bluuebunny/crossref_metadata_2025 huggingface dataset:
Electrospun Silk Fibroin-CNT Composite Fibers: Characterization and Application in Mediating Fibroblast Stimulation
Authors: Rathnayake Rathnayake A. C. , Yoon Shinhae , Zheng Shuyao , Clutter Elwin D. , Wang Rong R.
Published: December 2022

Electrospinning is a simple, low-cost, and highly efficient technique to generate desirable nano/microfibers from polymer solutions. Silk fibroin (SF), a biopolymer found in Bombyx mori cocoons, has attracted attention for various biomedical applications. In this study, functionalized CNT was incorporated in SF to generate biocomposite fibers by electrospinning. The electrospun (E-spun) fibers were well aligned with morphology mimicking the locally oriented ECM proteins in connective tissues. While as-spun fibers dissolved in water in just two minutes, ethanol vapor post-treatment promoted β-sheet formation leading to improved fiber stability in an aqueous environment (>14 days). The addition of a minute amount of CNT effectively improved the E-spun fiber alignment and mechanical strength while retained high biocompatibility and biodegradability. The fibers’ electrical conductivity increased by 13.7 folds and 21.8 folds, respectively, in the presence of 0.1 w% and 0.2 w% CNT in SF fibers. With aligned SF-CNT 0.1 % fibers as a cell culture matrix, we found electrical stimulation effectively activated fibroblasts from patients of pelvic organ prolapse (POP), a connective tissue disorder. The stimulation boosted the fibroblasts’ productivity of collagen III (COLIII) and collagen I (COLI) by 74 folds and 58 folds, respectively, and reduced the COLI to COLIII ratio favorable for tissue repair. The developed material and method offer a simple, direct, and effective way to remedy the dysfunctional fibroblasts of patients for personalized cell therapeutic treatment of diseases and health conditions associated with collagen disorder.
Generation of Controlled Micrometric Fibers inside Printed Scaffolds Using Standard FDM 3D Printers
Authors: del Barrio Cortés Elisa , Matutano Molina Clara , Rodríguez-Lorenzo Luis , Cubo-Mateo Nieves
Published: December 2022

New additive manufacturing techniques, such as melting electro-writing (MEW) or near-field electrospinning (NFES), are now used to include microfibers inside 3D printed scaffolds as FDM printers present a limited resolution in the XY axis, not making it easy to go under 100 µm without dealing with nozzle troubles. This work studies the possibility of creating reproducible microscopic internal fibers inside scaffolds printed by standard 3D printing. For this purpose, novel algorithms generating deposition routines (G-code) based on primitive geometrical figures were created by python scripts, modifying basic deposition conditions such as temperature, speed, or material flow. To evaluate the influence of these printing conditions on the creation of internal patterns at the microscopic level, an optical analysis of the printed scaffolds was carried out using a digital microscope and subsequent image analysis with ImageJ software. To conclude, the formation of heterogeneously shaped microfilaments (48 ± 12 µm, mean ± S.D.) was achieved in a standard FDM 3D Printer with the strategies developed in this work, and it was found that the optimum conditions for obtaining such microfibers were high speeds and a reduced extrusion multiplier.
Progresses and Perspectives of Near-Infrared Emission Materials with “Heavy Metal-Free” Organic Compounds for Electroluminescence
Authors: Xiong Wenjing , Zhang Cheng , Fang Yuanyuan , Peng Mingsheng , Sun Wei
Published: December 2022

Organic/polymer light-emitting diodes (OLEDs/PLEDs) have attracted a rising number of investigations due to their promising applications for high-resolution fullcolor displays and energy-saving solid-state lightings. Near-infrared (NIR) emitting dyes have gained increasing attention for their potential applications in electroluminescence and optical imaging in optical tele-communication platforms, sensing and medical diagnosis in recent decades. And a growing number of people focus on the “heavy metal-free” NIR electroluminescent materials to gain more design freedom with cost advantage. This review presents recent progresses in conjugated polymers and organic molecules for OLEDs/PLEDs according to their different luminous mechanism and constructing systems. The relationships between the organic fluorophores structures and electroluminescence properties are the main focus of this review. Finally, the approaches to enhance the performance of NIR OLEDs/PLEDs are described briefly. We hope that this review could provide a new perspective for NIR materials and inspire breakthroughs in fundamental research and applications.
Influences of the Braking Effect of Ruler EMBr on Molten Steel Flow and Steel–Slag Interface Fluctuation in a Continuous Casting Mold
Authors: Xu Lin , Pei Qun-Wu , Han Ze-Feng , Cui Jie , Pan Hong-Gang , Yao Yan-Tao
Published: December 2022

Electromagnetic braking (EMBr) technology, as one of the most effective technologies in the continuous casting process, provides an effective tool for improving the internal and external defects of steel products. Specifically, the EMBr technology takes the benefit of the generation of Lorentz force to decrease flow instability, mold powder entrapment, and surface defects, if applied properly. For this purpose, to gain a clear understanding of the effect of EMBr technology on the continuous casting process, a commonly used EMBr technology, namely ruler EMBr technology, is applied in the current work to investigate the dynamic behaviors of molten steel flow and steel–slag interface fluctuation inside a slab mold. Furthermore, to obtain a desirable braking effect of the ruler EMBr technology, operational parameters including the magnetic flux density, submerged entry nozzle (SEN) depth, and magnetic pole location are numerically investigated. The results demonstrate that the braking effect exerted by the ruler EMBr device is favorable for suppressing the impact of upward stream on the steel–slag interface with the magnetic flux density exceeding 0.3 T. For the influence of the SEN depth and magnetic pole location on the effect of ruler EMBr mold, the results show that a steady jet flow pattern can be obtained through the adjustment of a location between the ruler EMBr device and the SEN depth. For instance, when the ruler EMBr device installation position of 225 mm corresponds to the SEN depth of 150 mm, the upward deflection of jet stream is suppressed and a stable interface fluctuation profile is formed. With this adjustment, the possibility of mold flux entrapment is decreased.
Using Particle Residence Time Distributions as an Experimental Approach for Evaluating the Performance of Different Designs for a Pilot-Scale Spray Dryer
Authors: Zhou Zelin , Langrish Timothy A. G. , Cai Sining
Published: December 2022

The performances of four different designs for a pilot-scale spray dryer have been evaluated and compared based on experimentally measured particle residence time distributions (RTD), recovery rates and physical properties of spray-dried fresh skim milk. The RTDs have been measured using a dye pulse injection method, and the measurements have been fitted to models using continuous stirred-tank reactors in series (CSTR-TIS) for quantitative performance evaluation and comparison. Conical drying chambers and a box connection design have been used in the latest dryer design to reduce the amount of wall deposition and provide a smoother gas flow pattern. The particle-to-gas mean residence time ratio for the latest design is significantly closer to unity (1.6 s/s to 1.0 s/s) compared with earlier designs (2.6 s/s to 1.5 s/s). The latest design has a wider spread of RTD (n = 5–8) compared with earlier designs (n = 13–18), which may be linked to the recirculation zone in the box connection. Although the latest design has a wider spread of RTD, the conical design has shown promising results compared with a cylindrical drying chamber in terms of overall wall deposition behaviours.
Gravitational Surface Vortex Formation and Suppression Control: A Review from Hydrodynamic Characteristics
Authors: Zheng Gaoan , Gu Zeheng , Xu Weixin , Lu Bin , Li Qihan , Tan Yunfeng , Wang Chengyan , Li Lin
Published: December 2022

The energy-conversion stability of hydropower is critical to satisfy the growing demand for electricity. In low-head hydropower plants, a gravitational surface vortex is easily generated, which causes irregular shock vibrations that damage turbine performance and input-flow stability. The gravitational surface vortex is a complex fluid dynamic problem with high nonlinear features. Here, we thoroughly investigate its essential hydrodynamic properties, such as Ekman layer transport, heat/mass transfer, pressure pulsation, and vortex-induced vibration, and we note some significant scientific issues as well as future research directions and opportunities. Our findings show that the turbulent Ekman layer analytical solution and vortex multi-scale modeling technology, the working condition of the vortex across the scale heat/mass transfer mechanism, the high-precision measurement technology for high-speed turbulent vortexes, and the gas–liquid–solid three-phase vortex dynamics model are the main research directions. The vortex-induced vibration transition mechanism of particle flow in complex restricted pipelines, as well as the improvement of signal processing algorithms and a better design of anti-spin/vortex elimination devices, continue to draw attention. The relevant result can offer a helpful reference for fluid-induced vibration detection and provide a technical solution for hydropower energy conversion.
Behavioral Model Deployment for the Transportation Projects within a Smart City Ecosystem: Cases of Germany and South Korea
Authors: Shvetsova Olga , Bialevich Anastasiya , Kim Jihee , Voronina Mariia
Published: December 2022

This research focused on a behavioral model as a significant tangible enabler for smart city plans and initiatives across Asian and EU regions as per transportation projects. This study aimed to create a behavioral model to serve as a planning tool for policymakers, planners, and implementers of transportation initiatives in smart cities. The paper discusses the validity of the proposed model framework for fostering the diffusion of a successful smart city project transformation in a general smart city ecosystem and particularly within the transportation industry. The framework was verified using three different methods: literature review to give a speculative understanding of current smart city approaches; case studies from Germany and South Korea smart city ecosystems that were selected and applied against the behavioral model; and finally, desktop research (behavioral model) performed for smart city project development. As a result, the authors recognized key variables for deriving a possible successful behavioral model as a suggested efficient framework for further smart city strategic projects. Researchers developed and tested, with two validated examples, the suggested behavioral model for smart city projects with a focus on the transportation industry. Results of this survey could help stakeholders in different countries analyze factors influencing decision-making processes and adopt smart city projects to local business environments.
Modern Technologies of Hydrogen Production
Authors: Stenina Irina , Yaroslavtsev Andrey
Published: December 2022

Transitioning to energy-saving and renewable energy sources is impossible without accelerated development of hydrogen energy and hydrogen technologies. This review summarizes the state-of-the-art and recent advances of various hydrogen production processes, including but not limited to thermochemical and electrolytic processes. Their opportunities and limitations, operating conditions, and catalysts are discussed. Nowadays, most hydrogen is still produced by steam reforming of methane, its partial oxidation, or coal gasification. Considerable attention is also paid to natural gas pyrolysis. However, hydrogen produced using these technologies has a lot of impurities and needs additional purification. A series of technologies for hydrogen purification, including its filtration through palladium alloy membranes, and membrane catalysis, allowing hydrogen production and purification in one stage, are discussed. The main way to produce carbon-free hydrogen is water electrolysis using low-cost energy from nuclear or renewable sources. Both conventional and novel methods of hydrogen storage and transportation, which are an important part of the hydrogen economy, are reviewed. Biohydrogen production technologies are also discussed. Finally, prospects for further work in this field are provided. This review will be useful to researchers and manufacturers working in this field.
The Potential of Valorized Sisal Decorticated Waste in Rearing of Black Soldier Fly
Authors: Konyo Aziza Athumani , Machunda Revocatus , Pasape Liliane , Mshandete Anthony
Published: December 2022

The use of sisal decorticated waste (SDW) for various applications is limited due to its high acidic content. This is the first study of its kind regarding the use of SDW as a substrate for the growth of the black soldier fly (BSF). Pre-treatment was a necessary and challenging step performed on the waste to meet the minimum requirements for the rearing of BSF. The SDW was sun dried, sieved, and decomposited and neutralized to form the final products that were used for the rearing of BSF. The resultant waste had fourteen (14) elements; the essential elemental form results were Ca, P, K, Mn, Fe, Cu, and Zn at varying levels, which are all essential for animal growth. The SDW contained 10 ± 0.01 percent of crude protein, 11 ± 0.02 moisture and energy (1615 kcal/g of sisal decorticated waste). The sun dried BSF larvae were reared on SDW that contained 53 ± 0.005 percent of crude protein, 4 ± 0.01 percent of crude fat, a moisture content of (10 ± 0.1)%, carbohydrate percent of (43 ± 0.01)%, and ash percent of (37 ± 0.08). When rearing was finished, 3000 g of dried pre-treated waste yielded more wet BSF larvae, (336 ± 41.3) g, compared to 3000 g of fruit waste, which yielded (244 ± 4.16) g of wet BSF larvae. Therefore, based on this study, SDW is a promising potential feed for rearing BSF because it had a better reduction of the waste by 52%. Furthermore, the harvested BSF larvae contained sufficient nutritional value to feed poultry and fish.
Symbolic Religious Landscape: Religious and Patriotic Symbolism in the Pilgrimage Centres in Poland
Authors: Liro Justyna , Sołjan Izabela , Bilska-Wodecka Elzbieta
Published: December 2022

Combining religious and national symbolism is not a new phenomenon. There are known examples of countries where there were or still are connections between nationality and religion. Pilgrimage centres are examples of symbolic religious landscapes based on the presence of the sacred. Such anthropogenic landscapes are a visible result of culture formed under the influence of religion, a special spiritual and often national heritage expressed through sacred objects, visual evidence of religiousness and, likewise, national identity. Here, we present a detailed analysis of religious and patriotic symbolism present in the largest pilgrimage centres in Poland. Additionally, the paper discusses a. the historical and socio-cultural conditions of the presence and significance of these elements in religious landscapes; b. the strong relationships between religiousness and the sense of national identity, and c. the resulting significant importance of pilgrimage centres in the development and consolidation of a sense of national identity. Symbolic elements in the analysed pilgrimage centres refer to both universal religious content and cults popular in the Roman Catholic Church, as well as to the identity of the analysed places. In addition to religious symbolism, national and patriotic symbols often occur in Polish sanctuaries. Their occurrence is historically conditioned and, to a large extent, results from the strong ties between religiousness and national identity.
A Lightweight Object Detector Based on Spatial-Coordinate Self-Attention for UAV Aerial Images
Authors: Liu Chen , Yang Degang , Tang Liu , Zhou Xun , Deng Yi
Published: December 2022

Object detection is one of the most widespread applications for numerous Unmanned Aerial Vehicle (UAV) tasks. Due to the shooting angle and flying height of the UAV, compared with general scenarios, small objects account for a large proportion of aerial images, and common object detectors are not extremely effective in aerial images. Moreover, since the computing resources of UAV platforms are generally limited, the deployment of common detectors with a large number of parameters on UAV platforms is difficult. This paper proposes a lightweight object detector YOLO-UAVlite for aerial images. Firstly, the spatial attention module and coordinate attention module are modified and combined to form a novel Spatial-Coordinate Self-Attention (SCSA) module, which integrates spatial, location, and channel information to enhance object representation. On this basis, we construct a lightweight backbone, named SCSAshufflenet, which combines the Enhanced ShuffleNet (ES) network with the proposed SCSA module to improve feature extraction and reduce model size. Secondly, we propose an improved feature pyramid model, namely Slim-BiFPN, where we construct new lightweight convolutional blocks to reduce the information loss during the feature map fusion process while reducing the model weights. Finally, the localization loss function is modified to increase the bounding box regression rate while improving the localization accuracy. Extensive experiments conducted on the VisDrone-DET2021 dataset indicate that, compared with the YOLOv5-N baseline, the proposed YOLO-UAVlite reduces the number of parameters by 25.8% and achieves gains of 10.9% in mAP0.50. Compared with other lightweight detectors, both the mAP and the number of parameters are improved.
Image Texture as Quality Indicator for Optical DEM Generation: Geomorphic Applications in the Arid Central Andes
Authors: Purinton Benjamin , Mueting Ariane , Bookhagen Bodo
Published: December 2022

The generation of Digital Elevation Models (DEMs) through stereogrammetry of optical satellite images has gained great popularity across various disciplines. For the analysis of these DEMs, it is important to understand the influence of the input data and different processing steps and parameters employed during stereo correlation. Here, we explore the effects that image texture, as well as the use of different matching algorithms (Block Matching (BM) and More Global Matching (MGM)), can have on optical DEMs derived from the flexible, open-source Ames Stereo Pipeline. Our analysis relies on a ∼2700 km2 clip of a SPOT6 tristereo scene covering the hyperarid, vegetation-free Pocitos Basin and adjacent mountain ranges in the northwestern Argentine Andes. A large, perfectly flat salt pan (paleolake bed) that covers the center of this basin is characterized by strong contrasts in image texture, providing a unique opportunity to quantitatively study the relationship between image texture and DEM quality unaffected by topography. Our findings suggest that higher image texture, measured by panchromatic variance, leads to lower DEM uncertainty. This improvement continues up to ∼103 panchromatic variance, above which further improvements in DEM quality are independent of local image texture but instead may have sensor or geometric origins. Based on this behavior, we propose that image texture may serve as an important proxy of DEM quality prior to stereo correlation and can help to set adequate processing parameters. With respect to matching algorithms, we observe that MGM improves matching in low-texture areas and overall generates a smoother surface that still preserves complex, narrow (i.e., ridge and valley) features. Based on this sharper representation of the landscape, we conclude that MGM should be preferred for geomorphic applications relying on stereo-derived DEMs. However, we note that the correlation kernel selected for stereo-matching must be carefully chosen depending on local image texture, whereby larger kernels generate more accurate matches (less artifacts) at the cost of smoothing results. Overall, our analysis suggests a path forward for the processing and fusion of overlapping satellite images with suitable view-angle differences to improve final DEMs.
Intelligent Estimating the Tree Height in Urban Forests Based on Deep Learning Combined with a Smartphone and a Comparison with UAV-LiDAR
Authors: Xuan Jie , Li Xuejian , Du Huaqiang , Zhou Guomo , Mao Fangjie , Wang Jingyi , Zhang Bo , Gong Yulin , Zhu Di’en , Zhou Lv , Huang Zihao , Xu Cenheng , Chen Jinjin , Zhou Yongxia , Chen Chao , Tan Cheng , Sun Jiaqian
Published: December 2022

In this paper, a method for extracting the height of urban forest trees based on a smartphone was proposed to efficiently and accurately determine tree heights. First, a smartphone was used to obtain person–tree images, LabelImg was used to label the images, and a dataset was constructed. Secondly, based on a deep learning method called You Only Look Once v5 (YOLOv5) and the small-hole imaging and scale principles, a person–tree scale height measurement model was constructed. This approach supports recognition and mark functions based on the characteristics of a person and a tree in a single image. Finally, tree height measurements were obtained. By using this method, the heights of three species in the validation set were extracted; the range of the absolute error was 0.02 m–0.98 m, and the range of the relative error was 0.20–10.33%, with the RMSE below 0.43 m, the rRMSE below 4.96%, and the R2 above 0.93. The person–tree scale height measurement model proposed in this paper greatly improves the efficiency of tree height measurement while ensuring sufficient accuracy and provides a new method for the dynamic monitoring and investigation of urban forest resources.
A Method Based on Improved iForest for Trunk Extraction and Denoising of Individual Street Trees
Authors: Li Zhiyuan , Wang Jian , Zhang Zhenyu , Jin Fengxiang , Yang Juntao , Sun Wenxiao , Cao Yi
Published: December 2022

Currently, the street tree resource survey using Mobile laser scanning (MLS) represents a hot spot around the world. Refined trunk extraction is an essential step for 3D reconstruction of street trees. However, due to scanning errors and the effects of occlusion by various types of features in the urban environment, street tree point cloud data processing has the problem of excessive noise. For the noise points that are difficult to remove using statistical methods in close proximity to the tree trunk, we propose an adaptive trunk extraction and denoising method for street trees based on an improved iForest (Isolation Forest) algorithm. Firstly, to extract the individual tree trunk points, the trunk and the crown are distinguished from the individual tree point cloud through point cloud slicing. Next, the iForest algorithm is improved by conducting automatic calculation of the contamination and further used to denoise the tree trunk point cloud. Finally, the method is validated with five datasets of different scenes. The results indicate that our method is robust and effective in extracting and denoising tree trunks. Compared with the traditional Statistical Outlier Removal (SOR) filter and Radius filter denoising methods, the denoising accuracy of the proposed method can be improved by approximately 30% for noise points close to tree trunks. Compared to iForest, the proposed method automatically calculates the contamination, improving the automation of the algorithm. Our method can provide more precise trunk point clouds for 3D reconstruction of street trees.
Urban Area Mapping Using Multitemporal SAR Images in Combination with Self-Organizing Map Clustering and Object-Based Image Analysis
Authors: Amitrano Donato , Di Martino Gerardo , Iodice Antonio , Riccio Daniele , Ruello Giuseppe
Published: December 2022

Mapping urban areas from space is a complex task involving the definition of what should be considered as part of an urban agglomerate beyond the built-up features, thus modelling the transition of a city into the surrounding landscape. In this paper, a new technique to map urban areas using multitemporal synthetic aperture radar data is presented. The proposed methodology exploits innovative RGB composites in combination with self-organizing map (SOM) clustering and object-based image analysis. In particular, the clustered product is used to extract a coarse urban area map, which is then refined using object-based processing. In this phase, Delaunay triangulation and the spatial relationship between the identified urban regions are used to model the urban–rural gradient between a city and the surrounding landscape. The technique has been tested in different scenarios representative of structurally different cities in Italy and Germany. The quality of the obtained products is assessed by comparison with the Urban Atlas of the European Environmental Agency, showing good agreement with the adopted reference data despite their different taxonomies.
Identification of Time-Varying External Force Using Group Sparse Regularization and Redundant Dictionary
Authors: Liu Huanlin , Ma Hongwei
Published: December 2022

How to accurately identify unknown time-varying external force from measured structural responses is an important engineering problem, which is critical for assessing the safety condition of the structure. In the context of a few available accelerometers, this paper proposes a novel time-varying external force identification method using group sparse regularization based on the prior knowledge in the redundant dictionary. Firstly, the relationship between time-varying external force and acceleration responses is established, and a redundant dictionary is designed to create a sparse expression of external force. Then, the relevance of atoms in the redundant dictionary is revealed, and this prior knowledge is used to determine the group structures of atoms. As a result, a force identification governing equation is formulated, and the group sparse regularization is reasonably introduced to ensure the accuracy of the identified results. The contribution of this paper is that the group structures of atoms are reasonably determined based on prior knowledge, and the complexity in the process for identifying external force from measured acceleration responses is reduced. Finally, the effectiveness of the proposed method is demonstrated by numerical simulations and an experimental structure. The illustrated results show that, compared with the force identification method based on the standard l1-norm regularization, the proposed method can further improve the identified accuracy of unknown external force and greatly enhance the computational efficiency for the force identification problem.
Image Generation from Text Using StackGAN with Improved Conditional Consistency Regularization
Authors: Tominaga Rihito , Seo Masataka
Published: December 2022

Image generation from natural language has become a very promising area of research on multimodal learning in recent years. In recent years, the performance of this theme has improved rapidly, and the release of powerful tools has caused a great response in various places. The Stacked Generative Adversarial Networks (StackGAN) model is a representative method to generate images from text descriptions. Although it can generate high-resolution images, it involves several limitations; some of the images generated are typically unintelligible, and mode collapse may occur. Therefore, in this study, we aim to solve these two problems to generate images that follow a given text description more closely. First, we incorporate a new consistency regularization technique for conditional generation tasks into StackGAN, called Improved Consistency Regularization or ICR. The ICR technique learns the meaning of data by matching the semantic information of input data before and after data augmentation, and can also stabilize learning in adversarial networks. In this research, this method mainly suppresses mode collapse by expanding the variation of generated images. However, this method may lead to excessive variations in the generated images, which may result in images that do not match the meaning of the input text or that are ambiguous. Therefore, we further propose a new regularization method called ICCR as a modification of ICR, which is designed to perform conditional generation tasks and eliminate the negative impacts of the generator. This method realized the generation of various images along the input text. The proposed StackGAN with ICCR performed 16% better than StackGAN and 4% better than StackGAN with ICR and AttnGAN on the Inception Score using the CUB dataset. AttnGAN, similar to StackGAN, is a GAN-based text-to-image model that incorporates the attention mechanism, which has achieved great results in recent years. It is very important that our proposed model, which incorporates ICCR into a simple model, obtained better results than AttnGAN. In addition, StackGAN with ICCR was effective in eliminating mode collapse. The probability of mode collapse in the original StackGAN was 20%, while in StackGAN with ICCR the probability was 0%. In the questionnaire survey, our proposed method was rated 18% higher than StackGAN with ICR. This indicates that ICCR is more effective for conditional tasks than ICR.
Development of a Quantitative Chromatographic Fingerprint Analysis Method for Sugar Components of Xiaochaihu Capsules Based on Quality by Design Concept
Authors: Lan Jing , Wu Gelin , Wu Linlin , Qu Haibin , Gong Ping , Xie Yongjian , Zhou Peng , Gong Xingchu
Published: December 2022

Background: Xiaochaihu capsule is composed of seven traditional Chinese medicines. The pharmacopoeia only focuses on the quantitative detection of baicalin, which cannot fully reflect the quality of the preparation. Some medium polar components were used to establish the fingerprint of Xiaochaihu capsule, but there was no report on the strong polar components. Methods: A high performance liquid chromatography-corona charged aerosol detection technology was used to establish a fingerprint analysis method for Xiaochaihu capsules following an analytical quality by design approach. Definitive screening designed experiments were used to optimize the method parameters. A stepwise regression method was used to build quantitative models. The method operable design region was calculated using the experimental error simulation method. Plackett–Burman designed experiments were carried out to test robustness. Results: The contents of four components were simultaneously determined. There were seven common peaks in the fingerprint. The common peak area accounted for 91.72%. Both fingerprint and quantitative analysis methods were validated as applicable in the methodology study. The quantitative fingerprint analysis method for sugar components can fill the gap in the detection of strong polar components in the existing methods. It provides a new technology for the comprehensive overall evaluation of Xiaochaihu capsule.
“I’ve Lived Everything They Are Trying to Teach Me”: Latinx Immigrant Youth’s Ambivalent Educational Mobility in White Evangelical Universities
Authors: Flores Andrea
Published: December 2022

Undocumented Latinx youth in Tennessee envision higher education as the single pathway to enable their upward economic and social mobility. Many of these young people enroll in private, historically white, Evangelical Christian colleges that provide financial support otherwise unavailable to undocumented youth. At the same time, university actors struggle to meet students’ other needs as undocumented and minoritized individuals. Based on long-term ethnographic fieldwork, I demonstrate how youth struggle with the hidden personal costs of educational access and the upward mobility it promises. Most significantly, Latinx immigrant youth must navigate the tensions between the ever-present legacy of racial segregation and animus in Evangelical traditions and their status as the embodiment of newfound, institutionally desirable “diversity.” As these students negotiate deeply racialized social and academic orders, they grow ambivalent about the promises of educational mobility, particularly if that mobility is contingent upon conforming to “respectable” forms of diversity. Centering youth’s ambivalence reveals both the contingent value of educational mobility to those experiencing it and the limits of university policies intended to increase educational access.
Are Infinite-Failure NHPP-Based Software Reliability Models Useful?
Authors: Li Siqiao , Dohi Tadashi , Okamura Hiroyuki
Published: December 2022

In the literature, infinite-failure software reliability models (SRMs), such as Musa-Okumoto SRM (1984), have been demonstrated to be effective in quantitatively characterizing software testing processes and assessing software reliability. This paper primarily focuses on the infinite-failure (type-II) non-homogeneous Poisson process (NHPP)-based SRMs and evaluates the performances of these SRMs comprehensively by comparing with the existing finite-failure (type-I) NHPP-based SRMs. In more specific terms, to describe the software fault-detection time distribution, we postulate 11 representative probability distribution functions that can be categorized into the generalized exponential distribution family and the extreme-value distribution family. Then, we compare the goodness-of-fit and predictive performances with the associated 11 type-I and type-II NHPP-based SRMs. In numerical experiments, we analyze software fault-count data, collected from 16 actual development projects, which are commonly known in the software industry as fault-count time-domain data and fault-count time-interval data (group data). The maximum likelihood method is utilized to estimate the model parameters in both NHPP-based SRMs. In a comparison of the type-I with the type-II, it is shown that the type-II NHPP-based SRMs could exhibit better predictive performance than the existing type-I NHPP-based SRMs, especially in the early stage of software testing.
The Evolutionary Game of Cooperative Air Pollution Management under Complex Networks
Authors: Song Yi , Chang Dan , Cui Lizhu
Published: December 2022

In this paper, based on complex networks and evolutionary game theory, we use Text Mining and Analytics, MATLAB Simulation, and other technical means to study the decision-making process of each subject in the collaborative air pollution management network, taking the “limited rational” local government as the decision-making subject of the evolutionary game. The study finds that the cooperative network of small-scale air pollution management is a very important element in the evolutionary process. The small-scale air pollution collaborative governance network has the effect of significantly improving the evolution speed of local government collaborative governance decisions in the network. It can better mobilize local governments to participate in collaborative air pollution governance and realize the cooperative emergence with the ratio interval of income heterogeneity in [0.6, 1], preference heterogeneity in [1.2, 1.4], and allocation heterogeneity in [0.6, 1]. The results of the study are consistent with the actual situation, which verifies the validity and operability of the model. Finally, the article also proposes countermeasures to improve the dilemma of air pollution synergy caused by regional heterogeneity.
Stakeholders’ Perspectives on Implementing “Internationalization at Home” for China’s International Education Sustainability: Challenges and Strategies
Authors: Li Jian , Xue Eryong
Published: December 2022

The purpose of this study was to examine stakeholders’ perspectives on implementing “internationalization at home” for international education sustainability in China in the post-epidemic era. We conducted semi-structured interviews with 26 (12 male and 14 female) participants to explore their opinions on the challenges of and strategies for the implementation of “internationalization at home” in higher education institutions. It was found that an institutional international curriculum mechanism construction is needed. The cross-cultural teaching capacity of faculty members needs to be promoted. Funding resources and organizational guarantees need to be provided. International campus environments and cultures need to be built. The discussion and conclusions are provided in the last section.
Empathize with Whom? Adopting a Design Thinking Mind-Set to Stimulate Sustainability Initiatives in Chinese SMEs
Authors: Storm John , Smith Adam
Published: December 2022

This paper highlights the growing importance towards supporting Chinese Small to Medium sized Enterprises (SMEs) in adopting pro-active and collaborative behaviors that stimulate sustainability initiatives. Equating to 90% of enterprises in the country and contributing towards 60% of the nation’s gross domestic product (GDP), China’s SMEs are recognized for their willingness to engage in change, raising the potential for exploring and embarking on new ventures. This paper proposes that a design thinking mind-set, rooted in empathic stakeholder dialogue, conceptually supports SMEs to engage with the increasingly complex challenges that envelop China’s society, culture, economy, and environment. Discussion emphasizes that proactively including and responding to the various and ambiguous needs of stakeholders leads to increased opportunity for innovation and new ways of thinking, both being vital for sustainable and responsible growth. Equally, the questions of whom to empathize with and how should SMEs empathize are postulated as roadblocks to the adoption of design thinking in SMEs. This paper proposes a model for addressing those challenges.
Comprehensive Analysis of PV and Wind Energy Integration into MMC-HVDC Transmission Network
Authors: Hossain Md Ismail , Shafiullah Md , Al-Sulaiman Fahad A. , Abido Mohammad A.
Published: December 2022

Renewable energy will play a vital role in greenhouse gas emissions reduction. However, renewable energy is located far away from the load center. Modular multilevel converter-(MMC) based VSC-HVDC systems became competitive for remotely located renewable energy grid integration. Unlike the average model for MMC and renewable energy side converter, this paper presents a detailed model-based control and analysis of the MMC-HVDC system for solar and wind energy integration. Furthermore, it optimally tracks PV energy employing the modified incremental conductance method and wind energy using field-oriented control. Instead of decoupled control, a feedforward controller is utilized to establish a standalone AC voltage for renewable energy grid integration. This work considers a doubly fed induction generator (DFIG), permanent magnet synchronous generator (PMSG), and squirrel cage induction generator (SCIG) for wind energy integration. The results from MATLAB/SIMULINK platform agree with the controller hardware in the loop results from RTDS-dSPACE platform. The results confirmed the optimum solar and wind energy tracking during wind speed, irradiance, and temperature variations. However, it improved the fault ride-through capability during balanced and unbalanced low voltage disturbances at the point of common coupling (PCC) of AC grid.
Investigating Consumer Values of Secondhand Fashion Consumption in the Mass Market vs. Luxury Market: A Text-Mining Approach
Authors: ul Hasan H M Rakib , Lang Chunmin , Xia Sibei
Published: December 2022

The purpose of this research is to investigate consumer values of secondhand fashion (SHF) consumption from online platforms in both the mass market and luxury market. The luxury SHF business is closely related to the business of SHF mass market. A cross-market value analysis would provide better insights to understand consumers’ motivations to purchase secondhand fashion products. Three mass-market SHF companies and three luxury SHF companies were selected as sample companies based on their revenues in the U.S. market. Consumers’ comments and reviews from a third-party business review site, trustpilot.com, were collected using the web-scraping technique. Recurrent Neural Network (RNN) analysis, as part of a machine learning algorithm, was applied to detect the most co-occurring word combinations and underlying values discussed in the reviews. The findings identified major consumer-perceived ‘source of values’, i.e., ‘possession/ownership transfer’, and ‘interaction between consumer to business platform’, which might negatively impact the performance of the business of SHF mass market compared to the luxury SHF business. The ’possession/ownership transfer’ source-related values are the most ignored value in the SHF mass market. By adopting the product-oriented value strategy practiced in the luxury market, SHF mass market might improve the consumer perception of product-related value areas.
Increasing Biowaste and Manure in Biogas Feedstock Composition in Luxembourg: Insights from an Agent-Based Model
Authors: Bayram Alper , Marvuglia Antonino , Myridinas Maria , Porcel Marta
Published: December 2022

Biowaste and manure are resources readily available as feedstock for biogas production. Possible scenarios with increased use of biowaste and manure for biogas production in the Grand Duchy of Luxembourg are investigated in this study using an Agent-Based Model (ABM) coupled with Life Cycle Assessment (LCA). ABMs are particularly suitable to simulate human-natural systems, since they allow modelers to consider behavioral aspects of individuals. On the other hand, when it comes to the assessment of a system’s environmental sustainability, LCA is largely recognized as a sound methodology and widely used in research, industry, and policy making. The paper simulates three different scenarios that reproduce 10 years and can help policymakers building emission mitigation strategies. The aim is to increase the number of biogas plants or change the feedstock composition for anaerobic digestion in Luxembourg whilst observing the expected environmental impacts generated by these changes. The first scenario (Scenario A) is the baseline scenario, which simulates the current situation, with 24 operating biogas plants. The results of Scenario A show that, on average, 63.02 GWh of electricity production per year is possible from biogas. The second scenario (Scenario B) foresees an increase in the manure share (which is initially 63%) in the biogas feedstock composition along with an increase in the number of biogas production plants. The third scenario (Scenario C) only concerns increasing the amount of manure in the feedstock composition without the introduction of new plants. The results of Scenario C show that an 11% increase in electricity production is possible if more farms contribute to the production by bringing their excess manure to the biogas plant. This value is even higher (14%) in Scenario D where more biowaste is made available. The aggregated life-cycle impact assessment (LCIA) single scores, calculated with the ReCiPe method, show that Scenario C has the lowest impacts (although by only around 7% compared to the worst performing scenario, i.e., Scenario D), while Scenario D allows the highest electricity production (71.87 GWh in the last year of the simulation). As a result, the inclusion of more livestock farms into already established biogas cooperatives (as in Scenario C) can pave the way for an increase in electricity production from renewables and can bring a reduction in environmental impacts (more than 35% for the Terrestrial Ecotoxicity impact category and more than 27% in categories such as Agricultural Land Occupation, Marine Eutrophication and Water Depletion), thanks to the exploitation of manure for biogas production.
Sustainability of an Open-Loop GWHP System in an Italian Alpine Valley
Authors: Cappellari Davide , Piccinini Leonardo , Pontin Alessandro , Fabbri Paolo
Published: December 2022

Shallow geothermal systems (SGSs) for building climatization represent an advantageous alternative to traditional air-conditioning systems, resulting in economic and environmental benefits. Installation of these systems requires knowledge of site-specific geological and hydrogeological conditions, which in feasibility studies are often evaluated only at the single plant scale, lacking a comprehensive view and risking not to guarantee the system sustainability over time. In this paper a methodology for the sustainable design of SGSs is presented. The methodology is developed from an example on the aquifer scale in Longarone (Belluno, Italy), where three groundwater heat pumps (GWHPs) were installed in an industrial area located in a mountain basin hosting a coarse-grained phreatic aquifer, characterized by sediments with high hydraulic conductivity and proximal to a large river (Piave River). Open-loop systems were first analyzed through numerical modeling using FEFLOW software, identifying peculiar features of the aquifer, due to its interaction with surface waters, and suggesting the possibility of its greater geothermal exploitation. Subsequently, a relationship between flow rates and thermal plume extensions was obtained, which is useful to providing support in the evaluation of potential interference with neighboring systems. The study at the aquifer scale proved representative of the system, highlighting the criticalities of the area, such as trends of aquifer temperature alteration, interference between plants, and thermal feedback.
Analyzing Express Revenue Spatial Association Network’s Characteristics and Effects: A Case Study of 31 Provinces in China
Authors: Wang Guipu , Yang Jianyu , Xu Fangtang
Published: December 2022

In recent years, China’s express delivery industry has experienced significant regional differences in income, which may endanger the sustainable development of the economy and affect the sustainable development of society. To study the development setup and influencing factors of the express delivery industry in various regions of China, based on the income data of provincial express delivery, we constructed a spatial correlation network of provincial express revenue in China using the social network analysis method and analyzed its network characteristics and evolution rules. Additionally, the relationship between network structure parameters and express revenue was tested using the spatial Durbin model. The results reveal that the spatial correlation network of China’s provincial express revenue changes from decentralization to agglomeration, and the connection between the express industry in each province is constantly strengthened. The provinces in the eastern coastal areas exhibit a higher degree of centrality and effective scale in the network, and the limit system is smaller, which indicates that these provinces have higher importance and control ability in the network. Simultaneously, a moderating effect exists between network structure parameters and express income, indicating that network structure parameters affect express income through human capital input. This study provides theoretical research and empirical support to promote the coordinated and sustainable development of the express delivery industry.