bluuebunny/crossref_metadata_2025 huggingface dataset:
Ashwagandha (Withania somnifera)—Current Research on the Health-Promoting Activities: A Narrative Review
Authors: Mikulska Paulina , Malinowska Marta , Ignacyk Miłosz , Szustowski Paweł , Nowak Joanna , Pesta Karolina , Szeląg Monika , Szklanny Damian , Judasz Eliza , Kaczmarek Gabriela , Ejiohuo Ovinuchi Prince , Paczkowska-Walendowska Magdalena , Gościniak Anna , Cielecka-Piontek Judyta
Published: March 2023

In recent years, there has been a significant surge in reports on the health-promoting benefits of winter cherry (Withania somnifera), also known as Ashwagandha. Its current research covers many aspects of human health, including neuroprotective, sedative and adaptogenic effects and effects on sleep. There are also reports of anti-inflammatory, antimicrobial, cardioprotective and anti-diabetic properties. Furthermore, there are reports of reproductive outcomes and tarcicidal hormone action. This growing body of research on Ashwagandha highlights its potential as a valuable natural remedy for many health concerns. This narrative review delves into the most recent findings and provides a comprehensive overview of the current understanding of ashwagandha’s potential uses and any known safety concerns and contraindications.
Genome-Wide Identification and Analysis of MYB Transcription Factor Family in Hibiscus hamabo
Authors: Liu Dina , Gu Chunsun , Fu Zekai , Wang Zhiquan
Published: March 2023

MYB transcription factors constitute one of the largest gene families in plants and play essential roles in the regulation of plant growth, responses to stress, and a wide variety of physiological and biochemical processes. In this study, 204 MYB proteins (HhMYBs) were identified in the Hibiscus hamabo Sieb. et Zucc (H. hamabo) genome and systematically analyzed based on their genomic sequence and transcriptomic data. The candidate HhMYB proteins and MYBs of Arabidopsis thaliana were divided into 28 subfamilies based on the analysis of their phylogenetic relationships and their motif patterns. Expression analysis using RNA-seq and quantitative real-time PCR (qRT-PCR) indicated that most HhMYBs are differentially regulated under drought and salt stresses. qRT-PCR analysis of seven selected HhMYBs suggested that the HhMYB family may have regulatory roles in the responses to stress and hormones. This study provides a framework for a more comprehensive analysis of the role of MYBs in the response to abiotic stress in H. hamabo.
Genetic Relationships of 118 Castanea Specific Germplasms and Construction of Their Molecular ID Based on Morphological Characteristics and SSR Markers
Authors: Bai Xiaoqian , Zhang Shijie , Wang Wu , Chen Yu , Zhao Yuqiang , Shi Fenghou , Zhu Cancan
Published: March 2023

To understand the genetic relationships of Castanea species, 16 phenotypic traits were measured, simple sequence repeat (SSR) markers were analyzed, and molecular identity cards (IDs) were constructed for 118 Castanea materials using fluorescent capillary electrophoresis. The coefficient of variation values of the 16 morphological traits of the test materials ranged from 11.11% to 60.38%. A total of 58 alleles were detected using six pairs of SSR core primers, with an average number of 9.7 alleles per locus. The average number of valid alleles per locus was 3.9419 and the proportion of valid alleles was 40.78%. A total of 105 genotypes were detected, and the number of genotypic species that could be amplified per primer pair ranged from 8 to 26. The mean value of the observed heterozygosity was 0.4986. The variation in the He, H, and PIC values was similar; the size of I value was approximately 2.21 times larger, and its mean number of variations was 0.7390, 0.7359, 0.6985, and 1.6015, respectively. The classification of 118 Castanea species was performed using three analytical methods: structure analysis, neighbor-joining (NJ) cluster analysis, and principal coordinate analysis (PCoA), and the results of the three methods were in high agreement. Six pairs of SSR core primers with high polymorphism and strong discriminatory properties were used to identify 118 Castanea plants, and a unique molecular ID card was constructed for each material. These results provide insight into the genetic diversity and population structure of Castanea plants and a theoretical basis for improving the phenomenon of mixed varieties and substandard plants in the Castanea plant market.
Do Nitrogen and Phosphorus Additions Affect Nitrogen Fixation Associated with Tropical Mosses?
Authors: Avila Clasen Lina , Permin Aya , Horwath Aline B. , Metcalfe Daniel B. , Rousk Kathrin
Published: March 2023

Tropical cloud forests are characterized by abundant and biodiverse mosses which grow epiphytically as well as on the ground. Nitrogen (N)-fixing cyanobacteria live in association with most mosses, and contribute greatly to the N pool via biological nitrogen fixation (BNF). However, the availability of nutrients, especially N and phosphorus (P), can influence BNF rates drastically. To evaluate the effects of increased N and P availability on BNF in mosses, we conducted a laboratory experiment where we added N and P, in isolation and combined, to three mosses (Campylopus sp., Dicranum sp. and Thuidium peruvianum) collected from a cloud forest in Peru. Our results show that N addition almost completely inhibited BNF within a day, whereas P addition caused variable results across moss species. Low N2 fixation rates were observed in Campylopus sp. across the experiment. BNF in Dicranum sp. was decreased by all nutrients, while P additions seemed to promote BNF in T. peruvianum. Hence, each of the three mosses contributes distinctively to the ecosystem N pool depending on nutrient availability. Moreover, increased N input will likely significantly decrease BNF associated with mosses also in tropical cloud forests, thereby limiting N input to these ecosystems via the moss-cyanobacteria pathway.
Effects of Dietary Rare Earth Chitosan Chelate on Performance, Egg Quality, Immune and Antioxidant Capacity, and Intestinal Digestive Enzyme Activity of Laying Hens
Authors: Lu Xinxin , Chang Xinyu , Zhang Haijun , Wang Jing , Qiu Kai , Wu Shugeng
Published: March 2023

Rare earth chitosan chelate salt (RECC) is a potential feed additive and is a product of the chelation effect between rare earth ions and chitosan. This research study aims to explore the effects of dietary RECC on performance, egg quality, intestinal digestive function, and the immune and antioxidant capacity of laying hens in the late phase of production. A total of 360 56-week-old Dawu Jinfeng laying hens were randomly allotted into four treatment groups with six replicates per treatment and 15 birds per replicate. The laying hens were fed the basal diet supplemented with, respectively, 0 (control: CON), 100 (R1), 200 (R2), and 400 (R3) mg/kg for 8 weeks. Dietary RECC significantly improved average daily feed intake (ADFI) and average daily egg yield in both linear and quadratic manner (p < 0.05). In addition, albumen height and HU were improved significantly (p < 0.05) in a dose-dependent manner of RECC. In addition, a significant decrease (p < 0.05) in serum TP, IgA, and MDA for the R1 group and IgG in the R2 group were notable, while the increase in serum TP and decrease in T-AOC were found for R3 dietary group compared to CON (p < 0.05). The level of intestinal IL-2 and TNF-α was decreased by dietary RECC (p < 0.01). The activities of the digestive enzyme (α-Amylase, lipase, and Trypsin) showed a quadratic change with an increase and then decrease in response to increasing dose of RECC, 200 mg/kg RECC significantly increased the activity of lipase and Trypsin (p < 0.01). Supplementation of dietary RECC at low doses compared to higher doses impacted positive effects on the antioxidant capacity and immune function (p < 0.05). The utilization of RECC as a feed additive in the diet of aged laying hens exerted beneficial effects on egg production, albumen quality, humoral immunity, inflammatory response, and activity of digestive enzymes. Thus, the regulation of antioxidant capacity and duodenal function via increased enzyme activity and immune and inflammatory response were critical to the improvement of laying performance and egg quality in aged hens. The optimal supplemental dose is 100–200 mg/kg.
Fabrication of 3D Printed Ceramic Part Using Photo-Polymerization Process
Authors: Lim Da-Sol , Chung Jin-Kyo , Yun Ji-Sun , Park Min-Soo
Published: March 2023

Ceramics are high-strength and high-temperature resistant materials that are used in various functional parts. However, due to the high strength and brittleness properties, there are many difficulties in the fabrication of complex shapes. Therefore, there are many studies related to the fabrication of ceramic parts using 3D printing technology optimized for complex shapes. Among them, studies using photo-polymerization (PP) 3D printing technology with excellent dimensional accuracy and surface quality have received the most widespread attention. To secure the physical properties of sintered ceramic, the content and distribution of materials are important. This study suggests a novel 3D printing process based on a high-viscosity composite resin that maximizes the content of zirconia ceramics. For reliable printing, the developed 3D printers that can adjust the process environment were used. To minimize warpage and delamination, the divided micro square pattern images were irradiated in two separate intervals of 1.6 s each while maintaining the internal chamber temperature at 40 °C. This contributed to improved stability and density of the sintered structures. Ultimately, the ceramic parts with a Vickers hardness of 12.2 GPa and a relative density of over 95% were able to be fabricated based on a high-viscosity resin with 25,000 cps.
Pyrolysis of Waste Tires: A Review
Authors: Han Wenwen , Han Deshang , Chen Hongbo
Published: March 2023

Waste tires are known as “black pollution”, which is difficult to degrade. The safe handling and recycling of waste tires have always been the focus of and difficulty for the global rubber industry. Pyrolysis can not only solve the problem of environmental pollution but also completely treat the waste tires and recover valuable pyrolysis products. This paper summarizes research progress on the pyrolysis of waste tires, including the pyrolysis mechanism; the important factors affecting the pyrolysis of waste tires (pyrolysis temperature and catalysts); and the composition, properties, and applications of the three kinds of pyrolysis products. The composition and yield of pyrolysis products can be regulated by pyrolysis temperature and catalysts, and pyrolysis products can be well used in many industrial occasions after different forms of post-treatment.
Delamination of Novel Carbon Fibre-Based Non-Crimp Fabric-Reinforced Thermoplastic Composites in Mode I: Experimental and Fractographic Analysis
Authors: Mohsin Muhammad Ameerul Atrash , Iannucci Lorenzo , Greenhalgh Emile S.
Published: March 2023

Delamination, a form of composite failure, is a significant concern in laminated composites. The increasing use of out-of-autoclave manufacturing techniques for automotive applications, such as compression moulding and thermoforming, has led to increased interest in understanding the delamination resistance of carbon-fibre-reinforced thermoplastic (CFRTP) composites compared to traditional carbon-fibre-reinforced thermosetting (CFRTS) composites. This study evaluated the mode I (opening) interlaminar fracture toughness of two non-crimp fabric (NCF) biaxial (0/90°) carbon/thermoplastic composite systems: T700/polyamide 6.6 and T700/polyphenylene sulphide. The mode I delamination resistance was determined using the double cantilever beam (DCB) specimen. The results were analysed and the Mode I interlaminar fracture toughness was compared. Additionally, the fractographic analysis (microstructure characterisation) was conducted using a scanning electron microscope (SEM) to examine the failure surface of the specimens.
Effects of Translucency and Thickness of Lithium Disilicate-Reinforced Glass-Ceramic Veneers on the Degree of Conversion of a Purely Light-Curing Bonding Resin: An In Vitro Study
Authors: Poca Anthony , De Peretti Della Rocca Kenza , Nasr Karim , Ducassé Romain , Canceill Thibault
Published: March 2023

The objective of this study was to evaluate the variations in the degree of conversion (DC) of a light-curing composite resin when the thickness or the translucency of lithium disilicate-enriched glass-ceramic veneers are modified. IPS e. max® CAD blocks of the MT-A2, LT-A2 and MO1 types were cut to obtain four slices with thicknesses ranging from 0.6 mm to 1 mm. A strictly light-curing composite resin (G-aenial Universal Injectable) was injected in the empty part of a silicone mold so that the veneer could then be inserted under digital pressure to the stop. A 40 s light cure (1400 mW/cm2) was then performed. Resin samples were analyzed using Fourier transform infrared (FTIR) spectroscopy. When the degree of translucency of the ceramic was modified, a decrease in the resin conversion rate was noted, but with a non-significant global p-value (p = 0.062). Interestingly, the degree of conversion of the light-curing composite resin was also modified when the ceramic’s thickness increased, especially when it was over 1 mm (DC0.6 > DC0.7 > DC0.8 > DC1; p < 0.0001). This confirms that the degree of conversion of a bonding material is very dependent on the ceramic’s thickness. Contradictory data are, however, found in the literature, where there are reports of an absence of a difference between the DC obtained with thicknesses of ceramics of 0.7 and 2 mm.
Wearable and Stretchable SEBS/CB Polymer Conductive Strand as a Piezoresistive Strain Sensor
Authors: Jamatia Thaiskang , Matyas Jiri , Olejnik Robert , Danova Romana , Maloch Jaroslav , Skoda David , Slobodian Petr , Kuritka Ivo
Published: March 2023

A wearable and stretchable strain sensor with a gauge factor above 23 was prepared using a simple and effective technique. Conducting nanocomposite strands were prepared from styrene-b-(ethylene-co-butylene)-b-styrene triblock copolymer (SEBS) and carbon black (CB) through a solvent-processing method that uses a syringe pump. This novel nanocomposite preparation technique is a straightforward and cost-effective process and is reported in the literature for the first time. The work included two stages: the flexible nanocomposite preparation stage and the piezoresistive sensor stage. Depending on its molecular structure, the thermoelastic polymer SEBS is highly resilient to stress and strain. The main aim of this work is to fabricate a highly flexible and piezoresistive nanocomposite fibre/strand. Among the prepared composites, a composite corresponding to a composition just above the percolation threshold was selected to prepare the strain sensor, which exhibited good flexibility and conductivity and a large piezoresistive effect that was linearly dependent on the applied strain. The prepared nanocomposite sensor was stitched onto a sports T-shirt. Commercially available knee and elbow sleeves were also purchased, and the nanocomposite SEBS/CB strands were sewn separately on the two sleeves. The results showed a high sensitivity of the sensing element in the case of breathing activity (normal breathing, a 35% change, and deep breathing at 135%, respectively). In the case of knee and elbow movements, simultaneous measurements were performed and found that the sensor was able to detect movement cycles during walking.
Nanostructured Polyaniline Films Functionalized through Auxiliary Nitrogen Addition in Atmospheric Pressure Plasma Polymerization
Authors: Kim Jae Young , Jang Hyojun , Lee Ye Rin , Kim Kangmin , Suleiman Habeeb Olaitan , Park Choon-Sang , Shin Bhum Jae , Jung Eun Young , Tae Heung-Sik
Published: March 2023

Polyaniline (PANI) was synthesized from liquid aniline, a nitrogen-containing aromatic compound, through the atmospheric pressure (AP) plasma process using a newly designed plasma jet array with wide spacing between plasma jets. To expand the area of the polymerized film, the newly proposed plasma jet array comprises three AP plasma jet devices spaced 7 mm apart in a triangular configuration and an electrodeless quartz tube capable of applying auxiliary gas in the center of the triangular plasma jets. The vaporized aniline monomer was synthesized into a PANI film using the proposed plasma array device. The effects of nitrogen gas addition on the morphological, chemical, and electrical properties of PANI films in AP argon plasma polymerization were examined. The iodine-doped PANI film was isolated from the atmosphere through encapsulation. The constant electrical resistance of the PANI film indicates that the conductive PANI film can achieve the desired resistance by controlling the atmospheric exposure time through encapsulation.
Coagulative Nucleation in the Copolymerization of Methyl Methacrylate–Butyl Acrylate under Monomer-Starved Conditions
Authors: Castellanos Sujey G. , Fernández-Escamilla V. Vladimir A. , Corona-Rivera Miguel Á. , González-Iñiguez Karla J. , Barrera Arturo , Moscoso-Sánchez Francisco J. , Figueroa-Ochoa Edgar B. , Ceja Israel , Rabelero Martín , Aguilar Jacobo
Published: March 2023

Coagulative nucleation in the copolymerization of methyl methacrylate–butyl acrylate (MMA-BA) via semicontinuous emulsion heterophase polymerization (SEHP) under monomer-starved conditions in latexes with high solid content (50.0 wt %) and low concentrations of surfactant is reported. The SEHP technique allows the obtention of latex with high colloidal stability and has potential industrial application in polymer synthesis. High instantaneous conversions (>90%) and a high-ratio polymerization rate/addition rate (Rp/Ra) ≥ 0.9 were obtained at low times until the final copolymerization, which confirmed the starved conditions in the systems at the highest surfactant concentrations. The particle size exhibited a linear size increment at conversions between 0 and 40% induced by homogeneous nucleation, a transition region between 40 and 50%, and non-linear behavior at higher conversions by coagulative nucleation. These three behaviors were also observed in the particle surfactant coverage area (Sc), Z-potential, particle coagulation rate (dNp/dt) by the Smoluchowski model, final particle size (Dpz), and number particle (Np) through the reaction. By means of transmission electron microscopy (TEM) images, the onset of coagulation was observed from 50% of conversion until the end of the reaction. In addition, in both processes of copolymerization, tacticity was displayed (mainly syndiotacticity).
Application of Drug Efficiency Index Metric for Analysis of Post-Traumatic Stress Disorder and Treatment Resistant Depression Gene Expression Profiles
Authors: Borisov Nicolas , Ilnytskyy Yaroslav , Byeon Boseon , Kovalchuk Olga , Kovalchuk Igor
Published: March 2023

Post-traumatic stress disorder (PTSD) is a severe mental illness with grave social, political, economic, and humanitarian implications. To apply the principles of personalized omics-based medicine to this psychiatric problem, we implemented our previously introduced drug efficiency index (DEI) to the PTSD gene expression datasets. Generally, omics-based personalized medicine evaluates individual drug action using two classes of data: (1) gene expression, mutation, and Big Data profiles, and (2) molecular pathway graphs that reflect the protein–protein interaction. In the particular case of the DEI metric, we evaluate the drug action according to the drug’s ability to restore healthy (control) activation levels of molecular pathways. We have curated five PTSD and one TRD (treatment-resistant depression) cohorts of next-generation sequencing (NGS) and microarray hybridization (MH) gene expression profiles, which, in total, comprise 791 samples, including 379 cases and 413 controls. To check the applicability of our DEI metrics, we have performed three differential studies with gene expression and pathway activation data: (1) case samples vs. control samples, (2) case samples after treatment or/and observation vs. before treatment, and (3) samples from patients positively responding to the treatment vs. those responding negatively or non-responding patients. We found that the DEI values that use the signaling pathway impact activation (SPIA) metric were better than those that used the Oncobox pathway activation level (Oncobox PAL) approach. However, SPIA, Oncobox PAL, and DEI evaluations were reliable only if there were differential genes between case and control, or treated and untreated, samples.
Pilgrimage as Self-Discovery in an Ecological Community
Authors: Redick Kip
Published: March 2023

Pilgrims become open to self-discovery in a Gestalt of the personal and communal. Traditional pilgrimage integrates pilgrims into a community of faith. Long-distance hikes through wilderness, such as the Appalachian Trail, may be interpreted from this lens. However, the environment/space/place of wilderness situates pilgrims beyond a traditional religious frame. The sacred does not manifest from a schema of established religious symbols because wilderness trails disrupt preconceptions, breaking through the self’s strategies of centering. The disruption of one’s prior orientation to community, customs, and conventions that form the self’s symbolic schema opens the hiker to what Levinas references as “the delirium that comes from God [as] a divine release of the soul from the yoke of custom and convention”. The other as transcendent presents herself and exceeds “the idea of the other in me,” (Levinas). Pilgrims may discover an existential dialogue with the sacred other, the human or extra-human. The Gestalt of the personal and communal extends beyond traditional boundaries, encompassing human and extra-human beings.
Changing Tourism Trends of the Benedictine Abbey in Tihany: Best Practices of a Hungarian Monastic Community
Authors: Fekete Dávid , Barkó Gábor Ágoston , Dömötör Mihály , Czakó Katalin
Published: March 2023

The touristic use of sacred sites is a widespread practice in Hungary. Throughout the past centuries, Christianity has dominated the history of the Carpathian Basin. The Hungarian State’s strong affiliation with the Roman Catholic Church dates back to its foundation over a 1000 years ago. Due to its location on the shore of Lake Balaton and outstanding natural environment, the Benedictine Tihany Abbey is a major touristic destination and a popular place of pilgrimage in Hungary. The objective of the present paper is to examine how touristic activities contribute to the livelihood of a Hungarian monastic community and local economic development in the 21st century. To answer the research questions, the study primarily relied on literature and documentary analysis, in-depth interviews, and the construction and processing of a database. The research revealed that in addition to the classic elements (e.g., guided tours in the abbey, museum exhibitions, concerts, etc.), the program offer developed by the monastic community of Tihany is capable of addressing new target audiences: the rapidly rising number of tourists from the Far East (mainly Japan, China, South Korea, and Russia). Despite remaining considerably below visitor numbers recorded by the abbey in the mid-1990s, a significant increase in visitor numbers was observed in 2018–2019. The economic activities of the Benedictines of Tihany comprise a number of different areas beyond tourism, e.g., agricultural production and candle making, both of which are integrated into their tourism offer and feature among touristic programs. As a major employer, the abbey exerts a positive impact on the population retention capacity of the settlement by offering jobs to local residents, and it also contributes to population growth by attracting a large number of highly skilled professionals who choose to settle down in the region. The paper shows the evidence of the pandemic on pilgrimage and religious tourism in Tihany.
Revolutionizing Social Robotics: A Cloud-Based Framework for Enhancing the Intelligence and Autonomy of Social Robots
Authors: Elfaki Abdelrahman Osman , Abduljabbar Mohammed , Ali Luqman , Alnajjar Fady , Mehiar Dua’a , Marei Ashraf M. , Alhmiedat Tareq , Al-Jumaily Adel
Published: March 2023

Social robots have the potential to revolutionize the way we interact with technology, providing a wide range of services and applications in various domains, such as healthcare, education, and entertainment. However, most existing social robotics platforms are operated based on embedded computers, which limits the robot’s capabilities to access advanced AI-based platforms available online and which are required for sophisticated physical human–robot interactions (such as Google Cloud AI, Microsoft Azure Machine Learning, IBM Watson, ChatGPT, etc.). In this research project, we introduce a cloud-based framework that utilizes the benefits of cloud computing and clustering to enhance the capabilities of social robots and overcome the limitations of current embedded platforms. The proposed framework was tested in different robots to assess the general feasibility of the solution, including a customized robot, “BuSaif”, and commercialized robots, “Husky”, “NAO”, and “Pepper”. Our findings suggest that the implementation of the proposed platform will result in more intelligent and autonomous social robots that can be utilized by a broader range of users, including those with less expertise. The present study introduces a novel methodology for augmenting the functionality of social robots, concurrently simplifying their utilization for non-experts. This approach has the potential to open up novel possibilities within the domain of social robotics.
A Hybrid ENSO Prediction System Based on the FIO−CPS and XGBoost Algorithm
Authors: Kuang Zhiyuan , Song Yajuan , Wu Jie , Fu Qiuying , Shu Qi , Qiao Fangli , Song Zhenya
Published: March 2023

Accurate prediction of the El Niño–Southern Oscillation (ENSO) is crucial for climate change research and disaster prevention and mitigation. In recent decades, the prediction skill for ENSO has improved significantly; however, accurate forecasting at a lead time of more than six months remains challenging. By using a machine learning method called eXtreme Gradient Boosting (XGBoost), we corrected the ENSO predicted results from the First Institute of Oceanography Climate Prediction System version 2.0 (FIO−CPS v2.0) based on the satellite remote sensing sea surface temperature data, and then developed a dynamic and statistical hybrid prediction model, named FIO−CPS−HY. The latest 15 years (2007–2021) of independent testing results showed that the average anomaly correlation coefficient (ACC) and root mean square error (RMSE) of the Niño3.4 index from FIO−CPS v2.0 to FIO−CPS−HY for 7− to 13−month lead times could be increased by 57.80% (from 0.40 to 0.63) and reduced by 24.79% (from 0.86 °C to 0.65 °C), respectively. The real−time predictions from FIO−CPS−HY indicated that the sea surface state of the Niño3.4 area would likely be in neutral conditions in 2023. Although FIO−CPS−HY still has some biases in real−time prediction, this study provides possible ideas and methods to enhance short−term climate prediction ability and shows the potential of integration between machine learning and numerical models in climate research and applications.
An Atmospheric Phase Correction Method Based on Normal Vector Clustering Partition in Complicated Conditions for GB-SAR
Authors: Ou Pengfei , Lai Tao , Huang Shisheng , Chen Wu , Weng Duojie
Published: March 2023

Atmospheric phase is the main factor affecting the accuracy of ground-based synthetic aperture radar. The atmospheric phase screen (APS) may be very complicated, due to the drastic changes in atmospheric conditions, and the conventional correction methods based on regression models cannot fit and correct it effectively. Partition correction is a feasible path to improve atmospheric phase correction (APC) accuracy for complicated APS, but the overfitting problem cannot be ignored. In this article, we propose a clustering partition method, based on the normal vector of APS, which can partition the complicated APS more reasonably, and then perform APC based on the partition results. APC, and simulation experiments on measurement data, suggests that the proposed method achieves higher accuracy than the conventional model-based methods for complicated APS and avoids severe overfitting, realizing the balance between accuracy and credibility. This article verifies the feasibility and effectiveness of using APS distribution information to guide the partition and conduct APC.
A New Method for False Alarm Suppression in Heterogeneous Change Detection
Authors: Xu Cong , Liu Baisen , He Zishu
Published: March 2023

Heterogeneous change detection has a wide range of applications in many fields. However, to date, many existing problems of heterogeneous change detection, such as false alarm suppression, have not been specifically addressed. In this article, we discuss the problem of false alarm suppression and propose a new method based on the combination of a convolutional neural network (CNN) and graph convolutional network (GCN). This approach employs a two-channel CNN to learn the feature maps of multitemporal images and then calculates difference maps of different scales, which means that both low-level and high-level features contribute equally to the change detection. The GCN, with a newly built convolution kernel (called the partially absorbing random walk convolution kernel), classifies these difference maps to obtain the inter-feature relationships between true targets and false ones, which can be represented by an adjacent matrix. We use pseudo-label samples to train the whole network, which means our method is unsupervised. Our method is verified on two typical data sets. The experimental results indicate the superiority of our method compared to some state-of-the-art approaches, which proves the efficacy of our method in false alarm suppression.
Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data
Authors: Wang Jianbo , Wang Jinyang , Chen Shunde , Luo Jianbo , Sun Mingzhi , Sun Jialong , Yuan Jiajia , Guo Jinyun
Published: March 2023

Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 109 m3 from July 2002 to December 2022, with a growth rate of 5.3766 × 108 m3/a. The growth rate from January 2005 to January 2015 is 4.4850 × 108 m3/a, and from January 2015 to December 2022, the growth rate is 8.9206 × 108 m3/a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years.
Comparing Global Sentinel-2 Land Cover Maps for Regional Species Distribution Modeling
Authors: Venter Zander S. , Roos Ruben E. , Nowell Megan S. , Rusch Graciela M. , Kvifte Gunnar M. , Sydenham Markus A. K.
Published: March 2023

Mapping the spatial and temporal dynamics of species distributions is necessary for biodiversity conservation land-use planning decisions. Recent advances in remote sensing and machine learning have allowed for high-resolution species distribution modeling that can inform landscape-level decision-making. Here we compare the performance of three popular Sentinel-2 (10-m) land cover maps, including dynamic world (DW), European land cover (ELC10), and world cover (WC), in predicting wild bee species richness over southern Norway. The proportion of grassland habitat within 250 m (derived from the land cover maps), along with temperature and distance to sandy soils, were used as predictors in both Bayesian regularized neural network and random forest models. Models using grassland habitat from DW performed best (RMSE = 2.8 ± 0.03; average ± standard deviation across models), followed by ELC10 (RMSE = 2.85 ± 0.03) and WC (RMSE = 2.87 ± 0.02). All satellite-derived maps outperformed a manually mapped Norwegian land cover dataset called AR5 (RMSE = 3.02 ± 0.02). When validating the model predictions of bee species richness against citizen science data on solitary bee occurrences using generalized linear models, we found that ELC10 performed best (AIC = 2278 ± 4), followed by WC (AIC = 2367 ± 3), and DW (AIC = 2376 ± 3). While the differences in RMSE we observed between models were small, they may be significant when such models are used to prioritize grassland patches within a landscape for conservation subsidies or management policies. Partial dependencies in our models showed that increasing the proportion of grassland habitat is positively associated with wild bee species richness, thereby justifying bee conservation schemes that aim to enhance semi-natural grassland habitat. Our results confirm the utility of satellite-derived land cover maps in supporting high-resolution species distribution modeling and suggest there is scope to monitor changes in species distributions over time given the dense time series provided by products such as DW.
Human Activity Classification Based on Dual Micro-Motion Signatures Using Interferometric Radar
Authors: Hassan Shahid , Wang Xiangrong , Ishtiaq Saima , Ullah Nasim , Mohammad Alsharef , Noorwali Abdulfattah
Published: March 2023

Micro-Doppler signatures obtained from the Doppler radar are generally used for human activity classification. However, if the angle between the direction of motion and radar antenna broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human body reduce significantly, thereby degrading the performance of the classification algorithm. For the accurate classification of different human activities irrespective of trajectory, we propose a new algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric micro-motion signatures, using an interferometric radar. First, the motion of different parts of the human body is simulated using motion capture (MOCAP) data, which is further utilized for radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms obtained from time-frequency analysis of a single Doppler receiver and interferometric output data, respectively, are fed as input to the deep convolutional neural network (DCNN) for feature extraction and the training/testing process. The performance of the proposed algorithm is analyzed and compared with a micro-Doppler signatures-based classifier. Results show that a dual micro-motion-based DCNN classifier using an interferometric radar is capable of classifying different human activities with an accuracy level of 98%, where Doppler signatures diminish considerably, providing insufficient information for classification. Verification of the proposed classification algorithm based on dual micro-motion signatures is also performed using a real radar test dataset of different human walking patterns, and a classification accuracy level of approximately 90% is achieved.
Significant Inverse Influence of Tropical Indian Ocean SST on SIF of Indian Vegetation during the Summer Monsoon Onset Phase
Authors: Varghese Roma , Behera Swadhin K. , Behera Mukunda Dev
Published: March 2023

Sea surface temperature (SST) substantially influences the land climate conditions through the co-variability of multiple climate variables, which in turn affect the structural and functional characteristics of terrestrial vegetation. Our study explored the varying responses of vegetation photosynthesis in India to the SST variations in the tropical Indian Ocean during the summer monsoon. To characterise the terrestrial photosynthetic activity, we used solar-induced chlorophyll fluorescence (SIF). Our results demonstrated a significant negative SST-SIF relationship during the onset phase of the summer monsoon: the SIF anomalies in the northern and central Indian regions decrease when strong warm SST anomalies persist in the tropical Indian Ocean. Further, SIF anomalies increase with cold anomalies of SST. However, the negative SST anomalies in the tropical Indian Ocean are less impactful on SIF anomalies relative to the positive SST anomalies. The observed statistically significant SST–SIF link is feasible through atmospheric teleconnections. During monsoon onset, positive SST anomalies in the tropical Indian Ocean favour weakened monsoon flow, decreasing moisture transport from the ocean to the Indian mainland. The resultant water deficiency, along with the high air temperature, created a stress condition and reduced the photosynthetic rate, thus demonstrating negative SIF anomalies across India. Conversely, negative SST anomalies strengthened monsoon winds in the onset period and increased moisture availability across India. Negative air temperature anomalies also dampen water stress conditions and increased photosynthetic activity, resulting in positive SIF anomalies. The identified SST-SIF relationship would be beneficial to generate a simple framework that aids in the detection of the probable impact on vegetation growth across India associated with the rapidly varying climate conditions in the Indian Ocean.
Soft-Sensor System for Grasp Type Recognition in Underactuated Hand Prostheses
Authors: De Arco Laura , Pontes María José , Segatto Marcelo E. V. , Monteiro Maxwell E. , Cifuentes Carlos A. , Díaz Camilo A. R.
Published: March 2023

This paper presents the development of an intelligent soft-sensor system to add haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed, one for finger joint angles and the other for fingertips’ contact force. Three sensor fabrications were tested for the angle sensor by axially rotating the sensors in four positions. The configuration with the most similar response in the four rotations was chosen. The chosen sensors presented a polynomial response with R2 higher than 92%. The tactile force sensors tracked the force made over the objects. Almost all sensors presented a polynomial response with R2 higher than 94%. The system monitored the prosthesis activity by recognizing grasp types. Six machine learning algorithms were tested: linear regression, k-nearest neighbor, support vector machine, decision tree, k-means clustering, and hierarchical clustering. To validate the algorithms, a k-fold test was used with a k = 10, and the accuracy result for k-nearest neighbor was 98.5%, while that for decision tree was 93.3%, enabling the classification of the eight grip types.
Machine Learning-Assisted Improved Anomaly Detection for Structural Health Monitoring
Authors: Samudra Shreyas , Barbosh Mohamed , Sadhu Ayan
Published: March 2023

The importance of civil engineering infrastructure in modern societies has increased lately due to the growth of the global economy. It forges global supply chains facilitating enormous economic activity. The bridges usually form critical links in complex supply chain networks. Structural health monitoring (SHM) of these infrastructures is essential to reduce life-cycle costs, and determine their remaining life using advanced sensing techniques and data fusion methods. However, the data obtained from the SHM systems describing the health condition of the infrastructure systems may contain anomalies (i.e., distortion, drift, bias, outlier, noise etc.). An automated framework is required to accurately classify these anomalies and evaluate the current condition of these systems in a timely and cost-effective manner. In this paper, a recursive and interpretable decision tree framework is proposed to perform multiclass classification of acceleration data collected from a real-life bridge. The decision nodes of the decision tree are random forest classifiers that are invoked recursively after synthetically augmenting the training data before successive iterations until suitable classification performance is obtained. This machine-learning-based classification model evolved from a simplistic decision tree where statistical features are used to perform classification. The feature vectors defined for training the random forest classifiers are calculated using similar statistical features that are easy to interpret, enhancing the interpretability of the classifier models. The proposed framework could classify non-anomalous (i.e., normal) time-series of the test dataset with 98% accuracy.
Evaluation of Sit-to-Stand Movement in Older Adults with Locomotive Syndrome Using the Nintendo Wii Balance Board
Authors: Yamako Go , Punchihewa Niroshan G. , Arakawa Hideki , Tajima Takuya , Chosa Etsuo
Published: March 2023

Standing up from a chair is a mechanically demanding daily motion, and its biomechanics represent motor performance. In older adults with locomotive syndrome (LS), sit-to-stand (STS) movement with adequate postural control is essential to prevent falls. This study evaluated the characteristics of dynamic balance during STS movement on older adults with LS. A total of 116 participants aged ≥65 years were divided into Non-LS, LS stage 1, and LS stage 2 groups using the LS risk test. The participants were instructed to stand on the Nintendo Wii Balance Board as quickly as possible, and the STS movement was quantified using the vertical ground reaction force (VGRF) and center of pressure (CoP). The STS score, which represented dynamic balance, was significantly different among the groups (p < 0.001). The rate of VGRF development was significantly lower in the LS stages 1 and 2 than in the Non-LS group (p < 0.001). On the other hand, the total distance of the CoP path did not differ among the groups (p = 0.211). These findings indicated a reduction of postural control in older adults with LS. The STS score emphasized the importance of balance training to prevent falls in older adults with LS.
A Recognition Method for Road Hypnosis Based on Physiological Characteristics
Authors: Wang Bin , Shi Huili , Chen Longfei , Wang Xiaoyuan , Wang Gang , Zhong Fusheng
Published: March 2023

Road hypnosis is a state which is easy to appear frequently in monotonous scenes and has a great influence on traffic safety. The effective detection for road hypnosis can improve the intelligent vehicle. In this paper, the simulated experiment and vehicle experiment are designed and carried out to obtain the physiological characteristics data of road hypnosis. A road hypnosis recognition model based on physiological characteristics is proposed. Higher-order spectra are used to preprocess the electrocardiogram (ECG) and electromyography (EMG) data, which can be further fused by principal component analysis (PCA). The Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbor (KNN) models are constructed to identify road hypnosis. The proposed model has good identification performance on road hypnosis. It provides more alternative methods and technical support for real-time and accurate identification of road hypnosis. It is of great significance to improve the intelligence and active safety of intelligent vehicles.
The Network Bass Model with Behavioral Compartments
Authors: Modanese Giovanni
Published: March 2023

A Bass diffusion model is defined on an arbitrary network, with the additional introduction of behavioral compartments, such that nodes can have different probabilities of receiving the information/innovation from the source and transmitting it to other nodes. The dynamics are described by a large system of non-linear ordinary differential equations, whose numerical solutions can be analyzed in dependence on diffusion parameters, network parameters, and relations between the compartments. For example, in a simple case with two compartments (Enthusiasts and Sceptics about the innovation), we consider cases in which the “publicity” and imitation terms act differently on the compartments, and individuals from one compartment do not imitate those of the other, thus increasing the polarization of the system and creating sectors of the population where adoption becomes very slow. For some categories of scale-free networks, we also investigate the dependence on the features of the networks of the diffusion peak time and of the time at which adoptions reach 90% of the population.
Identifying and Ranking the Dimensions of Urban Resilience and Its Effect on Sustainable Urban Development in Tongdejie, China
Authors: Xie Jiarong
Published: March 2023

Urban resilience strengthens urban sustainability and leads to sustainable solutions in the process of promoting sustainable development. Paying attention to the benefits of urban resilience to strengthen sustainable urban systems is vital to achieve our desired future. This study aims to identify and classify the key indicators of resilience in Tongdejie, a residential area in Guangzhou, China, as well as to examine and compare these indicators with sustainable development indicators. Fuzzy AHP was used to rank the most important indicators in Tongdejie. The results showed that the first three important indicators were economic indicators, and the economic dimension with a weight of 0.41 was higher than the other four indicators. Then, social and cultural indicators took second place with a weight of 0.194, and the management and institutional indicators took third place with a weight of 0.194. Structural–physical and environmental dimensions were ranked fourth and fifth, respectively. From the obtained results and their comparison with the sustainable indicators, in addition to ranking the importance of these indicators and incorporating the research related to urban construction development indicators, it can be concluded that these two concepts have a direct relationship with each other. In order to attain a desired and resilient urban future, it is important to pay attention to the indications and advantages of resilience. This leads to the development and stability of urban systems.
Optimal Control of Industrial Pollution under Stochastic Differential Models
Authors: Xiao Lu , Ding Huacong , Zhong Yu , Wang Chaojie
Published: March 2023

Considering that the amount of waste generated by an industrial enterprise is affected by many uncertain factors, such as the quality of raw materials and the state of equipment. The process is not deterministic, as assumed in most existing studies. In this paper, we propose a stochastic impulse control model to characterize the process of pollution control. The Quasi-Variational Inequality (QVI) method is implemented to solve the optimization problem. Our results show that the optimal control strategy for an industrial enterprise is to perform at a fixed intensity when the pollution reaches the threshold level. In addition, sensitivity analysis of parameters is implemented to illustrate the impact of higher growth rates and volatility on the optimal control strategy. The paper provides a decision basis for industrial enterprises to do pollution control efficiently.