bluuebunny/crossref_metadata_2025 huggingface dataset:
Biopolymer Material from Human Spongiosa for Regenerative Medicine Application
Authors: Tsiklin Ilya L. , Pugachev Evgeniy I. , Kolsanov Alexandr V. , Timchenko Elena V. , Boltovskaya Violetta V. , Timchenko Pavel E. , Volova Larisa T.
Published: February 2022

Natural biopolymers demonstrate significant bone and connective tissue-engineering application efficiency. However, the quality of the biopolymer directly depends on microstructure and biochemical properties. This study aims to investigate the biocompatibility and microstructural properties of demineralized human spongiosa Lyoplast® (Samara, Russian Federation). The graft’s microstructural and biochemical properties were analyzed by scanning electron microscopy (SEM), micro-computed tomography, Raman spectroscopy, and proteomic analysis. Furthermore, the cell adhesion property of the graft was evaluated using cell cultures and fluorescence microscopy. Microstructural analysis revealed the hierarchical porous structure of the graft with complete removal of the cellular debris and bone marrow components. Moreover, the proteomic analysis confirmed the preservation of collagen and extracellular proteins, stimulating and inhibiting cell adhesion, proliferation, and differentiation. We revealed the adhesion of chondroblast cell cultures in vitro without any evidence of cytotoxicity. According to the study results, demineralized human spongiosa Lyoplast® can be effectively used as the bioactive scaffold for articular hyaline cartilage tissue engineering.
Metallo-Supramolecular Complexation Behavior of Terpyridine- and Ferrocene-Based Polymers in Solution—A Molecular Hydrodynamics Perspective
Authors: Perevyazko Igor , Mikusheva Nina , Lezov Alexey , Gubarev Alexander , Enke Marcel , Winter Andreas , Schubert Ulrich S. , Tsvetkov Nikolay
Published: February 2022

The contribution deals with the synthesis of the poly(methacrylate)-based copolymers, which contain ferrocene and/or terpyridine moieties in the side chains, and the subsequent analysis of their self-assembly behavior upon supramolecular/coordination interactions with Eu3+ and Pd2+ ions in dilute solutions. Both metal ions provoke intra and inter molecular complexation that results in the formation of large supra-macromolecular assembles of different conformation/shapes. By applying complementary analytical approaches (i.e., sedimentation-diffusion analysis in the analytical ultracentrifuge, dynamic light scattering, viscosity and density measurements, morphology studies by electron microscopy), a map of possible conformational states/shapes was drawn and the corresponding fundamental hydrodynamic and macromolecular characteristics of metallo-supramolecular assemblies at various ligand-to-ion molar concentration ratios (M/L) in extremely dilute polymer solutions (c[η]≈0.006) were determined. It was shown that intramolecular complexation is already detected at (L≈0.1), while at M/L>0.5 solution/suspension precipitates. Extreme aggregation/agglomeration behavior of such dilute polymer solutions at relatively “high” metal ion content is explained from the perspective of polymer-solvent and charge interactions that will accompany the intramolecular complexation due to the coordination interactions.
Polysaccharides Composite Materials as Carbon Nanoparticles Carrier
Authors: Krystyjan Magdalena , Khachatryan Gohar , Khachatryan Karen , Krzan Marcel , Ciesielski Wojciech , Żarska Sandra , Szczepankowska Joanna
Published: February 2022

Nanotechnology is a dynamically developing field of science, due to the unique physical, chemical and biological properties of nanomaterials. Innovative structures using nanotechnology have found application in diverse fields: in agricultural and food industries, where they improve the quality and safety of food; in medical and biological sciences; cosmetology; and many other areas of our lives. In this article, a particular attention is focused on carbon nanomaterials, especially graphene, as well as carbon nanotubes and carbon quantum dots that have been successfully used in biotechnology, biomedicine and broadly defined environmental applications. Some properties of carbon nanomaterials prevent their direct use. One example is the difficulty in synthesizing graphene-based materials resulting from the tendency of graphene to aggregate. This results in a limitation of their use in certain fields. Therefore, in order to achieve a wider use and better availability of nanoparticles, they are introduced into matrices, most often polysaccharides with a high hydrophilicity. Such composites can compete with synthetic polymers. For this purpose, the carbon-based nanoparticles in polysaccharides matrices were characterized. The paper presents the progress of ground-breaking research in the field of designing innovative carbon-based nanomaterials, and applications of nanotechnology in diverse fields that are currently being developed is of high interest and shows great innovative potential.
Water-Based Highly Stretchable PEDOT:PSS/Nonionic WPU Transparent Electrode
Authors: Kim Youngno , Yoo Sinseok , Kim Jung-Hyun
Published: February 2022

Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) has the merits of high electrical conductivity and solution processability, and can be dispersed in water. To improve the stretchability of PEDOT:PSS-based transparent electrode films, the intrinsically conducting polymer PEDOT:PSS was blended with highly stretchable nonionic waterborne polyurethane (WPU) and coated on a thermoplastic polyurethane (TPU) film. Nonionic WPU has good compatibility with PEDOT:PSS, without affecting the acidity. WPU undergoes hydrogen bonding and coulombic attractions with PEDOT:PSS. With variation of the WPU content, differences in the electrical properties, such as the sheet resistance and mechanical stretchability, of the coated thin films were observed. The film with 2.0 wt% WPU could be stretched to 400% of the electrode surface without damage to the surface of the electrode films. The WPU and TPU films both have a polyester group, which provides good adhesion between the WPU-based transparent electrodes and the TPU substrate films. A stretchable alternating current electroluminescence (ACEL) device was constructed by using the water-based PEDOT:PSS/nonionic WPU composite as both the bottom and top transparent electrodes. The fabricated ACEL remained its initial luminance in the 500% stretched state.
Polysaccharide-Drug Conjugates: A Tool for Enhanced Cancer Therapy
Authors: Yadav Neena , Francis Arul Prakash , Priya Veeraraghavan Vishnu , Patil Shankargouda , Mustaq Shazia , Khan Sameer Saeed , Alzahrani Khalid J. , Banjer Hamsa Jameel , Mohan Surapaneni Krishna , Mony Ullas , Rajagopalan Rukkumani
Published: February 2022

Cancer is one of the most widespread deadly diseases, following cardiovascular disease, worldwide. Chemotherapy is widely used in combination with surgery, hormone and radiation therapy to treat various cancers. However, chemotherapeutic drugs can cause severe side effects due to non-specific targeting, poor bioavailability, low therapeutic indices, and high dose requirements. Several drug carriers successfully overcome these issues and deliver drugs to the desired sites, reducing the side effects. Among various drug delivery systems, polysaccharide-based carriers that target only the cancer cells have been developed to overcome the toxicity of chemotherapeutics. Polysaccharides are non-toxic, biodegradable, hydrophilic biopolymers that can be easily modified chemically to improve the bioavailability and stability for delivering therapeutics into cancer tissues. Different polysaccharides, such as chitosan, alginates, cyclodextrin, pullulan, hyaluronic acid, dextran, guar gum, pectin, and cellulose, have been used in anti-cancer drug delivery systems. This review highlights the recent progress made in polysaccharides-based drug carriers in anti-cancer therapy.
Special Features of Polyester-Based Materials for Medical Applications
Authors: Darie-Niță Raluca Nicoleta , Râpă Maria , Frąckowiak Stanisław
Published: February 2022

This article presents current possibilities of using polyester-based materials in hard and soft tissue engineering, wound dressings, surgical implants, vascular reconstructive surgery, ophthalmology, and other medical applications. The review summarizes the recent literature on the key features of processing methods and potential suitable combinations of polyester-based materials with improved physicochemical and biological properties that meet the specific requirements for selected medical fields. The polyester materials used in multiresistant infection prevention, including during the COVID-19 pandemic, as well as aspects covering environmental concerns, current risks and limitations, and potential future directions are also addressed. Depending on the different features of polyester types, as well as their specific medical applications, it can be generally estimated that 25–50% polyesters are used in the medical field, while an increase of at least 20% has been achieved since the COVID-19 pandemic started. The remaining percentage is provided by other types of natural or synthetic polymers; i.e., 25% polyolefins in personal protection equipment (PPE).
Evaluation of Surfactants on Graphene Dispersion and Thermal Performance for Heat Dissipation Coating
Authors: Cheng Chia , Shi Wen-Hao , Teng Tun-Ping , Yang Chii-Rong
Published: February 2022

With the development of thin and high-power electronic devices, heat dissipation has become an important and urgent issue in thermal management. In this study, a water-based epoxy was used as a polymer matrix to prepare heat dissipation coatings utilizing low volatile organic compounds, which were environmentally friendly and had a high heat-dissipating performance. Graphene flakes, multi-walled carbon nanotubes and aluminum oxide particles were used as fillers for preparing the heat dissipation coating. The graphene flakes and multi-walled carbon nanotubes were dispersed in a water-based epoxy by adding sodium dihexyl sulfosuccinate and poly (dimethyldiallylammonium chloride). These two surfactants were combined as a dispersant to improve the dispersibility of the carbon nanomaterials in the water-based epoxy. The synergistic effect of the well-dispersed fillers improved the heat-dissipating performance. The experimental results show that the infrared emissivity of the heat dissipation film was 0.96 after filling 30 wt% aluminum oxide particles, 2 wt% graphene flakes and 2 wt% multi-walled carbon nanotubes into a water-based epoxy. The heat dissipation film reduced the thermal equilibrium temperature of the bare copper panel by 17.8 °C under a heating power of 10 W. The film was applied in a heat dissipation test on a 15 W LED bulb, and the thermal equilibrium temperature was reduced by 21.3 °C. The results demonstrate that the carbon nanomaterial-based heat dissipation coating with a water-based epoxy could significantly reduce the thermal equilibrium temperature, giving a high potential for the application of thermal management.
Deterioration Mapping of RC Bridge Elements Based on Automated Analysis of GPR Images
Authors: Abdul Rahman Mohammed , Zayed Tarek , Bagchi Ashutosh
Published: February 2022

Ground-Penetrating Radar (GPR) is a popular non-destructive technique for evaluating RC bridge elements as it can identify major subsurface defects within a short span of time. The data interpretation of the GPR profiles based on existing amplitude-based approaches is not completely reliable when compared to the actual condition of concrete with destructive measures. An alternative image-based analysis considers GPR as an imaging tool wherein an experienced analyst marks attenuated areas and generates deterioration maps with greater accuracy. However, this approach is prone to human errors and is highly subjective. The proposed model aims to improve it through automated detection of hyperbolas in GPR profiles and classification based on mathematical modeling. Firstly, GPR profiles are pre-processed, and hyperbolic reflections were detected in them based on a trained classifier using the Viola–Jones Algorithm. The false positives are eliminated, and missing regions are identified automatically across the top/bottom layer of reinforcement based on user-interactive regional comparison and statistical analysis. Subsequently, entropy, a textural factor, is evaluated to differentiate the detected regions closely equivalent to the human visual system. These detected regions are finally clustered based on entropy values using the K-means algorithm and a deterioration map is generated which is robust, reliable, and corresponds to the in situ state of concrete. A case study of a parking lot demonstrated good correspondence of deterioration maps generated by the developed model when compared with both amplitude- and image-based analysis. These maps can facilitate structural inspectors to locally identify deteriorated zones within structural elements that require immediate attention for repair and rehabilitation.
The Current Crustal Vertical Deformation Features of the Sichuan–Yunnan Region Constrained by Fusing the Leveling Data with the GNSS Data
Authors: Zhang Yong , Xu Caijun , Zheng Zhijiang , Liang Hongbao , Zhu Shuang
Published: February 2022

This study uses the least squares collocation method to fuse the leveling vertical deformation velocity in the Sichuan–Yunnan region with the GNSS observations of this region from 320 stations in the China Crustal Movement Observation Network (CMONOC) and the China Continental Tectonic Environment Monitoring Network (CMTEMN) from 1999 to 2017. Such fusion is to improve the accuracy of the vertical deformation rates in large spatial scales. The fused vertical deformation results show that: (1) the fused deformation field has a uniform spatial distribution, and shows detailed change characteristics of key regions; (2) the current vertical crustal motion in this region is featured by the contemporaneous occurrence of crustal compression, shortening and uplift and basin extensional subsidence; (3) most areas in this region experience uplifts, as the lateral push of the Qinghai–Tibet Plateau was blocked by the Sichuan Basin. The areas on the northwest side of the Longmenshan fault and the Lijiang-Xiaojinhe fault are dominated by uplifts, with the velocity of 1.5 mm/a–5.5 mm/a, and the region on the southeast side has slight uplifts, with the velocity of 1.0 mm/a–1.5 mm/a; (4) many areas have high gradient vertical deformation, especially the region close to the Wenshan fault and on the two sides of the Yarlung Zangbo fault that has the value of 3.0–4.0 × 10−8/a, deserving further attention to be paid to the long-term earthquake hazards.
Comparison of Aerial and Ground 3D Point Clouds for Canopy Size Assessment in Precision Viticulture
Authors: Pagliai Andrea , Ammoniaci Marco , Sarri Daniele , Lisci Riccardo , Perria Rita , Vieri Marco , D’Arcangelo Mauro Eugenio Maria , Storchi Paolo , Kartsiotis Simon-Paolo
Published: February 2022

In precision viticulture, the intra-field spatial variability characterization is a crucial step to efficiently use natural resources by lowering the environmental impact. In recent years, technologies such as Unmanned Aerial Vehicles (UAVs), Mobile Laser Scanners (MLS), multispectral sensors, Mobile Apps (MA) and Structure from Motion (SfM) techniques enabled the possibility to characterize this variability with low efforts. The study aims to evaluate, compare and cross-validate the potentiality and the limits of several tools (UAV, MA, MLS) to assess the vine canopy size parameters (thickness, height, volume) by processing 3D point clouds. Three trials were carried out to test the different tools in a vineyard located in the Chianti Classico area (Tuscany, Italy). Each test was made of a UAV flight, an MLS scanning over the vineyard and a MA acquisition over 48 geo-referenced vines. The Leaf Area Index (LAI) were also assessed and taken as reference value. The results showed that the analyzed tools were able to correctly discriminate between zones with different canopy size characteristics. In particular, the R2 between the canopy volumes acquired with the different tools was higher than 0.7, being the highest value of R2 = 0.78 with a RMSE = 0.057 m3 for the UAV vs. MLS comparison. The highest correlations were found between the height data, being the highest value of R2 = 0.86 with a RMSE = 0.105 m for the MA vs. MLS comparison. For the thickness data, the correlations were weaker, being the lowest value of R2 = 0.48 with a RMSE = 0.052 m for the UAV vs. MLS comparison. The correlation between the LAI and the canopy volumes was moderately strong for all the tools with the highest value of R2 = 0.74 for the LAI vs. V_MLS data and the lowest value of R2 = 0.69 for the LAI vs. V_UAV data.
Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales
Authors: Ramadhan Ravidho , Yusnaini Helmi , Marzuki Marzuki , Muharsyah Robi , Suryanto Wiwit , Sholihun Sholihun , Vonnisa Mutya , Harmadi Harmadi , Ningsih Ayu Putri , Battaglia Alessandro , Hashiguchi Hiroyuki , Tokay Ali
Published: February 2022

Accurate precipitation observations are crucial for water resources management and as inputs for a gamut of hydrometeorological applications. Precipitation data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) have recently been widely used to complement traditional rain gauge systems. However, the satellite precipitation data needs to be validated before being widely used in the applications and this is still missing over the Indonesian maritime continent (IMC). We conducted a validation of the IMERG product version 6 for this region. The evaluation was carried out using gauge data in the period from 2016 to 2020 for three types of IMERG: Early (E), Late (L), and Final (F) from annual, monthly, daily and hourly data. In general, the annual and monthly data from IMERG showed a good correlation with the rain gauge, with the mean correlation coefficient (CC) approximately 0.54–0.78 and 0.62–0.79, respectively. About 80% of stations in the IMC area showed a very good correlation between gauge data and IMERG-F estimates (CC = 0.7–0.9). For the daily assessment, the CC value was in the range of 0.39 to 0.44 and about 40% of stations had a correlation of 0.5–0.7. IMERG had a fairly good ability to detect daily rain in which the average probability of detection (POD) for all stations was above 0.8. However, the false alarm ratio (FAR) value is quite high (<0.5). For hourly data, IMERG’s performance was still poor with CC around 0.03–0.28. For all assessments, IMERG generally overestimated rainfall in comparison with rain gauge. The accuracy of the three types of IMERG in IMC was also influenced by season and topography. The highest and lowest CC values were observed for June–July–August and December–January–February, respectively. However, categorical statistics (POD, FAR and critical success index) did not show any clear seasonal variation. The CC value decreased with higher altitude, but with slight difference for each IMERG type. For all assessments conducted, IMERG-F generally showed the best rainfall observations in IMC, but with slightly difference from IMERG-E and IMERG-L. Thus, IMERG-E and IMERG-L data that had a faster latency than IMERG-F show potential to be used in rainfall observations in IMC.
Nonlocal Feature Selection Encoder–Decoder Network for Accurate InSAR Phase Filtering
Authors: Pu Liming , Zhang Xiaoling , Zhou Liming , Li Liang , Shi Jun , Wei Shunjun
Published: February 2022

Accurate interferometric phase filtering is an essential step in InSAR data processing. The existing deep learning-based phase-filtering methods were developed based on local neighboring pixels and only use local phase information. The idea of nonlocal processing has been proven to be very effective for improving the accuracy of interferometric phase filtering. In this paper, we propose a deep convolutional neural network-based nonlocal InSAR filtering method via a nonlocal phase filtering network (NL-PFNet) based on the encoder–decoder structure and nonlocal feature selection strategy. Thanks to the powerful phase feature extraction ability of the encoder–decoder structure and the utilization of nonlocal phase information, NL-PFNet can predict an accurately filtered interferometric phase after training using a large number of interferometric phase images with different noise levels. Experiments on both simulated and real InSAR data show that the proposed method significantly outperforms three traditional well-established methods and another deep learning-based method. Compared with the InSAR-BM3D filter and another deep learning-based method, the mean square error of the proposed method is 25% and 11% lower when processing simulated data, respectively, and when processing the real Sentinel-1 interferometric phase, the no-reference evaluation metric Q of the proposed method is 25% and 9% higher, respectively. In addition, the running time of the proposed method is tens of times less than that of the traditional filtering methods.
Functional Analysis for Habitat Mapping in a Special Area of Conservation Using Sentinel-2 Time-Series Data
Authors: Pesaresi Simone , Mancini Adriano , Quattrini Giacomo , Casavecchia Simona
Published: February 2022

The mapping and monitoring of natural and semi-natural habitats are crucial activities and are regulated by European policies and regulations, such as the 92/43/EEC. In the Mediterranean area, which is characterized by high vegetational and environmental diversity, the mapping and monitoring of habitats are particularly difficult and often exclusively based on in situ observations. In this scenario, it is necessary to automate the generation of updated maps to support the decisions of policy makers. At present, the availability of high spatiotemporal resolution data provides new possibilities for improving the mapping and monitoring of habitats. In this work, we present a methodology that, starting from remotely sensed time-series data, generates habitat maps using supervised classification supported by Functional Data Analysis. We constructed the methodology using Sentinel-2 data in the Mediterranean Special Area of Conservation “Gola di Frasassi” (Code: IT5320003). In particular, the training set uses 308 field plots with 11 target classes (five forests, two shrubs, one grassland, one mosaic, one extensive crop, and one urban land). Starting from vegetation index time-series data, Functional Principal Component Analysis was applied to derive FPCA scores and components. In particular, in the classification stage, the FPCA scores are considered as features. The obtained results out-performed a previous map derived from photo-interpretation by domain experts. We obtained an overall accuracy of 85.58% using vegetation index time-series, topography, and lithology data. The main advantages of the proposed approach are the capability to efficiently compress high dimensional data (dense remote-sensing time series) providing results in a compact way (e.g., FPCA scores and mean seasonal time profiles) that: (i) facilitate the link between remote sensing with habitat mapping and monitoring and their ecological interpretation and (ii) could be complementary to species-based approaches in plant community ecology and phytosociology.
Transient Scattering Echo Simulation and ISAR Imaging for a Composite Target-Ocean Scene Based on the TDSBR Method
Authors: Guo Guangbin , Guo Lixin , Wang Rui , Liu Wei , Li Long
Published: February 2022

We propose an inverse synthetic aperture radar (ISAR) imaging algorithm for a composite target-ocean scene based on time-domain shooting and bouncing rays (TDSBR) method. To do this, we develop the TDSBR method to simulate the electromagnetic scattering echoes from a composite target-ocean scene. Then, we derive the ISAR imaging formulas based on electromagnetic scattering field expressions. The demodulation, pulse compression, and azimuth inverse fast Fourier transform are performed on the scattering echo data to obtain ISAR images. Linear frequency modulation (LFM) pulses are usually used in traditional radar imaging. However, its long pulse width leads to more computation time for simulations. To increase efficiency, we replace LFM pulses with modulating Gaussian pulses. As a result, we modify the matched filter to achieve pulse compression under modulating Gaussian pulse excitations. Simulation results confirm that this proposed method improves efficiency while ensuring image quality. This proposed technique fully incorporates the electromagnetic scattering mechanism and is accurate. It effectively combines the electromagnetic scattering algorithm and the radar imaging algorithm. Finally, we present ISAR images simulated by this method. These results confirm the feasibility and efficiency of this technique.
Copper-Electroplating-Modified Liquid Metal Microfluidic Electrodes
Authors: Gong Jiahao , Liu Bingxin , Zhang Pan , Zhang Huimin , Gui Lin
Published: February 2022

Here, we report a novel technology for the fabrication of copper-electroplating-modified liquid metal microelectrodes. This technology overcomes the complexity of the traditional fabrication of sidewall solid metal electrodes and successfully fabricates a pair of tiny stable solid-contact microelectrodes on both sidewalls of a microchannel. Meanwhile, this technology also addresses the instability of liquid metal electrodes when directly contacted with sample solutions. The fabrication of this microelectrode depends on controllable microelectroplating of copper onto the gallium electrode by designing a microelectrolyte cell in a microfluidic chip. Using this technology, we successfully fabricate various microelectrodes with different microspacings (from 10 μm to 40 μm), which were effectively used for capacitive sensing, including droplet detection and oil particle counting.
Characterization of English Braille Patterns Using Automated Tools and RICA Based Feature Extraction Methods
Authors: Shokat Sana , Riaz Rabia , Rizvi Sanam Shahla , Khan Inayat , Paul Anand
Published: February 2022

Braille is used as a mode of communication all over the world. Technological advancements are transforming the way Braille is read and written. This study developed an English Braille pattern identification system using robust machine learning techniques using the English Braille Grade-1 dataset. English Braille Grade-1 dataset was collected using a touchscreen device from visually impaired students of the National Special Education School Muzaffarabad. For better visualization, the dataset was divided into two classes as class 1 (1–13) (a–m) and class 2 (14–26) (n–z) using 26 Braille English characters. A position-free braille text entry method was used to generate synthetic data. N = 2512 cases were included in the final dataset. Support Vector Machine (SVM), Decision Trees (DT) and K-Nearest Neighbor (KNN) with Reconstruction Independent Component Analysis (RICA) and PCA-based feature extraction methods were used for Braille to English character recognition. Compared to PCA, Random Forest (RF) algorithm and Sequential methods, better results were achieved using the RICA-based feature extraction method. The evaluation metrics used were the True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy, Area Under the Receiver Operating Curve (AUC) and F1-Score. A statistical test was also performed to justify the significance of the results.
How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
Authors: Huang Shuyue , Liang Lena Jingen , Choi Hwansuk Chris
Published: February 2022

Service failure is inevitable. Although empirical studies on the outcomes and processes of service failures have been conducted in the hotel industry, the findings need more exploration to understand how different segments perceive service failures and the associated emotions differently. This approach enables hotel managers to develop more effective strategies to prevent service failures and implement more specific service-recovery actions. For analysis, we obtained a nine-year (2010–2018) longitudinal dataset containing 1224 valid respondents with 73,622 words of textual content from a property affiliated with an international hotel brand in Canada. A series of text-mining and natural language processing (NLP) analyses, including frequency analysis and word cloud, sentiment analysis, word correlation, and TF–IDF analysis, were conducted to explore the information hidden in the massive amount of unstructured text data. The results revealed the similarities and differences between groups (i.e., men vs. women and leisure vs. business) in reporting service failures. We also carefully examined different meanings of words that emerged from the text-mining results to ensure a more comprehensive understanding of the guest experience.
Research and Application of Supersaturated Dissolved Oxygen Technology Combined with Magnetization Technology in the Improvement of Water Quality: Taking the South-to-North Water Diversion Project of China as a Pilot Project
Authors: Yao Yinlei , He Kang , Li Yuying , Zhang Xia , Ma Zhaolong , Cui Zhenzhen , Zheng Wenjun , Messyasz Beata , Chen Xiaonuo
Published: February 2022

Supersaturated dissolved oxygen and magnetization (SDOXM) technology is a composite technology that combines supersaturated dissolved oxygen with water magnetization technologies. Compared with conventional water purification technology, the advantages introduced by such technology include obvious and efficient improvement in purified water quality without adding any chemicals, removing sludge and changing the original function of the river. In this study, taking the Middle-Route (MR) of the South-to-North Water Diversion Project of China (SNWDPC) as a pilot project, the effects of the SDOXM composite process on microbial activity, phytoplankton community structure, and removal efficiency of the main nutritional indexes in the canal of the MR were evaluated. Aiming at static and flowing water bodies, this study was divided into two parts: static and dynamic experiments (two groups: a group with artificial aquatic plants and another group without artificial aquatic plants). The performance of the SDOXM system was assessed by monitoring the organic matter removal as well as the relative light unit (RLU) of ATP, changes in the community structure of phytoplankton, and the effects of artificial aquatic plants as a biofilm carrier on organic matter removal and microbial activity. During the study period, SDOXM technology was able to increase the concentration of dissolved oxygen (DO) in water and maintained the state of supersaturation for more than three days. The removal effect of organic matter from water was obvious. The community structure of phytoplankton changed from the Bacillariophyta-Cyanophyta type to the Bacillariophyta-Cyanobacteria type. Finally, the introduction of artificial aquatic plants has contributed to the improvement in water quality. Therefore, SDOXM technology can be used as a new water quality improvement technology to enhance the self-restoration ability of a river natural ecology.
The Role of Farmers’ Umbrella Organizations in Building Transformative Capacity around Grassroots Innovations in Rural Agri-Food Systems in Guatemala
Authors: Ortiz Rosalba , Peris Jordi
Published: February 2022

Family farmers and grassroots innovations can enable transitions to more sustainable food systems. The study explores the roles umbrella farmers’ organizations play in building transformative capacity through grassroots innovations in rural food systems in Guatemala. An analytical framework based on the notion of transformative capacity and socio-technical transitions is used to explore the specific factors enabling and limiting the transformative potential of grassroots innovations in a rural setting. A case study in rural Huehuetenango, Guatemala is presented, using interviews and focus groups discussions with relevant stakeholders engaged in the development process. Perceptions from interviews and focus groups discussions highlight the catalyst role played by the umbrella farmers’ organization as the main enabling factor to increase transformative capacity of grassroots innovations. The umbrella organization plays a key role in enabling farmers to pursue socio-technical transformations and in moving grassroots innovations outside a niche sphere. It contributes to creating coherence towards a common sustainability vision, supporting innovation and experimentation, and providing technical assistance around core development processes. In addition, it navigates across different levels of agency (households, communities, networks, and institutions) and different interaction scales (local, department, and national). However, gender and multi-generational gaps have been identified as limiting factors that would require further analysis.
An Unsupervised Mutual Information Feature Selection Method Based on SVM for Main Transformer Condition Diagnosis in Nuclear Power Plants
Authors: Yu Wenmin , Yu Ren , Tao Jun
Published: February 2022

Dissolved gas in oil (DGA) is a common means of monitoring the condition of an oil-immersed transformer. The concentration of dissolved gas and the ratio of different gases are important indexes to judge the condition of power transformers. Monitoring devices for dissolved gas in oil are widely installed in main transformers, but there are few recorded fault data of main transformers. The special operation and maintenance modes of main transformers leads to the fault modes particularity of main transformers. In order to solve the problem of insufficient samples and the feature uncertainty, this paper puts forward an unsupervised mutual information method to select the feature verified by the optimized support vector machine (SVM) model of particle swarm optimization (PSO) method and tries to find the feature sequence with better performance. The methos is validated by data from nuclear power transformers.
Sanitation in Mexico: An Overview of Its Realization as a Human Right
Authors: García-Searcy Vanesa , Villada-Canela Mariana , Arredondo-García María Concepción , Anglés-Hernández Marisol , Pelayo-Torres María Candelaria , Daesslé Luis Walter
Published: February 2022

Adequate sanitation is essential for health, human well-being, the preservation of water resources, biodiversity, and the full enjoyment of human rights. Sanitation is a human right, and although it is linked to the human right to water (HRW), it has specific characteristics that deserve particular attention. These components are (1) availability; (2) quality; (3) physical accessibility; (4) affordability; (5) acceptability; (6) equality and non-discrimination; (7) government management and inter-institutional coordination; (8) access to information and participation; and (9) environmental protection. These components enable the analysis of other aspects that may not be fully considered from the traditional sanitation approach. This research aims to analyze the context of the realization of the human right to sanitation (HRS) in Mexico, for a future comprehensive assessment. The study was conducted through documentary and national news coverage research. It concluded that there are still significant challenges to realizing this human right in Mexico. Findings suggest that besides the infrastructure and pollution difficulties, there are other problems like physical and economic access to sanitation for the vulnerable population, inequality between urban and rural areas, the lack of maintenance work of sanitation infrastructure, and the lack of effective coordination between the authorities responsible for sanitation.
An Exploratory Case Study on the Metrics and Performance of IoT Investment in Japanese Manufacturing Firms
Authors: Fukuzawa Mitsuhiro , Sugie Ryosuke , Park Youngwon , Shi Jin
Published: February 2022

This study investigates the objectives, approval process, decision criteria, metrics, and performance of the Internet of Things (IoT) system investment in four Japanese manufacturing firms through exploratory case analysis. This study conducted semi-structured interviews and several workshops with practitioners to collect, confirm, supplement, and verify the interviews data and the researcher’s interpretations. The study clarifies the actual status of investment activities in IoT systems and the essential common issues. In addition, this study shows that IoT investments in Japanese companies improve production activities’ efficiency. However, collaboration among divisions and departments other than production is not sufficient. This paper also contributes to constructing an analytical framework for comprehensively clarifying IT system investment decision-making and investment effects. These findings will be one of the reference points of the IoT system investment project and will contribute to the recent digital transformation movement in many manufacturing firms.
Earthquake Resilience Framework for a Stormwater Pipe Infrastructure System Integrating the Best Worst Method and Dempster–Shafer Theory
Authors: Garshasbi Maryam , Kabir Golam
Published: February 2022

Stormwater pipe infrastructure is a fundamental requirement of any nation, but pipes can be damaged in natural disasters. Consequently, evaluating the resilience of stormwater infrastructure to earthquake damage is an essential duty for any city because it outlines the capability to recover from a disaster after the event. The resilience quantification process needs various data types from various sources, and uncertainty and partial data may be included. This study recommends a resilience assessment framework for stormwater pipe infrastructure facing earthquake hazards using Hierarchical Evidential Reasoning (HER) on the basis of the Dempster–Shafer (D-S) theory. The developed framework was implemented in the City of Regina, SK, Canada to quantify the resilience of the stormwater pipe infrastructure. First, various resilience factors were identified from the literature. Based on experts’ judgment, the weight of these factors was determined using the Best Worst Method (BWM). After that, the resilience was determined using the D–S theory. Finally, sensitivity analysis was conducted to examine the sensitivity of the factors of the recommended hierarchical stormwater infrastructure resilience model. The recommended earthquake resilience assessment model produced satisfying outcomes, which showed the condition state of resilience with the degree of uncertainty.
Toward Sustainability: Dynamics of Total Carbon Dioxide Emissions, Aggregate Income, Non-Renewable Energy, and Renewable Power
Authors: Chen Wan-Jiun
Published: February 2022

The purpose of energy sustainability policy is to support both economic growth and environmental quality. With climate change accelerating, economies must reduce carbon emissions. Low-carbon economics can balance the oft-contradictory policy aims of income growth and carbon reduction. Carbon pricing and renewable substitutes can pave the way. This analysis probes the dynamics of the adjustments toward the ideals of low-carbon economics through Granger causality testing of total carbon emissions, income, nonrenewable energy consumption, and renewable power. Cointegration regressions and a panel data vector error correction model are used to demonstrate the aforementioned variables’ long-term balance and short-term adjustment, respectively. Two panels of countries, namely 18 European Union and 32 Organization of Economic Co-operation and Development countries, are investigated with 1990–2021 data. Determinants for the success of low-carbon development and the implications of border regulations and taxation of carbon footprint are also discussed. Economic competitiveness, as well as increases in commodity prices, would initially emerge as interferences and then induce carbon reduction and accelerate the adoption and development of green technology.
The Application of a Pavement Distress Detection Method Based on FS-Net
Authors: Hou Yun , Dong Yuanshuai , Zhang Yanhong , Zhou Zuofeng , Tong Xinlong , Wu Qingquan , Qian Zhenyu , Li Ran
Published: February 2022

In order to solve the problem of difficulties in pavement distress detection in the field of pavement maintenance, a pavement distress detection algorithm based on a new deep learning method is proposed. Firstly, an image data set of pavement distress is constructed, including large-scale image acquisition, expansion and distress labeling; secondly, the FReLU structure is used to replace the leaky ReLU activation function to improve the ability of two-dimensional spatial feature capture; finally, in order to improve the detection ability of this model for long strip pavement distress, the strip pooling method is used to replace the maximum pooling method commonly used in the existing network, and a new method is formed which integrates the FReLU structure and the strip pooling method, named FS-Net in this paper. The results show that the average accuracy of the proposed method is 4.96% and 3.67% higher than that of the faster R-CNN and YOLOv3 networks, respectively. The detection speed of 4 K images can reach about 12 FPS. The accuracy and computational efficiency can meet the actual needs in the field of road detection. A set of lightweight detection equipment for highway pavement was formed in this paper by purchasing hardware, developing software, designing brackets and packaging shells, and the FS-Net was burned into the equipment. The recognition rate of pavement distress is more than 90%, and the measurement error of the crack width is within ±0.5 mm through application testing. The lightweight detection equipment for highway pavement with burning of the pavement distress detection algorithm based on FS-Net can detect pavement conditions quickly and identify the distress and calculate the distress parameters, which provide a large amount of data support for the pavement maintenance department to make maintenance decisions.
Ecological Quality Response to Multi-Scenario Land-Use Changes in the Heihe River Basin
Authors: Wang Shengtang , Ge Yingchun
Published: February 2022

To investigate the spatial-temporal effects of land-use changes on ecological quality and future trends, an integrated framework combining the Dyna-CLUE model and the remote sensing ecological index (RSEI) was developed. Land-use changes from 2000 to 2035 were simulated and projected under the current trend scenario (CTS), economic development scenario (EDS) and ecological protection scenario (EPS) in the Heihe River Basin, while the RSEI was predicted using the elastic net regression (machine learning method); finally, the predicted results were synthesized and analyzed. The results showed that forest, grassland and water were positively correlated with ecological quality, with the green space coverage under the CTS, EPS and EDS accounting for 34.15%, 70.65% and 34.72% of the total transferred land area, respectively. The increase in the area of build-up land and unutilized land was detrimental to ecological quality, with the area of building land in the EDS being 1.75 times larger than in the year 2000. The EDS contributes to the sustainable development of the upstream area and the EPS is more conducive to the midstream and downstream areas by limiting the expansion of build-up land and by developing unutilized land in a limited way to increase the area of green space after reconciling economic conditions. Projection results promote the rational allocation of various land-use types in the future (semi) arid region, such as artificial forestation, unutilized land development and restriction of urban expansion, and also lay the foundation for the formulation of policies such as water allocation and ecological protection to facilitate the sustainable development of regional society, economy and ecology.
A Threshold Model of Tailings Sand Liquefaction Based on PSO-SVM
Authors: Jin Jiaxu , Yuan Shihao , Cui Hongzhi , Xiao Xiaochun , Jia Baoxin
Published: February 2022

The liquefaction of tailings sand caused by seismic loads is a major problem in ensuring the safety of tailings ponds. Liquefaction may cause uncontrolled fluidized failure of the dam body, causing considerable damage to the lives, property and environment of people downstream. In this paper, a prototype tailings sand is used as the material to consider the main factors affecting liquefaction (i.e., dynamic load, soil quality, burial and static conditions). By embedding acceleration, pore pressure and earth pressure sensors in the rigid design of the self-designed rigid model box, different types of seismic waves of different ground motion amplitudes (PGA) were induced in a shaking table test of tailings sand liquefaction. The seismic intensity, waveform (class II, III and IV seismic waves) and active earth pressure of the PGA characterizing dynamic factors were obtained, and the static factors were characterized. The dynamic shear stress ratio, the peak acceleration of the earthquake, the pore pressure of the drainage factor and the buried depth (overlying effective pressure) characterize the soil conditions. SPSS software was used to analyze the factor dimension reduction, and the most suitable factors for factor analysis were obtained. Particle Swarm Optimization (PSO) was used to optimize the parameters, and the improved PSO-SVM algorithm was compared with the existing genetic algorithm (GA) and grid node search (GS). The algorithm used in this paper is fast and has a relatively high accuracy rate of 92.7%. The established threshold model method is of great significance to predict the liquefaction of tailings sand soil under the action of ground motions and to carry out safety managemenin advance, which can provide a certain reference for the project.
Decoupling Regional Economic Growth from Industrial CO2 Emissions: Empirical Evidence from the 13 Prefecture-Level Cities in Jiangsu Province
Authors: Liu Jingxing , Li Hailing , Liu Tianqi
Published: February 2022

Amid global climate warming, it is necessary to explore the decoupling relationship between economic growth (EG) and industrial CO2 emissions (ICEs). This paper uses the Tapio decoupling model and the logarithmic mean Divisia index (LMDI) model synthetically to analyze the decoupling relationship between EG and ICEs and reveal the driving force of growth of CO2 emissions (CEs) based on ICE panel data from 13 prefecture-level cities in Jiangsu province from 2011 to 2015. From the research results, it can be seen that the decoupling status in southern Jiangsu, middle Jiangsu and northern Jiangsu presented weak decoupling (WD), weak negative decoupling (WND) and WD, respectively. In 2011–2013, seven prefecture-level cities exhibited states of WD, and strong decoupling (SD) occurred only in Zhenjiang, with a decoupling index value of −0.3359. In 2013–2015, five prefecture-level cities exhibited states of WD. The energy intensity and carbon emission intensity had negative inhibitory effects on ICEs, and economic development and the energy structure had positive promotion effects on ICEs. The research results have important theoretical and practical significance for future energy savings, carbon emissions reductions and the realization of a low-carbon economy in the economic development of Jiangsu.