bluuebunny/crossref_metadata_2025 huggingface dataset:
Observational Methods in Studies of Infant and Young Child Feeding Practices in Low- and Middle-Income Countries: A Twenty-Year Retrospective Review
Authors: Schwendler Teresa R. , Na Muzi , Keller Kathleen L. , Jensen Leif , Kodish Stephen R.
Published: January 2024

This narrative review describes the observational approaches used to study infant and young child feeding (IYCF) practices in low- and middle-income countries (LMICs) published between 2001 and 2021. Articles were included in this narrative review if they were (1) original peer-reviewed articles published in English in PubMed and Web of Science; (2) published between 1 January 2001, and 31 December 2021; (3) conducted in an LMIC; and (4) employed observations and focused on IYCF practices among children aged 6–59 months. The studies (n = 51) revealed a wide-ranging application of direct meal and full-day observations, as well as indirect spot checks, to study IYCF. The findings revealed that meal observations were typically conducted during a midday meal using precise recording approaches such as video and aimed to understand child–caregiver interactions or specialized nutritious food (SNF) usage. Conversely, full-day observations lasted between 6 and 12 h and often used a field notes-based recording approach. Behaviors occurring outside of mealtime, such as snacking or interhousehold food sharing, were also a primary focus. Finally, spot checks were conducted to indirectly assess SNF compliance during both announced and unannounced visits. This review highlights the adaptability of observations across contexts and their versatility when used as a primary data collection tool to help monitor and evaluate nutrition programs.
Zinc Deficiency Promotes Calcification in Vascular Smooth Muscle Cells Independent of Alkaline Phosphatase Action and Partly Impacted by Pit1 Upregulation
Authors: Alcantara Ethel H. , Kwon Jae-Hee , Kang Min-Kyung , Cho Young-Eun , Kwun In-Sook
Published: January 2024

Inorganic phosphate (Pi) is a critical determinant of calcification, and its concentration is regulated by alkaline phosphatase (ALP) and Pit1. ALP is a key regulator of osteogenic calcification and acts by modulating local inorganic phosphate (Pi) concentrations through hydrolyzing pyrophosphate in the extracellular matrix (ECM). Pit1, a sodium-dependent phosphate transporter, regulates calcification via facilitating phosphate uptake within the cells. To investigate whether zinc differentially regulates osteoblastic and vascular calcifications, we examined ALP activity and Pit1 in osteoblastic and vascular smooth muscle cells (VSMCs). Our findings demonstrate that calcification in osteoblastic MC3T3-E1 cells is decreased via diminished ALP action under zinc deficiency. In contrast, zinc-deficiency-induced calcification in VSMCs is independent of ALP action, as demonstrated by very weak ALP activity and expression in calcified VSMCs. In zinc-deficient A7r5 VSMC, P accumulation increased with increasing Na phosphate concentration (3–7 mM) but not with β-GP treatment, which requires ALP activity to generate Pi. Ca deposition also increased with Na phosphate in a dose-dependent manner; in contrast, β-GP did not affect Ca deposition. In osteoblastic cells, Pit1 expression was not affected by zinc treatments. In contrast, Pit1 expression is highly upregulated in A7r5 VSMC under zinc deficiency. Using phosphonoformic acid, a competitive inhibitor of Pit1, we showed that calcification is inhibited in both A7r5 and MC3T3-E1 cells, indicating a requirement for Pit1 in both calcifications. Moreover, the downregulation of VSMC markers under zinc deficiency was restored by blocking Pit1. Taken together, our results imply that zinc-deficiency-induced calcification in VSMC is independent of ALP action in contrast to osteoblastic calcification. Moreover, Pit1 expression in VSMCs is a target for zinc deficiency and may mediate the inhibition of VSMC marker expression under zinc deficiency.
Nutrient Density, Added Sugar, and Fiber Content of Commercially Available Fruit Snacks in the United States from 2017 to 2022
Authors: Fu Hao , Lee Chi Hyun , Nolden Alissa A. , Kinchla Amanda J.
Published: January 2024

Fruit snacks have become a popular and convenient snacking choice and have the potential to contribute to a well-balanced diet. However, the nutritional quality of fruit snack products has not yet been studied. The objective of the present study is to provide a nutritional assessment of the fruit snack product category. This study used the Mintel Global New Product Database to collect data about fruit snack products launched in the United States from 2017 to 2022. Fruit snack products (n = 2405) are divided into nine product categories based on product characteristics. Nutrition composition was assessed using a comprehensive score, Nutrient Rich Food (NRF) model, and by examining individual components (added sugar and fiber). The results show that dried fruit has the highest nutrient density, fiber content, and the lowest added sugar content. Conversely, fruit-flavored snacks have the lowest nutrient density, fiber content, and added sugar content. Currently, fruit puree, canned fruit with juice, and dried fruit are the only fruit snacks that meet the current recommendations set by the USDA Dietary Guidelines. Future directions for the fruit snack category should consider decreasing the added sugar content, increasing the fiber content, and enhancing their sensory profile to improve the overall nutrient density.
Acute Kidney Injury Caused by Rhabdomyolysis Is Ameliorated by Serum Albumin-Based Supersulfide Donors through Antioxidative Pathways
Authors: Ikeda-Imafuku Mayumi , Fukuta Tatsuya , Tuan Giam Chuang Victor , Sawa Tomohiro , Maruyama Toru , Otagiri Masaki , Ishida Tatsuhiro , Ishima Yu
Published: January 2024

Oxidative stress is responsible for the onset and progression of various kinds of diseases including rhabdomyolysis-induced acute kidney injury (AKI). Antioxidants are, therefore, thought to aid in the recovery of illnesses linked to oxidative stress. Supersulfide species have been shown to have substantial antioxidative activity; however, due to their limited bioavailability, few supersulfide donors have had their actions evaluated in vivo. In this study, human serum albumin (HSA) and N-acetyl-L-cysteine polysulfides (NACSn), which have polysulfides in an oxidized form, were conjugated to create a supersulfide donor. HSA is chosen to be a carrier of NACSn because of its extended blood circulation and high level of biocompatibility. In contrast to a supersulfide donor containing reduced polysulfide in HSA, the NACSn-conjugated HSAs exhibited stronger antioxidant activity than HSA and free NACSn without being uptaken by the cells in vitro. The supersulfide donor reduced the levels of blood urea nitrogen and serum creatinine significantly in a mouse model of rhabdomyolysis-induced AKI. Supersulfide donors significantly reduced the expression of oxidative stress markers in the kidney. These results indicate that the developed supersulfide donor has the therapeutic effect on rhabdomyolysis-induced AKI.
Influence of Long-Term Anti-Seizure Medications on Redox Parameters in Human Blood
Authors: Jakovljević Danijel , Nikolić Milan , Jovanović Vesna , Vidonja Uzelac Teodora , Nikolić-Kokić Aleksandra , Novaković Emilija , Miljević Čedo , Milovanović Maja , Blagojević Duško
Published: January 2024

Background: Epilepsy is a chronic brain disease affecting millions of people worldwide, but little is known about the impact of anti-seizure medications on redox homeostasis. Methods: This study aimed to compare the effects of the long-term use of oral anti-seizure medications in monotherapy (lamotrigine, carbamazepine, and valproate) on antioxidant enzymes: superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase, haemoglobin, and methaemoglobin content in erythrocytes, and concentrations of total proteins and thiols, nitrites, lipid peroxides and total glutathione in the plasma of epilepsy patients and drug-naïve patients. Results: The results showed that lamotrigine therapy led to lower superoxide dismutase activity (p < 0.005) and lower concentrations of total thiols (p < 0.01) and lipid peroxides (p < 0.01) compared to controls. On the other hand, therapy with carbamazepine increased nitrite levels (p < 0.01) but reduced superoxide dismutase activity (p < 0.005). In the valproate group, only a decrease in catalase activity was observed (p < 0.005). Canonical discriminant analysis showed that the composition of antioxidant enzymes in erythrocytes was different for both the lamotrigine and carbamazepine groups, while the controls were separated from all others. Conclusions: Monotherapy with anti-seizure medications discretely alters redox homeostasis, followed by distinct relationships between antioxidant components.
Biomolecules to Biomarkers? U87MG Marker Evaluation on the Path towards Glioblastoma Multiforme Pathogenesis
Authors: Pokorná Markéta , Kútna Viera , Ovsepian Saak V. , Matěj Radoslav , Černá Marie , O’Leary Valerie Bríd
Published: January 2024

The heterogeneity of the glioma subtype glioblastoma multiforme (GBM) challenges effective neuropathological treatment. The reliance on in vitro studies and xenografted animal models to simulate human GBM has proven ineffective. Currently, a dearth of knowledge exists regarding the applicability of cell line biomolecules to the realm of GBM pathogenesis. Our study’s objectives were to address this preclinical issue and assess prominin-1, ICAM-1, PARTICLE and GAS5 as potential GBM diagnostic targets. The methodologies included haemoxylin and eosin staining, immunofluorescence, in situ hybridization and quantitative PCR. The findings identified that morphology correlates with malignancy in GBM patient pathology. Immunofluorescence confocal microscopy revealed prominin-1 in pseudo-palisades adjacent to necrotic foci in both animal and human GBM. Evidence is presented for an ICAM-1 association with degenerating vasculature. Significantly elevated nuclear PARTICLE expression from in situ hybridization and quantitative PCR reflected its role as a tumor activator. GAS5 identified within necrotic GBM validated this potential prognostic biomolecule with extended survival. Here we present evidence for the stem cell marker prominin-1 and the chemotherapeutic target ICAM-1 in a glioma animal model and GBM pathology sections from patients that elicited alternative responses to adjuvant chemotherapy. This foremost study introduces the long non-coding RNA PARTICLE into the context of human GBM pathogenesis while substantiating the role of GAS5 as a tumor suppressor. The validation of GBM biomarkers from cellular models contributes to the advancement towards superior detection, therapeutic responders and the ultimate attainment of promising prognoses for this currently incurable brain cancer.
Revealing the Effect of the Molecular Weight Distribution on the Chain Diffusion and Crystallization Process under a Branched Trimodal Polyethylene System
Authors: Cai Min , He Xuelian , Liu Boping
Published: January 2024

With the increasing demand for high-end materials, trimodal polyethylene (PE) has become a research hotspot in recent years due to its superior performance compared with bimodal PE. By means of molecular dynamics (MD) simulations, we aim to expound the effect of the molecular weight distribution (MWD) on the mechanism of nucleation and crystallization of trimodal PE. The crystallization rate is faster when short-chain branching is distributed on a single backbone compared to that on two backbones. In addition, as the content of high molecular weight backbone decreases, the time required for nucleation decreases, but the crystallization rate slows down. This is because low molecular weight backbones undergo intra-chain nucleation and crystallize earlier due to the high diffusion capacity, which leads to entanglement that prevents the movement of medium or high molecular weight backbones. Furthermore, crystallized short backbones hinder the movement and crystallization of other backbones. What is more, a small increase in the high molecular weight branched backbone of trimodal PE can make the crystallinity greater than that of bimodal PE, but when the content of high molecular weight backbone is too high, the crystallinity decreases instead, because the contribution of short and medium backbones to high crystallinity is greater than that of long backbones.
S/N/O-Enriched Carbons from Polyacrylonitrile-Based Block Copolymers for Selective Separation of Gas Streams
Authors: Gómez-Díaz Diego , Domínguez-Ramos Lidia , Malucelli Giulio , Freire María Sonia , González-Álvarez Julia , Lazzari Massimo
Published: January 2024

A series of polyacrylonitrile (PAN)-based block copolymers with poly(methyl methacrylate) (PMMA) as sacrificial bock were synthesized by atom transfer radical polymerization and used as precursors for the synthesis of porous carbons. The carbons enriched with O- and S-containing groups, introduced by controlled oxidation and sulfuration, respectively, were characterized by Raman spectroscopy, scanning electron microscopy, and X-ray photoelectron spectrometry, and their surface textural properties were measured by a volumetric analyzer. We observed that the presence of sulfur tends to modify the structure of the carbons, from microporous to mesoporous, while the use of copolymers with a range of molar composition PAN/PMMA between 10/90 and 47/53 allows the obtainment of carbons with different degrees of porosity. The amount of sacrificial block only affects the morphology of carbons stabilized in oxygen, inducing their nanostructuration, but has no effect on their chemical composition. We also demonstrated their suitability for separating a typical N2/CO2 post-combustion stream.
Monitoring Diesel Spills in Freezing Seawater under Windy Conditions Using C-Band Polarimetric Radar
Authors: Zabihi Mayvan Mahdi , Asihene Elvis , Desmond Durell , Hicks Leah , Polcwiartek Katarzyna , Stern Gary A. , Isleifson Dustin
Published: January 2024

The risk of oil spills in the Arctic is growing rapidly as anthropogenic activities increase due to climate-driven sea ice loss. Detecting and monitoring fuel spills in the marine environment is imperative for enacting an efficient response to mitigate the risk. Microwave radar systems can be used to address this issue; therefore, we examined the potential of C-band polarimetric radar for detecting diesel fuel in freezing seawater under windy environmental conditions. We present results from a mesocosm experiment, where we introduced diesel fuel to a seawater-filled cylindrical tub at the Sea-ice Environmental Research Facility (SERF), University of Manitoba. We characterized the temporal evolution of the diesel-contaminated seawater and sea ice by monitoring the normalized radar cross section (NRCS) and polarimetric parameters (i.e., copolarization ratio (Rco), cross-polarization ratio (Rxo), entropy (H), mean-alpha (α), conformity coefficient (μ), and copolarization correlation coefficient (ρco)) at 20° and 25° incidence angles. Three stages were identified, with notably different NRCS and polarimetric results, related to the thermophysical conditions. The transition from calm conditions to windy conditions was detected by the 25° incidence angle, whereas the transition from open water to sea ice was more apparent at 20°. The polarimetric analysis demonstrated that the conformity coefficient can have distinctive sensitivities to the presence of wind and sea ice at different incidence angles. The H versus α scatterplot showed that the range of distribution is dependent upon wind speed, incidence angle, and oil product. The findings of this study can be used to further improve the capability of existing and future C-band dual-polarization radar satellites or drone systems to detect and monitor potential diesel spills in the Arctic, particularly during the freeze-up season.
Influence of Flight Altitude and Surface Characteristics on UAS-LiDAR Ground Height Estimate Accuracy in Juncus roemerianus Scheele-Dominated Marshes
Authors: Amelunke Michael , Anderson Carlton P. , Waldron Margaret C. B. , Raber George T. , Carter Gregory A.
Published: January 2024

Management and monitoring of vulnerable coastal marshes rely on accurate ground height estimates. However, marsh surface characteristics such as vegetation and water presence complicate aerial remote sensing of the ground. Towards developing an improved understanding and techniques for these remote sensing efforts, this study established relationships among data collection flight altitude, surface characteristics, and ground height estimate accuracy within Juncus roemerianus Scheele-dominated marshes. Uncrewed Aerial System (UAS) Light Detection and Ranging (LiDAR) sampling was conducted at five altitudes for five marsh sites and one local control site. Survey-grade topographic measurements and marsh surface characteristics were recorded at each site for comparison. Root Mean Square Error (RMSE) and linear mixed-effects modeling were used to quantify relationships among vertical error, altitude, and surface characteristics. For low (24–72 m) and high (96–120 m) altitudes Above Ground Level (AGL), the RMSE values were 49 cm and 17 cm, respectively. Despite this appreciable improvement in accuracy with increasing flight altitude, point density values of these datasets limit applications. Linear mixed-effects modeling further emphasized the complex relationships between sensor footprint size, surface characteristics, and ground height estimates. These findings have direct implications for elevation modeling and monitoring efforts of frequently inundated, coastal marshes.
Recent Phenomenal and Investigational Subsurface Landslide Monitoring Techniques: A Mixed Review
Authors: Ebrahim Kyrillos M. P. , Gomaa Sherif M. M. H. , Zayed Tarek , Alfalah Ghasan
Published: January 2024

Landslides are a common and challenging geohazard that may be caused by earthquakes, rainfall, or manmade activity. Various monitoring strategies are used in order to safeguard populations at risk from landslides. This task frequently depends on the utilization of remote sensing methods, which include the observation of Earth from space, laser scanning, and ground-based interferometry. In recent years, there have been notable advancements in technologies utilized for monitoring landslides. The literature lacks a comprehensive study of subsurface monitoring systems using a mixed review approach that combines systematic and scientometric methods. In this study, scientometric and systematic analysis was used to perform a mixed review. An in-depth analysis of existing research on landslide-monitoring techniques was conducted. Surface-monitoring methods for large-scale landslides are given first. Next, local-scale landslide subsurface monitoring methods (movement, forces and stresses, water, temperature, and warning signs) were examined. Next, data-gathering techniques are shown. Finally, the physical modeling and prototype field systems are highlighted. Consequently, key findings about landslide monitoring are reviewed. While the monitoring technique selection is mainly controlled by the initial conditions of the case study, the superior monitoring technique is determined by the measurement accuracy, spatiotemporal resolution, measuring range, cost, durability, and applicability for field deployment. Finally, research suggestions are proposed, where developing a superior distributed subsurface monitoring system for wide-area monitoring is still challenging. Interpolating the complex nonlinear relationship between subsurface monitoring readings is a clear gap to overcome. Warning sign systems are still under development.
Stereo Matching Method for Remote Sensing Images Based on Attention and Scale Fusion
Authors: Wei Kai , Huang Xiaoxia , Li Hongga
Published: January 2024

With the development of remote sensing satellite technology for Earth observation, remote sensing stereo images have been used for three-dimensional reconstruction in various fields, such as urban planning and construction. However, remote sensing images often contain noise, occluded regions, untextured areas, and repeated textures, which can lead to reduced accuracy in stereo matching and affect the quality of 3D reconstruction results. To reduce the impact of complex scenes in remote sensing images on stereo matching and to ensure both speed and accuracy, we propose a new end-to-end stereo matching network based on convolutional neural networks (CNNs). The proposed stereo matching network can learn features at different scales from the original images and construct cost volumes with varying scales to obtain richer scale information. Additionally, when constructing the cost volume, we introduce negative disparity to adapt to the common occurrence of both negative and non-negative disparities in remote sensing stereo image pairs. For cost aggregation, we employ a 3D convolution-based encoder–decoder structure that allows the network to adaptively aggregate information. Before feature aggregation, we also introduce an attention module to retain more valuable feature information, enhance feature representation, and obtain a higher-quality disparity map. By training on the publicly available US3D dataset, we obtain an accuracy of 1.115 pixels in end-point error (EPE) and 5.32% in the error pixel ratio (D1) on the test dataset, and the inference speed is 92 ms. Comparing our model with existing state-of-the-art models, we achieve higher accuracy, and the network is beneficial for the three-dimensional reconstruction of remote sensing images.
Sustainable Monitoring of Mining Activities: Decision-Making Model Using Spectral Indexes
Authors: Michałowska Krystyna , Pirowski Tomasz , Głowienka Ewa , Szypuła Bartłomiej , Malinverni Eva Savina
Published: January 2024

In response to the escalating demand for mineral resources and the imperative for sustainable management of natural assets, the development of effective methods for monitoring mining excavations is essential. This study presents an innovative decision-making model that employs a suite of spectral indices for the sustainable monitoring of mining activities. The integration of the Combinational Build-up Index (CBI) with additional spectral indices such as BRBA and BAEI, alongside multitemporal analysis, enhances the detection and differentiation of mining areas, ensuring greater stability and reliability of results, particularly when applied to single datasets from the Sentinel-2 satellite. The research indicates that the average accuracy of excavation detection (overall accuracy, OA) for all test fields and data is approximately 72–74%, varying with the method employed. Utilizing a single CBI index often results in a significant overestimation of producer’s accuracy (PA) over user’s accuracy (UA), by about 10–14%. Conversely, the introduction of a set of three complementary indices achieves a balance between PA and UA, with discrepancies of approximately 1–3%, and narrows the range of result variations across different datasets. Furthermore, the study underscores the limitations of employing average threshold values for excavation monitoring and suggests the adoption of dedicated monthly thresholds to diminish accuracy variability. These findings could have considerable implications for the advancement of autonomous and largely automated systems for the surveillance of illegal mining excavations, providing a predictable and reliable methodology for remote sensing applications in environmental monitoring.
A Framework for Fine-Grained Land-Cover Classification Using 10 m Sentinel-2 Images
Authors: Zhang Wenge , Yang Xuan , Yuan Zhanliang , Chen Zhengchao , Xu Yue
Published: January 2024

Land-cover mapping plays a crucial role in resource detection, ecological environmental protection, and sustainable development planning. The existing large-scale land-cover products with coarse spatial resolution have a wide range of categories, but they suffer from low mapping accuracy. Conversely, land-cover products with fine spatial resolution tend to lack diversity in the types of land cover they encompass. Currently, there is a lack of large-scale land-cover products simultaneously possessing fine-grained classifications and high accuracy. Therefore, we propose a mapping framework for fine-grained land-cover classification. Firstly, we propose an iterative method for developing fine-grained classification systems, establishing a classification system suitable for Sentinel-2 data based on the target area. This system comprises 23 fine-grained land-cover types and achieves the most stable mapping results. Secondly, to address the challenges in large-scale scenes, such as varying scales of target features, imbalanced sample quantities, and the weak connectivity of slender features, we propose an improved network based on Swin-UNet. This network incorporates a pyramid pooling module and a weighted combination loss function based on class balance. Additionally, we independently trained models for roads and water. Guided by the natural spatial relationships, we used a voting algorithm to integrate predictions from these independent models with the full classification model. Based on this framework, we created the 2017 Beijing–Tianjin–Hebei regional fine-grained land-cover product JJJLC-10. Through validation using 4254 sample datasets, the results indicate that JJJLC-10 achieves an overall accuracy of 80.3% in the I-level validation system (covering seven land-cover types) and 72.2% in the II-level validation system (covering 23 land-cover types), with kappa coefficients of 0.7602 and 0.706, respectively. In comparison with widely used land-cover products, JJJLC-10 excels in accurately depicting the spatial distribution of various land-cover types and exhibits significant advantages in terms of classification quantity and accuracy.
Long-Term Dynamics of Atmospheric Sulfur Dioxide in Urban and Rural Regions of China: Urbanization and Policy Impacts
Authors: Wang Fang , Shaheen Abdallah , Yousefi Robabeh , Ge Quansheng , Wu Renguang , Lelieveld Jos , Kaskaoutis Dimitris G. , Lu Zifeng , Zhan Yu , Zhou Yuyu
Published: January 2024

High levels of sulfur dioxide (SO2) due to human activities pose a serious air pollution issue in China, especially in urban agglomerations. However, limited research has investigated the impact of anthropogenic emissions on higher SO2 concentrations in urban regions compared to rural areas in China. Here, we analyzed the trends in SO2 concentrations from 1980 to 2021 in China using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) dataset. SO2 column concentrations from the Copernicus Atmosphere Monitoring Service (CAMS) and the Ozone Monitoring Instrument (OMI) during the years 2007–2021 were also examined for validation and comparison purposes. Eight representative areas, including four urban regions (Pearl River Delta [PRD], Beijing-Tianjin-Hebei [BTH], Yangtze River Delta [YRD], and Sichuan Basin [SCB]) and four rural regions (Northeast Region [NER], Mongolian Region [MR], West Region [WR], and Tibetan Plateau Region [TR]) were selected for the analysis. Overall, a significant but fluctuating increase in SO2 concentrations over China was observed during 1980–2021. During 1980–1997 and 2000–2010, there was an increase in SO2 concentration, while during 1997–2000 and 2010–2021, a decreasing trend was observed. The average increase in SO2 concentration was approximately 16 times higher in urban regions than in the rural background. We also found that SO2 dynamics were highly associated with expansion of urban areas, population density, and gross domestic product. Nonetheless, since 2007, SO2 concentrations have exhibited a downward trend, which is mainly attributed to the air pollution policies implemented by the Chinese government. Our findings highlight the need for further studies on the impact of SO2 on regional climate change in China.
Selection of Landsat-8 Operational Land Imager (OLI) Optimal Band Combinations for Mapping Alteration Zones
Authors: Yang Chen , Jia Hekun , Dong Lifang , Zhao Haishi , Zhao Minghao
Published: January 2024

In typical alteration extraction methods, e.g., band math and principal component analysis (PCA), the bands or band combinations unitized to extract altered minerals are usually selected based on empirical models or previous rules. This results in significant differences in the alteration of mineral mapping even in the same area, thus greatly increasing the uncertainty of mineral resource prediction. In this paper, an intelligent alteration extraction approach was proposed in which an optimization algorithm, i.e., a genetic algorithm (GA), was introduced into the PCA; this approach is termed GA-PCA and is used for selecting the optimized band combinations of mineralized alterations. The proposed GA-PCA was employed to map iron oxides and hydroxyl minerals using the most commonly adopted multispectral data, i.e., Landsat-8 OLI data, at the Lalingzaohuo polymetallic deposits, China. The results showed that the spectral characteristics of GA-PCA-selected OLI band combinations in the research area were beneficial for enhancing alteration information and were more capable of suppressing the interference of vegetation information. The mapping alteration zones using the GA-PCA approach had a higher agreement with known ore spots, i.e., 25% and 33.3% in ferrous-bearing and hydroxyl-bearing deposits, compared to the classical PCA. Furthermore, two predicted targets (not shown in the classical PCA results) were precisely obtained via analyzing the GA-PCA alteration maps combined with the ore-forming geological conditions of the mine and its tectonic characteristics. This indicated that the intelligent selection of mineral alteration band combinations increased the reliability of remote sensing-based mineral exploration.
Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by Multi-Objective Genetic Optimization of Sensor Placement and Strain Pre-Extrapolation
Authors: Del Priore Emiliano , Lampani Luca
Published: January 2024

The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as “shape sensing”. The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors.
Analyzing the Automatic Power Level Control Effect of a Signal Generator in RF Power Sensor Calibration by a Direct Comparison Transfer Method and a Millimeter Wave Application
Authors: Danaci Erkan
Published: January 2024

Most calibration laboratories prefer the Direct Comparison Transfer Method (DCTM) for a reliable and accurate calibration of power sensors in the radio frequency (RF) scope. Most studies suggest using this calibration method, with its automatic power level control (APLC) of RF signal generators. The APLC is preferred to keep the output power level of the signal generator the same, while the power sensor is calibrated and the reference power sensor is connected to the measurement system. The known APLC mechanisms are also explained for the DCTM, and a comparison of the calibration factor values carried out with and without the automatic power level control process in the DCTM is also given in this study. RF power sensor calibrations with coaxial and waveguide connector types are examined with DCTM in this study as well. This study shows that the DCTM, unless with APLC, should be applied for the waveguide power sensor’s calibration at millimeter wave frequencies.
X as a Passive Sensor to Identify Opinion Leaders: A Novel Method for Balancing Visibility and Community Engagement
Authors: Furini Marco
Published: January 2024

The identification of opinion leaders is a matter of great significance for companies and authorities, as these individuals are able to shape the opinions and attitudes of entire societies. In this paper, we consider X (formerly Twitter) as a passive sensor to identify opinion leaders. Given the unreliability of the traditional follower count metric due to the presence of fake accounts and farm bots, our approach combines the measures of visibility and community engagement to identify these influential individuals. Through an experimental evaluation involving approximately 4 million tweets, we showed two important findings: (i) relying solely on follower count or post frequency is inadequate for accurately identifying opinion leaders, (ii) opinion leaders are able to build community and gain visibility around specific themes. The results showed the benefits of using X as a passive sensor to identify opinion leaders, as the proposed method offers substantial advantages for those who are involved in social media communication strategies, including political campaigns, brand monitoring, and policymaking.
Enhancing Seismic Damage Detection and Assessment in Highway Bridge Systems: A Pattern Recognition Approach with Bayesian Optimization
Authors: Liang Xiao
Published: January 2024

Highway bridges stand as paramount elements within transportation infrastructure systems. The ability to ensure swift recovery after extreme events, such as earthquakes, is a fundamental trait of resilient communities. Consequently, expediting the recovery process necessitates near real-time diagnosis of structural damage to provide dependable information. In this study, a data-driven approach for damage detection and assessment is investigated, focusing on bridge columns—the pivotal supporting elements of bridge systems—based on simulations derived from nonlinear time history analysis. This research introduces a set of cumulative intensity-based damage features, whose efficacy is demonstrated through unsupervised learning techniques. Leveraging the support vector machine, a prominent pattern recognition algorithm in supervised learning, alongside Bayesian optimization with a Gaussian process, seismic damage detection and assessment are explored. Encouragingly, the methodology yields high estimation accuracies for both binary outcomes (indicating the presence of damage or the occurrence of collapse) and multi-class classifications (indicating the severity of damage). This breakthrough opens avenues for the practical implementation of on-board sensor computing, enabling near real-time damage detection and assessment in bridge structures.
Adaptive Optimization Method for Prediction and Compensation of Thin-Walled Parts Machining Deformation Based on On-Machine Measurement
Authors: Wu Long , Wang Aimin , Wang Kang , Xing Wenhao , Xu Baode , Zhang Jiayu , Yu Yuan
Published: January 2024

Thin-walled aluminum alloy parts are widely used in the aerospace field because of their favorable characteristics that cater to various applications. However, they are easily deformed during milling, leading to a low pass rate of workpieces. On the basis of on-machine measurement (OMM) and surrogate stiffness models (SSMs), we developed an iterative optimization compensation method in this study to overcome the machining deformation of thin-walled parts. In the error compensation process, the time-varying factors of workpiece stiffness and the impact of prediction model errors were considered. First, we performed machining deformation simulation and information extraction on the key nodes of the machined surface, and an SSM containing the stiffness information of discrete nodes of each cutting layer was established. Subsequently, the machining errors were monitored through intermittent OMM to suppress the adverse impact of prediction model errors. Further, an interlayer correction coefficient was introduced in the compensation process to iteratively correct the prediction model error of each node in the SSM along the depth direction, and a correction coefficient between parts was introduced to realize the iterative correction of the prediction model for the same node position between different parts. Finally, the feasibility of the proposed method was verified through multiple sets of actual machining experiments on thin-walled parts with added pads.
Modeling and Simulation of Shape Control Based on Digital Twin Technology in Hot Strip Rolling
Authors: Sun Youzhao , Li Jingdong , Sun Yamin , Song Lebao , Yang Quan , Wang Xiaochen
Published: January 2024

Focusing on the problem of strip shape quality control in the finishing process of hot rolling, a shape model based on metal flow and stress release with the application of varying contact rolling parameters is introduced. Combined with digital twin technology, the digital twin framework of the shape model is proposed, which realizes the deep integration between physical time–space and virtual time–space. With the utilization of the historical data, the parameters are optimized iteratively to complete the digital twin of the shape model. According to the schedule, the raw material information is taken as the input to obtain the simulation of the strip shape, which shows a variety of export shape conditions. The prediction absolute error of the crown and flatness are less than 5 μm and 5 I-unit, respectively. The results prove that the proposed shape simulation model with strong prediction performance can be effectively applied to hot rolling production. In addition, the proposed model provides operators with a reference for the parameter settings for actual production and promotes the intelligent application of a shape control strategy.
GLFNet: Combining Global and Local Information in Vehicle Re-Recognition
Authors: Yang Yinghan , Liu Peng , Huang Junran , Song Hongfei
Published: January 2024

Vehicle re-identification holds great significance for intelligent transportation and public safety. Extracting vehicle recognition information from multi-view vehicle images has become one of the challenging problems in the field of vehicle recognition. Most recent methods employ a single network extraction structure, either a single global or local measure. However, for vehicle images with high intra-class variance and low inter-class variance, exploring globally invariant features and discriminative local details is necessary. In this paper, we propose a Feature Fusion Network (GLFNet) that combines global and local information. It utilizes global features to enhance the differences between vehicles and employs local features to compactly represent vehicles of the same type. This enables the model to learn features with a large inter-class distance and small intra-class distance, significantly improving the model’s generalization ability. Experiments show that the proposed method is competitive with other advanced algorithms on three mainstream road traffic surveillance vehicle re-identification benchmark datasets.
A Review on Concrete Structural Properties and Damage Evolution Monitoring Techniques
Authors: Zhang Jinghua , Peng Lisha , Wen Shuzhi , Huang Songling
Published: January 2024

Concrete structures have emerged as some of the most extensively utilized materials in the construction industry due to their inherent plasticity and high-strength characteristics. However, due to the temperature fluctuations, humidity, and damage caused by human activities, challenges such as crack propagation and structural failures pose threats to the safety of people’s lives and property. Meanwhile, conventional non-destructive testing methods are limited to defect detection and lack the capability to provide real-time monitoring and evaluating of concrete structural stability. Consequently, there is a growing emphasis on the development of effective techniques for monitoring the health of concrete structures, facilitating prompt repairs and mitigation of potential instabilities. This paper comprehensively presents traditional and novel methods for concrete structural properties and damage evolution monitoring, including emission techniques, electrical resistivity monitoring, electromagnetic radiation method, piezoelectric transducers, ultrasonic techniques, and the infrared thermography approach. Moreover, the fundamental principles, advantages, limitations, similarities and differences of each monitoring technique are extensively discussed, along with future research directions. Each method has its suitable monitoring scenarios, and in practical applications, several methods are often combined to achieve better monitoring results. The outcomes of this research provide valuable technical insights for future studies and advancements in the field of concrete structural health monitoring.
RTK+OSNMA Positioning for Road Applications: An Experimental Performance Analysis in Finland
Authors: Vallet García José M. , Bhuiyan M. Zahidul H.
Published: January 2024

We compare the performance of dual-band (GPS L1/L2 and Galileo E1/E5a) real-time kinematic (RTK) positioning in an open sky and urban scenarios in southern Finland using two different authentication schemes: one using only satellites authenticated by Galileo’s open service navigation message authentication (OSNMA) service (which at the moment of our tests led to using only authenticated Galileo satellites) and the other with no authentication. The results show the actual trade-off between accuracy and availability vs. authenticity associated with using only OSNMA-authenticated satellites, while the authentication of only Galileo satellites is possible (e.g., a drop of RTK positioning availability from 96.67 to 86.01% in our open sky and from 73.55 to 18.65% in our urban scenarios, respectively), and an upper bound of the potential performance that could be reached in similar experimental conditions had the authentication of GPS satellites been supported (e.g., an overall 14 cm and 10.20 m 95% horizontal accuracy in our open sky and urban scenarios, with below 30, 20 and 10 cm during 97.39, 96.03 and 92.43% of the time in the open sky and 49.12, 45.96 and 39.63% in the urban scenarios, respectively).
Cost–Benefit Analysis of Distributed Energy Systems Considering the Monetization of Indirect Benefits
Authors: Zhao Yutong , Zeng Shuang , Ding Yifeng , Ma Lin , Wang Zhao , Liang Anqi , Ren Hongbo
Published: January 2024

Driven by market value, a co-benefits assessment framework to encompass various benefits arising from distributed energy systems is developed. Using a monetization approach, a quantitative analysis model is established to evaluate both direct and indirect benefits. According to the simulation results of typical distributed energy systems, the distributed photovoltaic (PV) system demonstrates superior economic performance compared with the gas-fired distributed energy system, highlighting its potential for widespread commercialization. Moreover, the inclusion of indirect benefits significantly enhances the economic viability of the distributed energy system. While the PV system exhibits a more favorable promotional impact, it also renders the gas-fired distributed energy system commercially feasible.
Status of Sino–Russian Trade in Agricultural Products: Dual Consideration Based on Characteristics and Growth
Authors: Fu Jing , Tong Guangji
Published: January 2024

The security of agricultural product supply is crucial for social development. For populous and resource-rich countries like China and Russia, strengthening their bilateral trade in agricultural products is essential to ensure stability and security in the global food market. However, current research on their agricultural product trade lacks sufficient detail. This article aims to analyze the overall trend of agricultural product trading between China and Russia based on trade data from 2007 to 2021. Different trade indexes will be used to examine the four aspects of the agricultural product trade characteristics of both countries. The constant market share model will be employed to analyze the growth factors of agricultural product trade between the two nations, providing insights into the driving force behind changes in agricultural exports. The findings of this study demonstrate that bilateral agricultural trade between China and Russia shows complementarity and high trade intensity. Intra-industry trade represents the primary form of agricultural product exchange, while inter-industry trade serves as a supplemental form of trade. In terms of trade growth impact, the bilateral agricultural trade between China and Russia is primarily influenced by the effect of product structure, followed by competitiveness. Therefore, in the face of uncertain situations, such as counter-globalization and localized trade frictions, specific recommendations will be provided to optimize the trade structure of agricultural products between the two nations.
The COVID-19 Pandemic Response and Its Impact on Post-Corona Health Emergency and Disaster Risk Management in Türkiye
Authors: Erbay Hasan
Published: January 2024

The COVID-19 pandemic, which has affected the entire world, has not only created a number of emerging issues for each country, especially in the field of public health, but has also provided a number of opportunities for risk management, alternative strategies and completely new ways of looking at challenges. This brief report examines the COVID-19 pandemic response in Türkiye and the possible implications of the experience for future responses to other health emergencies and disaster risk management, based on the lessons learned. This study uses publicly available literature from government, private sector and academic sources to analyse the conflicts, changes and lessons learned during the COVID-19 pandemic, which are components of the World Health Organization (WHO) Health Emergency and Disaster Risk Management (Health EDRM) framework. The COVID-19 experience in Türkiye has several aspects, including the significant role of healthcare workers, the existence of an effective health system accustomed to emergencies, applications based on information technologies, the partial transparency of public authorities in providing information and a socio-cultural environment related to cooperation on prevention strategies, including wearing masks and vaccination. Challenges in Türkiye include distance learning in schools, lockdowns that particularly affect the elderly, ensuring environmental sustainability, hesitation about the effectiveness of social/financial support programs, the socio-cultural trivialisation of pandemics after a while and the relaxation of prevention strategies. Lessons learned from the COVID-19 pandemic include the value of transparency in public health/healthcare information, the strengthening of all aspects of the health system in terms of health workers and the importance of a balanced economy prepared for foreseeable risks.