optical data are usable even without supplementary data.
Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox
Authors: Wang Weibing , Guo Shuai , Zhao Shuanfeng , Lu Zhengxiong , Xing Zhizhong , Jing Zelin , Wei Zheng , Wang Yuan
Published: May 2023

This paper introduces a fault diagnosis method for mine scraper conveyor gearbox gears using motor current signature analysis (MCSA). This approach solves problems related to gear fault characteristics that are affected by coal flow load and power frequency, which are difficult to extract efficiently. A fault diagnosis method is proposed based on variational mode decomposition (VMD)–Hilbert spectrum and ShuffleNet-V2. Firstly, the gear current signal is decomposed into a series of intrinsic mode functions (IMF) by using VMD, and the sensitive parameters of VMD are optimized by using a genetic algorithm (GA). The Sensitive IMF algorithm judges the modal function sensitive to fault information after VMD processing. By analyzing the local Hilbert instantaneous energy spectrum for fault-sensitive IMF, an accurate expression of signal energy changing with time is obtained to generate the local Hilbert immediate energy spectrum dataset of different fault gears. Finally, ShuffleNet-V2 is used to identify the gear fault state. The experimental results show that the accuracy of the ShuffleNet-V2 neural network is 91.66% after 778 s.
Improved Algorithms of Data Processing for Dispersive Interferometry Using a Femtosecond Laser
Authors: Liu Tao , Wu Jiucheng , Suzuki Amane , Sato Ryo , Matsukuma Hiraku , Gao Wei
Published: May 2023

Two algorithms of data processing are proposed to shorten the unmeasurable dead-zone close to the zero-position of measurement, i.e., the minimum working distance of a dispersive interferometer using a femtosecond laser, which is a critical issue in millimeter-order short-range absolute distance measurement. After demonstrating the limitation of the conventional data processing algorithm, the principles of the proposed algorithms, namely the spectral fringe algorithm and the combined algorithm that combines the spectral fringe algorithm with the excess fraction method, are presented, together with simulation results for demonstrating the possibility of the proposed algorithms for shortening the dead-zone with high accuracy. An experimental setup of a dispersive interferometer is also constructed for implementing the proposed data processing algorithms over spectral interference signals. Experimental results demonstrate that the dead-zone using the proposed algorithms can be as small as half of that of the conventional algorithm while measurement accuracy can be further improved using the combined algorithm.
A Soft Sensor Model of Sintering Process Quality Index Based on Multi-Source Data Fusion
Authors: Li Yuxuan , Jiang Weihao , Shi Zhihui , Yang Chunjie
Published: May 2023

In complex industrial processes such as sintering, key quality variables are difficult to measure online and it takes a long time to obtain quality variables through offline testing. Moreover, due to the limitations of testing frequency, quality variable data are too scarce. To solve this problem, this paper proposes a sintering quality prediction model based on multi-source data fusion and introduces video data collected by industrial cameras. Firstly, video information of the end of the sintering machine is obtained via the keyframe extraction method based on the feature height. Secondly, using the shallow layer feature construction method based on sinter stratification and the deep layer feature extraction method based on ResNet, the feature information of the image is extracted at multi-scale of the deep layer and the shallow layer. Then, combining industrial time series data, a sintering quality soft sensor model based on multi-source data fusion is proposed, which makes full use of multi-source data from various sources. The experimental results show that the method effectively improves the accuracy of the sinter quality prediction model.
Co-Designing Digital Technologies for Improving Clinical Care in People with Parkinson’s Disease: What Did We Learn?
Authors: Monje Mariana , Grosjean Sylvie , Srp Martin , Antunes Laura , Bouça-Machado Raquel , Cacho Ricardo , Domínguez Sergio , Inocentes John , Lynch Timothy , Tsakanika Argyri , Fotiadis Dimitrios , Rigas George , Růžička Evžen , Ferreira Joaquim , Antonini Angelo , Malpica Norberto , Mestre Tiago , Sánchez-Ferro Álvaro ,   iCARE-PD Consortium
Published: May 2023

The healthcare model is shifting towards integrated care approaches. This new model requires patients to be more closely involved. The iCARE-PD project aims to address this need by developing a technology-enabled, home-based, and community-centered integrated care paradigm. A central part of this project is the codesign process of the model of care, exemplified by the active participation of patients in the design and iterative evaluation of three sensor-based technological solutions. We proposed a codesign methodology used for testing the usability and acceptability of these digital technologies and present initial results for one of them, MooVeo. Our results show the usefulness of this approach in testing the usability and acceptability as well as the opportunity to incorporate patients’ feedback into the development. This initiative will hopefully help other groups incorporate a similar codesign approach and develop tools that are well adapted to patients’ and care teams’ needs.
Corner-Point and Foreground-Area IoU Loss: Better Localization of Small Objects in Bounding Box Regression
Authors: Cai Delong , Zhang Zhaoyun , Zhang Zhi
Published: May 2023

Bounding box regression is a crucial step in object detection, directly affecting the localization performance of the detected objects. Especially in small object detection, an excellent bounding box regression loss can significantly alleviate the problem of missing small objects. However, there are two major problems with the broad Intersection over Union (IoU) losses, also known as Broad IoU losses (BIoU losses) in bounding box regression: (i) BIoU losses cannot provide more effective fitting information for predicted boxes as they approach the target box, resulting in slow convergence and inaccurate regression results; (ii) most localization loss functions do not fully utilize the spatial information of the target, namely the target’s foreground area, during the fitting process. Therefore, this paper proposes the Corner-point and Foreground-area IoU loss (CFIoU loss) function by delving into the potential for bounding box regression losses to overcome these issues. First, we use the normalized corner point distance between the two boxes instead of the normalized center-point distance used in the BIoU losses, which effectively suppresses the problem of BIoU losses degrading to IoU loss when the two boxes are close. Second, we add adaptive target information to the loss function to provide richer target information to optimize the bounding box regression process, especially for small object detection. Finally, we conducted simulation experiments on bounding box regression to validate our hypothesis. At the same time, we conducted quantitative comparisons of the current mainstream BIoU losses and our proposed CFIoU loss on the small object public datasets VisDrone2019 and SODA-D using the latest anchor-based YOLOv5 and anchor-free YOLOv8 object detection algorithms. The experimental results demonstrate that YOLOv5s (+3.12% Recall, +2.73% mAP@0.5, and +1.91% mAP@0.5:0.95) and YOLOv8s (+1.72% Recall and +0.60% mAP@0.5), both incorporating the CFIoU loss, achieved the highest performance improvement on the VisDrone2019 test set. Similarly, YOLOv5s (+6% Recall, +13.08% mAP@0.5, and +14.29% mAP@0.5:0.95) and YOLOv8s (+3.36% Recall, +3.66% mAP@0.5, and +4.05% mAP@0.5:0.95), both incorporating the CFIoU loss, also achieved the highest performance improvement on the SODA-D test set. These results indicate the effectiveness and superiority of the CFIoU loss in small object detection. Additionally, we conducted comparative experiments by fusing the CFIoU loss and the BIoU loss with the SSD algorithm, which is not proficient in small object detection. The experimental results demonstrate that the SSD algorithm incorporating the CFIoU loss achieved the highest improvement in the AP (+5.59%) and AP75 (+5.37%) metrics, indicating that the CFIoU loss can also improve the performance of algorithms that are not proficient in small object detection.
Location-Based Augmented Reality for Cultural Heritage Communication and Education: The Doltso District Application
Authors: Kleftodimos Alexandros , Evagelou Athanasios , Triantafyllidou Amalia , Grigoriou Magdalini , Lappas Georgios
Published: May 2023

Location-based Augmented Reality applications are increasingly used in many research and commercial fields. Some of the fields that these applications are used are recreational digital games, tourism, education, and marketing. This study aims to present a location-based augmented reality (AR) application for cultural heritage communication and education. The application was created to inform the public, especially K12 students, about a district of their city with cultural heritage value. Furthermore, Google Earth was utilized to create an interactive virtual tour for consolidating the knowledge acquired by the location-based AR application. A scheme for evaluating the AR application was also constructed using factors suitable for location-based applications: challenge, educational usefulness (knowledge), collaboration, and intention to reuse. A sample of 309 students evaluated the application. Descriptive statistical analysis showed that the application scored well in all factors, especially in challenge and knowledge (mean values 4.21 and 4.12). Furthermore, structural equation modeling (SEM) analysis led to a model construction that represents how the factors are causally related. Based on the findings, the perceived challenge significantly influenced the perceived educational usefulness (knowledge) (b = 0.459, sig = 0.000) and interaction levels (b = 0.645, sig = 0.000). Interaction amongst users also had a significant positive impact on users’ perceived educational usefulness (b = 0.374, sig = 0.000), which in turn influenced users’ intention to reuse the application (b = 0.624, sig = 0.000).
Capillary Zone Electrophoresis with Light-Emitting Diode-Induced Fluorescence Detection for the Analysis of Monoclonal Antibodies: Detector Optimization through Design of Experiments and Comparison to UV Detection
Authors: Zagst Holger , Hartung Sophie , Menges Dina-Mareike , Wittmann Antonia , Wätzig Hermann
Published: May 2023

Capillary zone electrophoresis (CZE) is an important technique for the analysis of monoclonal antibodies (mAbs). A recently released light-emitting diode (LED)-induced fluorescence (LEDIF) detector equipped with a 275 nm LED for the detection of proteins through their native fluorescence was used in this study and compared to results obtained using the predominant detection mode, the measurement of the absorption of ultraviolet light (UV detection). This was accomplished using an established CZE method for the analysis of three mAbs: NISTmAb, matuzumab, and Intact Mass Check Standard (Waters). For this purpose, the detector’s settings were first optimized using a design of experiments approach. Three factors, rise time, photomultiplier high voltage supply, and acquisition frequency, were optimized by means of a D-optimal design. The optimal settings were then used for the investigation of signal-to-noise ratios (S/Ns), linearity, and precision. LEDIF detection offered a similar separation quality, up to 12 times higher S/Ns, and lower limits of detection compared to UV detection. Repeatability was excellent, with relative standard deviations (RSDs) of approximately 1% for percentage areas. For intermediate precision, RSDs of <2% (n = 3 × 10) were typically achieved. Overall, LEDIF detection was found to be an excellent and easily optimizable alternative to UV detection.
Accelerating the Construction of a Unified Domestic Market to Promote Sustainable Economic Development: Mechanisms, Challenges and Countermeasures—A Perspective Based on the General Law of the Market Economy and Chinese Reality
Authors: Shi Jiaxian , Qi Changbin , Mu Xinxin
Published: May 2023

Accelerating the development of a unified domestic market is essential for establishing a high-level socialist market economy, which will also serve as the foundation for promoting China’s sustainable economic development. Referencing the general law of the market economy, this study analyses the internal mechanisms for accelerating the formation of a unified domestic market to promote the sustainable development of China’s economy from four dimensions, including system guarantee, impetus condition, material basis and administrative guarantee. Furthermore, an empirical analysis is conducted to identify existing challenges in this regard using the data of China’s Marketisation Index Report by Province from 2016 to 2021, which include insufficient comprehension of the concept of a unified domestic market, institutional barriers, a low degree of opening up to the outside world and uneven infrastructure development. Corresponding policy suggestions include strengthening theoretical study, research and publicity; intensifying the unification of fundamental institutional systems and market rules; and accelerating high-level opening to the outside world and market facilities’ high-quality connectivity. This research provides a theoretical and policy reference for the sustainable development of China’s economy that is broadly applicable.
Morphological Differences between Virgin and Secondary Fibers
Authors: Małachowska Edyta , Dubowik Marcin , Przybysz Piotr
Published: May 2023

The properties of the fibers determine the quality of the pulp and, thus, the quality of the paper made from it. Recognition of properties, which fiber and paper pulp should be characterized by, in order to achieve required paper properties, is, therefore, a subject of research and interest of many papermaking research experts and scientists. Fibers are subject to deformation and possible weakening under the influence of chemical and mechanical factors, and therefore the quality of the fibers decreases each time they are used in production when it comes to recycled pulps. Then again, the key factor determining the quality of the primary fiber is the degree of pulp delignification. In the article, an attempt was made to define the impact of delignification of virgin pulp on morphological properties of fibers, and compare them with the properties of recycled paper pulp, in order to find correlations. The current economic and raw material situations in the wood market force one to seek new solutions to limit the use of virgin fibers, which is extremely important for the economy of the paper mill, environmental protection and raw material management.
Distributed Control Algorithm for DC Microgrid Using Higher-Order Multi-Agent System
Authors: Ahsan Muhammad , Rodriguez Jose , Abdelrahem Mohamed
Published: May 2023

During the last decade, DC microgrids have been extensively researched due to their simple structure compared to AC microgrids and increased penetration of DC loads in modern power networks. The DC microgrids consist of three main components, that is, distributed generation units (DGU), distributed non-linear load, and interconnected power lines. The main control tasks in DC microgrids are voltage stability at the point of common coupling (PCC) and current sharing among distributed loads. This paper proposes a distributed control algorithm using the higher-order multi-agent system for DC microgrids. The proposed control algorithm uses communication links between distributed multi-agents to acquire information about the neighbors’ agents and perform the desired control actions to achieve voltage balance and current sharing among distributed DC loads and DGUs. In this research work, non-linear ZIP loads and dynamical RLC lines are considered to construct the model. The dynamical model of the power lines and DGU are used to construct the control objective for each distributed DGU that is improved using the multi-agent system-based distributed current control. The closed-loop stability analysis is performed at the equilibrium points, and control gains are derived. Finally, simulations are performed using MATLAB/Simulink environment to verify the performance of the proposed control method.
Numerical Optimization of Mini Centrifuge Modelling Test Design of Excavation Unloading Influence on Existing Tunnel Controlled by Partition Piles
Authors: Wang Bingyi , Du Yiming , Diao Yu , Zhao Xiangyu
Published: May 2023

Partition piles are widely used in tunnel displacement control under excavation. However, the control effect of partition piles has not been systematically studied, and the centrifuge model test is lacking. In view of this situation, the centrifuge model test design of the deformation of the adjacent existing tunnel caused by the excavation in terms of the partition piles under the dry sand foundation was carried out, and a series of optimization designs were carried out in the experimental design process according to the numerical simulation results. In addition, the numerical simulation of the centrifuge model test was carried out, and the calculated results were discussed and analyzed. The conclusions were obtained as follows: The magnesium-aluminum alloy materials were used to model the structural members, and its elastic modulus was close to that of common reinforced concrete; the high precision digital image correlation technology (DIC) was used to measure the horizontal and vertical displacement of the tunnel. The results of numerical simulation show that the embedded partition piles can play a better role in controlling the tunnel displacement than the non-partition piles and the non-embedded partition piles.
A Cooperative Transmission Scheme in Radio Frequency Energy-Harvesting WBANs
Authors: Hu Juncheng , Xu Gaochao , Hu Liang , Li Shujing
Published: May 2023

Wireless Body Area Network (WBAN) plays an important role in e-health, sports training, and entertainment to monitor human bodies wirelessly and remotely. One critical challenge for WBAN is to guarantee the quality of user experience and improve the network performance within such a resource-constrained and dynamic network. In the proposed paper, we investigate a cooperative radio frequency energy harvesting-based WBAN. Herein, we primarily focus on improving the energy efficiency and network performance through intelligent cooperation among nodes, allowing sensors with sufficient energy to assist other sensors in data uploading. We propose a relay selection method that considers both energy demand and energy harvest efficiency. Each sensor calculates the transmission power threshold required for data uploading based on the perceived channel state and determines whether it can act as a potential relay node in conjunction with its own energy harvest efficiency. The coordinator is responsible for optimizing collaborative transmission plans based on real-time network status. Experimental results show that the cooperative scheme performs better than the common single-hop scheme in terms of packet reception rate and packet arrival rate. In a network consisting of 10 sensors, the increase in packet reception rate ranges from 4.9% to 7.8% when the sensors are placed in preset fixed positions. When the sensors are randomly placed, the increase in packet reception rate ranges from 0.9% to 7.9% and from 0.7% to 7.4%, corresponding to δ values of 0.7 and 0.9, respectively.
Effects of Content Characteristics and Improvement in User Satisfaction on the Reuse of Home Fitness Application
Authors: Nam Moonju , Song Taegeon , Kim Dasran , Jang Kukjin , Kim Jinsick , Koo Byeongsoo , Lee Jooyeoun , Chung Myoungsug
Published: May 2023

As the global fitness industry rapidly digitalizes, home fitness applications have emerged as a convenient solution for people to exercise anytime, anywhere. This study investigated the mediation effect of exercise satisfaction on the relationship between content characteristics and continuance intention to use home fitness applications. This study provided guidance for developing effective home fitness applications by analyzing which content characteristics can increase the continuance intention to use home fitness applications. The mediation effect of exercise satisfaction on content characteristics and continuance intention to use was examined using a structural equation model analysis, and 330 survey responses from individuals who have used home fitness applications for exercise were analyzed. The results confirmed that the enhancement of the content characteristics exerted a positive effect on exercise satisfaction and continuance intention to use. Additionally, the enhancement of exercise satisfaction characteristics positively affected the continuance intention to use. Further, exercise satisfaction was observed to exert a significant mediating effect on the relationship between the content characteristics and continuance intention to use home fitness applications. These findings suggest that it is essential to devote significant attention to enhancing content characteristics and exercise satisfaction in the development of home fitness applications.