bluuebunny/crossref_metadata_2025 huggingface dataset:
Causal Link between Gut Microbiota, Neurophysiological States, and Bone Diseases: A Comprehensive Mendelian Randomization Study
Authors: Luo Shaoting , Chen Zhiyang , Deng Linfang , Chen Yufan , Zhou Weizheng , Canavese Federico , Li Lianyong
Published: September 2023

Increasing evidence highlights a robust correlation between the gut microbiota and bone diseases; however, the existence of a causal relationship between them remains unclear. In this study, we thoroughly examined the correlation between gut microbiota and skeletal diseases using genome-wide association studies. Linkage disequilibrium score regression and Mendelian randomization were used to probe genetic causality. Furthermore, the potential mediating role of neuropsychological states (i.e., cognition, depression, and insomnia) between the gut microbiota and bone diseases was evaluated using mediation analysis, with genetic colocalization analysis revealing potential targets. These findings suggest a direct causal relationship between Ruminococcaceae and knee osteoarthritis (OA), which appears to be mediated by cognitive performance and insomnia. Similarly, a causal association was observed between Burkholderiales and lumbar pelvic fractures, mediated by cognitive performance. Colocalization analysis identified a shared causal variant (rs2352974) at the TRAF-interacting protein locus for cognitive ability and knee OA. This study provides compelling evidence that alterations in the gut microbiota can enhance cognitive ability, ameliorate insomnia, and potentially reduce the risk of site-specific fractures and OA. Therefore, strategies targeting gut microbiota optimization could serve as novel and effective preventive measures against fractures and OA.
Reply to Consavage Stanley, K.; Kraak, V.I. Comment on “Lau et al. Trends in Beef Intake in the United States: Analysis of the National Health and Nutrition Examination Survey, 2001–2018. Nutrients 2023, 15, 2475”
Authors: Lau Clara S. , Fulgoni Victor L. , Van Elswyk Mary E. , McNeill Shalene H.
Published: September 2023

We thank the authors [...]
Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables
Authors: Martin-Morales Agustin , Yamamoto Masaki , Inoue Mai , Vu Thien , Dawadi Research , Araki Michihiro
Published: September 2023

Cardiovascular disease (CVD) is one of the primary causes of death around the world. This study aimed to identify risk factors associated with CVD mortality using data from the National Health and Nutrition Examination Survey (NHANES). We created three models focusing on dietary data, non-diet-related health data, and a combination of both. Machine learning (ML) models, particularly the random forest algorithm, demonstrated robust consistency across health, nutrition, and mixed categories in predicting death from CVD. Shapley additive explanation (SHAP) values showed age, systolic blood pressure, and several other health factors as crucial variables, while fiber, calcium, and vitamin E, among others, were significant nutritional variables. Our research emphasizes the importance of comprehensive health evaluation and dietary intake in predicting CVD mortality. The inclusion of nutrition variables improved the performance of our models, underscoring the utility of dietary intake in ML-based data analysis. Further investigation using large datasets with recurring dietary recalls is necessary to enhance the effectiveness and interpretability of such models.
Evidence-Based Practice Competency of Registered Nurses in the Greek National Health Service
Authors: Schetaki Stefania , Patelarou Evridiki , Giakoumidakis Konstantinos , Kleisiaris Christos , Patelarou Athina
Published: September 2023

Nurses’ competency toward evidence-based practice (EBP) has been extensively investigated by several studies worldwide. However, factors affecting the competence of Greek nurses working in the NHS have not been fully investigated in terms of EBP. Thus, this study aimed to explore the impact of the individual qualifications of nurses on their competence toward EBP. Data from 473 registered nurses working in 10 hospitals in the Greek National Health Service (NHS) were collected between October and December 2020 using a convenience sampling method in a cross-section design. The Greek version of the 35-item (five-point Likert scale) Evidence-Based Practice Competency Questionnaire for Professional Registered Nurses (EBP-COQ Prof) was used to assess the competence level of nurses, focusing on attitudes, skills, and knowledge, as well as the utilization of EBP in clinical practice. One-way ANOVA and Pearson coefficient tests were applied to compare the possible differences among variables (two or more groups) as appropriate. A multi-factorial regression model was applied to explore participants’ qualifications, including demographics (MSc degree, gender, English language knowledge, etc.) as independent variables, and to control for potential confounding effects toward EBP competency. The p-values < 0.05 were considered statistically significant. The mean age of the 473 participants (402 women and 71 men) was 44.7 ± 9.2 years old. The mean value of competence subscales was found as follows: attitudes 3.9 ± 0.6, knowledge 3.7 ± 0.6, skills 3.1 ± 0.8, and utilization 3.4 ± 0.7. A multivariate regression analysis revealed that associates of “Master’s degree” (t = 3.039, p = 0.003), “Writing an academic article” (3.409, p = 0.001), “Working in a University clinic” (2.203, p = 0.028), and “Computer Skills” (2.404, p = 0.017) positively affected “Attitudes”, “Knowledge”, “Skills”, and “Utilization”, respectively. The research data suggest that nurses working in the Greek NHS were limited in competence regarding EBP in comparison with other European countries. Therefore, vocational, educational, and training programs tailored to EBP enhancement are crucially important. This study was not registered.
Mathematical Model of the Spread of Hantavirus Infection
Authors: Gutiérrez-Jara Juan Pablo , Muñoz-Quezada María Teresa , Córdova-Lepe Fernando , Silva-Guzmán Alex
Published: September 2023

A mathematical epidemiological model incorporating the mobility of rodents and human groups among zones of less or major contact between them is presented. The hantavirus infection dynamics is expressed using a model type SEIR (Susceptible-Exposed-Infectious-Removed), which incorporates the displacement of the rodent and the human, between the urban and rural sector, the latter being subdivided in populated and non-populated. The results show the impact that rodent or human displacement may have on the propagation of hantavirus infection. Human mobility is more significant than rodents in increasing the number of hantavirus infection cases. The results found may be used as a reference by the health authorities to develop more specific campaigns on the territorial dynamics of the rodent, attend to the mobility of humans in these territories, mainly agricultural and forestry workers, and strengthen control-prevention actions in the community, to prevent future outbreaks that are fatal.
Commensal Fitness Advantage May Contribute to the Global Dissemination of Multidrug-Resistant Lineages of Bacteria—The Case of Uropathogenic E. coli
Authors: Fuzi Miklos , Sokurenko Evgeni
Published: September 2023

It is widely accepted that favorable fitness in commensal colonization is one of the prime facilitators of clonal dissemination in bacteria. The question arises as to what kind of fitness advantage may be wielded by uropathogenic strains of the two predominant fluoroquinolone- and multidrug-resistant clonal groups of E. coli—ST131-H30 and ST1193, which has permitted their unprecedented pandemic-like global expansion in the last few decades. The colonization-associated genes’ content, carriage of low-cost plasmids, and integrons with weak promoters could certainly contribute to the fitness of the pandemic groups, although those genetic factors are common among other clonal groups as well. Also, ST131-H30 and ST1193 strains harbor fluoroquinolone-resistance conferring mutations targeting serine residues in DNA gyrase (GyrA-S83) and topoisomerase IV (ParC-S80) that, in those clonal backgrounds, might result in a commensal fitness benefit, i.e., beyond the antibiotic resistance per se. This fitness gain might have contributed not only to the widespread dissemination of these major clones in the healthcare setting but also to their long-term colonization of healthy individuals and, thus, circulation in the community, even in a low or no fluoroquinolone use environment. This evolutionary shift affecting commensal E. coli, initiated by mutations co-favorable in both antibiotics-treated patients and healthy individuals warrants more in-depth studies to monitor further changes in the epidemiological situation and develop effective measures to reduce the antibiotic resistance spread.
Conformational Dynamics of Biopolymers in the Course of Their Interaction: Multifaceted Approaches to the Analysis by the Stopped-Flow Technique with Fluorescence Detection
Authors: Kuznetsov Nikita A.
Published: September 2023

This review deals with modern approaches to systematic research on molecular-kinetic mechanisms of damage recognition and removal by pro- and eukaryotic enzymes of DNA base excision repair. To this end, using DNA glycosylases from different structural families as an example—as well as apurinic/apyrimidinic endonuclease, which differs structurally and catalytically from DNA glycosylases—a comprehensive methodology is described in detail regarding studies on the mechanisms of action of DNA repair enzymes in humans and in Escherichia coli. This methodology is based on kinetic, thermodynamic, and mutational analyses of alterations in the conformation of molecules of an enzyme and of DNA during their interaction in real time. The described techniques can be used to analyze any protein–protein or protein–nucleic acid interactions.
Prediction of Shock Wave Velocity Induced by a Combined Millisecond and Nanosecond Laser Based on Convolution Neural Network
Authors: Li Jingyi , Zhang Wei , Li Ye , Jin Guangyong
Published: September 2023

The variation of shock-wave velocity with time induced by a millisecond-nanosecond combined pulse laser (CPL) on silicon is investigated. The convolution neural network (CNN) is used to predict the shock-wave velocity induced by a single ns laser and CPL with a ns laser energy density of 6, 12 and 24 J/cm2, ms laser energy density of 0 and 226.13 J/cm2, and pulse delay of 0, 0.4 and 0.8 ms. The four-layer CNN model was applied, ns laser energy density, ms laser energy density, pulse delay and time were set as the input parameter, while the shock-wave velocity was set as the output parameter. The correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) of the CNN model on the test data set was 0.9865, 3.54 and 3.01, respectively. This indicated that the CNN model shows a high reliability in the prediction of CPL-induced shock-wave velocity with limited experimental data.
Antioxidant and Antiproliferative Activities of Phenolic Extracts of Eriobotrya japonica (Thunb.) Lindl. Fruits and Leaves
Authors: Pawłowska Agata Maria , Żurek Natalia , Kapusta Ireneusz , De Leo Marinella , Braca Alessandra
Published: September 2023

Increasing interest in new sources of secondary metabolites as biologically active substances has resulted in an advanced study of many plant species. Loquat (Eriobotrya japonica (Thunb.) Lindl. = Rhaphiolepis bibas (Lour.) Galasso & Banfi, Rosaceae family), an evergreen, subtropical fruit tree, native to China and Japan, but cultivated in southern countries of Europe, is a species commonly used in folk medicine and may be an excellent source of bioactive compounds. Therefore, the aim of the present study was to evaluate the profile of the phenolic constituents of E. japonica fruits and leaves originating from Tuscany (Italy), as well as their in vitro antioxidant and chemopreventive activities on human cancer cell lines breast adenocarcinoma (MCF-7), colon adenocarcinoma (Caco-2 and HT-29), and glioblastoma (U87MG). Results revealed that the extract of leaves displayed higher antioxidant and anticancer potential than the fruit extract and contained 25 individual phenolic compounds that have been characterized and quantified by the UPLC-PDA-MS method. The antiproliferative activity was correlated with the content of polyphenolic compounds indicating that both fruits and leaves are a good source of antioxidants and may be exploited as nutraceuticals enriching food or as components for the cosmetic/pharmaceutical industry.
Rowanberry—A Source of Bioactive Compounds and Their Biopharmaceutical Properties
Authors: Arvinte Ofelia Marioara , Senila Lăcrimioara , Becze Anca , Amariei Sonia
Published: September 2023

After a period of intense development in the synthesis pharmaceutical industry, plants are making a comeback in the public focus as remedies or therapeutic adjuvants and in disease prevention and ensuring the wellbeing and equilibrium of the human body. Plants are being recommended more and more in alimentation, in their natural form, or as extracts, supplements or functional aliments. People, in general, are in search of new sources of nutrients and phytochemicals. As a result, scientific research turns to lesser known and used plants, among them being rowanberries, a species of fruit very rich in nutrients and underused due to their bitter astringent taste and a lack of knowledge regarding the beneficial effects of these fruit. Rowan fruits (rowanberries) are a rich source of vitamins, polysaccharides, organic acids and minerals. They are also a source of natural polyphenols, which are often correlated with the prevention and treatment of modern world diseases. This article presents the existing data regarding the chemical composition, active principles and biopharmaceutical properties of rowan fruits and the different opportunities for their usage.
Organosilicon Compounds in Hot-Melt Adhesive Technologies
Authors: Czakaj Jakub , Sztorch Bogna , Romanczuk-Ruszuk Eliza , Brząkalski Dariusz , Przekop Robert E.
Published: September 2023

Hot-melt adhesives (HMAs) are thermoplastic materials that can bond various substrates by solidifying rapidly upon cooling from the molten state, and their modification with organosilicon compounds can result in crosslinking behavior, characteristic of gels. Organosilicon compounds are hybrid molecules that have both inorganic and organic components and can enhance the properties and performance of HMAs. The gel aspect of HMA with and without organosilicon modifiers can be considered in organosilicon-modified systems, the modifiers are often either sol–gel condensation products or their mechanism of action on the adherent surface can be considered of sol–gel type. The purpose of this manuscript is to present the current state of the art on the formulation, characterization, and application of HMAs and optimize their performance with organosilicon compounds for application in various industries such as automotive, construction, and photovoltaics. This review covers articles published within the period of 2018–2022. The article is divided into sections, in which information about hot-melt adhesives is described at the beginning. The following part of the presented review focuses on the composition of hot-melt adhesives, which takes into account the use of organosilicon compounds. The last part of this review outlines the future trends in hot-melt adhesives.
Donor-Acceptor-Based Organic Polymer Semiconductor Materials to Achieve High Hole Mobility in Organic Field-Effect Transistors
Authors: Ren Shiwei , Wang Zhuoer , Zhang Wenqing , Ding Yubing , Yi Zhengran
Published: September 2023

Organic polymer semiconductor materials are conveniently tuned to energy levels because of their good chemically modifiable properties, thus enhancing their carrier transport capabilities. Here, we have designed and prepared a polymer with a donor-acceptor structure and tested its potential as a p-type material for organic field-effect transistor (OFET) applications using a solution-processing method. The conjugated polymers, obtained via the polymerization of the two monomers relying on the Stille coupling reaction, possess extremely high molecular weights and thermodynamic stability. Theoretical-based calculations show that PDPP-2S-Se has superior planarity, which is favorable for carrier transport within the main chain. Photophysical and electrochemical measurements systematically investigated the properties of the material and the energy levels with respect to the theoretical values. The maximum hole mobility of the PDPP-2S-Se-based OFET device is 0.59 cm2 V−1 s−1, which makes it a useful material for potential organic electronics applications.
Influence of Thermal Treatment and Acetic Acid Concentration on the Electroactive Properties of Chitosan/PVA-Based Micro- and Nanofibers
Authors: Bernal Rigel Antonio Olvera , Olekhnovich Roman O. , Uspenskaya Mayya V.
Published: September 2023

This study presents, for the first time, a comprehensive investigation of the influence of pre- and post-fabrication parameters for the electroactive properties of electrospun chitosan/PVA-based micro- and nanofibers. Chitosan/PVA fibers were fabricated using electrospinning, characterized, and tested as electroactive materials. Solutions with different acetic acid contents (50, 60, 70, and 80 v/v%) were used, and the rheological properties of the solutions were analyzed. Characterization techniques, such as rheology, conductivity, optical microscopy, a thermogravimetric analysis, differential scanning calorimetry, a tensile test, and FT-IR spectroscopy, were utilized. Fiber mats from the various solutions were thermally treated, and their electroactive behavior was examined under a constant electric potential (10 V) at different pHs (2–13). The results showed that fibers electrospun from 80% acetic acid had a lower electroactive response and dissolved quickly. However, thermal treatment improved the stability and electroactive response of all fiber samples, particularly the ones spun with 80% acetic acid, which exhibited a significant increase in speed displacement from 0 cm−1 (non-thermally treated) to 1.372 cm−1 (thermally treated) at a pH of 3. This study sheds light on the influence of pre- and post-fabrication parameters on the electroactive properties of chitosan/PVA fibers, offering valuable insights for the development of electroactive materials in various applications.
Security of Cyber-Physical Systems of Chemical Manufacturing Industries Based on Blockchain
Authors: Deng Wu , Fan Wei , Li Zhenzhen , Cui Chi , Ji Xu , He Ge
Published: September 2023

The traditional manufacturing systems are often enterprise-centric systems, whereas the modern chemical industry is oriented towards industrial chain integration. Enterprise entities present a loosely coupled state at the scale of the industrial chain, with decentralized characteristics. This poses greater challenges and requirements for the industrial safety system. Based on the characteristics of the chemical manufacturing industry and blockchain, the application of the information security of blockchain in the chemical manufacturing industry is studied herein and the cyber-physical systems security architecture model of dual blockchains is proposed. The first-layer blockchain is applied at the system’s core function level to solve security issues at the system level and provide security guarantees for communication, transactions, and billing between users and manufacturers. Meanwhile, the second layer involves the system resource layer, which not only solves the security problem of cross-level platform data interaction, but also enables the point-to-point security of the device-level cyber-physical system to ensure internal equipment communication information security. A domestic commercial concrete manufacturing company’s real production and operation data were used to simulate basic functions such as transaction requests, trade success, and blockchain queries. After multiple tests, results show that its basic blockchain, query response, transaction creation, and block creation functions are all finished within milliseconds, meeting the industrial requirements. Its safety verification can meet the requirements of safety, efficiency, and low latency for production control in chemical industry sites, proving the feasibility of applying the dual blockchain model in the chemical manufacturing industry. Based on data security, privacy, and integrity requirements, the blockchain technology proposed in this article provides a more efficient, transparent, and secure operation and management solution for the chemical industry.
Promoting Effect of Microwave Field on Gas Phase Diffusion Limited Magnetite Reduction in Carbon Monoxide
Authors: Zhou Meijie , Ai Liqun , Hong Lukuo , Sun Caijiao , Tong Shuai
Published: September 2023

To investigate the effect of microwave irradiation on the rate of magnetite reduction while increasing the gas phase diffusion rate limit, the microstructure and kinetics of CO reduction of magnetite powder were studied. The investigation was conducted through microwave irradiation and conventional heating at 900~1100 °C. Under the two heating methods, the iron crystal is selectively reduced and gradually expanded along the direction normal to the length of the ore powder, forming a strip of iron crystal that penetrates the powder and expands outward across the width. The microwave field can effectively improve the sintering of minerals. The changes in Avrami exponents m and k in the reduction process were determined by combining the Johnson–Mehl–Avrami (JMA) model with the lnln method. The microwave field did not change the limiting step. Microwave irradiation proves to be the most effective means to enhance both the initial reduction rate and the rate during the primary iron crystal precipitation phase. The morphology of the iron crystal takes on a dense punctate shape, influenced by the rate of diffusion control.
Pope Francis: Master of Imaginative Discernment through Storytelling, Metaphors, and Symbols
Authors: Kim Young Hoon , Pinto Paul Rolphy
Published: September 2023

This paper attempts to deal with an essential dimension of the process of discernment as Francis develops it, namely, the narrative. The paper treats the imaginative discernment exemplified in his use of storytelling, metaphors, and symbols to open a creative forum for discerning sacred truths in our personal lives. To justify the appropriateness of Francis’s use of imagination in discernment, the first part of the paper analyzes Ignatius’s use of the imagination, especially in the Rules for Discernment of Spirits. The second and lengthier part of the paper turns to Pope Francis’s skilled narrative use of metaphors and symbols. He adopts the Ignatian imaginative style with metaphors that appeal to contemporary seekers. The various metaphorical twists that he incorporates into this experience have a privileged place in his spiritual pedagogy of accompanying, discerning, and walking together with people during difficult times. The metaphorical style of Ignatius and Francis allows for a comprehensive understanding of the role that imagination can play in the discernment of spirits. Besides enriching our understanding of discernment, this style can facilitate a Christian spirituality that enhances the search for and discovery of meaning within our contemporary lives.
Pricing of Averaged Variance, Volatility, Covariance and Correlation Swaps with Semi-Markov Volatilities
Authors: Swishchuk Anatoliy , Franco Sebastian
Published: September 2023

In this paper, we consider the problem of pricing variance, volatility, covariance and correlation swaps for financial markets with semi-Markov volatilities. The paper’s motivation derives from the fact that in many financial markets, the inter-arrival times between book events are not independent or exponentially distributed but instead have an arbitrary distribution, which means they can be accurately modelled using a semi-Markov process. Through the results of the paper, we hope to answer the following question: Is it possible to calculate averaged swap prices for financial markets with semi-Markov volatilities? This question has not been considered in the existing literature, which makes the paper’s results novel and significant, especially when one considers the increasing popularity of derivative securities such as swaps, futures and options written on the volatility index VIX. Within this paper, we model financial markets featuring semi-Markov volatilities and price-averaged variance, volatility, covariance and correlation swaps for these markets. Formulas used for the numerical evaluation of averaged variance, volatility, covariance and correlation swaps with semi-Markov volatilities are presented as well. The formulas that are detailed within the paper are innovative because they provide a new, simplified method to price averaged swaps, which has not been presented in the existing literature. A numerical example involving the pricing of averaged variance, volatility, covariance and correlation swaps in a market with a two-state semi-Markov process is presented, providing a detailed overview of how the model developed in the paper can be used with real-life data. The novelty of the paper lies in the closed-form formulas provided for the pricing of variance, volatility, covariance and correlation swaps with semi-Markov volatilities, as they can be directly applied by derivative practitioners and others in the financial industry to price variance, volatility, covariance and correlation swaps.
Detection of Winter Heat Wave Impact on Surface Runoff in a Periglacial Environment (Ny-Ålesund, Svalbard)
Authors: Salzano Roberto , Cerrato Riccardo , Scoto Federico , Spolaor Andrea , Valentini Emiliana , Salvadore Marco , Esposito Giulio , Sapio Serena , Taramelli Andrea , Salvatori Rosamaria
Published: September 2023

The occurrence of extreme warm events in the Arctic has been increasing in recent years in terms of their frequency and intensity. The assessment of the impact of these episodes on the snow season requires further observation capabilities, where spatial and temporal resolutions are key constraints. This study targeted the snow season of 2022 when a winter rain-on-snow event occurred at Ny-Ålesund in mid-March. The selected methodology was based on a multi-scale and multi-platform approach, combining ground-based observations with satellite remote sensing. The ground-based observation portfolio included meteorological measurements, nivological information, and the optical description of the surface in terms of spectral reflectance and snow-cover extent. The satellite data were obtained by the Sentinel-2 platforms, which provided ten multi-spectral acquisitions from March to July. The proposed strategy supported the impact assessment of heat waves in a periglacial environment, describing the relation and the timing between rain-on-snow events and the surface water drainage system. The integration between a wide range of spectral, time, and spatial resolutions enhanced the capacity to monitor the evolution of the surface water drainage system, detecting two water discharge pulsations, different in terms of duration and effects. This preliminary study aims to improve the description of the snow dynamics during those extreme events and to assess the impact of the produced break during the snow accumulation period.
Understanding Road Traffic Evolution in an Urbanizing Context through Statistical and Geospatial Analysis
Authors: Shen Wencang , Niu Lu , Liang Yingzi
Published: September 2023

Urbanization, an accelerated process, is inherently coupled with complex issues, including the evolution of road traffic systems. This diversity in urbanization and transport infrastructure largely hinges on economic status and geographic positioning across cities. Leveraging the capabilities of remote sensing and Geographic Information Systems (GIS) in processing geospatial big data, this paper evaluates the urbanization level (UL) and road traffic level (RTL) in 212 prefecture-level cities using statistical and geospatial grid data. We aim to dissect the impact of UL on RTL, thus highlighting the specific challenges and opportunities across regions and pinpointing optimal urban development models. Our findings demonstrate (1) rapid development in UL across all cities, juxtaposed with a surge and then stagnation in RTL; (2) a positive correlation between UL and RTL that grows over time but weakens in later stages; (3) differentiated development models in different city tiers and regions; and (4) region-specific development models and optimization policies aimed at enhancing the symbiosis of urbanization and road traffic. This study underscores the pivotal role of the integration of statistical and geospatial data in understanding the dynamic intersection of urbanization and road traffic systems.
The MAMA Algorithm for Fast Computations of Upwelling Far- and Mid-Infrared Radiances in the Presence of Clouds
Authors: Martinazzo Michele , Maestri Tiziano
Published: September 2023

A methodology for the computation of spectrally resolved upwelling radiances in the presence of atmospheric diffusive layers is presented. The algorithm, called MAMA (Martinazzo–Maestri), provides fast simulations over the whole longwave spectrum, with high accuracy, particularly for optically thin scattering layers like cirrus clouds. The solution is obtained through a simplification of the multiple-scattering term in the general equation of the radiative transfer in a plane-parallel assumption. The scattering contribution is interpreted as a linear combination of the mean ambient radiances involved in the forward and back-scatter processes, which are multiplied by factors derived from the diffusive features of the layer. For this purpose, a fundamental property of the layer is introduced, named the angular back-scattering coefficient, which describes the fraction of radiation coming from a hemisphere and back-scattered into a specific direction (the observer in our case). This property, easily derived from the phase function of the particle size distribution, can be calculated from any generic single-scattering properties database, which allows for simple upgrades of the reference optical properties within the code. The paper discusses the solutions for mean upward and downward ambient radiances and their use in the simplification of the general radiative transfer equation for thermal infrared. To assess the algorithm performance, the results obtained with the MAMA code are compared with those derived with a discrete ordinate-based radiative transfer model for a large range of physical and optical properties of ice and liquid water clouds and for multiple atmospheric conditions. It is demonstrated that, for liquid water clouds, the MAMA code accuracy is mostly within 0.4 mW/(m2cm−1sr) with respect to the reference code both at far- and mid-infrared wavelengths. Ice cloud spectra are also accurately simulated at mid-infrared for all realistic cloud cases, which makes the MAMA code suitable for the analysis of any spectral measurements of current satellite infrared sounders. At far infrared, the MAMA accuracy is excellent when ice clouds with an optical depth of less than 2 are considered, which is particularly valuable since cirrus clouds are one of the main targets of the future mission FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) of the European Space Agency. In summary, the MAMA method allows computations of cloudy sky high-resolution radiances over the full longwave spectrum (4–100 μm) in less than a second (for pre-computed gas optical depths and on a standard personal computer). The algorithm exploits the fundamental properties of the scattering layers, and the code can be easily updated in relation to new scattering properties.
CNN and Transformer Fusion for Remote Sensing Image Semantic Segmentation
Authors: Chen Xin , Li Dongfen , Liu Mingzhe , Jia Jiaru
Published: September 2023

Semantic segmentation of remote sensing images has been widely used in environmental protection, geological disaster discovery, and natural resource assessment. With the rapid development of deep learning, convolutional neural networks (CNNs) have dominated semantic segmentation, relying on their powerful local information extraction capabilities. Due to the locality of convolution operation, it can be challenging to obtain global context information directly. However, Transformer has excellent potential in global information modeling. This paper proposes a new hybrid convolutional and Transformer semantic segmentation model called CTFuse, which uses a multi-scale convolutional attention module in the convolutional part. CTFuse is a serial structure composed of a CNN and a Transformer. It first uses convolution to extract small-size target information and then uses Transformer to embed large-size ground target information. Subsequently, we propose a spatial and channel attention module in convolution to enhance the representation ability for global information and local features. In addition, we also propose a spatial and channel attention module in Transformer to improve the ability to capture detailed information. Finally, compared to other models used in the experiments, our CTFuse achieves state-of-the-art results on the International Society of Photogrammetry and Remote Sensing (ISPRS) Vaihingen and ISPRS Potsdam datasets.
Empirical Correlation Weighting (ECW) Spatial Interpolation Method for Satellite Aerosol Optical Depth Products by MODIS AOD over Northern China in 2016
Authors: Wang Yang , Zhang Xianmei , Zhou Pei , Fan Meng
Published: September 2023

Satellite aerosol products are pivotal in studies of regional air quality and global climate change. Compared with accurate in situ observations, satellite measurements provide valuable large-scale atmospheric information. However, limitations such as clouds and retrieval assumptions result in a significant number of missing values in satellite aerosol optical depth (AOD) products, which severely hampers the representativeness. To address this issue, spatial interpolation of the AOD data is necessary to improve data coverage. In this study, one year of AOD observation data from the MODIS C6.1 version was applied to analyze the spatiotemporal correlated characteristics. The statistical parameters were used as dynamic interpolation weights to develop a novel interpolation method called empirical correlation weighting (ECW) based on MODIS AOD over Northern China in 2016. The ECW interpolation results were obtained at a 0.05° resolution (~5 km). The results showed that the spatial coverage of the Deep Blue (DB) and Dark Target (DT) products increased from 43.88% to 70.65% and from 15.04% to 32.62%, respectively. The reconstruction of the ECW method illustrated good agreement with original values in three cases and in two experimental areas. The mean absolute error (MAE) and root mean square error (RMSE) in the two experiments were 0.1171 and 0.0809, and 0.1212 and 0.0838, respectively, indicating that the ECW exhibited the better accuracy than ordinary Kriging (OK) and Thin Plate Spline (TPS). The AERONET validation results indicated that the values of RMSE and MAE were slightly higher after interpolation compared with those before interpolation, maintaining relatively low values, 0.241 and 0.257, 0.140 and 0.150, respectively.
The Effects of Olive Cake Supplementation on Feedlot Performance and Longissimus Muscle Fatty Acid Composition of American Wagyu Steers and In Vitro Rumen Fermentation Characteristics
Authors: Tangredi Briana V. , Loh Huey Yi , Thorndyke Meghan P. , Guimaraes Octavio , Engle Terry E.
Published: September 2023

Wagyu cattle are well known for their greater intramuscular fat content which improves eating quality for consumers. These experiments were designed to investigate the impact of olive cake supplementation on Wagyu steer growth performance, longissimus muscle fatty acid composition, and in vitro rumen fermentation characteristics. We hypothesized that feeding with olive cake would improve animal performance and increase oleic acid (C18:1cis9) composition in the longissimus muscle. Experiment 1: Eighty-three American Wagyu steers (725 ± 10.7 kg) were used in this experiment. Steers were blocked by initial body weight (BW) and randomly assigned within blocks to one of two treatments. Treatments consisted of (1) control (basal ration with no olive cake) or (2) control diet + 5% supplemental olive. Steers were housed in feedlot pens (n = 4 steers/pen; 11 replicates/treatment) and fed a finishing diet typical for Wagyu cattle. Steers were individually weighed every 28 d throughout the 177 d experiment. Longissimus muscle samples were obtained for fatty acid analysis at the time of slaughter. Experiment 2: Rumen fluid from three beef steers (480 ± 10 kg) fitted with rumen canulae was used to investigate the impact of olive cake on in vitro rumen fermentation characteristics. Treatments consisted of (1) control (no olive cake) or (2) 5% olive cake. Results: Experiment 1: Steers receiving olive cake had a lower (p < 0.05) final BW and DM intake when compared to steers receiving the control diet. Longissimus muscle C18:2 and C22:6 n-3 were greater (p < 0.05) and C18:1cis9 tended (p < 0.06) to be greater in steers receiving olive cake when compared to controls. Experiment 2: Dry matter, acid detergent fiber, and neutral detergent fiber disappearance and molar proportions of short chain fatty acids were similar across treatments. The weight percentage of C14:1 was greater in in vitro rumen fluid fermented with olive cake when compared to controls. All other long chain fatty acids were similar across treatments. Under the conditions of this experiment, feeding olive cake at 5% of the diet DM reduced live animal performance and had minimal impacts on longissimus muscle fatty acid composition and in vitro rumen fermentation characteristics.
Long-Term Analysis of Aerosol Concentrations Using a Low-Cost Sensor: Monitoring African Dust Outbreaks in a Suburban Environment in the Canary Islands
Authors: Alonso-Pérez Silvia , López-Solano Javier
Published: September 2023

This study presents the results of the long-term monitoring of PM10 and PM2.5 concentrations using a low-cost particle sensor installed in a suburban environment in the Canary Islands. A laser-scattering Nova Fitness SDS011 sensor was operated continuously for approximately three and a half years, which is longer than most other studies using this type of sensor. The impact of African dust outbreaks on the aerosol concentrations was assessed, showing a significant increase in both PM10 and PM2.5 concentrations during the outbreaks. Additionally, a good correlation was found with a nearby reference instrument of the air quality network of the Canary Islands’ government. The correlation between the PM10 and PM2.5 concentrations, the effect of relative humidity, and the stability of the sensor were also investigated. This study highlights the potential of this kind of sensor for long-term air quality monitoring with a view to developing extensive and dense low-cost air quality networks that are complementary to official air quality networks.
Efficient and Automatic Breast Cancer Early Diagnosis System Based on the Hierarchical Extreme Learning Machine
Authors: Lyu Songyang , Cheung Ray C. C.
Published: September 2023

Breast cancer is the leading type of cancer in women, causing nearly 600,000 deaths every year, globally. Although the tumors can be localized within the breast, they can spread to other body parts, causing more harm. Therefore, early diagnosis can help reduce the risks of this cancer. However, a breast cancer diagnosis is complicated, requiring biopsy by various methods, such as MRI, ultrasound, BI-RADS, or even needle aspiration and cytology with the suggestions of specialists. On certain occasions, such as body examinations of a large number of people, it is also a large workload to check the images. Therefore, in this work, we present an efficient and automatic diagnosis system based on the hierarchical extreme learning machine (H-ELM) for breast cancer ultrasound results with high efficiency and make a primary diagnosis of the images. To make it compatible to use, this system consists of PNG images and general medical software within the H-ELM framework, which is easily trained and applied. Furthermore, this system only requires ultrasound images on a small scale, of 28×28 pixels, reducing the resources and fulfilling the application with low-resolution images. The experimental results show that the system can achieve 86.13% in the classification of breast cancer based on ultrasound images from the public breast ultrasound images (BUSI) dataset, without other relative information and supervision, which is higher than the conventional deep learning methods on the same dataset. Moreover, the training time is highly reduced, to only 5.31 s, and consumes few resources. The experimental results indicate that this system could be helpful for precise and efficient early diagnosis of breast cancers with primary examination results.
An Automatic Calibration Method for the Field of View Aberration in a Risley-Prism-Based Image Sensor
Authors: Lin Zhonglin , Liu Wenchao , Gan Jinyu , Lu Jilian , Huang Feng , Wu Xianyu , Wang Weixiong
Published: September 2023

Risley-prism-based image sensors can expand the imaging field of view through beam control. The larger the top angle of the prism, the higher the magnification of the field of view, but at the same time, it aggravates the problem of imaging aberrations, which also puts higher requirements on the aberration correction method for the Risley-prism-based image sensor. To improve the speed, accuracy, and stability of the aberration correction process, an automatic calibration method for the Risley-prism-based image sensor is proposed based on a two-axis turntable. The image datasets of the calibration plate with different prism rotation angles and object distances are acquired using a two-axis turntable. Then, the images of the calibration plate are pre-processed using the bicubic interpolation algorithm. The calibration parameters are finally calculated, and parameter optimization is performed. The experimental results verify the feasibility of this automated calibration method. The reprojection error of the calibration is within 0.26 pixels when the distance of the imaging sensor is 3.6 m from the object, and the fine aberration correction results are observed.
Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression
Authors: Chung Jiwon , Lee Kyungho
Published: September 2023

Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as “IF” and “THEN”, and operators, such as “>“ and “<“, to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall.
Nearest Neighbour Node Deployment Algorithm for Mobile Sensor Networks
Authors: Ghahroudi Mahsa Sadeghi , Shahrabi Alireza , Boutaleb Tuleen
Published: September 2023

Many animal aggregations display remarkable collective coordinated movements on a large scale, which emerge as a result of distributed local decision-making by individuals. The recent advances in modelling the collective motion of animals through the utilisation of Nearest Neighbour rules, without the need for centralised coordination, resulted in the development of self-deployment algorithms in Mobile Sensor Networks (MSNs) to achieve various types of coverage essential for different applications. However, the energy consumption associated with sensor movement to achieve the desired coverage remains a significant concern for the majority of algorithms reported in the literature. In this paper, the Nearest Neighbour Node Deployment (NNND) algorithm is proposed to efficiently provide blanket coverage across a given area while minimising energy consumption and enhancing fault tolerance. In contrast to other algorithms that sequentially move sensors, NNND leverages the power of parallelism by employing multiple streams of sensor motions, each directed towards a distinct section of the area. The cohesion of each stream is maintained by adaptively choosing a leader for each stream while collision avoidance is also ensured. These properties contribute to minimising the travel distance within each stream, resulting in decreased energy consumption. Additionally, the utilisation of multiple leaders in NNND eliminates the presence of a single point of failure, hence enhancing the fault tolerance of the area coverage. The results of our extensive simulation study demonstrate that NNND not only achieves lower energy consumption but also a higher percentage of k-coverage.
An Adaptive Multi-Scale Network Based on Depth Information for Crowd Counting
Authors: Zhang Peng , Lei Weimin , Zhao Xinlei , Dong Lijia , Lin Zhaonan
Published: September 2023

Crowd counting, as a basic computer vision task, plays an important role in many fields such as video surveillance, accident prediction, public security, and intelligent transportation. At present, crowd counting tasks face various challenges. Firstly, due to the diversity of crowd distribution and increasing population density, there is a phenomenon of large-scale crowd aggregation in public places, sports stadiums, and stations, resulting in very serious occlusion. Secondly, when annotating large-scale datasets, positioning errors can also easily affect training results. In addition, the size of human head targets in dense images is not consistent, making it difficult to identify both near and far targets using only one network simultaneously. The existing crowd counting methods mainly use density plot regression methods. However, this framework does not distinguish the features between distant and near targets and cannot adaptively respond to scale changes. Therefore, the detection performance in areas with sparse population distribution is not good. To solve such problems, we propose an adaptive multi-scale far and near distance network based on the convolutional neural network (CNN) framework for counting dense populations and achieving a good balance between accuracy, inference speed, and performance. However, on the feature level, in order to enable the model to distinguish the differences between near and far features, we use stacked convolution layers to deepen the depth of the network, allocate different receptive fields according to the distance between the target and the camera, and fuse the features between nearby targets to enhance the feature extraction ability of pedestrians under nearby targets. Secondly, depth information is used to distinguish distant and near targets of different scales and the original image is cut into four different patches to perform pixel-level adaptive modeling on the population. In addition, we add density normalized average precision (nAP) indicators to analyze the accuracy of our method in spatial positioning. This paper validates the effectiveness of NF-Net on three challenging benchmarks in Shanghai Tech Part A and B, UCF_ CC_50, and UCF-QNRF datasets. Compared with SOTA, it has more significant performance in various scenarios. In the UCF-QNRF dataset, it is further validated that our method effectively solves the interference of complex backgrounds.