bluuebunny/crossref_metadata_2025 huggingface dataset:
Seed Size, Seed Dispersal Traits, and Plant Dispersion Patterns for Native and Introduced Grassland Plants
Authors: Tuthill Jane E. , Ortega Yvette K. , Pearson Dean E.
Published: February 2023

Most terrestrial plants disperse by seeds, yet the relationship between seed mass, seed dispersal traits, and plant dispersion is poorly understood. We quantified seed traits for 48 species of native and introduced plants from the grasslands of western Montana, USA, to investigate the relationships between seed traits and plant dispersion patterns. Additionally, because the linkage between dispersal traits and dispersion patterns might be stronger for actively dispersing species, we compared these patterns between native and introduced plants. Finally, we evaluated the efficacy of trait databases versus locally collected data for examining these questions. We found that seed mass correlated positively with the presence of dispersal adaptations such as pappi and awns, but only for introduced plants, for which larger-seeded species were four times as likely to exhibit dispersal adaptations as smaller-seeded species. This finding suggests that introduced plants with larger seeds may require dispersal adaptations to overcome seed mass limitations and invasion barriers. Notably, larger-seeded exotics also tended to be more widely distributed than their smaller-seeded counterparts, again a pattern that was not apparent for native taxa. These results suggest that the effects of seed traits on plant distribution patterns for expanding populations may be obscured for long-established species by other ecological filters (e.g., competition). Finally, seed masses from databases differed from locally collected data for 77% of the study species. Yet, database seed masses correlated with local estimates and generated similar results. Nonetheless, average seed masses differed up to 500-fold between data sources, suggesting that local data provides more valid results for community-level questions.
Chemical Structure and Microscopic Morphology Changes of Dyed Wood Holocellulose Exposed to UV Irradiation
Authors: Shi Hui , Ni Yongqing , Guo Hongwu , Liu Yi
Published: February 2023

Dyed wood is prone to photoaging when exposed to UV irradiation which decreases its decorative effect and service life. Holocellulose, as the main component of dyed wood, has a photodegradation behavior which is still unclear. To investigate the effect of UV irradiation on chemical structure and microscopic morphology changes of dyed wood holocellulose, Maple birch (Betulacostata Trautv) dyed wood and holocellulose were exposed to UV accelerated aging treatment; the photoresponsivity includes crystallization, chemical structure, thermal stability, and microstructure were studied. Results showed that UV radiation has no significant effect on the lattice structure of dyed wood fibers. The wood crystal zone diffraction 2θ and layer spacing was basically unchanged. With the UV radiation time extension, the relative crystallinity of dyed wood and holocellulose showed a trend of increasing first and then decreasing, but the overall change was not significant. The relative crystallinity change range of the dyed wood was not more than 3%, and the dyed holocellulose was not more than 5%. UV radiation caused the molecular chain chemical bond in the non-crystalline region of dyed holocellulose to break, the fiber underwent photooxidation degradation, and the surface photoetching feature was prominent. Wood fiber morphology was damaged and destroyed, finally leading to the degradation and corrosion of the dyed wood. Studying the photodegradation of holocellulose is helpful to understand the photochromic mechanism of dyed wood, and, further, to improve its weather resistance.
A Shape Memory Alloy-Based Soft Actuator Mimicking an Elephant’s Trunk
Authors: Kang Minchae , Han Ye-Ji , Han Min-Woo
Published: February 2023

Soft actuators that execute diverse motions have recently been proposed to improve the usability of soft robots. Nature-inspired actuators, in particular, are emerging as a means of accomplishing efficient motions based on the flexibility of natural creatures. In this research, we present an actuator capable of executing multi-degree-of-freedom motions that mimics the movement of an elephant’s trunk. Shape memory alloys (SMAs) that actively react to external stimuli were integrated into actuators constructed of soft polymers to imitate the flexible body and muscles of an elephant’s trunk. The amount of electrical current provided to each SMA was adjusted for each channel to achieve the curving motion of the elephant’s trunk, and the deformation characteristics were observed by varying the quantity of current supplied to each SMA. It was feasible to stably lift and lower a cup filled with water by using the operation of wrapping and lifting objects, as well as effectively performing the lifting task of surrounding household items of varying weights and forms. The designed actuator is a soft gripper that incorporates a flexible polymer and an SMA to imitate the flexible and efficient gripping action of an elephant trunk, and its fundamental technology is expected to be used as a safety-enhancing gripper that requires environmental adaptation.
Asymmetric Monomer Design Enables Structural Control of M(Salen)-Type Polymers
Authors: Novozhilova Maria , Polozhentseva Julia , Karushev Mikhail
Published: February 2023

Conductive and electrochemically active polymers consisting of Salen-type metal complexes as building blocks are of interest for energy storage and conversion applications. Asymmetric monomer design is a powerful tool for fine-tuning the practical properties of conductive electrochemically active polymers but has never been employed for polymers of M(Salen)]. In this work, we synthesize a series of novel conducting polymers composed of a nonsymmetrical electropolymerizable copper Salen-type complex (Cu(3-MeOSal–Sal)en). We show that asymmetrical monomer design provides easy control of the coupling site via polymerization potential control. With in-situ electrochemical methods such as UV-vis-NIR (ultraviolet-visible-near infrared) spectroscopy, EQCM (electrochemical quartz crystal microbalance), and electrochemical conductivity measurements, we elucidate how the properties of these polymers are defined by chain length, order, and cross-linking. We found that the highest conductivity in the series has a polymer with the shortest chain length, which emphasizes the importance of intermolecular iterations in polymers of [M(Salen)].
Shear Bond Strength of Resin Luting Materials to Lithium Disilicate Ceramic: Correlation between Flexural Strength and Modulus of Elasticity
Authors: Irie Masao , Okada Masahiro , Maruo Yukinori , Nishigawa Goro , Matsumoto Takuya
Published: February 2023

This study investigates the effect of the curing mode (dual-cure vs. self-cure) of resin cements (four self-adhesive and seven conventional cements) on their flexural strength and flexural modulus of elasticity, alongside their shear bond strength to lithium disilicate ceramics (LDS). The study aims to determine the relationship between the bond strength and LDS, and the flexural strength and flexural modulus of elasticity of resin cements. Twelve conventional or adhesive and self-adhesive resin cements were tested. The manufacturer’s recommended pretreating agents were used where indicated. The shear bond strengths to LDS and the flexural strength and flexural modulus of elasticity of the cement were measured immediately after setting, after one day of storage in distilled water at 37 °C, and after 20,000 thermocycles (TC 20k). The relationship between the bond strength to LDS, flexural strength, and flexural modulus of elasticity of resin cements was investigated using a multiple linear regression analysis. For all resin cements, the shear bond strength, flexural strength, and flexural modulus of elasticity were lowest immediately after setting. A clear and significant difference between dual-curing and self-curing modes was observed in all resin cements immediately after setting, except for ResiCem EX. Regardless of the difference of the core-mode condition of all resin cements, flexural strengths were correlated with the LDS surface upon shear bond strengths (R2 = 0.24, n = 69, p < 0.001) and the flexural modulus of elasticity was correlated with them (R2 = 0.14, n = 69, p < 0.001). Multiple linear regression analyses revealed that the shear bond strength was 17.877 + 0.166, the flexural strength was 0.643, and the flexural modulus was (R2 = 0.51, n = 69, p < 0.001). The flexural strength or flexural modulus of elasticity may be used to predict the bond strength of resin cements to LDS.
Docking Design of the Different Microcapsules in Aqueous Solution and Its Quantitative On-Off Study
Authors: Tan Hongfei , Zhao Dan , Liu Mingxing , Hong Zongguo , Liu Jingxue , Dai Kang , Xiao Xincai
Published: February 2023

To avoid risk, spacecraft docking technologies can transport batches of different astronauts or cargoes to a space station. Before now, spacecraft-docking multicarrier/multidrug delivery systems have not been reported on. Herein, inspired by spacecraft docking technology, a novel system including two different docking units, one made of polyamide (PAAM) and on of polyacrylic acid (PAAC), grafted respectively onto polyethersulfone (PES) microcapsules, is designed, based on intermolecular hydrogen bonds in aqueous solution. VB12 and vancomycin hydrochloride were chosen as the release drugs. The release results show that the docking system is perfect, and has a good responsiveness to temperature when the grafting ratio of PES-g-PAAM and PES-g-PAAC is close to 1:1. Below 25 °C, this system exhibited an “off” effect because the polymer chains on the microcapsule’s surface produced intermolecular hydrogen bonds. Above 25 °C, when the hydrogen bonds were broken, the microcapsules separated from each other, and the system exhibited an “on” state. The results provide valuable guidance for improving the feasibility of multicarrier/multidrug delivery systems.
High-Durability Photothermal Slippery Surfaces for Droplet Manipulation Based on Ultraviolet Lithography
Authors: Wen Tong , Zhang Chen , Gong Yanyan , Liu Zezhi , Zhao Wei , Zhan Yongjie , Zhang Ce , Wang Kaige , Bai Jintao
Published: February 2023

Photothermal slippery surface has broad applications in many research fields for noncontacting, loss-free, and flexible droplet manipulation capability. In this work, with specific morphologic parameters and modified base materials doped by Fe3O4, a high-durability photothermal slippery surface (HD-PTSS) was proposed and implemented based on ultraviolet (UV) lithography to achieve repeatability of more than 600 cycles. The instantaneous response time and transport speed of HD-PTSS were related to near-infrared ray (NIR) powers and droplet volume. Meanwhile, the durability was closely related to the morphology of HD-PTSS, which impacts the recovering of a lubricant layer. The droplet manipulation mechanism of HD-PTSS was discussed in depth, and the Marangoni effect was found to be the key factor for the durability of HD-PTSS.
Solid State Polymerization of Biodegradable Poly(butylene sebacate-co-terephthalate): A Rapid, Facile Method for Property Enhancement
Authors: Lim Daegyu , Park Su-il
Published: February 2023

Poly(butylene sebacate-co-terephthalate) (PBSeT) has generated attention as a promising biopolymer for preparing bioplastics. However, there are limited studies on the synthesis of PBSeT, impeding its commercialization. Herein, with a view to addressing this challenge, biodegradable PBSeT was modified using solid state polymerization (SSP) with various ranges of time and temperature. The SSP used three different temperatures below the melting temperature of PBSeT. The polymerization degree of SSP was investigated using Fourier-transform infrared spectroscopy. The changes in the rheological properties of PBSeT after SSP were investigated using a rheometer and an Ubbelodhe viscometer. Differential scanning calorimetry and X-ray diffraction showed that the crystallinity of PBSeT was higher after SSP. The investigation revealed that after SSP for 40 min at 90 °C, PBSeT exhibited higher intrinsic viscosity (increased from 0.47 to 0.53 dL/g), crystallinity, and complex viscosity than PBSeT polymerized at other temperatures. However, a high SSP processing time resulted in a decrease in these values. In this experiment, SSP was most effectively performed in the temperature range closest to the melting temperature of PBSeT. This indicates that SSP could be a facile and rapid method for improving the crystallinity and thermal stability of synthesized PBSeT.
Synthesis of Xanthan Gum Anchored α-Fe2O3 Bionanocomposite Material for Remediation of Pb (II) Contaminated Aquatic System
Authors: Alharthi Fahad A. , Alshammari Riyadh H. , Hasan Imran
Published: February 2023

Increases in community and industrial activities have led to disturbances of the environmental balance and the contamination of water systems through the introduction of organic and inorganic pollutants. Among the various inorganic pollutants, Pb (II) is one of the heavy metals possessing non-biodegradable and the most toxic characteristics towards human health and the environment. The present study is focussed on the synthesis of efficient and eco-friendly adsorbent material that can remove Pb (II) from wastewater. A green functional nanocomposite material based on the immobilization of α-Fe2O3 nanoparticles with xanthan gum (XG) biopolymer has been synthesized in this study to be applied as an adsorbent (XGFO) for sequestration of Pb (II). Spectroscopic techniques such as scanning electron microscopy with energy dispersive X-ray (SEM-EDX), Fourier transform infrared (FTIR), transmission electron microscopy (TEM), X-ray diffraction (XRD), ultraviolet visible (UV-Vis) and X-ray photoelectron spectroscopy (XPS) were adopted for characterizing the solid powder material. The synthesized material was found to be rich in key functional groups such as –COOH and –OH playing important roles in binding the adsorbate particles through ligand-to-metal charge transfer (LMCT). Based on the preliminary results, adsorption experiments were conducted, and the data obtained were applied to four different adsorption isotherm models, viz the Langmuir, Temkin, Freundlich and D–R models. Based on the high values of R2 and low values of χ2, the Langmuir isotherm model was found to be the best model for simulation of data for Pb (II) adsorption by XGFO. The value of maximum monolayer adsorption capacity (Qm) was found to be 117.45 mg g−1 at 303 K, 126.23 mg g−1 at 313 K, 145.12 mg g−1 at 323 K and 191.27 mg g−1 at 323 K. The kinetics of the adsorption process of Pb (II) by XGFO was best defined by the pseudo-second-order model. The thermodynamic aspect of the reaction suggested that the reaction is endothermic and spontaneous. The outcomes proved that XGFO can be utilized as an efficient adsorbent material for the treatment of contaminated wastewater.
FeV LDH Coated on Sandpaper as an Electrode Material for High-Performance Flexible Energy Storage Devices
Authors: Park Jihyeon , Kim Youngsu , Kitchamsetti Narasimharao , Jo Seungju , Lee Seungjae , Song Jubin , Park Wook , Kim Daewon
Published: February 2023

Recently, considerable research efforts to achieve advanced design of promising electroactive materials as well as unique structures in supercapacitor electrodes have been explored for high-performance energy storage systems. We suggest the development of novel electroactive materials with an enlarged surface area for sandpaper materials. Based on the inherent micro-structured morphologies of the sandpaper substrate, nano-structured Fe-V electroactive material can be coated on it by facile electrochemical deposition technique. A hierarchically designed electroactive surface is covered with FeV-layered double hydroxide (LDH) nano-flakes on Ni-sputtered sandpaper as a unique structural and compositional material. The successful growth of FeV-LDH is clearly revealed by surface analysis techniques. Further, electrochemical studies of the suggested electrodes are carried out to optimize the Fe-V composition as well as the grit number of the sandpaper substrate. Herein, optimized Fe0.75V0.25 LDHs coated on #15000 grit Ni-sputtered sandpaper are developed as advanced battery-type electrodes. Finally, along with the negative electrode of activated carbon and the FeV-LDH electrode, it is utilized for hybrid supercapacitor (HSC) assembly. The fabricated flexible HSC device indicates high energy and power density by showing excellent rate capability. This study is a remarkable approach to improving the electrochemical performance of energy storage devices using facile synthesis.
Experimental Investigation of Effect of L-Profile Hybrid Aluminium/GFRP to the Axial and Lateral Characteristic
Authors: Nugraha Ariyana Dwiputra , Alandro Daffa , Kusumawanto Arif , Junianto Endro , Perwara Budi , Kumar Vishnu Vijay , Santos Gil Nonato C. , Sentanuhady Jayan , Norcahyo Rachmadi , Muflikhun Muhammad Akhsin
Published: February 2023

The current study investigates the effect of a hybrid L-profile aluminium/glass-fiber-reinforced polymer stacking sequence under axial and lateral compression loads. Four stacking sequences are studied: aluminium (A)—glass-fiber (GF)—AGF, GFA, GFAGF, and AGFA. In the axial compression test, the aluminium/GFRP hybrid tends to crush in a more progressive and stable failure than the net aluminium and net GFRP specimens, with a relatively more stable load-carrying capacity throughout the experimental tests. The AGF stacking sequence was second, with an energy absorption of 145.31 kJ, following AGFA at 157.19 kJ. The load-carrying capacity of AGFA was the highest, with an average peak crushing force of 24.59 kN. The second-highest peak crushing force, 14.94 kN, was achieved by GFAGF. The highest amount of energy absorption, 157.19 J, was achieved by the AGFA specimen. The lateral compression test showed a significant increase in load-carrying and energy absorption capacity in the aluminium/GFRP hybrid specimens compared to the net GFRP specimens. AGF had the highest energy absorption with 10.41 J, followed by AGFA with 9.49 J. AGF also had the highest peak crushing force with 2.98 kN, followed by AGFA with 2.16 kN. The most crashworthy stacking sequence among the four variations tested in this experimental research was the AGF stacking sequence because of its great load-carrying capacity, energy absorption, and specific energy absorption in axial and lateral loading. The study provides greater insight into the failure of hybrid composite laminates under lateral and axial compression.
Estimation of Chlorine Concentration in Water Distribution Systems Based on a Genetic Algorithm
Authors: Gómez-Coronel Leonardo , Delgado-Aguiñaga Jorge Alejandro , Santos-Ruiz Ildeberto , Navarro-Díaz Adrián
Published: February 2023

This paper proposes a methodology based on a genetic algorithms (GA) to calibrate the parameters of a chlorine decay model in a water distribution system (WDS). The proposed methodology first contemplates that a GA is implemented using historical measurements of chlorine concentration at some sensed nodes to calibrate the unknown values corresponding to both the bulk and wall reaction coefficients. Once both parameters are estimated, the optimal-fit chlorine decay model is used to predict the decay of chlorine concentration in the water at each node for any concentration input at the pumping station. Then, a second GA-based algorithm is implemented to obtain the minimal chlorine concentration needed at the input to ensure that every node in the system meets the official normativity requirements for free chlorine in a WDS. The proposed methodology performed satisfactorily for a WDS simulated in EPANET with a GA implemented in MATLAB, both for the estimation of the reaction coefficients and the optimization of the required chlorine concentration at the input. Simulation results illustrate the performance of the proposed algorithm.
Dynamic Parameter Simulations for a Novel Small-Scale Power-to-Ammonia Concept
Authors: Koschwitz Pascal , Bellotti Daria , Sanz Miguel Cámara , Alcaide-Moreno Antonio , Liang Cheng , Epple Bernd
Published: February 2023

Ammonia is a promising carbon-free energy vector, hydrogen carrier, and efficient means for long-time hydrogen storage. Power-to-ammonia-to-power concepts, powered exclusively by electricity from renewable sources, will leave the carbon economy behind and enter a truly renewable era. However, the fluctuating nature of renewables requires a good dynamic behavior of such concepts. Employing the software Aspen Plus Dynamics®, this paper investigates the dynamic behavior of a novel containerized power-to-ammonia solution to be tested at the University of Genova in 2023. Implementing a novel kinetic reaction model, the impacts of several deviations from the optimal values of the cycle parameters are investigated. The simulations provide practical guidance on how to best and safely operate the cycle. A total of ten scenarios were simulated, of which six are acceptable, two are desirable, and two should be avoided. However, all scenarios can be safely controlled by the control infrastructure.
The Odor of a Holy Fame: The Problematic Charisma of King Louis IX (1214–1270)
Authors: Pelissie du Rausas Amicie
Published: February 2023

By attributing the term “charismatic” to Saint Louis, Jacques Le Goff identified two sources of charisma: sacred kingship and personal holiness. Without denying these aspects of the holy king’s reputation, we should investigate the nature of the charismatic relationship that linked Louis IX to his contemporaries. The sacrality of Louis IX pre-existed him; his sanctity is a post-death construction. What are the attributes of the living character that would allow us to recognize a charismatic personality? This paper argues that the religious aura of the king, which best echoes the Paulinian version of charisma, was sometimes at odds with the political expectations levied on a medieval ruler, which a Weberian definition of charisma helps to define. In this light, the crusades provided a unique setting where the king’s Christ-like qualities and his political leadership could be reconciled. To conduct this argument, this paper proposes to look for the symptoms of Louis IX’s living charisma in the reactions of his contemporaries, based on the re-examination of classical sources on the life of the king, carefully contextualized.
Cryptocurrency Risks, Fraud Cases, and Financial Performance
Authors: Kerr David S. , Loveland Karen A. , Smith Katherine Taken , Smith Lawrence Murphy
Published: February 2023

In this study, we examine major cryptocurrencies, present notable fraud cases, describe fraud risks, and analyze cryptocurrency financial performance. People debate whether cryptocurrency is an investment opportunity, the new Dutch Tulip Bubble, or a giant Ponzi scheme. There have been a number of high-profile fraud cases associated with cryptocurrencies, such as the FTX scandal in late 2022, thereby making fraud a real concern to current and potential future investors. Regarding financial performance, cryptocurrencies experienced a major collapse in value in the most recent period of the study, about three times worse than the major stock market indices. While in prior periods, cryptocurrencies have significantly outperformed stock market indices, recent fraud cases and the extreme volatility of cryptocurrencies indicate that investing in cryptocurrencies comes with much higher risk than traditional stock market investments. The debate over the investment potential of cryptocurrencies continues, whether they have long term value or are simply the new Dutch Tulip Bubble. The study’s findings will be useful to investors, regulators, and academic researchers regarding the cryptocurrency industry.
Mapping the Distribution and Dynamics of Coniferous Forests in Large Areas from 1985 to 2020 Combining Deep Learning and Google Earth Engine
Authors: Liu Lizhi , Zhang Qiuliang , Guo Ying , Chen Erxue , Li Zengyuan , Li Yu , Wang Bing , Ri Ana
Published: February 2023

Mapping the distribution of coniferous forests is of great importance to the sustainable management of forests and government decision-making. The development of remote sensing, cloud computing and deep learning has provided the support of data, computing power and algorithms for obtaining large-scale forest parameters. However, few studies have used deep learning algorithms combined with Google Earth Engine (GEE) to extract coniferous forests in large areas and the performance remains unknown. In this study, we thus propose a cloud-enabled deep-learning approach using long-time series Landsat remote sensing images to map the distribution and obtain information on the dynamics of coniferous forests over 35 years (1985–2020) in the northwest of Liaoning, China, through the combination of GEE and U2-Net. Firstly, to assess the reliability of the proposed method, the U2-Net model was compared with three Unet variants (i.e., Resnet50-Unet, Mobile-Unet and U-Net) in coniferous forest extraction. Secondly, we evaluated U2-Net’s temporal transferability of remote sensing images from Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI. Finally, we compared the results obtained by the proposed approach with three publicly available datasets, namely GlobeLand30-2010, GLC_FCS30-2010 and FROM_GLC30-2010. The results show that (1) the cloud-enabled deep-learning approach proposed in this paper that combines GEE and U2-Net achieves a high performance in coniferous forest extraction with an F1 score, overall accuracy (OA), precision, recall and kappa of 95.4%, 94.2%, 96.6%, 95.5% and 94.0%, respectively, outperforming the other three Unet variants; (2) the proposed model trained by the sample blocks collected from a specific time can be applied to predict the coniferous forests in different years with satisfactory precision; (3) Compared with three global land-cover products, the distribution of coniferous forests extracted by U2-Net was most similar to that of actual coniferous forests; (4) The area of coniferous forests in Northwestern Liaoning showed an upward trend in the past 35 years. The area of coniferous forests has grown from 945.64 km2 in 1985 to 6084.55 km2 in 2020 with a growth rate of 543.43%. This study indicates that the proposed approach combining GEE and U2-Net can extract coniferous forests quickly and accurately, which helps obtain dynamic information and assists scientists in developing sustainable strategies for forest management.
Cropland Productivity Evaluation: A 100 m Resolution Country Assessment Combining Earth Observation and Direct Measurements
Authors: Csikós Nándor , Szabó Brigitta , Hermann Tamás , Laborczi Annamária , Matus Judit , Pásztor László , Szatmári Gábor , Takács Katalin , Tóth Gergely
Published: February 2023

A methodology is presented for the quantitative assessment of soil biomass productivity at 100 m spatial resolution on a national scale. The traditional land evaluation approach—where crop yield is the dependent variable—was followed using measured yield and net primary productivity data derived from satellite images, together with digital soil and climate maps. In addition to characterizing of soil biomass productivity based on measured data, the weight of soil properties on productivity was also quantified to provide measured soil health and soil quality indicators as an information base for designing sustainable land management practices. To produce these results, we used only the Random Forest method for our calculations. The study considers high-input agriculture, which is predominant in the country. Biomass productivity indices for the main crops (wheat, maize and sunflowers) and general productivity indices were calculated for the whole agricultural area of Hungary. Results can be implemented in cadastral systems, in applied in agricultural and rural development programs. The assessment can be repeated for monitoring purposes to support general monitoring objectives as well as for reporting in relation to the United Nations Sustainable Development Goals. However, on the basis of the results, we also propose a method for periodically updating the assessment, which can also be used for monitoring biomass productivity in the context of climate change, land degradation and the development of cultivation technology.
Evaluation of GPM-IMERG Precipitation Product at Multiple Spatial and Sub-Daily Temporal Scales over Mainland China
Authors: Zhou Zehui , Lu Dekai , Yong Bin , Shen Zhehui , Wu Hao , Yu Lei
Published: February 2023

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) provides new-generation satellite precipitation datasets with high spatio-temporal resolution and accuracy, which is widely applied in hydrology and meteorology. However, most examinations of the IMERG were conducted on daily, monthly, and annual scales, and inadequate research focused on the sub-daily scale. Thus, this study set up four sub-daily scales (1 h, 3 h, 12 h, and 24 h at 0.1° spatial resolution) and four spatial scales (0.1°, 0.25°, 0.5°, and 1° at 1 h temporal resolution) to finely evaluate the performance of IMERG products in the summer seasons from 2014 to 2019 over mainland China. The precipitation amount (PA), frequency (PF), and intensity (PI) were adopted to assess the performance of the IMERG referenced by the ground-based precipitation product of the China Meteorological Administration (CMA). The results show that the IMERG can capture the spatial patterns of precipitation characters over mainland China, but the PA and PI are overestimated and the PF is underestimated, and the evaluation results are highly sensitive to the different temporal and spatial resolutions. Compared with fine spatio-temporal scales, the performance of the IMERG is significantly improved when scaled up to coarser scales. Moreover, the IMERG shows a better performance of PA and PI in larger regions and during longer periods. This study provided a reference for the application of IMERG products in different spatial and temporal scales.
Joint Communication and Jamming System Design Based on Filter Bank Multicarrier Chirp Waveform: Using for Curvilinear Flight Scenario
Authors: Liu Gaogao , Yang Wenbo , Bao Yuqian , Wang Youming , Li Peng
Published: February 2023

A joint communication jamming waveform is proposed in this study based on the FBMC- chirp. To increase the number of false targets in a single pulse period, the chirp signal is modulated to different subcarrier groups. Since the subcarriers of the FBMC-OQAM signal are orthogonal, the signals are naturally orthogonal. This allows the transmitter and receiver to be separated and achieve multiple false target jamming, allowing the CFAR threshold to be raised by about 20 dB and protecting the target from detection. The ratio of the frequency shift of the designed jamming signal to the frequency modulation depends on the delay time, making the joint signal more robust in response to jamming and resistant to frequency modulation. The use of intercepted radar signals allowed channel estimation, providing high-speed digital transmission while ensuring multi-false-target jamming. The simulation results show that the joint signal has jamming effects on the pulse Doppler radar. The proposed FBMC chirp joint waveform requires about 20 dB less jamming signal ratio than the existing method, and thus the energy saved can ensure the robust performance of the communication subsystem in the joint communication jamming system. The proposed system has excelled in communication rate and bit error rate performance, ensuring that instructions are accurately and completely transmitted while implementing effective jamming.
A Data-Centric Approach for Wind Plant Instance-Level Segmentation Using Semantic Segmentation and GIS
Authors: de Carvalho Osmar Luiz Ferreira , de Carvalho Junior Osmar Abílio , de Albuquerque Anesmar Olino , Orlandi Alex Gois , Hirata Issao , Borges Díbio Leandro , Gomes Roberto Arnaldo Trancoso , Guimarães Renato Fontes
Published: February 2023

Wind energy is one of Brazil’s most promising energy sources, and the rapid growth of wind plants has increased the need for accurate and efficient inspection methods. The current onsite visits, which are laborious and costly, have become unsustainable due to the sheer scale of wind plants across the country. This study proposes a novel data-centric approach integrating semantic segmentation and GIS to obtain instance-level predictions of wind plants by using free orbital satellite images. Additionally, we introduce a new annotation pattern, which includes wind turbines and their shadows, leading to a larger object size. The elaboration of data collection used the panchromatic band of the China–Brazil Earth Resources Satellite (CBERS) 4A, with a 2-m spatial resolution, comprising 21 CBERS 4A scenes and more than 5000 wind plants annotated manually. This database has 5021 patches, each with 128 × 128 spatial dimensions. The deep learning model comparison involved evaluating six architectures and three backbones, totaling 15 models. The sliding windows approach allowed us to classify large areas, considering different pass values to obtain a balance between performance and computational time. The main results from this study include: (1) the LinkNet architecture with the Efficient-Net-B7 backbone was the best model, achieving an intersection over union score of 71%; (2) the use of smaller stride values improves the recognition process of large areas but increases computational power, and (3) the conversion of raster to polygon in GIS platforms leads to highly accurate instance-level predictions. This entire pipeline can be easily applied for mapping wind plants in Brazil and be expanded to other regions worldwide. With this approach, we aim to provide a cost-effective and efficient solution for inspecting and monitoring wind plants, contributing to the sustainability of the wind energy sector in Brazil and beyond.
Quantifying the Effects of Snow on the Beginning of Vegetation Growth in the Mongolian Plateau
Authors: Zhang Xiang , Sa Chula , Hai Quansheng , Meng Fanhao , Luo Min , Gao Hongdou , Zhang Haochen , Yin Chaohua , Zhang Yuhui , Sun Hui
Published: February 2023

Snow is one of the important water sources for vegetation growth in the Mongolian Plateau, and temporal and spatial changes to it have a profound impact on terrestrial vegetation phenology. In recent years, due to global climate change, the snow associated with the different vegetation types of the Mongolian Plateau has changed substantially, and the mechanism of the resulting change in the vegetation growth date needs to be studied. To address this issue, we used the modified Carnegie Ames Stanford Approach (CASA) model was to estimate the start of growing season net primary productivity (SOSNPP) for different types of vegetation over the Mongolian Plateau from 2001 to 2019. An extensive study of the spatial changes in the SOSNPP and the responses reflected by the winter snow cover fraction (SCFWinter), spring snow melting date (SMDSpring), and SOSNPP to influencing factors is of great significance for ecosystem maintenance. We observed: (1) Different vegetation types exhibited similar changes; SCFWinter underwent a significant decrease of −0.2%, and SMDSpring followed a slow downward trend of −0.59 day of the year (DOY)/year for the whole study area. (2) In the Mongolia Plateau, SOSNPP showed a trend of significant decrease of −0.53 DOY/year. (3) The local hydrothermal condition coupling relationship effect on different vegetation types. Spring temperature (TEMSpring) has a direct effect on vegetation SOSNPP, with a path coefficient of −0.09 in the Mongolian Plateau. SCFWinter and SMDSpring were shown through a path analysis to employ different effects on vegetation SOSNPP. SMDSpring has a direct effect on vegetation SOSNPP, with a path coefficient of 0.53. (4) The SMDSpring and PRESpring factors have a significant impact on vegetation SOSNPP, and they account for 21.11% and 21.26% of the whole study area SOSNPP, respectively. This study is expected to promote the examination of the snow phonological parameters of different related vegetation types and theoretical research on SOSNPP.
Characteristics of Raindrop Size Distributions in the Southwest Mountain Areas of China According to Seasonal Variation and Rain Types
Authors: Wu Haopeng , Niu Shengjie , Zhou Yue , Sun Jing , Lv Jingjing , He Yixiao
Published: February 2023

The precipitation and raindrop size distribution (RSD) characteristics of the four seasons and different rain types were studied using a PARSIVEL2 raindrop disdrometer set in the southwest mountain areas of China from 2019 to 2021. The seasonal precipitation in the southwest mountain areas was mainly stratiform rain. The peaks of the RSD were about 1–2 orders of magnitude higher than those in the plains. The convective rain in spring and autumn was very close to the ocean-like convective mass. The local shape–slope (μ–Λ), radar reflectivity–rain rate (Z–R), and kinetic energy–rain rate (KE–R) relationships were further derived, and the diversity of these relationships was mainly due to the variability of the RSDs. In addition, the differences in the RSD characteristics between the top and the foot of the mountain during a typical precipitation process in the summer of 2020 were further compared. It was found that the number density of the small particles at the top of the mountain was higher than that at the foot of the mountain due to the broken large raindrops caused by the high wind speed, while the high evaporation rate, strong convective available potential energy (CPAE), and water vapor content at the foot of the mountain could strengthen the RSD, making the number density of the large raindrops at the foot of the mountain higher than that at the top.
Hybrid Recommendation Network Model with a Synthesis of Social Matrix Factorization and Link Probability Functions
Authors: Kumar Balraj , Sharma Neeraj , Sharma Bhisham , Herencsar Norbert , Srivastava Gautam
Published: February 2023

Recommender systems are becoming an integral part of routine life, as they are extensively used in daily decision-making processes such as online shopping for products or services, job references, matchmaking for marriage purposes, and many others. However, these recommender systems are lacking in producing quality recommendations owing to sparsity issues. Keeping this in mind, the present study introduces a hybrid recommendation model for recommending music artists to users which is hierarchical Bayesian in nature, known as Relational Collaborative Topic Regression with Social Matrix Factorization (RCTR–SMF). This model makes use of a lot of auxiliary domain knowledge and provides seamless integration of Social Matrix Factorization and Link Probability Functions into Collaborative Topic Regression-based recommender systems to attain better prediction accuracy. Here, the main emphasis is on examining the effectiveness of unified information related to social networking and an item-relational network structure in addition to item content and user-item interactions to make predictions for user ratings. RCTR–SMF addresses the sparsity problem by utilizing additional domain knowledge, and it can address the cold-start problem in the case that there is hardly any rating information available. Furthermore, this article exhibits the proposed model performance on a large real-world social media dataset. The proposed model provides a recall of 57% and demonstrates its superiority over other state-of-the-art recommendation algorithms.
X-ray Single Exposure Imaging and Image Processing of Objects with High Absorption Ratio
Authors: Liu Yanxiu , Li Kaitai , Ding Dan , Li Ye , Zhao Peng
Published: February 2023

The dynamic range of an X-ray digital imaging system is very important when detecting objects with a high absorption ratio. In this paper, a ray source filter is used to filter the low-energy ray components which have no penetrating power to the high absorptivity object to reduce the X-ray integral intensity. This enables the effective imaging of the high absorptivity objects and avoids the image saturation of low absorptivity objects, thus achieving single exposure imaging of high absorption ratio objects. However, this method will reduce the image contrast and weaken the image structure information. Therefore, this paper proposes a contrast enhancement method for X-ray images based on Retinex. Firstly, based on Retinex theory, the multi-scale residual decomposition network decomposes the image into an illumination component and a reflection component. Then, the contrast of the illumination component is enhanced through the U-Net model with the global–local attention mechanism, and the reflection component is enhanced in detail using the anisotropic diffused residual dense network. Finally, the enhanced illumination component and the reflected component are fused. The results show that the proposed method can effectively enhance the contrast in X-ray single exposure images of the high absorption ratio objects, and can fully display the structure information of images on devices with low dynamic range.
3D Scanner-Based Identification of Welding Defects—Clustering the Results of Point Cloud Alignment
Authors: Hegedűs-Kuti János , Szőlősi József , Varga Dániel , Abonyi János , Andó Mátyás , Ruppert Tamás
Published: February 2023

This paper describes a framework for detecting welding errors using 3D scanner data. The proposed approach employs density-based clustering to compare point clouds and identify deviations. The discovered clusters are then classified according to standard welding fault classes. Six welding deviations defined in the ISO 5817:2014 standard were evaluated. All defects were represented through CAD models, and the method was able to detect five of these deviations. The results demonstrate that the errors can be effectively identified and grouped according to the location of the different points in the error clusters. However, the method cannot separate crack-related defects as a distinct cluster.
Photocatalytic Organic Contaminant Degradation of Green Synthesized ZrO2 NPs and Their Antibacterial Activities
Authors: Chelliah Parvathiraja , Wabaidur Saikh Mohammad , Sharma Hari Prapan , Majdi Hasan Sh. , Smait Drai Ahmed , Najm Mohammed Ayyed , Iqbal Amjad , Lai Wen-Cheng
Published: February 2023

The green synthesis of metal oxide nanoparticles is an efficient, simple, and chemical-free method of producing nanoparticles. The present work reports the synthesis of Murraya koenigii-mediated ZrO2 nanoparticles (ZrO2 NPs) and their applications as a photocatalyst and antibacterial agent. Capping and stabilization of metal oxide nanoparticles were achieved by using Murraya koenigii leaf extract. The optical, structural, and morphological valance of the ZrO2 NPs were characterized using UV-DRS, FTIR, XRD, and FESEM with EDX, TEM, and XPS. An XRD analysis determined that ZrO2 NPs have a monoclinic structure and a crystallite size of 24 nm. TEM and FESEM morphological images confirm the spherical nature of ZrO2 NPs, and their distributions on surfaces show lower agglomerations. ZrO2 NPs showed high optical absorbance in the UV region and a wide bandgap indicating surface oxygen vacancies and charge carriers. The presence of Zr and O elements and their O=Zr=O bonds was categorized using EDX and FTIR spectroscopy. The plant molecules’ interface, bonding, binding energy, and their existence on the surface of ZrO2 NPs were established from XPS analysis. The photocatalytic degradation of methylene blue using ZrO2 NPs was examined under visible light irradiation. The 94% degradation of toxic MB dye was achieved within 20 min. The antibacterial inhibition of ZrO2 NPs was tested against S. aureus and E. coli pathogens. Applications of bio-synthesized ZrO2 NPs including organic substance removal, pathogenic inhibitor development, catalysis, optical, and biomedical development were explored.
Intimate Partner Violence in Vulnerable Contexts: A Case Study
Authors: Mañas Carmen , Martínez María A. , Burgueño Francisca
Published: February 2023

A case study of domestic abuse is presented from the perspective of the socio-structural basis of gender violence. The research analyzes gender violence based on the accounts of a group of 30 women who have suffered abuse and have filed reports at the Judicial Unit for victims of domestic violence in the city of Cuenca (Ecuador). Survivors agreed to express their voices and experiences voluntarily and in a natural context. The results demonstrate, on one hand, the enormous weight of social pressure and stereotypes, with the resulting fear, guilt, and sense of helplessness. On the other hand, the guarantees of safety and assistance from the institutions that should protect them have not always been within reach, nor have they been sufficient.
The Distribution of Unpaid Domestic Work in Hungarian Stay-at-Home Father—Working-Mother Families
Authors: Drjenovszky Zsófia , Sztáray Kézdy Éva
Published: February 2023

The purpose of this paper is to analyze the Hungarian stay-at-home father—working-mother families from a point of view of how the distribution of unpaid domestic work develops during the period when the father stays at home with his child(ren). It answers the question of what principles and implemented practices govern the division of household labour in these families. According to the perception of the traditional parental role, unpaid domestic work, such as family duties, routine housework, and care work are the responsibility of mothers, while fathers are responsible for the financial stability of the family. In addition to the once prevailing breadwinner father role, nowadays the egalitarian model is becoming increasingly prominent. In parallel, a new father type appears according to which a good father wants to be more involved in the everyday life of the child. An extreme case of this type of involvement is when the father stays at home with his child(ren) and becomes the primary caregiver, and the mother assumes the role of breadwinner. The ratio of such families is growing around the world, but we still know relatively little about them. Our gap-filling, qualitative research is based on semi-structured in-depth interviews with 31 Hungarian stay-at-home fathers supplemented by a short questionnaire with their partners. The data were analyzed by thematic analysis method. As a main conclusion we could identify two clearly distinguishable groups among the examined couples: families with a rather traditional approach, and families having more egalitarian values concerning gender roles. However, the findings suggest that all of these families can be characterised by egalitarian sharing practices of duties, and at the same time by undoing gender. The distribution of routine housework and care work is based on a time availability perspective, which does not specify any masculine or feminine family duty or work.
A Machine Learning Approach to Predicting Academic Performance in Pennsylvania’s Schools
Authors: Chen Shan , Ding Yuanzhao
Published: February 2023

Academic performance prediction is an indispensable task for policymakers. Academic performance is frequently examined using classical statistical software, which can be used to detect logical connections between socioeconomic status and academic performance. These connections, whose accuracy depends on researchers’ experience, determine prediction accuracy. To eliminate the effects of logical relationships on such accuracy, this research used ‘black box’ machine learning models extended with education and socioeconomic data on Pennsylvania to predict academic performance in the state. The decision tree, random forest, logistic regression, support vector machine, and neural network achieved testing accuracies of 48%, 54%, 50%, 51%, and 60%, respectively. The neural network model can be used by policymakers to forecast academic performance, which in turn can aid in the formulation of various policies, such as those regarding funding and teacher selection. Finally, this study demonstrated the feasibility of machine learning as an auxiliary educational decision-making tool for use in the future.