bluuebunny/crossref_metadata_2025 huggingface dataset:
The Structures and Compositions Design of the Hollow Micro–Nano-Structured Metal Oxides for Environmental Catalysis
Authors: Xu Jingxin , Bian Yufang , Tian Wenxin , Pan Chao , Wu Cai-e , Xu Leilei , Wu Mei , Chen Mindong
Published: July 2024

In recent decades, with the rapid development of the inorganic synthesis and the increasing discharge of pollutants in the process of industrialization, hollow-structured metal oxides (HSMOs) have taken on a striking role in the field of environmental catalysis. This is all due to their unique structural characteristics compared to solid nanoparticles, such as high loading capacity, superior pore permeability, high specific surface area, abundant inner void space, and low density. Although the HSMOs with different morphologies have been reviewed and prospected in the aspect of synthesis strategies and potential applications, there has been no systematic review focusing on the structures and compositions design of HSMOs in the field of environmental catalysis so far. Therefore, this review will mainly focus on the component dependence and controllable structure of HSMOs in the catalytic elimination of different environmental pollutants, including the automobile and stationary source emissions, volatile organic compounds, greenhouse gases, ozone-depleting substances, and other potential pollutants. Moreover, we comprehensively reviewed the applications of the catalysts with hollow structure that are mainly composed of metal oxides such as CeO2, MnOx, CuOx, Co3O4, ZrO2, ZnO, Al3O4, In2O3, NiO, and Fe3O4 in automobile and stationary source emission control, volatile organic compounds emission control, and the conversion of greenhouse gases and ozone-depleting substances. The structure–activity relationship is also briefly discussed. Finally, further challenges and development trends of HSMO catalysts in environmental catalysis are also prospected.
Flexible and Disposable Hafnium Nitride Extended Gates Fabricated by Low-Temperature High-Power Impulse Magnetron Sputtering
Authors: Yang Chia-Ming , Wei Chao-Hui , Chang Jia-Yuan , Lai Chao-Sung
Published: July 2024

To obtain a high-performance extended gate field-effect transistor for pH detection, hafnium nitride (HfN) was first fabricated on an indium tin oxide on polyethylene terephthalate (ITO/PET) substrate using a high-power impulse magnetron sputter system (HiPIMS) in this study. It can be easily applied in biomedical diagnostic and environmental monitoring applications with the advantages of flexible, disposable, cost-effective, and reliable components. Various duty cycle conditions in HiPIMSs were designed to investigate the corresponding sensing performance and material properties including surface morphology and composition. As the duty cycle increased, the grain size of HfN increased. Additionally, X-ray photoelectron spectroscopy (XPS) analysis illustrated the presence of HfOxNy on the deposited HfN surface. Both behaviors could result in a better pH sensing performance based on the theory of the site-binding model. Subsequently, HfN with a 15% duty cycle exhibited excellent pH sensitivity and linearity, with values of 59.3 mV/pH and 99.8%, respectively; its hysteresis width and drift coefficient were −1 mV and 0.5 mV/h, respectively. Furthermore, this pH-sensing performance remained stable even after 2000 repeated bending cycles. These results indicate the potential and feasibility of this HiPIMS-deposited HfN for future wearable chemical applications.
Langmuir–Blodgett Transfer of Nanocrystal Monolayers: Layer Compaction, Layer Compression, and Lattice Stretching of the Transferred Layer
Authors: Patel Reken N. , Goodfellow Brian , Heitsch Andrew T. , Smilgies Detlef-M. , Korgel Brian A.
Published: July 2024

Grazing incidence small angle X-ray scattering (GISAXS) was used to study the structure and interparticle spacing of monolayers of organic ligand-stabilized iron oxide nanocrystals floating at the air–water interface on a Langmuir trough, and after transfer to a solid support via the Langmuir–Blodgett technique. GISAXS measurements of the nanocrystal arrangement at the air–water interface showed that lateral compression decreased the interparticle spacing of continuous films. GISAXS also revealed that Langmuir–Blodgett transfer of the nanocrystal layers to a silicon substrate led to a stretching of the film, with a significant increase in interparticle spacing.
Exploring the Microstructural Effect of FeCo Alloy on Carbon Microsphere Deposition and Enhanced Electromagnetic Wave Absorption
Authors: Jia Xiaoshu , Zhang Heng , Liu Fang , Yi Qiaojun , Li Chaolong , Wang Xiao , Piao Mingxing
Published: July 2024

The rational design of magnetic carbon composites, encompassing both their composition and microstructure, holds significant potential for achieving exceptional electromagnetic wave-absorbing materials (EAMs). In this study, FeCo@CM composites were efficiently fabricated through an advanced microwave plasma-assisted reduction chemical vapor deposition (MPARCVD) technique, offering high efficiency, low cost, and energy-saving benefits. By depositing graphitized carbon microspheres, the dielectric properties were significantly enhanced, resulting in improved electromagnetic wave absorption performances through optimized impedance matching and a synergistic effect with magnetic loss. A systematic investigation revealed that the laminar-stacked structure of FeCo exhibited superior properties compared to its spherical counterpart, supplying a higher number of exposed edges and enhanced catalytic activity, which facilitated the deposition of uniform and low-defect graphitized carbon microspheres. Consequently, the dielectric loss performance of the FeCo@CM composites was dramatically improved due to increased electrical conductivity and the formation of abundant heterogeneous interfaces. At a 40 wt% filling amount and a frequency of 7.84 GHz, the FeCo@CM composites achieved a minimum reflection loss value of −58.2 dB with an effective absorption bandwidth (fE) of 5.13 GHz. This study presents an effective strategy for developing high-performance EAMs.
Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing
Authors: Zhu Li , Lin Junchen , Zhu Yixin , Wu Jie , Wan Xiang , Sun Huabin , Yu Zhihao , Xu Yong , Tan Cheeleong
Published: July 2024

Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device’s electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at −0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence.
The Role of Gut Microbiome in Sleep Quality and Health: Dietary Strategies for Microbiota Support
Authors: Sejbuk Monika , Siebieszuk Adam , Witkowska Anna Maria
Published: July 2024

Dietary components, including dietary fiber, unsaturated fatty acids, and polyphenols, along with meal timing and spacing, significantly affect the microbiota’s capacity to produce various metabolites essential for quality sleep and overall health. This review explores the role of gut microbiota in regulating sleep through various metabolites such as short-chain fatty acids, tryptophan, serotonin, melatonin, and gamma-aminobutyric acid. A balanced diet rich in plant-based foods enhances the production of these sleep-regulating metabolites, potentially benefiting overall health. This review aims to investigate how dietary habits affect gut microbiota composition, the metabolites it produces, and the subsequent impact on sleep quality and related health conditions.
Chemical Characterization and Enantioselective Analysis of Tagetes filifolia Lag. Essential Oil and Crude Extract
Authors: Morocho Vladimir , Chamba Anghela , Pozo Paulo , Montalván Mayra , Suárez Alírica I.
Published: July 2024

The essential oil (EO) of Tagetes filifolia Lag. was obtained from dried plant material through Clevenger-type steam distillation and analyzed using gas chromatography–mass spectrometry (GC/MS), a gas chromatography–flame ionization detector (GC/FID) and enantioselective gas chromatography. The results showed 50 compounds (93.33%) with a predominance of oxygenated monoterpenes. The main components were trans-anethole (55.57 ± 9.83%), tridecene <1-> (8.66 ± 0.01), methyl chavicol (5.81 ± 0.85%) and Neophytadiene (3.45 ± 0.88) Enantioselective analysis revealed linalool and  ionone as enantiomers. The identification of secondary metabolites from the ethyl acetate extract obtained by maceration was performed by GC-MS, NMR and by a literature comparison, determining the presence of mostly trans-anethole and a mixture of two triterpenes, fernenol and lupeol.
Microbial Organic Fertilizer Combined with Magnetically Treated Water Drip Irrigation Promoted the Stability of Desert Soil Aggregates and Improved the Yield and Quality of Jujubes
Authors: Tao Wanghai , Shao Fanfan , Yan Haokui , Wang Quanjiu
Published: July 2024

In the southern Xinjiang region of China, developing efficient irrigation and fertilization strategies to enhance resource utilization and prevent desertification is of critical importance. This study focuses on jujubes in Xinjiang, China, and involves a three-year field experiment aimed at exploring the optimal application strategy of magnetically treated water combined with microbial organic fertilizer to provide scientific support for high-quality jujube production. The experiment included a control group (using only fresh water, denoted as CK) and combinations of magnetically treated water drip irrigation with varying amounts of microbial organic fertilizer: in 2021, treatments included M0 (only irrigating with magnetically treated water), M6 (0.6 t/ha), M12 (1.2 t/ha), M18 (1.8 t/ha), and M24 (2.4 t/ha); in 2022 and 2023, treatments included M0, M6 (0.6 t/ha), M12 (1.2 t/ha), M24 (2.4 t/ha), and M48 (4.8 t/ha). This study investigated the effects of magnetically treated water drip irrigation combined with microbial organic fertilizer on soil physical properties, hydraulic parameters, enzyme activity, aggregate stability, and jujube yield and quality. The application of microbial organic fertilizer significantly reduced the soil bulk density by 3.07% to 11.04% and increased soil porosity by 1.97% to 14.75%. Soil saturated hydraulic conductivity gradually decreased with the increasing amount of microbial organic fertilizer, with a reduction range of 5.95% to 13.69%, while the water-holding capacity significantly improved (from 0.217 cm3/cm3 to 0.264 cm3/cm3). Additionally, microbial organic fertilizer significantly enhanced the activities of urease, catalase, and sucrase in the soil and significantly increased the proportion of large soil aggregates. Jujube yield increased by 3.66% to 21.38%, and the quality significantly improved, as evidenced by the increase in soluble sugar and flavonoid content. The Gauss model calculation results recommended 3.09 t·hm2 as the optimal amount of microbial organic fertilizer for comprehensively improving jujube yield and quality. These findings indicate that magnetically treated water drip irrigation combined with high amounts of microbial organic fertilizer significantly improved soil physical properties, hydraulic parameters, enzyme activity, aggregate stability, and jujube yield and quality, providing scientific evidence for desert soil improvement and agricultural production.
Control Efficacy of the Bt Maize Event DBN3601T Expressing Cry1Ab and Vip3Aa Proteins against Beet Armyworm, Spodoptera exigua (Hübner), in China
Authors: Song Cheng , Yang Xianming , He Limei , Wang Wenhui , Wu Kongming
Published: July 2024

The beet armyworm, Spodoptera exigua (Hübner), is a major pest of maize, cotton, soybean, and many other crops globally. Despite the widespread deployment of Bt transgenic maize for pest control worldwide, the efficacy of Bt lepidopteran-resistant transgenic maize in managing S. exigua remains rarely studied. In this study, we quantified the expression level of pyramided Cry1Ab and Vip3Aa toxins in Bt maize (event DBN3601T) and evaluated their control efficiency against S. exigua under both laboratory and field conditions. The enzyme-linked immunosorbent assay (ELISA) results showed that the expression levels of Cry1Ab and Vip3Aa proteins in DBN3601T maize tissues followed a decreasing order as follows: V5-leaf > V8-leaf > VT-tassel > R2-kernel > R1-silk. Diet-overlay assay results showed that the LC50 values of Cry1Ab and Vip3Aa proteins against S. exigua larvae were 11.66 ng/cm2 and 27.74 ng/cm2, respectively, with corresponding GIC50 values at 1.59 ng/cm2 and 7.93 ng/cm2. Bioassay using various tissues of the DBN3601T maize indicated that after 7 days of infestation, mortality rates of neonates and third-instar larvae ranged from 86% to 100% and 58% to 100%, respectively. Mortality was highest on V5 and V8 leaves, followed by R2-kernel, VT-tassel, and R1-silk. Field trials demonstrated that DBN3601T maize exhibited significantly lower larval density, damage rate, and leaf damage score compared to non-Bt maize. Field cage trial showed that the control efficacy of DBN3601T maize at the vegetative stage could reach 98%. These findings provide a theoretical basis for utilizing Bt transgenic maize to enhance the sustainable management of S. exigua in Asia.
Biochar Blended with Alkaline Mineral Can Better Inhibit Lead and Cadmium Uptake and Promote the Growth of Vegetables
Authors: Huang Lianxi , Chen Weisheng , Wei Lan , Li Xiang , Huang Yufen , Huang Qing , Liu Chuanping , Liu Zhongzhen
Published: July 2024

Three successive vegetable pot experiments were conducted to assess the effects on the long-term immobilization of heavy metals in soil and crop yield improvement after the addition of peanut shell biochar and an alkaline mineral to an acidic soil contaminated with lead and cadmium. Compared with the CK treatment, the change rates of biomass in the edible parts of the three types of vegetables treated with B0.3, B1, B3, B9, R0.2 and B1R0.2 were −15.43%~123.30%, 35.10%~269.09%, 40.77%~929.31%, −26.08%~711.99%, 44.14%~1067.12% and 53.09%~1139.06%, respectively. The cadmium contents in the edible parts of the three vegetables treated with these six additives reduced by 2.08%~13.21%, 9.56%~24.78%, 9.96%~35.61%, 41.96%~78.42%, −4.19%~57.07% and 12.43%~65.92%, respectively, while the lead contents in the edible parts reduced by −15.70%~59.47%, 6.55%~70.75%, 3.40%~80.10%, 55.26%~89.79%, 11.05%~70.15% and 50.35%~79.25%, respectively. Due to the increases in soil pH, soil cation-exchange capacity and soil organic carbon content, the accumulation of Cd and Pb in the vegetables was most notably reduced with a high dosage of 9% peanut shell biochar alone, followed by the addition of a low dosage of 1% peanut shell biochar blended with 0.2% alkaline mineral. Therefore, the addition of a low dosage of 1% peanut shell biochar blended with 0.2% alkaline mineral was the best additive in increasing the vegetable biomass, whereas the addition of 9% peanut shell biochar alone was the worst. Evidently, the addition of 0.2% alkaline mineral can significantly reduce the amount of peanut shell needed for passivating heavy metals in soil, while it also achieves the effect of increasing the vegetable yield.
Morphological and Anatomical Differentiation of Potamogeton gramineus in Relation to the Presence of Invasive Species Elodea nuttallii: A Case Study from Vlasina Lake, Serbia
Authors: Nikolić Danijela , Jenačković Gocić Dragana , Raca Irena , Đorđević Miodrag , Savić Ana , Jušković Marina
Published: July 2024

Elodea nuttallii represents non-native and highly invasive species in Europe that significantly influence freshwater plant communities by decreasing the diversity of native species. This study aimed to determine whether the morphological and anatomical features of Potamogeton gramineus, a native species in Vlasina Lake, differ between sites where it coexists with E. nuttallii and those where E. nuttallii is not present. Environmental variables such as water depth, temperature, pH, conductivity, saturation, and O2 concentration were included in the analysis. Analyses were conducted on 32 morphological and anatomical features of P. gramineus collected from six sites within Vlasina Lake, comprising three sites where E. nuttallii was present and three sites where it was absent. The datasets containing morphometric and environmental variables underwent analysis using standard univariate techniques (Descriptive, ANOVA), Tukey’s Honest Significant Difference (HSD) test, Student’s t-test, and the Mann–Whitney U test, as well as multivariate statistical methods such as Canonical Discriminant Analysis (CDA). The results show the presence of morphological differentiation among P. gramineus individuals across the analyzed sites. These findings suggest that morphological and anatomical features, such as epidermis, mesophyll, palisade, and aerenchyma tissue thickness in floating leaves, number, length, width, and the surface area of stomata, as well as the width of submersed leaves and stem aerenchyma tissue thickness, effectively differentiate individuals that coexist with E. nuttallii and individuals that growth without its presence. Moreover, they indicate that P. gramineus exhibits a notable ability to modify its morphological traits in response to invasion.
Technical Principles and Clinical Applications of Electrical Impedance Tomography in Pulmonary Monitoring
Authors: Cui Ziqiang , Liu Xinyan , Qu Hantao , Wang Huaxiang
Published: July 2024

Pulmonary monitoring is crucial for the diagnosis and management of respiratory conditions, especially after the epidemic of coronavirus disease. Electrical impedance tomography (EIT) is an alternative non-radioactive tomographic imaging tool for monitoring pulmonary conditions. This review proffers the current EIT technical principles and applications on pulmonary monitoring, which gives a comprehensive summary of EIT applied on the chest and encourages its extensive usage to clinical physicians. The technical principles involving EIT instrumentations and image reconstruction algorithms are explained in detail, and the conditional selection is recommended based on clinical application scenarios. For applications, specifically, the monitoring of ventilation/perfusion (V/Q) is one of the most developed EIT applications. The matching correlation of V/Q could indicate many pulmonary diseases, e.g., the acute respiratory distress syndrome, pneumothorax, pulmonary embolism, and pulmonary edema. Several recently emerging applications like lung transplantation are also briefly introduced as supplementary applications that have potential and are about to be developed in the future. In addition, the limitations, disadvantages, and developing trends of EIT are discussed, indicating that EIT will still be in a long-term development stage before large-scale clinical applications.
CORAL—Catamaran for Underwater Exploration: Development of a Multipurpose Unmanned Surface Vessel for Environmental Studies
Authors: Cocchi Luca , Muccini Filippo , Locritani Marina , Spinelli Leonardo , Cocco Michele
Published: July 2024

CORAL (Catamaran fOr UndeRwAter expLoration) is a compact, unmanned catamaran-type vehicle designed and developed to assist the scientific community in exploring marine areas such as inshore regions that are not easily accessible by traditional vessels. This vehicle can operate in different modalities: completely autonomous, semi-autonomous, or remotely assisted by the operator, thus accommodating various investigative scenarios. CORAL is characterized by compact dimensions, a very low draft and a total electric propulsion system. The vehicle is equipped with a single echo-sounder, a 450 kHz Side Scan Sonar, an Inertial Navigation System assisted by a GPS receiver and a pair of high-definition cameras for recording both above and below the water surface. Here, we present results from two investigations: the first conducted in the tourist harbour in Pozzuoli Gulf and the second in the Riomaggiore-Manarola marine area within the Cinque Terre territory (Italy). Both surveys yielded promising results regarding the potentiality of CORAL to collect fine-scale submarine elements such as anthropic objects, sedimentary features, and seagrass meadow spots. These capabilities characterize the CORAL system as a highly efficient investigation tool for depicting shallow bedforms, reconstructing coastal dynamics and erosion processes and monitoring the evolution of biological habitats.
Automated Method for Intracranial Aneurysm Classification Using Deep Learning
Authors: Hlavata Roberta , Kamencay Patrik , Radilova Martina , Sykora Peter , Hudec Robert
Published: July 2024

Intracranial aneurysm (IA) is now a common term closely associated with subarachnoid hemorrhage. IA is the bulging of a blood vessel caused by a weakening of its wall. This bulge can rupture and, in most cases, cause internal bleeding. In most cases, internal bleeding leads to death or other fatal consequences. Therefore, the development of an automated system for detecting IA is needed to help physicians make more accurate diagnoses. For this reason, we have focused on this problem. In this paper, we propose a 2D Convolutional Neural Network (CNN) based on a network commonly used for data classification in medicine. In addition to our proposed network, we also tested ResNet 50, ResNet 101 and ResNet 152 on a publicly available dataset. In this case, ResNet 152 achieved better results than our proposed network, but our network was significantly smaller and the classifications took significantly less time. Our proposed network achieved an overall accuracy of 98%. This result was achieved on a dataset consisting of 611 images. In addition to the mentioned networks, we also experimented with the VGG network, but it was not suitable for this type of data and achieved only 20%. We compare the results in this work with neural networks that have been verified by the scientific community, and we believe that the results obtained by us can help in the creation of an automated system for the detection of IA.
Measurement Error Analysis of Seawater Refractive Index: A Measurement Sensor Based on a Position-Sensitive Detector
Authors: Zhou Guanlong , Li Liyan , Zhou Yan , Chen Xinyu
Published: July 2024

The seawater refractive index is an essential parameter in ocean observation, making its high-precision measurement necessary. This can be effectively achieved using a position-sensitive detector-based measurement system. However, in the actual measurement process, the impact of the jitter signal measurement error on the results cannot be ignored. In this study, we theoretically analysed the causes of long jitter signals during seawater refractive index measurements and quantified the influencing factors. Through this analysis, it can be seen that the angle between the two windows in the seawater refractive index measurement area caused a large error in the results, which could be effectively reduced by controlling the angle to within 2.06°. At the same time, the factors affecting the position-sensitive detector’s measurement accuracy were analysed, with changes to the background light, the photosensitive surface’s size, and the working environment’s temperature leading to its reduction. To address the above factors, we first added a 0.9 nm bandwidth, narrow-band filter in front of the detector’s photosensitive surface during system construction to filter out any light other than that from the signal light source. To ensure the seawater refractive index’s measuring range, a position-sensitive detector with a photosensitive surface size of 4 mm × 4 mm was selected; whereas, to reduce the working environment’s temperature variation, we partitioned the measurement system. To validate the testing error range of the optimised test system, standard seawater samples were measured under the same conditions, showing a reduction in the measurement system’s jitter signal from 0.0022 mm to 0.0011 mm, before and after optimisation, respectively, as well as a reduction in the refractive index’s deviation. The experimental results show that the refractive index of seawater was effectively reduced by adjusting the measurement system’s optical path and structure.
Electrodermal Activity (EDA) Morphologies and Prediction of Engagement with Simple Moving Average Crossover: A Mixed-Method Study
Authors: Nandipati Kishore Kumar , Pal Sonika , Mitra Ritayan
Published: July 2024

Electrodermal Activity (EDA), which primarily indicates arousal through sympathetic nervous system activity, serves as a tool to measure constructs like engagement, cognitive load, performance, and stress. Despite its potential, empirical studies have often yielded mixed results and found it of limited use. To better understand EDA, we conducted a mixed-methods study in which quantitative EDA profiles and survey data were investigated using qualitative interviews. This study furnishes an EDA dataset measuring the engagement levels of seven participants who watched three videos for 4–10 min. The subsequent interviews revealed five EDA morphologies with varying short-term signatures and long-term trends. We used this dataset to demonstrate the moving average crossover, a novel metric for EDA analysis, in predicting engagement–disengagement dynamics in such data. Our contributions include the creation of the detailed dataset, comprising EDA profiles annotated with qualitative data, the identification of five distinct EDA morphologies, and the proposition of the moving average crossover as an indicator of the beginning of engagement or disengagement in an individual.
FMCW Radar Human Action Recognition Based on Asymmetric Convolutional Residual Blocks
Authors: Zhang Yuan , Tang Haotian , Wu Ye , Wang Bolun , Yang Dalin
Published: July 2024

Human action recognition based on optical and infrared video data is greatly affected by the environment, and feature extraction in traditional machine learning classification methods is complex; therefore, this paper proposes a method for human action recognition using Frequency Modulated Continuous Wave (FMCW) radar based on an asymmetric convolutional residual network. First, the radar echo data are analyzed and processed to extract the micro-Doppler time domain spectrograms of different actions. Second, a strategy combining asymmetric convolution and the Mish activation function is adopted in the residual block of the ResNet18 network to address the limitations of linear and nonlinear transformations in the residual block for micro-Doppler spectrum recognition. This approach aims to enhance the network’s ability to learn features effectively. Finally, the Improved Convolutional Block Attention Module (ICBAM) is integrated into the residual block to enhance the model’s attention and comprehension of input data. The experimental results demonstrate that the proposed method achieves a high accuracy of 98.28% in action recognition and classification within complex scenes, surpassing classic deep learning approaches. Moreover, this method significantly improves the recognition accuracy for actions with similar micro-Doppler features and demonstrates excellent anti-noise recognition performance.
An Underwater Image Denoising Method Based on High-Frequency Abrupt Signal Separation and Hybrid Attention Mechanism
Authors: Huo Chunling , Zhang Da , Yang Huanyu
Published: July 2024

During underwater image processing, image quality is affected by the absorption and scattering of light in water, thus causing problems such as blurring and noise. As a result, poor image quality is unavoidable. To achieve overall satisfying research results, underwater image denoising is vital. This paper presents an underwater image denoising method, named HHDNet, designed to address noise issues arising from environmental interference and technical limitations during underwater robot photography. The method leverages a dual-branch network architecture to handle both high and low frequencies, incorporating a hybrid attention module specifically designed for the removal of high-frequency abrupt noise in underwater images. Input images are decomposed into high-frequency and low-frequency components using a Gaussian kernel. For the high-frequency part, a Global Context Extractor (GCE) module with a hybrid attention mechanism focuses on removing high-frequency abrupt signals by capturing local details and global dependencies simultaneously. For the low-frequency part, efficient residual convolutional units are used in consideration of less noise information. Experimental results demonstrate that HHDNet effectively achieves underwater image denoising tasks, surpassing other existing methods not only in denoising effectiveness but also in maintaining computational efficiency, and thus HHDNet provides more flexibility in underwater image noise removal.
All-Fiber Flexible Electrochemical Sensor for Wearable Glucose Monitoring
Authors: Tang Zeyi , Jian Jinming , Guo Mingxin , Liu Shangjian , Ji Shourui , Li Yilong , Liu Houfang , Shao Tianqi , Gao Jian , Yang Yi , Ren Tianling
Published: July 2024

Wearable sensors, specifically microneedle sensors based on electrochemical methods, have expanded extensively with recent technological advances. Today’s wearable electrochemical sensors present specific challenges: they show significant modulus disparities with skin tissue, implying possible discomfort in vivo, especially over extended wear periods or on sensitive skin areas. The sensors, primarily based on polyethylene terephthalate (PET) or polyimide (PI) substrates, might also cause pressure or unease during insertion due to the skin’s irregular deformation. To address these constraints, we developed an innovative, wearable, all-fiber-structured electrochemical sensor. Our composite sensor incorporates polyurethane (PU) fibers prepared via electrospinning as electrode substrates to achieve excellent adaptability. Electrospun PU nanofiber films with gold layers shaped via thermal evaporation are used as base electrodes with exemplary conductivity and electrochemical catalytic attributes. To achieve glucose monitoring, gold nanofibers functionalized by gold nanoflakes (AuNFs) and glucose oxidase (GOx) serve as the working electrode, while Pt nanofibers and Ag/AgCl nanofibers serve as the counter and reference electrode. The acrylamide-sodium alginate double-network hydrogel synthesized on electrospun PU fibers serves as the adhesive and substance-transferring layer between the electrodes. The all-fiber electrochemical sensor is assembled layer-by-layer to form a robust structure. Given the stretchability of PU nanofibers coupled with a high specific surface area, the manufactured porous microneedle glucose sensor exhibits enhanced stretchability, superior sensitivity at 31.94 μA (lg(mM))−1 cm−2, a broad detection range (1–30 mM), and a significantly low detection limit (1 mM, S/N = 3), as well as satisfactory biocompatibility. Therefore, the novel electrochemical microneedle design is well-suited for wearable or even implantable continuous monitoring applications, thereby showing promising significant potential within the global arena of wearable medical technology.
Safety Evaluation for Fabricated Small Box Girder Bridges Based on Fuzzy Analytic Hierarchy Process and Monitoring Data
Authors: Yang Hongyin , Jiang Liangwei , Xu Feng , Gu Jianfeng , Ye Zhongtao , Peng Ya , Liu Zhangjun , Cheng Renhui
Published: July 2024

During the operation of fabricated small box girder bridges, which face safety issues such as structural degradation and failure, there is an urgent need to propose a safety evaluation method to cope with the possible risks. This article quantitatively evaluates the safety state of a fabricated small box girder bridge in Wuhan City based on Fuzzy Analytic Hierarchy Process (FAHP) and structural health monitoring (SHM) data. Firstly, the FAHP model is established, and stress, deformation, and temperature are selected as evaluation factors. The safety thresholds of stress and deformation are determined by combining the industry specifications and the historical statistical patterns of the massive SHM data. The temperature field of the bridge is simulated and analyzed by combining ANSYS, HYPERMESH, and TAITHREM, and the most unfavorable temperature gradient is determined as a threshold for the safety evaluation. Finally, the scores of indexes of the bridge are determined based on the measured SHM data, which in turn provides a quantitative description of the safety state. The results show that the thresholds determined by the joint industry specifications and the massive SHM data are reasonable; the temperature field simulation model established in this article is consistent with the measured results, and can accurately determine the temperature gradient of the bridge. The safety evaluation result from the FAHP model is the same as the field test results, which verifies the effectiveness and applicability of the proposed method to actual bridge projects.
Modal Identification Techniques for Concrete Dams: A Comprehensive Review and Application
Authors: Mostafaei Hasan
Published: July 2024

Throughout history, the implementation of structural health monitoring systems has played a crucial role in evaluating the responses of dams to environmental and human-induced threats. By continuously monitoring structural integrity and analyzing dynamic characteristics, these systems offer a robust alternative to traditional visual inspection methods, ensuring the long-term safety of dams. This paper delves into the intricate process of operational modal analysis applied to dams, encompassing data collection, preprocessing, and the utilization of diverse modal identification techniques across both time and frequency domains. Moreover, it explores innovative approaches aimed at overcoming challenges encountered in previous methodologies. Also, the evolution of automated modal identification techniques and their application in dams are investigated. It explores the advancements in this field and their implications for enhancing the efficiency and accuracy of modal analysis processes. Furthermore, this paper evaluates the effectiveness of damage detection methods in dams based on operational modal identification.
Caring without Tolerance: Care Literacy as an Enabler of an Inclusive Society
Authors: Costantini Hiroko , Nihei Misato , Ono Takazumi
Published: July 2024

Japanese society has been undergoing significant social changes in recent years, which has led to a greater variety of lived experiences in juxtaposition with pressures to conform from its group-oriented cultural context. Achieving inclusion in an increasingly heterogeneous society depends on how relatedness connects people, for example, in caring for others. The purpose of this study is to examine aspects of caring for others in Japanese society based on in-depth narrative interviews conducted in 2022 involving 18 informants. The fieldwork findings point to constraints on individual autonomy from relations stemming from care being intertwined with a broader relational context. Additionally, cultural conformity pressures lead to a propensity to assess social practices and, in turn, provide “excessive care”. Yet, such “excessive care”, as premised on cultural conformity, is at odds with increasingly heterogeneous choices. This leads to the emergence of intolerance, which supresses individuals’ autonomy and agency. Instead, to achieve an inclusive society, these findings point to the need for appropriate relations of understanding, tolerance and caring. This would be enabled by fostering “care literacy” across communities and stakeholders, thus supporting the transition towards a more inclusive society.
How to Reshape the Selection Boundaries between Traditional and Digital Supply Chain Finance Based on the Pledge Rate and Default Loss: Two Tripartite Game Models
Authors: Sun Xiang , Wang Yue , Huang Yinzi , Zhang Yue
Published: July 2024

The development of digital technologies such as blockchain has provided new possibilities for solving the financing difficulties of small and medium-sized enterprises (SMEs). In order to explore the mutual influence of the participants in the supply chain, this paper constructs two static tripartite game models for traditional and digital supply chain finance, including a small and medium-sized enterprise (SME), a core enterprise (CE), and a financial institution (FI). The conditions for SME, CE, and FI to participate in digital supply chain finance, and the equilibrium strategy (repayment, repayment, loan) after participating in digital supply chain finance, are figured out. It is found that compared with the traditional supply chain, the digital supply chain expands the probability range of repayment for SME and CE by the change of pledge rate and default loss and broadens the probability range of repayment for CE by the change of default loss. Further, compared with the traditional supply chain finance, the greater the pledge rate of digital supply chain finance and the smaller the default loss, the stronger the willingness of the SME and CE to participate in the digital supply chain and the lower the willingness of the FI. After the three parties participate in the digital supply chain, however, the conclusion is the opposite. The smaller the pledge rate and the greater the default loss, the stronger the repayment willingness for the SME and CE and the stronger the loan willingness of the FI. Therefore, it is suggested to find the critical values of pledge rate and default loss and raise these two variables to an appropriate range to encourage all parties to voluntarily and consistently participate in digital supply chain financing.
Navigating Strategic Balance: CEO Big Data Orientation, Environmental Investment, and Technological Innovation in Chinese Manufacturing
Authors: Wu Weiwei , Wang Xu
Published: July 2024

In the digital age, China’s economic development is transitioning from high speed to high quality. Through the application of digital technology, China’s manufacturing industry is moving toward more environmentally friendly and sustainable innovation, which makes it of great significance to study the effect of green investment and big data on innovation. Grounded in strategic management theory, this paper examines the interplay between CEO big data orientation, environmental investment, and their joint impact on technological innovation in manufacturing enterprises. Data are extracted from annual reports of listed Chinese manufacturing companies using computer-assisted text analysis methods and evaluated with negative binomial regression. The results indicate an inverted U-shaped relationship between CEO big data orientation and technological innovation. The results further explain that as CEO big data orientation increases, enterprises with higher levels of green investment will reach the peak of technological innovation performance earlier. According to China’s intelligent manufacturing in 2035, we have proposed some methods and suggestions for green investment and big data applications.
An Integrated Method for Cooperation Prediction in Complex Standard Networks
Authors: Yi Feiteng , Zhang Xuan , Zhang Jiali , Wei Yongchang
Published: July 2024

Standards play significant roles in the development of technology and economics, while the cooperation between drafters directly determines the quality of standard systems. The cooperation prediction is a significant while challenging problem for seeking new cooperation chances between drafting units due to their differences in experience and professional ability. In this study, an integrated artificial intelligence method is proposed for cooperation prediction using the link prediction method, text analysis, and network modeling. Specifically, we develop a multi-layer standard network formed by standard citation relationships and cooperation relationships between drafters. Then, a set of novel metrics is designed for predicting the cooperation between drafters considering the knowledge, experience, and professional capability. These metrics are further integrated into a neural network to improve the prediction accuracy. The priorities of our method in terms of prediction accuracy are verified with realistic data of Chinese environmental health standards. The prediction results provide strong support for the selection of drafters and further optimize the structure of standard systems.
Determination of River Ecological Flow Thresholds and Development of Early Warning Programs Based on Coupled Multiple Hydrological Methods
Authors: Zhang Xiaoyan , Yu Jiandong , Wang Liangguo , Zhang Rui
Published: July 2024

In order to safeguard the health of river ecosystems and maintain ecological balance, it is essential to rationally allocate water resources. This study utilized continuous runoff data from 1967 to 2020 at the Zhouqu Hydrological Station on the Bailong River. Five hydrological methods, tailored to the hydrological characteristics of the Zhouqu hydrological cross-section, were employed. These methods included the improved dynamic calculation method, the NGPRP method, the improved monthly frequency computation method, the improved RVA method, and the Tennant method. Ecological flow calculations were conducted to determine the ecological flow, with analysis carried out through the degree of satisfaction, economic benefits, and the nonlinear fitting of the GCAS model. We established an ecological flow threshold and early warning program for this specific hydrological cross-section. Ecological flow values calculated using different methods for each month of the year were compared. The improved RVA method and Tennant method resulted in small values ranging from 4.05 to 36.40 m3/s and 7.65 to 22.94 m3/s, respectively, with high satisfaction levels and economic benefits, but not conducive to ecologically sound development. In contrast, the dynamic calculation method, NGPRP method, and improved monthly frequency calculation method yielded larger ecological flow values in the ranges of 21.79–97.02 m3/s, 23.90–137.00 m3/s, and 28.50–126.00 m3/s, respectively, with poor fulfillment and economic benefits. Ecological flow thresholds were determined using the GCAS model, with values ranging from 16.72 to 114.58 m3/s during the abundant water period and from 5.03 to 63.63 m3/s during the dry water period. A three-level ecological warning system was proposed based on these thresholds, with the orange warning level indicating optimal sustainable development capacity for the Zhouqu Hydrological Station. This study provides valuable insights into the scientific management of water resources in the Bailong River Basin to ensure ecological security and promote sustainable development.
Exploring the Influence of the Digital Economy on Marine Pollution Mitigation: A Spatial Econometric Study of Coastal China
Authors: Xu Wangfang , Wang Qianqian , Wei Longbao
Published: July 2024

This work employs the spatial econometric model to explore the potential relation between the condition of marine ecosystems and the rapid development of the digital economy (DE), focusing on the coastal area of China. While the environmental benefits of the DE are well studied regarding the land and atmospheric pollution, its influence on marine pollution (MP) remains underexplored, and this work aims to fill in this gap. By analyzing panel data from 42 coastal cities in China using a spatial Durbin model to examine both the direct and indirect impacts of the DE on MP, the results highlight the positive role that the DE plays in reducing MP, benefitting not only the local marine environments but also those of neighboring areas through non-negligible spatial spillover effects. In addition, we find a non-linear, inverted U-shaped relationship between the DE and MP. These results are further confirmed through extensive robustness tests. This work enriches the field of environmental economics by reporting the first empirical study on the marine benefits of the DE and offers policy recommendations to optimize digital technologies for marine environmental preservation.
Research Trends and ‘Space-Sky-Ground-Underground’ Monitoring Technology Analysis of Landslide Hazard
Authors: Cheng Gang , Zhang Haoyu , Wang Ye , Shi Bin , Zhang Lei , Wu Jinghong , You Qinliang , Li Youcai , Shi Peiwei
Published: July 2024

Landslide is a typical geological disaster distributed in most countries worldwide. Due to long-term natural weathering and human engineering disturbances, the instability of landslides is prone to occur. Once monitoring and disposal methods are implemented inappropriately, they can lead to landslide hazards, seriously threatening the safety of people’s lives and property. For a long time, extensive research on landslide monitoring has been conducted from various countries, providing crucial technical support for reducing the incidence and severity of landslide hazards. However, considering the complex geological conditions of actual landslides and the direct impact of internal and external factors such as rainfall, storms, and earthquakes, the early warning accuracy of landslide hazards is still relatively low. Therefore, based on advanced research achievements, it is significant to carry out research on the current status and development trends of landslide monitoring technology. Based on the Web of Science core database, this study quantitatively analyzes the advanced research achievements in global landslide monitoring in the past decade using bibliometric analysis. A systematic analysis of landslide monitoring technology development is conducted according to each study’s publication time, keywords, and countries. On this basis, a multi-dimensional monitoring system for landslides was proposed, which utilizes the complementary advantages of multi-dimensional monitoring technology to achieve all-round, high-precision, and real-time monitoring of landslides. Finally, taking the Xinpu landslide in the Three Gorges Region of China as an example, a multi-source and multi-field-monitoring experiment was conducted. The application of landslide multi-field-monitoring technology provides an essential reference for monitoring, early warning, as well as the scientific prevention and control of landslide hazard.