bluuebunny/crossref_metadata_2025 huggingface dataset:
Association Between Dietary Diversity and Subjective Cognitive Decline in the Middle-Aged and Elderly Chinese Population: A Cross-Sectional Study
Authors: Gao Minjie , Wang Jing , Qiu Yue , Chen Yanan , Cao Qiancheng , Pan Yiru , Cao Yifei , Han Shufen , Yan Xiao , Xu Xianrong , Fang Xuexian , Lian Fuzhi
Published: October 2024

Background: This cross-sectional study aimed to examine the association between dietary diversity and risk of subjective cognitive decline (SCD), a precursor of dementia, in middle-aged and elderly Chinese populations residing in eastern China. Methods: Participants aged ≥ 45 years were recruited from a community in an eastern Chinese city after excluding potential objective cognitive impairment using the Mini-Cognitive Assessment Instrument (Mini-Cog). SCD was assessed using the Subjective Cognitive Decline Questionnaire-9 (SCD-Q9). Dietary data were collected using the Dietary Quality Questionnaire (DQQ), and the Food Group Diversity Score (FGDS) and the Consumed All Five Recommended Food Score (All-5) were calculated as indicators of dietary diversity. Odds ratios (ORs) and 95% confidence intervals (CIs) were computed to evaluate the associations of FGDS and All-5 scores with SCD after adjusting for age, sex, socioeconomic status, lifestyle factors, and health status. Results: Among the 871 participants, 358 (41.1%) were classified as having SCD. Compared with participants with the highest FGDS (≥8) and those with the highest All-5 score (5), those with the lowest FGDS (≤4) and the lowest All-5 score (≤3) exhibited 85% (OR = 1.85; 95% CI: 1.10–3.13; p = 0.02) and 90% (OR = 1.90; 95% CI: 1.21–2.97; p < 0.01) higher risk of SCD, respectively, after adjusting for all covariates. Fruits were the only food group among the All-5 components that demonstrated a significant association with SCD risk. Conclusions: Poor dietary diversity was associated with an elevated risk of SCD in middle-aged and older adults, and fruits were the food group with the most substantial effect.
In Silico Modeling of Myelin Oligodendrocyte Glycoprotein Disulfide Bond Reduction by Phosphine-Borane Complexes
Authors: Remtulla Raheem , Das Sanjoy Kumar , Levin Leonard A.
Published: October 2024

Background: Neurodegenerative diseases can cause vision loss by damaging retinal ganglion cells in the optic nerve. Novel phosphine-borane compounds (PBs) can protect these cells from oxidative stress via the reduction of disulfide bonds. However, the specific targets of these compounds are unknown. Proteomic evidence suggests that myelin oligodendrocyte glycoprotein (MOG) is a potential target. MOG is of significant interest due to its role in anti-MOG optic neuritis syndrome. Methods: We used in silico modeling to explore the structural consequences of cleaving the extracellular domain MOG disulfide bond, both in isolation and in complex with anti-MOG antibodies. The potential binding of PBs to this bond was examined using molecular docking. Results: Cleaving the disulfide bond of MOG altered the structure of MOG dimers and reduced their energetic favorability by 46.13 kcal/mol. The energy profiles of anti-MOG antibody complexes were less favorable when the disulfide bond of MOG was reduced in the monomeric state by 55.21 kcal/mol, but the reverse was true in the dimeric state. PBs exhibited reducing capabilities with the MOG extracellular disulfide bond, with this best-scoring compound binding with an energy of −28.54 kcal/mol to the MOG monomer and −24.97 kcal/mol to the MOG dimer. Conclusions: These findings suggest that PBs can affect the structure of MOG dimers and the formation of antibody complexes by reducing the MOG disulfide bond. Structural changes in MOG could have implications for neurodegenerative diseases and anti-MOG syndrome.
Effect of Particle Size on Physical Properties, Dissolution, In Vitro Antioxidant Activity, and In Vivo Hepatoprotective Properties of Tetrastigma hemsleyanum Diels et Gilg Powders
Authors: Zhang Zhiwen , Chen Yun , Wang Shaoxian , Tong Zheren , Luan Fujia , Jiang Binghong , Pu Faxiang , Xie Zhangfu , Wang Ping , Xu Zijin
Published: October 2024

Objective: The aim of this study was to analyze the effects of different particle sizes of Tetrastigma hemsleyanum Diels et Gilg (TDG) powders on physical properties, dissolution, in vitro antioxidant activity, and in vivo hepatoprotective properties. Methods: The particle size of TDG coarse powders (TDG-CP), TDG fine powders (TDG-FP), and TDG micro powders (TDG-MP) were measured by a laser particle size analyzer. The physical properties were measured according to the latest version of the Chinese Pharmacopoeia (Committee Chinese Pharmacopoeia 2020). The content of the total flavonoids, total polysaccharides, kaempferol-3-O-rutinoside, and rutin of TDG powders were determined using the NaNO2-Al (NO3)3 colorimetric method, the sulphate-phenol colorimetric method, and HPLC, respectively. In vitro dissolution and antioxidant activity were determined by the paddle method in phosphate buffer (pH 6.8) and the DPPH radical scavenging method, respectively. In addition, the liver tissue pathology was evaluated by hematoxylin and eosin staining (H&E), and the AST and ALT activities were measured by automatic biochemical analyzer. The superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH) activities were measured by using commercial analysis kits. Results: As the particle size decreases, the fluidity of TDG powders decreased and the porosity increased. In addition, there were no significant differences in physical properties between low temperature pulverized powders and room temperature pulverized powders. The final dissolution rates of the four bioactive ingredients in TDG-MP were found to be 85.06%, 85.61%, 83.88%, and 83.26%, respectively, whereas in TDG-CP, the dissolution rates were significantly lower at 18.79%, 17.96%, 22.46%, and 24.35%. The EC50 values of TDG-CP, TDG-FP, and TDG-MP on DPPH scavenging activity were 0.82, 0.31, and 0.10 mg/mL, respectively. The AST and ALT activities of the TDG-FP group and the TDG-MP group were significantly decreased and the SOD, CAT, and GSH activities were significantly increased when compared with that of the model group. The inflammatory cell infiltration and vacuolar degeneration of liver cells in the TDG-FP group and the TDG-MP group were significantly improved. Conclusions: The particle size of TDG powders had a significant effect on the physical properties and in vivo bioactivity. TDG pulverized to a fine particle size or smaller is a promising approach for clinical applications with improved physicochemical and biological properties.
Efficacy of Plant Tissue Culture Techniques for Eliminating Black Mulberry Idaeovirus (BMIV) from Infected Black Mulberry (Morus nigra)
Authors: Abdelwahab Elansary Doaa Waseem , Gürcan Kahraman , Roumi Vahid , Şimşek Özhan
Published: October 2024

Obtaining virus-free plants is a crucial step in disease management that enables reliable and profitable fruit farming. The present study applied various in vitro virus elimination protocols, including apical shoot culture, chemotherapy, thermotherapy, cryotherapy, and their combination, to eliminate black mulberry Idaeovirus (BMIV) from sour black mulberry. First, a shoot tip (0.5–2 mm) culture protocol was optimized, and four ribavirin concentrations (0, 10, 20, and 30 mg/L) were investigated over five weeks as a form of chemotherapy (ch). For the first thermotherapy treatment (Ch + Th1st), chemotherapy treatment was followed by a gradual increase in the temperature (24–33 °C). In another experiment (Th2nd + Ch), in vitro shoots were incubated in the dark for two weeks at two different temperatures (35 ± 1 °C and 37 ± 1 °C, for one week each). Subsequently, the shoot tips were incubated with various ribavirin doses. Finally, cryotherapy (Cr) was used with or without immersing the shoot tips in liquid nitrogen. A two-step RT-PCR was performed to assess the presence of the virus in 7–8-week-old in vitro plants. Th2nd + Ch significantly increased the shoot tip burst and plant survival/morphogenesis compared to the other treatments. Except for the application of cryotherapy, the protocols eliminated BMIV in different proportions, and the highest virus elimination rate (50%) was obtained by applying 30 mg/L ribavirin during the Ch + Th1st treatment. These findings are essential in preventing the dissemination of the virus and enabling the safe movement of germplasm around the world.
The Kinetics of Polymer Brush Growth in the Frame of the Reaction Diffusion Front Formalism
Authors: Polanowski Piotr , Sikorski Andrzej
Published: October 2024

We studied the properties of a reaction front that forms in irreversible reaction–diffusion systems with concentration-dependent diffusivities during the synthesis of polymer brushes. A coarse-grained model of the polymerization process during the formation of polymer brushes was designed and investigated for this purpose. In this model, a certain amount of initiator was placed on an impenetrable surface, and the “grafted from” procedure of polymerization was carried out. The system consisted of monomer molecules and growing chains. The obtained brush consisted of linear chains embedded in nodes of a face-centered cubic lattice with excluded volume interactions only. The simulations were carried out for high rafting densities of 0.1, 0.3, and 0.6 and for reaction probabilities of 0.02, 0.002, and 0.0002. Simulations were performed by means of the Monte Carlo method while employing the Dynamic Lattice Liquid model. Some universal behavior was found, i.e., irrespective of reaction rate and grafting density, the width of the reaction front as well as the height of the front show for long times the same scaling with respect to time. During the formation of the polymer layer despite the observed difference in dispersion of chain lengths for different grafting densities and reaction rates at a given layer height, the quality of the polymer layer does not seem to depend on these parameters.
Grafting of Lactic Acid and ε-Caprolactone onto Alpha-Cellulose and Sugarcane Bagasse Cellulose: Evaluation of Mechanical Properties in Polylactic Acid Composites
Authors: Valle Reyes Oscar Salvador , Orozco-Guareño Eulogio , Hernández-Montelongo Rosaura , Alvarado Mendoza Abraham Gabriel , Martínez Chávez Liliana , González Núñez Rubén , Aguilar Martínez Jacobo , Moscoso Sánchez Francisco Javier
Published: October 2024

In this paper, we present the synthesis of composite materials comprised of α-cellulose and sugarcane bagasse cellulose fibers grafted with lactic acid and ε-caprolactone. These fibers were incorporated as reinforcements into a PLA matrix by extrusion, producing composite materials with improved mechanical properties. The grafting of lactic acid and ε-caprolactone onto the fibers was confirmed by FTIR spectroscopy, demonstrating the chemical modification of the fibers. The morphology of the fibers and composites was analyzed through scanning electron microscopy (SEM), showing that the fibers are encapsulated within the polymeric matrix. This suggests good PLA–fiber interaction for the 90 PLA/10 α-Cel, 90 PLA/10 LAC-g-α-Cel, and 90 PLA/10 ε-CL-g-α-Cel composite materials. The obtained composite materials were tested under tensile loading. Incorporating 10 wt% of LAC-g-FBA-Cel and α-Cel-g-FBA-Cel grafted fibers into the PLA matrix improved the tensile modulus by 28% and 12%, respectively, compared with PLA. The maximum tensile strength values obtained were for composite materials with 10 wt% PLA/α-Cel, LAC-g-α-Cel, and FBA-Cel with 23, 27, and 37% concerning PLA. DSC thermal studies showed a reduction in the glass transition temperature in the composites with grafted fibers. The results suggest better interfacial adhesion between the PLA matrix and both grafted and non-grafted α-cellulose fibers, which contributes to the observed improvements in the mechanical and thermal properties of the composite materials. The results demonstrate that the composites can be produced through extrusion. Once the optimal concentration has been determined, α-cellulose or sugarcane bagasse grafted with lactic acid and ε-caprolactone can be incorporated into the PLA matrix, exhibiting adjustable properties.
Improving the Mechanical, Thermoelectric Insulations, and Wettability Properties of Acrylic Polymers: Effect of Silica or Cement Nanoparticles Loading and Plasma Treatment
Authors: Hussein Seenaa , Kadhem Saba , Ali Nadia , Alraih Alhafez , Abd-Elnaiem Alaa
Published: October 2024

The acrylic polymer composites in this study are made up of various weight ratios of cement or silica nanoparticles (1, 3, 5, and 10 wt%) using the casting method. The effects of doping ratio/type on mechanical, dielectric, thermal, and hydrophobic properties were investigated. Acrylic polymer composites containing 5 wt% cement or silica nanoparticles had the lowest abrasion wear rates and the highest shore-D hardness and impact strength. The increase in the inclusion of cement or silica nanoparticles enhanced surface roughness, water contact angle (WCA), and thermal insulation. Acrylic/cement composites demonstrated higher mechanical, electrical, and thermal insulation properties than acrylic/silica composites because of their lower particle size and their low thermal/electrical conductivity. Furthermore, to improve the surface hydrophobic characteristics of acrylic composites, the surface was treated with a dielectric barrier discharge (DBD) plasma jet. The DBD plasma jet treatment significantly enhanced the hydrophobicity of acrylic polymer composites. For example, the WCA of acrylic composites containing 5 wt% silica or cement nanoparticles increased from 35.3° to 55° and 44.7° to 73°, respectively, by plasma treatment performed at an Ar flow rate of 5 L/min and for an exposure interval of 25 s. The DBD plasma jet treatment is an excellent and inexpensive technique for improving the hydrophobic properties of acrylic polymer composites. These findings offer important perspectives on the development of materials coating for technical applications.
High-Velocity Impact Performance of Ballistic Fabric Using Core-Spun Compound Yarns
Authors: Yang Dan , Liu Shengdong , Zhang Weitian , Liu Qian , Yao Gaozheng , Zhu Kai
Published: October 2024

In this paper, the usage of core-spun compound yarns in ballistic fabric to improve ballistic performance is considered, as with the use of core-spun compound yarns, the yarn friction inside the fabric is enhanced, and, therefore, the energy absorption capability of the fabric is expected to increase. Three types of fabric were developed and compared. Fa refers to a woven type made with 100% Kevlar® filament yarns. Fb was woven with core-spun compound aramid yarns, which were made of Kevlar® filament yarns spun with staple aramid fiber. Fc was woven with core-spun compound polyester yarns, which were made of Kevlar® filament yarns spun with staple polyester fiber. There were two main purposes for comparing these types. The first was to confirm if the ballistic performance could be improved with the usage of core-spun compound yarns instead of pure filament yarns. The second was to investigate if different compositions of spun fiber would influence ballistic performance. The research results are positive and quite interesting. They show that the usage of core-spun compound yarn could indeed help to increase ballistic performance and that core-spun compound aramid yarns are better than core-spun compound polyester yarns in this function. The research was carried out using both ballistic tests and FEA models.
Atmospheric Cold Plasma to Maintain Sea Bass Quality: An Opportunity for International Fish Trade
Authors: Ulusoy Şafak , Akan Tamer , Kartal Sehban , Üçok Didem , Tosun Şehnaz Yasemin , Doğruyol Hande , Coşansu Serap , Bostan Kamil , Mol Sühendan
Published: October 2024

Whole chilled sea bass is an essential product for the European food market, and Türkiye is the foremost supplier. The importance of sea bass in the world food trade reveals the significance of food safety risks that may arise during or after harvest. This study aimed to examine the impact of atmospheric cold plasma (ACP) on delaying the spoilage of sea bass. The ACP is generated by an original device that produces a dielectric barrier discharge plasma using an alternating current (AC) power supply, applying a 30 kV high voltage with a sinusoidal frequency of 20 kHz. Whole sea bass samples were treated for 1 min (ACP1) or 7 min (ACP7), and then stored at 2 ± 1 °C. Sensory scores of plasma-treated sea bass were higher throughout the storage period. Both treatments decreased the initial bacterial load and delayed bacterial growth (p ≤ 0.05) during storage. The mesophilic aerobic bacteria count of control samples exceeded 6 log CFU/g on the second day of storage. However, ACP1 and ACP7 did not reach this value until the third and fourth days. The control samples had higher TMA-N and TBARS (p ≤ 0.05) than plasma-treated groups. The treatment did not significantly change the texture. Although ΔE was higher in ACP samples, a discoloration that could affect acceptability was not reported during the sensory test. Cold plasma can improve the overall market value by maintaining quality, benefiting the global fish trade. It has been shown that cold plasma has promising potential in the fresh fish industry.
Bioleaching of Industrial Metallic Steel Waste by Mixed Cultures of Thermoacidophilic Archaea
Authors: Memic Alma , Mashchenko Artem , Kölbl Denise , Schnideritsch Holger , Wohlmuth Dominik , Klösch Gerald , Milojevic Tetyana
Published: October 2024

Different mixed cultures of extremely thermoacidophilic microorganisms were used for bioleaching of metalliferous industrial dust waste derived from the basic oxygen furnace (BOF) steelmaking process. Such mixed cultures can extract various metals from multi-metallic BOF-dust waste, improving the metal dissolution and bioleaching performance in frames of metal recycling processes to assist circular economies and waste management. The results of the investigation showed that mixed cultures of thermoacidophilic archaea of the order Sulfolobales (Acidianus spp., Sulfolobus spp., and Metallosphaera sedula) during their growth in laboratory glass bioreactors provided a superior bioleaching system to Acidianus manzaensis alone. Depending on the composition of mixed thermoacidophilic cultures, extraction of various metals from BOF-dust could be achieved. Among the three different types of mixed cultures tested, the mixed culture system of A. manzaensis, A. brierleyi, and S. acidocaldarius was most effective for extraction of major elements (Fe, Ca, Zn, Mn, and Al). The mixed culture of A. manzaensis, A. brierleyi, and M. sedula showed high performance for bioleaching of most of the minor elements (Cu, Ni, Pb, Co, Mo, and Sr). The efficient ability of mixed cultures to colonise the mineral matrix of the metal waste product was observed via scanning electron microscopy, while their metal extraction capacities were analysed by inductively coupled plasma mass spectrometry. These investigations will promote the further design of microbial consortia in order to break down the solid matrix and efficiently extract metals from metalliferous waste materials.
How Not to Undiscipline Religion and Science: Indigenous Ecological Knowledge, Epistemic Resistance, and the Settler Imagination
Authors: Weaver Colin B.
Published: October 2024

Taking settler-environmental interest in Indigenous traditional ecological knowledge (ITEK) as a case study, this paper critically examines some ethico-political pitfalls that can accompany attempts to undiscipline the conceptual and academic boundaries between religion and science. Although settler interest in ITEK appears to heed calls to center Indigenous perspectives in response to ecological crises, I argue that in practice such turns repeatedly enact neocolonial maneuvers that risk obfuscating and exacerbating the settler-colonial status quo. Employing the analytic of biocolonialism, I focus in particular on the discursive construction of Indigenous knowledge as a universal good that any interested parties might access and circulate. I criticize this conception on anti-colonial grounds and propose that it depends on a picture of knowledge as such as an apolitical commodity. By way of parochializing that conception and loosening its grip on the settler-environmental imagination, I examine expressions of Indigenous epistemic resistance which generate a competing picture of knowledge as anti-public or secret. I conclude by suggesting that this second picture invites settler environmentalists to cultivate capacities of going without ITEK and claiming that analysts should continue to pursue the sort of critical and constructive work performed here if experiments in undisciplining are to cohere with anti-colonialism.
Parmenides as a Thinker of Fate
Authors: Torrijos-Castrillejo David
Published: October 2024

Although some ancient sources relate Parmenides to the religious doctrine of fate, this concept is not usually prominent in the scholarly presentation of the Eleatic thinker. Here, we offer a tentative interpretation of the notion of necessity in Parmenides’ poem, as a peculiar philosophical understanding of the presence of fate in reality. Necessity, divinised by Parmenides, implies that all things are bound together by the chains of fate. Therefore, his philosophical proposal consists in understanding this unity of reality originated by the connexion of fatal necessity. However, this presence of fate in all things is not bad news, but also means that everything is connected by Love.
Seven Decades of Surface Temperature Changes in Central European Lakes: What Is Next?
Authors: Ptak Mariusz , Amnuaylojaroen Teerachai , Sojka Mariusz
Published: October 2024

Lakes are vital components of the hydrosphere, holding both environmental and economic significance. In recent times, they have undergone transformations in one of their key characteristics—water temperature. Assessing the scale and pace of these changes depends on the length and accuracy of the available data. This study focuses on the two lakes in Poland (Białe Augustowskie and Studzieniczne) with the longest continuous water temperature records, ranging from 1954 to 2023. The results reveal a relatively stable thermal regime until the late 1980s (with changes that were statistically insignificant) and a significant shift over the past three decades, during which the water temperature increased at a rate of 0.5 °C per decade. Importantly, simulations indicate further warming of the water by the end of the 21st century. Depending on the chosen climate change scenario, the warming of both lakes is expected to continue, with the Shared Socioeconomic Pathways (SSP585) scenario projecting a steady increase of 0.5 °C per decade. Given the fundamental importance of water temperature in determining factors such as water quality, these future changes present a significant challenge for water management authorities in terms of maintaining and managing these ecosystems.
Application of Non-Parametric and Forecasting Models for the Sustainable Development of Energy Resources in Brazil
Authors: Saiki Gabriela Mayumi , Serrano André Luiz Marques , Rodrigues Gabriel Arquelau Pimenta , Bispo Guilherme Dantas , Gonçalves Vinícius Pereira , Neumann Clóvis , Albuquerque Robson de Oliveira , Bork Carlos Alberto Schuch
Published: October 2024

To achieve Sustainable Development Goal 7 (SDG7) and improve energy management efficiency, it is essential to develop models and methods to forecast and enhance the process accurately. These tools are crucial in shaping the national policymakers’ strategies and planning decisions. This study utilizes data envelopment analysis (DEA) and bootstrap computational methods to evaluate Brazil’s energy efficiency from 2004 to 2023. Additionally, it compares seasonal autoregressive integrated moving average (SARIMA) models and autoregressive integrated moving average (ARIMA) forecasting models to predict the variables’ trends for 2030. One significant contribution of this study is the development of a methodology to assess Brazil’s energy efficiency, considering environmental and economic factors to formulate results. These results can help create policies to make SDG7 a reality and advance Brazil’s energy strategies. According to the study results, the annual energy consumption rate is projected to increase by an average of 2.1% by 2030, which is accompanied by a trend of GDP growth. By utilizing existing technologies in the country, it is possible to reduce electricity consumption costs by an average of 30.58% while still maintaining the same GDP value. This demonstrates that sustainable development and adopting alternatives to minimize the increase in energy consumption can substantially impact Brazil’s energy sector, improving process efficiency and the profitability of the Brazilian industry.
MM-IRSTD: Conv Self-Attention-Based Multi-Modal Small and Dim Target Detection in Infrared Dual-Band Images
Authors: Yang Junyan , Ye Zhihui , Lin Jian , Chen Dongfang , Du Lingbian , Li Shaoyi
Published: October 2024

Infrared multi-band small and dim target detection is an important research direction in the fields of modern remote sensing and military surveillance. However, achieving high-precision detection remains challenging due to the small scale, low contrast of small and dim targets, and their susceptibility to complex background interference. This paper innovatively proposes a dual-band infrared small and dim target detection method (MM-IRSTD). In this framework, we integrate a convolutional self-attention mechanism module and a self-distillation mechanism to achieve end-to-end dual-band infrared small and dim target detection. The Conv-Based Self-Attention module consists of a convolutional self-attention mechanism and a multilayer perceptron, effectively extracting and integrating input features, thereby enhancing the performance and expressive capability of the model. Additionally, this module incorporates a dynamic weight mechanism to achieve adaptive feature fusion, significantly reducing computational complexity and enhancing the model’s global perception capability. During model training, we use a spatial and channel similarity self-distillation mechanism to drive model updates, addressing the similarity discrepancy between long-wave and mid-wave image features extracted through deep learning, thus improving the model’s performance and generalization capability. Furthermore, to better learn and detect edge features in images, this paper designs an edge extraction method based on Sobel. Finally, comparative experiments and ablation studies validate the advancement and effectiveness of our proposed method.
Optimized Soil Moisture Mapping Strategies on the Tibetan Plateau Using Downscaled and Interpolated Maps as Mutual Covariates
Authors: Zhang Mo , Ge Yong , Wang Jianghao
Published: October 2024

Accurate high-resolution soil moisture maps are crucial for a better understanding of hydrological processes and energy cycles. Mapping strategies such as downscaling and interpolation have been developed to obtain high-resolution soil moisture maps from multi-source inputs. However, research on the optimization performance of integrating downscaling and interpolation, especially through the use of mutual covariates, remains unclear. In this study, we compared four methods—two standalone methods based on downscaling and interpolation strategies and two combined methods that utilize soil moisture maps as mutual covariates within each strategy—in a case study of daily soil moisture mapping at a 1 km resolution in the Tibetan Plateau. We assessed mapping performance in terms of prediction accuracy and differences in spatial coverage. The results indicated that introducing interpolated soil moisture maps into the downscaling strategy significantly improved prediction accuracy (RMSE: −5.94%, correlation coefficient: +14.02%) but was limited to localized spatial coverage (6.9% of grid cells) near in situ sites. Conversely, integrating downscaled soil moisture maps into the interpolation strategy resulted in only modest gains in prediction accuracy (RMSE: −1.07%, correlation coefficient: +1.04%), yet facilitated broader spatial coverage (40.4% of grid cells). This study highlights the critical differences between downscaling and interpolation strategies in terms of accuracy improvement and spatial coverage, providing a reference for optimizing soil moisture mapping over large areas.
Multiple-Input Multiple-Output Microwave Tomographic Imaging for Distributed Photonic Radar Network
Authors: Noviello Carlo , Maresca Salvatore , Gennarelli Gianluca , Malacarne Antonio , Scotti Filippo , Ghelfi Paolo , Soldovieri Francesco , Catapano Ilaria , Scapaticci Rosa
Published: October 2024

This paper deals with the imaging problem from data collected by means of a microwave photonics-based distributed radar network. The radar network is leveraged on a centralized architecture, which is composed of one central unit (CU) and two transmitting and receiving dual-band remote radar peripherals (RPs), it is capable of collecting monostatic and multistatic phase-coherent data. The imaging is herein formulated as a linear inverse scattering problem and solved in a regularized way through the truncated singular value decomposition inversion scheme. Specifically, two different imaging schemes based on an incoherent fusion of the tomographic images or a fully coherent data processing are herein developed and compared. Experimental tests carried out in a port scenario for imaging both a stationary and a moving target are reported to validate the imaging approach.
The Effect of a Parcel-Aggregated Cropping Structure Mapping Method in Irrigation-Water Estimation in Arid Regions—A Case Study of the Weigan River Basin in Xinjiang
Authors: Wang Haoyu , Bai Linze , Wei Chunxia , Li Junli , Li Shuo , Zhou Chenghu , De Maeyer Philippe , Kou Wenqi , Zhang Chi , Shen Zhanfeng , Van de Voorde Tim
Published: October 2024

Effective management of agricultural water resources in arid regions relies on precise estimation of irrigation-water demand. Most previous studies have adopted pixel-level mapping methods to estimate irrigation-water demand, often leading to inaccuracies when applied in arid areas where land salinization is severe and where poorly growing crops cause the growing area to be smaller than the sown area. To address this issue and improve the accuracy of irrigation-water demand estimation, this study utilizes parcel-aggregated cropping structure mapping. We conducted a case study in the Weigan River Basin, Xinjiang, China. Deep learning techniques, the Richer Convolutional Features model, and the bilayer Long Short-Term Memory model were applied to extract parcel-aggregated cropping structures. By analyzing the cropping patterns, we estimated the irrigation-water demand and calculated the supply using statistical data and the water balance approach. The results indicated that in 2020, the cultivated area in the Weigan River Basin was 5.29 × 105 hectares, distributed over 853,404 parcels with an average size of 6202 m2. Based on the parcel-aggregated cropping structure, the estimated irrigation-water demand ranges from 25.1 × 108 m3 to 30.0 × 108 m3, representing a 5.57% increase compared to the pixel-level estimates. This increase highlights the effectiveness of the parcel-aggregated cropping structure in capturing the actual irrigation-water requirements, particularly in areas with severe soil salinization and patchy crop growth. The supply was calculated at 24.4 × 108 m3 according to the water balance approach, resulting in a minimal water deficit of 0.64 × 108 m3, underscoring the challenges in managing agricultural water resources in arid regions. Overall, the use of parcel-aggregated cropping structure mapping addresses the issue of irrigation-water demand underestimation associated with pixel-level mapping in arid regions. This study provides a methodological framework for efficient agricultural water resource management and sustainable development in arid regions.
Estimation of PM2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite
Authors: Zhang Ruijie , Chen Hui , Chen Ruizhi , Zhou Chunyan , Li Qing , Xie Huizhen , Wang Zhongting
Published: October 2024

The use of satellite data to estimate PM2.5 is an appropriate approach for long-term, substantial monitoring and assessment. To estimate PM2.5, the majority of the algorithms now in use utilize the top-of-atmosphere (TOA) reflectance or aerosol optical depth (AOD) derived from scalar satellite data. However, there is relatively little research on the retrieval of PM2.5 using multi-angle polarized data. With its directional polarimetric camera (DPC), the Chinese new-generation satellite Gaofen 5B (henceforth referred to as GF-5B) offers a unique opportunity to close this gap in multi-angle polarized observation data. In this research, we utilized TOA data from the DPC payload and applied the gradient boosting machine method to simulate the impact of the observation angle, wavelength, and polarization information on the accuracy of PM2.5 retrieval. We identified the optimal conditions for the effective estimation of PM2.5. The quantitative results indicated that, under these optimal conditions, the PM2.5 concentrations retrieved by GF-5B showed a strong correlation with the ground-based data, achieving an R2 of 0.9272 and an RMSE of 7.38 µg·m−3. By contrast, Himawari-8’s retrieval accuracy under similar data conditions consisted of an R2 of 0.9099 and RMSE of 7.42 µg·m−3, indicating that GF-5B offers higher accuracy. Furthermore, the retrieval results in this study demonstrated an R2 of 0.81 when compared to the CHAP dataset, confirming the feasibility and effectiveness of the use of GF-5B for PM2.5 retrieval and providing support for PM2.5 estimation through multi-angle polarized data.
Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images
Authors: Gu Xingjian , Yu Supeng , Huang Fen , Ren Shougang , Fan Chengcheng
Published: October 2024

Road extraction techniques based on remote sensing image have significantly advanced. Currently, fully supervised road segmentation neural networks based on remote sensing images require a significant number of densely labeled road samples, limiting their applicability in large-scale scenarios. Consequently, semi-supervised methods that utilize fewer labeled data have gained increasing attention. However, the imbalance between a small quantity of labeled data and a large volume of unlabeled data leads to local detail errors and overall cognitive mistakes in semi-supervised road extraction. To address this challenge, this paper proposes a novel consistency self-training semi-supervised method (CSSnet), which effectively learns from a limited number of labeled data samples and a large amount of unlabeled data. This method integrates self-training semi-supervised segmentation with semi-supervised classification. The semi-supervised segmentation component relies on an enhanced generative adversarial network for semantic segmentation, which significantly reduces local detail errors. The semi-supervised classification component relies on an upgraded mean-teacher network to handle overall cognitive errors. Our method exhibits excellent performance with a modest amount of labeled data. This study was validated on three separate road datasets comprising high-resolution remote sensing satellite images and UAV photographs. Experimental findings showed that our method consistently outperformed state-of-the-art semi-supervised methods and several classic fully supervised methods.
Study of the Tidal Variations in the Ionosphere and the MLT Region over Mohe and Beijing During Six Intense Geomagnetic Storms from 2016 to 2021
Authors: Ma Jiarong , Ma Zheng , Bao Jiaxin , Luo Jiahui , Yang Junfeng , Liu Dan
Published: October 2024

Geomagnetic storms can cause large variations in the ionosphere, but their impacts on the mesosphere and lower thermosphere (MLT) are not well understood. Based on the Total Electron Content (TEC) data and the meteor neutral winds data over Mohe (53.5°N, 122.3°E) and Beijing (40.3°N, 116.2°E), we analyze the tidal variations during six intense geomagnetic storms from 2016 to 2021. According to the six intense geomagnetic storms, we found that intense geomagnetic storms can lead to diurnal and semidiurnal tidal enhancements in TEC, while their influences on tidal variations in the MLT region are not always captured. Responses of tidal enhancement in the MLT region to the intense geomagnetic storms are more obvious at a lower latitude at Beijing, but the tidal amplitude changes are not proportional to the Dst indices. Some semidiurnal tides are significantly enhanced prior to the onset of geomagnetic storms, which needs to be statistically investigated in the future based on additional observations.
Impacts of Digital Elevation Model Elevation Error on Terrain Gravity Field Calculations: A Case Study in the Wudalianchi Airborne Gravity Gradiometer Test Site, China
Authors: Wang Lehan , Yang Meng , Huang Zhiyong , Feng Wei , Yan Xingyuan , Zhong Min
Published: October 2024

Accurate Digital Elevation Models (DEMs) are essential for precise terrain gravity field calculations, which are critical in gravity field modeling, airborne gravimeter and gradiometer calibration, and geophysical inversion. This study evaluates the accuracy of various satellite DEMs by comparing them with a LiDAR DEM at the Wudalianchi test site, a location requiring ultra-accurate terrain gravity fields. Major DEM error sources, particularly those related to vegetation, were identified and corrected using a least squares method that integrates canopy height, vegetation cover, NDVI, and airborne LiDAR DEM data. The impact of DEM vegetation errors on terrain gravity anomalies and gravity gradients was quantified using a partitioned adaptive gravity forward-modeling method at different measurement heights. The results indicate that the TanDEM-X DEM and AW3D30 DEM exhibit the highest vertical accuracy among the satellite DEMs evaluated in the Wudalianchi area. Vegetation significantly affects DEM accuracy, with vegetation-related errors causing an impact of approximately 0.17 mGal (RMS) on surface gravity anomalies. This effect is more pronounced in densely vegetated and volcanic regions. At 100 m above the surface and at an altitude of 1 km, vegetation height affects gravity anomalies by approximately 0.12 mGal and 0.07 mGal, respectively. Additionally, vegetation height impacts the vertical gravity gradient at 100 m above the surface by approximately 4.20 E (RMS), with errors up to 48.84 E over vegetation covered areas. The findings underscore the critical importance of using DEMs with vegetation errors removed for high-precision terrain gravity and gravity gradient modeling, particularly in applications such as airborne gravimeter and gradiometer calibration.
Evaluating Maize Residue Cover Using Machine Learning and Remote Sensing in the Meadow Soil Region of Northeast China
Authors: Liang Zhengwei , Du Jia , Yu Weilin , Zhuo Kaizeng , Shao Kewen , Zhang Weijian , Zhang Cangming , Qin Jie , Han Yu , Sui Bingrun , Song Kaishan
Published: October 2024

The management of crop residues in farmland is crucial for increasing soil organic matter and reducing soil erosion. Identifying the regional extent of crop residue cover (CRC) is vital for implementing conservation tillage and formulating agricultural subsidy policies. The Google Earth Engine (GEE) and remote sensing images from 2019 to 2023 were used to obtain spectral characteristics before the maize seedling stage in Northeast China, followed by constructing the CRC estimation models using machine learning algorithms. To avoid the impact of multicollinearity among data, three machine learning algorithms—ridge regression (RR), partial least squares regression (PLSR), and least absolute shrinkage and selection operator (LASSO)—were employed. By comparing the accuracy of these methods, the most accurate model was determined and applied to subsequent CRC estimation. Based on the estimated CRC and Conservation Technology Information Center definitions of tillage practices, the conservation tillage mapping was completed, and the spatiotemporal distribution characteristics were thoroughly analyzed. The following findings were demonstrated: (1) the PLSR-based model outperformed RR (Pearson’s correlation coefficient (r) = 0.8875, R2 = 0.7877, RMSE = 6.99%) and LASSO (r = 0.8903, R2 = 0.7926, RMSE = 6.88%) with higher accuracy (r = 0.9264, R2 = 0.8582, RMSE = 4.93%). (2) Over the five years, the average no-tillage (NT) proportion in the study area was 15.9%, reduced tillage (RT) was 17.8%, and conventional tillage (CT) was 66.3%. In 2020 and 2022, NT rates were significantly higher at 27.5% and 15.5%, while RT were 15.7% and 30.0%, respectively. (3) Compared to the Sanjiang and Liaohe Plains (RT = 1907 km2 and 1336 km2, and NT = 559 km2 and 585 km2, respectively), the Songnen Plain exhibited higher conservation tillage rates (where RT was 3791 km2 and NT was 1265 km2). This provides crucial scientific evidence for the management and planning of conservation tillage, thereby optimizing farmland production planning, enhancing production efficiency, and promoting the development of sustainable agricultural production systems.
Chemoresistive Gas Sensors Based on Electrospun 1D Nanostructures: Synergizing Morphology and Performance Optimization
Authors: Imash Aigerim , Smagulova Gaukhar , Kaidar Bayan , Keneshbekova Aruzhan , Kazhdanbekov Ramazan , Velasco Leticia Fernandez , Mansurov Zulkhair
Published: October 2024

Gas sensors are essential for safety and quality of life, with broad applications in industry, healthcare, and environmental monitoring. As urbanization and industrial activities intensify, the need for advanced air quality monitoring becomes critical, driving the demand for more sensitive, selective, and reliable sensors. Recent advances in nanotechnology, particularly 1D nanostructures like nanofibers and nanowires, have garnered significant interest due to their high surface area and improved charge transfer properties. Electrospinning stands out as a promising technique for fabricating these nanomaterials, enabling precise control over their morphology and leading to sensors with exceptional attributes, including high sensitivity, rapid response, and excellent stability in harsh conditions. This review examines the current research on chemoresistive gas sensors based on 1D nanostructures produced by electrospinning. It focuses on how the morphology and composition of these nanomaterials influence key sensor characteristics—sensitivity, selectivity, and stability. The review highlights recent advancements in sensors incorporating metal oxides, carbon nanomaterials, and conducting polymers, along with their modifications to enhance performance. It also explores the use of fiber-based composite materials for detecting oxidizing, reducing, and volatile organic compounds. These composites leverage the properties of various materials to achieve high sensitivity and selectivity, allowing for the detection of a wide range of gases in diverse conditions. The review further addresses challenges in scaling up production and suggests future research directions to overcome technological limitations and improve sensor performance for both industrial and domestic air quality monitoring applications.
Enhancement of Operational Safety in Marine Cargo Cranes on a Container Ship Through the Application of Authenticated Wi-Fi Based Wireless Data Transmission from Multiple Sensors
Authors: Abotaleb Mostafa , Mindykowski Janusz
Published: October 2024

The use of wireless technology in common marine engineering applications as a medium for data transaction in measurement and control systems, is not as popular as it should be. This article aims to demonstrate the advantages of using wireless technology in maritime engineering applications through a proposed Wi-Fi based wireless system dedicated to performance and safety monitoring in marine cargo cranes. The system is based on some concepts that were suggested in the earlier literature to perform authenticated data transmission from multiple sensors through using both the ESP-NOW protocol and the WebSerial remote serial monitor. The introduced system will be integrated with an already installed system in order to render the means for implementing effective principles in automation and control engineering, such as functional safety and predictive maintenance. Additionally, this article will highlight the economic efficiency of adopting wireless technology instead of cabling as a medium for data transaction in measurement and control systems in marine engineering applications such as cargo cranes.
Data-Driven Clustering Analysis for Representative Electric Vehicle Charging Profile in South Korea
Authors: Kim Kangsan , Kim Geumbee , Yoo Jiwon , Heo Jungeun , Cho Jaeyoung , Ryu Seunghyoung , Kim Jangkyum
Published: October 2024

As the penetration of electric vehicles (EVs) increases, an understanding of EV operation characteristics becomes crucial in various aspects, e.g., grid stability and battery degradation. This can be achieved through analyzing large amounts of EV operation data; however, the variability in EV data according to the user complicates unified data analysis and identification of representative patterns. In this research, a framework that captures EV charging characteristics in terms of charge–discharge area is proposed using actual field data. In order to illustrate EV operation characteristics in a unified format, an individual EV operation profile is modeled by the probability distribution of the charging start and end states of charge (SoCs).Then, hierarchical clustering analysis is employed to derive representative charging profiles. Using large amounts of real-world, vehicle-specific EV data in South Korea, the analysis results reveal that EV charging characteristics in terms of the battery charge–discharge area can be summarized into seven representative profiles.
Intelligent Human Operator Mental Fatigue Assessment Method Based on Gaze Movement Monitoring
Authors: Kashevnik Alexey , Kovalenko Svetlana , Mamonov Anton , Hamoud Batol , Bulygin Aleksandr , Kuznetsov Vladislav , Shoshina Irina , Brak Ivan , Kiselev Gleb
Published: October 2024

Modern mental fatigue detection methods include many parameters for evaluation. For example, many researchers use human subjective evaluation or driving parameters to assess this human condition. Development of a method for detecting the functional state of mental fatigue is an extremely important task. Despite the fact that human operator support systems are becoming more and more widespread, at the moment there is no open-source solution that can monitor this human state based on eye movement monitoring in real time and with high accuracy. Such a method allows the prevention of a large number of potential hazardous situations and accidents in critical industries (nuclear stations, transport systems, and air traffic control). This paper describes the developed method for mental fatigue detection based on human eye movements. We based our research on a developed earlier dataset that included captured eye-tracking data of human operators that implemented different tasks during the day. In the scope of the developed method, we propose a technique for the determination of the most relevant gaze characteristics for mental fatigue state detection. The developed method includes the following machine learning techniques for human state classification: random forest, decision tree, and multilayered perceptron. The experimental results showed that the most relevant characteristics are as follows: average velocity within the fixation area; average curvature of the gaze trajectory; minimum curvature of the gaze trajectory; minimum saccade length; percentage of fixations shorter than 150 ms; and proportion of time spent in fixations shorter than 150 milliseconds. The processing of eye movement data using the proposed method is performed in real time, with the maximum accuracy (0.85) and F1-score (0.80) reached using the random forest method.