bluuebunny/crossref_metadata_2025 huggingface dataset:
Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design
Authors: Awan Maaz Ali , Dalveren Yaser , Kara Ali , Derawi Mohammad
Published: August 2024

This research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of UAS flight: cruise, landing approach, and touchdown within a signal processing framework. Angle of arrival (AoA) estimation, traditionally employed in terrain mapping applications, is largely unexplored for UAS radar altimeters (RAs). Time-division multiplexing multiple input–multiple output (TDM-MIMO) is an efficient method for enhancing angular resolution without compromising the size, weight, and power (SWaP) characteristics. Accordingly, this work argues the potential of AoA estimation using TDM-MIMO to augment situational awareness in challenging landing scenarios. To this end, two corner cases comprising landing a small-sized drone on a platform in the middle of a water body are included. Likewise, for the touchdown stage, an improvised rendition of zoom fast Fourier transform (ZFFT) is investigated to achieve millimeter (mm)-level range accuracy. Aptly, it is proposed that a mm-level accurate RA may be exploited as a software redundancy for the critical weight-on-wheels (WoW) system in fixed-wing commercial UASs. Each stage is simulated as a radar scenario using the specifications of automotive radar operating in the 77–81 GHz band to optimize waveform design, setting the stage for field verification. This article addresses challenges arising from radial velocity due to UAS descent rates and terrain variation through theoretical and mathematical approaches for characterization and mandatory compensation. While constant false alarm rate (CFAR) algorithms have been reported for ground detection, a comparison of their variants within the scope UAS altimetry is limited. This study appraises popular CFAR variants to achieve optimized ground detection performance. The authors advocate for dedicated minimum operational performance standards (MOPS) for UAS RAs. Lastly, this body of work identifies potential challenges, proposes solutions, and outlines future research directions.
Integration of Deep Sequence Learning-Based Virtual GPS Model and EKF for AUV Navigation
Authors: Lv Peng-Fei , Lv Jun-Yi , Hong Zhi-Chao , Xu Li-Xin
Published: August 2024

To address the issue of increasing navigation errors in low-cost autonomous underwater vehicles (AUVs) operating without assisted positioning underwater, this paper proposes a Virtual GPS Model (VGPSM) based on deep sequence learning. This model is integrated with an Extended Kalman Filter (EKF) to provide a high-precision navigation solution for AUVs. The VGPSM leverages the time-series characteristics of data from sensors such as the Attitude and Heading Reference System (AHRS) and the Doppler Velocity Log (DVL) while the AUV is on the surface. It learns the relationship between these sensor data and GPS data by utilizing a hybrid model of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM), which are well-suited for processing and predicting time-series data. This approach constructs a virtual GPS model that generates virtual GPS displacements updated at the same frequency as the real GPS data. When the AUV navigates underwater, the virtual GPS displacements generated using the VGPSM in real-time are used as measurements to assist the EKF in state estimation, thereby enhancing the accuracy and robustness of underwater navigation. The effectiveness of the proposed method is validated through a series of experiments under various conditions. The experimental results demonstrate that the proposed method significantly reduces cumulative errors, with navigation accuracy improvements ranging from 29.2% to 69.56% compared to the standard EKF, indicating strong adaptability and robustness.
Temporal Monitoring of Simulated Burials in an Arid Environment Using RGB/Multispectral Sensor Unmanned Aerial Vehicles
Authors: Alawadhi Abdullah , Eliopoulos Constantine , Bezombes Frederic
Published: August 2024

For the first time, RGB and multispectral sensors deployed on UAVs were used to facilitate grave detection in a desert location. The research sought to monitor surface anomalies caused by burials using manual and enhanced detection methods, which was possible up to 18 months. Near-IR (NIR) and Red-Edge bands were the most suitable for manual detection, with a 69% and 31% success rate, respectively. Meanwhile, the enhanced method results varied depending on the sensor. The standard Reed–Xiaoli Detector (RXD) algorithm and Uniform Target Detector (UTD) algorithm were the most suitable for RGB data, with 56% and 43% detection rates, respectively. For the multispectral data, the percentages varied between the algorithms with a hybrid of the RXD and UTD algorithms yielding a 56% detection rate, the UTD algorithm 31%, and the RXD algorithm 13%. Moreover, the research explored identifying grave mounds using the normalized digital surface model (nDSM) and evaluated using the normalized difference vegetation index (NDVI) in grave detection. nDSM successfully located grave mounds at heights as low as 1 cm. A noticeable difference in NDVI values was observed between the graves and their surroundings, regardless of the extreme weather conditions. The results support the potential of using RGB and multispectral sensors mounted on UAVs for detecting burial sites in an arid environment.
An Information-Geometric Formulation of Pattern Separation and Evaluation of Existing Indices
Authors: Wang Harvey , Singh Selena , Trappenberg Thomas , Nunes Abraham
Published: August 2024

Pattern separation is a computational process by which dissimilar neural patterns are generated from similar input patterns. We present an information-geometric formulation of pattern separation, where a pattern separator is modeled as a family of statistical distributions on a manifold. Such a manifold maps an input (i.e., coordinates) to a probability distribution that generates firing patterns. Pattern separation occurs when small coordinate changes result in large distances between samples from the corresponding distributions. Under this formulation, we implement a two-neuron system whose probability law forms a three-dimensional manifold with mutually orthogonal coordinates representing the neurons’ marginal and correlational firing rates. We use this highly controlled system to examine the behavior of spike train similarity indices commonly used in pattern separation research. We find that all indices (except scaling factor) are sensitive to relative differences in marginal firing rates, but no index adequately captures differences in spike trains that result from altering the correlation in activity between the two neurons. That is, existing pattern separation metrics appear (A) sensitive to patterns that are encoded by different neurons but (B) insensitive to patterns that differ only in relative spike timing (e.g., synchrony between neurons in the ensemble).
Large-Scale Optimization among Photovoltaic and Concentrated Solar Power Systems: A State-of-the-Art Review and Algorithm Analysis
Authors: Wang Yi’an , Wu Zhe , Ni Dong
Published: August 2024

Large-scale optimization (LSO) problems among photovoltaic (PV) and concentrated solar power (CSP) systems are attracting increasing attention as they help improve the energy dispatch efficiency of PV and CSP systems to minimize power costs. Therefore, it is necessary and urgent to systematically analyze and summarize various LSO methods to showcase their advantages and disadvantages, ensuring the efficient operation of hybrid energy systems comprising different PV and CSP systems. This paper compares and analyzes the latest LSO methods for PV and CSP systems based on meta-heuristic algorithms (i.e., Particle Swarm Optimization, Genetic Algorithm, Enhanced Gravitational Search Algorithm, and Grey Wolf Optimization), numerical simulation and stochastic optimization methods (i.e., Constraint Programming, Linear Programming, Dynamic Programming Optimization Algorithm, and Derivative-Free Optimization), and machine learning-based AI methods (Double Grid Search Support Vector Machine, Long Short-Term Memory, Kalman Filter, and Random Forest). An in-depth analysis and A comparison of the essence and applications of these algorithms are conducted to explore their characteristics and suitability for PV and CSP or hybrid systems. The research results demonstrate the specificities of different LSO algorithms, providing valuable insights for researchers with diverse interests and guiding the selection of the most appropriate method as the solution algorithm for LSO problems in various PV and CSP systems. This also offers useful references and suggestions for extracting research challenges in LSO problems of PV and CSP systems and proposing corresponding solutions to guide future research development.
Research on Building Energy Consumption Prediction Based on Improved PSO Fusion LSSVM Model
Authors: Zhang Suli , Chang Yiting , Li Hui , You Guanghao
Published: August 2024

In urban building management, accurate prediction of building energy consumption is significant in realizing energy conservation and improving energy efficiency. Due to the complexity and variability of energy consumption data, existing prediction models face the challenge of difficult parameter selection, which directly affects their accuracy and application. To solve this problem, this study proposes an improved particle swarm algorithm (IPSO) for optimizing the parameters of the least squares support vector machine (LSSVM) and constructing an energy consumption prediction model based on IPSO-LSSVM. The model fully combines the advantages of LSSVM in terms of nonlinear fitting and generalization ability and uses the IPSO algorithm to adjust the parameters precisely. By analyzing the sample data characteristics and validating them on two different types of building energy consumption datasets, the results of the study show that, compared with traditional baseline models such as back-propagation neural networks (BP) and support vector regression (SVR), the model proposed in this study is more accurate and efficient in parameter selection and significantly reduces the prediction error rate. This improved approach not only improves the accuracy of building energy consumption prediction but also enhances the robustness and adaptability of the model, which provides reliable methodological support for the development of more effective energy-saving strategies and optimization of energy use to achieve the goal of energy-saving and consumption reduction and provides a new solution for the future management of building energy consumption.
Research Methods for Transient Stability Analysis of Power Systems under Large Disturbances
Authors: Wu Hao , Li Jing , Yang Haibo
Published: August 2024

Transient stability analysis is critical for maintaining the reliability and security of power systems. This paper provides a comprehensive review of research methods for transient stability analysis under large disturbances, detailing the modeling concepts and implementation approaches. The research methods for large disturbance transient stability analysis are categorized into five main types: simulation methods, direct methods, data-driven methods, analytical methods, and other methods. Within the analytical method category, several common analytical strategies are introduced, including the asymptotic expansion method, intrusive approximation method, and other analytical methods. The fundamental principles, characteristics, and recent research advancements of these methods are detailed, with particular attention to their performance in various aspects such as computational efficiency, accuracy, applicability to different system models, and stability region estimation. The advantages and disadvantages of each method are compared, offering insights to support further research into transient stability analysis for hybrid power grids under large disturbances.
Study on the Influencing Factors of Forest Tree-Species Classification Based on Landsat and Sentinel-2 Imagery
Authors: Lai Xin , Tang Xu , Ren Zhaotong , Li Yuecan , Huang Runlian , Chen Jianjun , You Haotian
Published: August 2024

Accurate forest tree-species classification not only provides data support for forest resource management but also serves as a crucial parameter for simulating various ecological processes. However, the results of forest tree-species classification have been affected by multiple factors, such as the spectral resolution, spatial resolution, and radiometric resolution of imagery, the classification algorithms used, the sample size, and the timing of image acquisition phases. Although there are many studies on the impact of individual factors on tree-species classification, there is a lack of systematic studies quantifying the magnitude of these factors’ influences, leading to uncertainties about the relative importance of different factors. In this study, Landsat-8, Landsat-9, and Sentinel-2 imagery was used as the foundational data, and random forest (RF), gradient tree boosting (GTB), and support vector machine (SVM) algorithms were employed to classify forest tree species. High-accuracy regional forest tree-species classification was achieved by exploring the impacts of spectral resolution, spatial resolution, radiometric resolution, classification algorithms, sample size, and image time phases. The results show that, for the commonly used Landsat-8, Landsat-9, and Sentinel-2 imagery, the tree-species classification results from Landsat-9 are the best, with an overall accuracy of 74.21% and a kappa of 0.71. Among the various influencing factors, the classification algorithm, image time phases, and sample size have relatively larger impacts on tree-species classification results, each exceeding 10%, while the positive impact of radiometric resolution is the smallest, at only 3.15%. Conversely, spectral and spatial resolutions had negative effects on tree-species classification results, at −4.09% and −1.4%, respectively. Based on the 30-m spring Landsat-9 and Sentinel-2 imagery, with 300 samples for each tree-species category, the classification results using the RF algorithm were the best, with an overall accuracy of 87.07% and a kappa coefficient of 0.85. The results indicate that different factors have different impacts on forest tree-species classification results, with classification algorithms, image time phases, and sample size having the largest impacts. Higher spatial and spectral resolutions do not improve the classification accuracy. Therefore, future studies should focus on selecting appropriate classification algorithms, sample sizes, and images from seasons with greater tree differences to improve tree-species classification results.
EIAGA-S: Rapid Mapping of Mangroves Using Geospatial Data without Ground Truth Samples
Authors: Zhao Yuchen , Wu Shulei , Zhang Xianyao , Luo Hui , Chen Huandong , Song Chunhui
Published: August 2024

Mangrove forests are essential for coastal protection and carbon sequestration, yet accurately mapping their distribution remains challenging due to spectral similarities with other vegetation. This study introduces a novel unsupervised learning method, the Elite Individual Adaptive Genetic Algorithm-Semantic Inference (EIAGA-S), designed for the high-precision semantic segmentation of mangrove forests using remote sensing images without the need for ground truth samples. EIAGA-S integrates an adaptive Genetic Algorithm with an elite individual’s evolution strategy, optimizing the segmentation process. A new Mangrove Enhanced Vegetation Index (MEVI) was developed to better distinguish mangroves from other vegetation types within the spectral feature space. EIAGA-S constructs segmentation rules through iterative rule stacking and enhances boundary information using connected component analysis. The method was evaluated using a multi-source remote sensing dataset covering the Hainan Dongzhai Port Mangrove Nature Reserve in China. The experimental results demonstrate that EIAGA-S achieves a superior overall mIoU (mean intersection over union) of 0.92 and an F1 score of 0.923, outperforming traditional models such as K-means and SVM (Support Vector Machine). A detailed boundary analysis confirms EIAGA-S’s ability to extract fine-grained mangrove patches. The segmentation includes five categories: mangrove canopy, other terrestrial vegetation, buildings and streets, bare land, and water bodies. The proposed EIAGA-S model offers a precise and data-efficient solution for mangrove semantic mapping while eliminating the dependency on extensive field sampling and labeled data. Additionally, the MEVI index facilitates large-scale mangrove monitoring. In future work, EIAGA-S can be integrated with long-term remote sensing data to analyze mangrove forest dynamics under climate change conditions. This innovative approach has potential applications in rapid forest change detection, environmental protection, and beyond.
Effects of Milling Methods on Cutting Performance of Wood–Plastic Composites Based on Principal Component Analysis
Authors: Zhu Yunqi , Buck Dietrich , Guan Jun , Song Meiqi , Tang Qi , Guo Xiaolei , Zhu Zhaolong
Published: August 2024

In the industrial machining of wood–plastic composites, optimization of cutting parameters is key to improving workpiece machinability. To explore the influence of different milling methods of straight-tooth milling, helical milling, and tapered milling on the machinability of wood–plastic composite, a milling experiment was performed. Cutting force, cutting temperature, and surface roughness were selected as evaluative factors. Based on experimental results, principal component analysis was used to analyze the significance of each factor’s contribution and to assess different milling methods of wood–plastic composite for different needs. By calculating the total score from principal component analysis, the optimized cutting mode was determined to be straight-tooth milling, with feed per tooth of 0.2 mm and cutting depth of 0.5 mm. Milling methods in order of decreasing cutting force were helical milling > straight-tooth milling > tapered milling. Milling methods in order of decreasing cutting temperature were helical milling > tapered milling > straight-tooth milling. In terms of the tradeoff between surface quality and processing efficiency, tapered milling is suitable for finishing, considering the machining quality, while helical milling is suitable for roughing, considering the machining efficiency. One of the contributions of this study is to link three separate milling study systems (straight-tooth milling, helical milling, and tapered milling) into one system.
The Characteristics of Visitor Behavior and Driving Factors in Urban Mountain Parks: A Case Study of Fuzhou, China
Authors: Fan Shiyuan , Huang Jingkai , Gao Chengfei , Liu Yuxiang , Zhao Shuang , Fang Wenqiang , Ran Chengyu , Jin Jiali , Fu Weicong
Published: August 2024

Previous studies have focused on the linear relationship between recreation behavior and environmental variables. However, to inform the planning and design of recreational spaces, it is essential to understand the factors that contribute to differences in the spatial distribution of recreation behavior. This study investigates the characteristics of visitor behavior in urban mountain parks in Fuzhou City, Fujian Province, China. It describes the distribution of tourist numbers and the diversity of behaviors in these parks and explores the landscape driving factors of visitor behavior, as well as the interaction effects between the factors from the perspective of spatial driving forces. The results indicate that (1) The observed behaviors in the three parks are primarily access behaviors. The number of visitors and the diversity of behaviors show a high level in the morning and evening and a low level in the midday. (2) There was minimal variation in behavioral composition and behavioral diversity among the study plots of different elevation gradients in the three parks. However, the contrasts between different landscape types were more pronounced, with impermeable plazas exhibiting the highest behavioral diversity and park roads demonstrating the most homogeneous behavioral diversity. (3) The impact of environmental factors was more pronounced than that of landscape pattern factors. The environmental factors that most strongly influenced passing, dynamic, and static behaviors were spatial connectivity value, hard space proportion, and number of recreational facilities, respectively. In contrast, the hard space proportion was the strongest driver of behavioral diversity. Moreover, the interaction between the hard space proportion and spatial connectivity value was more pronounced in driving behavioral diversity, as well as the three behaviors.
CrossDeFi: A Novel Cross-Chain Communication Protocol
Authors: Mohammed Abdul Shezon Saleem , Shrestha Anup , Yong Jianming
Published: August 2024

Decentralized finance (DeFi) is rapidly evolving, promising to revolutionize financial services through blockchain technology. Successful integration of asset transfers across diverse DeFi platforms hinges on effective interoperability and transaction finality, ensuring security and cost efficiency. This paper introduces CrossDeFi, a novel cross-chain communication protocol tailored to address the challenges posed by heterogeneity in consensus mechanisms, smart contracts, and token systems. CrossDeFi introduces two key mechanisms: Miner and bridge selection (MBS) and improved transfer confirmation (ITC). The MBS mechanism optimizes the selection of miners and bridges based on the unique characteristics of each blockchain, significantly improving transfer accuracy, cost efficiency, and speed. Meanwhile, the ITC mechanism leverages cryptographic primitives to secure asset transfer confirmations, ensuring robust transaction finality. The protocol’s effectiveness is demonstrated through detailed efficiency and security analyses, complemented by a prototype evaluation that showcases its capabilities in reducing transfer durations and costs. These findings underscore the potential of CrossDeFi to transform the DeFi ecosystem.
An Analysis of Water Leakages in Series Pipelines Using the Rigid Column Model
Authors: Fuertes-Miquel Vicente S. , Coronado-Hernández Oscar E. , Arrieta-Pastrana Alfonso
Published: August 2024

Water leakages are assessed with an extended period simulation (traditional analysis) in water distribution networks. However, when rapid manoeuvres regulate valves, the conventional analysis proves unsuitable for representing the water volume of leaks. Under these circumstances, the Rigid Water Column Model can be utilised to accurately compute the quantity of water volume leaks. This method has been applied to single and parallel pipelines to predict water leakages, considering the opening and closure manoeuvres of regulating valves. This research develops governing equations using the Rigid Column Model to predict water volume leaks in a general scheme of series pipelines, accounting for the opening and closure manoeuvres in regulating valves. The practical application involves two branches of a series pipeline with internal pipe diameters of 0.45 and 0.25 m and pipe lengths of 1200 and 1400 m, respectively. The results of this practical application demonstrate the accuracy of the Rigid Column Model and its relevance in real-world scenarios. For instance, the model shows that traditional analysis can overestimate water volume leaks by approximately 72% when a closure manoeuvre regulates valves for 200 s and by about 28% for 30 s, highlighting the need for a more accurate method.
Applications of Mittag–Leffler Functions on a Subclass of Meromorphic Functions Influenced by the Definition of a Non-Newtonian Derivative
Authors: Breaz Daniel , Karthikeyan Kadhavoor R. , Murugusundaramoorthy Gangadharan
Published: August 2024

In this paper, we defined a new family of meromorphic functions whose analytic characterization was motivated by the definition of the multiplicative derivative. Replacing the ordinary derivative with a multiplicative derivative in the subclass of starlike meromorphic functions made the class redundant; thus, major deviation or adaptation was required in defining a class of meromorphic functions influenced by the multiplicative derivative. In addition, we redefined the subclass of meromorphic functions analogous to the class of the functions with respect to symmetric points. Initial coefficient estimates and Fekete–Szegö inequalities were obtained for the defined function classes. Some examples along with graphs have been used to establish the inclusion and closure properties.
UCN-Centric Prognostic Model for Predicting Overall Survival and Immune Response in Colorectal Cancer
Authors: Liu Jia , Zhong Feiliang , Chen Yue
Published: August 2024

Colorectal cancer (CRC), a prevalent malignancy, ranks third in global incidence and second in mortality rates. Despite advances in screening methods such as colonoscopy, the accurate diagnosis of CRC remains challenging due to the absence of reliable biomarkers. This study aimed to develop a robust prognostic model for precise CRC outcome prediction. Employing weighted co-expression network analysis (WGCNA) and Cox regression analysis on data from The Cancer Genome Atlas (TCGA), we identified a panel of 12 genes strongly associated with patient survival. This gene panel facilitated accurate CRC outcome predictions, which is also validated via the external validation cohort GSE17536. We conducted further investigations into the key gene, urocortin (UCN), using single-cell transcriptomic data and immune infiltration analysis in CRC patients. Our results revealed a significant correlation between high UCN expression and the reduced prevalence of key immune cells, including B cells, CD4+ cytotoxic T cells, CD8+ T cells, and NKT cells. Functional experiments showed that UCN gene interference in the CRC cell lines significantly decreased cancer cell proliferation, underscoring UCN’s role in intestinal immunity modulation. The UCN-centric prognostic model developed enhances prognosis prediction accuracy and offers critical insights for CRC diagnosis and therapeutic interventions.
The Prevalence of Cancer Predisposition Syndromes (CPSs) in Children with a Neoplasm: A Cohort Study in a Central and Eastern European Population
Authors: Telman-Kołodziejczyk Gabriela , Strauss Ewa , Sosnowska-Sienkiewicz Patrycja , Januszkiewicz-Lewandowska Danuta
Published: August 2024

Importance: The etiology of pediatric cancers is often unclear; however, advancements in genetics have identified significant roles for genetic disorders in their development. Over time, the number of cancer predisposition syndromes (CPSs) and awareness of them have increased, providing the possibility of cancer prevention and early detection. Purpose: In this study, we present data concerning the number and type of oncological cases and their correlation with CPS occurrence in a cohort of Central and Eastern European pediatric patients. Materials: The data were collected between 2000 and 2019 at the Karol Jonscher Clinical Hospital of Poznan University of Medical Sciences, resulting in a cohort of 2190 cases in total, of which 193 children (8.81%) were confirmed to have a CPS. Results: CPSs occurred most frequently in infancy (22.90% of all children suffering from any diagnosed cancer during the first year of life; p < 0.0001), accounting for more than one-quarter of all CPS cases in our cohort. CPSs were least likely to be observed in patients aged 14 and 15 years (2.17% and 2.44% of children diagnosed with any of the listed cancers at the exact age, respectively; p < 0.05). Among CPSs, the most common were neurofibromatosis type I (NF1), Li–Fraumeni syndrome (LFS), and Down syndrome (DS). Conclusions: To conclude, it is important to emphasize the need for personalized treatment for each patient affected by both CPSs and subsequent cancer in order to reduce the toxicity of therapy and improve quality of life by reducing the risk of side effects.
Genomic Regions Associated with Growth and Reproduction Traits in Pink-Eyed White Mink
Authors: Shi Hongyu , Liu Linling , Larsen Peter Foged , Ding Yu , Zhang Tietao , Zhang Haihua , Liu Zongyue
Published: August 2024

In mink breeding, balanced selection for growth and reproductive features is essential because these traits are contradictory. The variables of total number born (TNB), number born alive (NBA), and body weight (BW) are highly valuable in terms of their importance in mink production. A comprehensive understanding of the molecular mechanisms that drive these features could offer vital insights into their genetic compositions. In the present study, the single-nucleotide polymorphism (SNP) genotypes of 219 minks were obtained via double digest restriction-site associated DNA sequencing (ddRAD-seq). Following several rounds of screening, about 2,415,121 high-quality SNPs were selected for a genome-wide association study (GWAS). The GWAS was used to determine BW and reproductive traits in pink-eyed white mink. It was suggested that SLC26A36, STXBP5L, and RPS 29 serve as potential genes for the total number of kits born (TNB), while FSCB, PDPN, NKX 2-1, NFKB 1, NFKBIA, and GABBR1 are key genes for the number born alive (NBA). Moreover, RTTN, PRPF31, MACROD1, and KYAT1 are possible BW genes based on association results and available functional data from gene and mammalian phenotype databases. These results offer essential information about the variety of mink and theoretical principles for applying mink breeds.
Steady-State Shear Rheology of Aqueous Noncolloidal Carbonate Suspensions
Authors: Marfo William Apau , Paso Kristofer Gunnar , Felix Maarten
Published: August 2024

Carbonate muds are essential sedimentary components in geological carbon cycles. Model carbonate muds are prepared from crushed, sieved carbonate rock. The carbonate rock particles are primarily smaller than 62.5 µm. Steady-state shear viscosity was measured for model carbonate muds prepared from three types of carbonate rock: limestone Grey, limestone Marl, and limestone Castleton. Model carbonate muds were prepared using fresh water or 3.5 g/L NaCl solution. The carbonate particle concentrations were 1.81 volume percent and 26.95 volume percent, representing semi-dilute and concentrated particle regimes, respectively. Carbonate mud viscosity was measured at temperatures ranging from 8 °C to 35 °C. Shear rates ranged from 60 s−1 to 2500 s−1. Pseudoplasticity occurs at low shear rates and is caused by the release of occluded water during shear-driven breakup and dispersal of particle aggregates. Shear thickening occurs at high shear rates and is caused by transient particle clusters, called hydroclusters, that are reinforced by lubrication forces or frictional particle contacts. Carbonate mud viscosity decreases at increasing temperatures. The presence of 3.5 g/L NaCl in the aqueous phase slightly increased the mud viscosity in the semi-dilute particle concentration regime because of a weak viscosifying effect of NaCl on the aqueous phase. In the concentrated particle regime, electrolytes screened electroviscous effects, reducing the viscosity of muds containing particles with electrically charged surfaces. In aqueous solution, limestone Marl had a comparatively high concentration of charged particles on its surface. Limestone Castleton had a comparatively low concentration of charged particles on its surface. Surface charges were not rheologically evident on limestone Grey.
Seismic Response of Pile Foundations in Clayey Soil Deposits Considering Soil Suction Changes Caused by Soil–Atmospheric Interactions
Authors: Shojaeian Ali , Bounds Tommy , Muraleetharan Kanthasamy K. , Miller Gerald
Published: August 2024

Extreme variations in weather patterns have become increasingly common across the Southern Great Plains of the United States. The soil layer in the active zone above the groundwater table is often subjected to moisture variations due to seasonal weather changes that will influence the behavior of soils, including their strength and stiffness parameters. Designing a pile foundation in seismic-prone areas without considering the moisture changes in soil interacting with piles may adversely impact the seismic performance of the piles. The main aim of this study is to investigate the role of soil moisture conditions and suction caused by soil–atmospheric interactions on the dynamic behavior of the pile foundations interacting with clayey soils. This study uses a stand-alone finite element computer code called DYPAC (Dynamic Piles Analysis Code) developed using the Beams on Nonlinear Winkler Foundation (BNWF) approach. The influence of soil suction is incorporated into the p-y curves and free-field soil displacements using site response analyses by employing the concept of apparent cohesion. To perform nonlinear site response analyses, DEEPSOIL software V6.1 is utilized. The variation in soil suction with depth along the pile is considered using unsaturated seepage analysis performed by employing the commercial software PLAXIS LE Groundwater for three different clayey soils with plasticity ranging from low to medium to high. The analyses were performed using actual past daily recorded weather data for a testbed that experienced significant back-to-back flash droughts in 2022. This study found that extreme weather events like flash droughts can significantly affect the soil suction and seismic performance of the piles interacting with the unsaturated clayey soils.
IgG-k/IgG-λ Para-Osseous Plasmacytoma Relapsed as Soft-Tissue Plasmacytoma with IgA-k Immunophenotype: A Case Report and Review of the Literature on Related Biochemical Aspects
Authors: Fazio Manlio , Sorbello Chiara Maria Catena , Del Fabro Vittorio , Romano Alessandra , Cannizzaro Maria Teresa , Parrinello Nunziatina Laura , Esposito Benedetta , Frazzetto Sara , Elia Federica , Di Raimondo Francesco , Conticello Concetta
Published: August 2024

Neoplastic plasma cells (PCs) proliferation at anatomic sites dislocated from the bone marrow (BM) or their contiguous growth from osseous lesions that disrupt the cortical bone is termed extramedullary multiple myeloma (EMD). EMD still remains challenging from a therapeutic and biological perspective. Pathogenesis has not been completely clarified, and it is generally associated with high-risk cytogenetics (HRCAs). In order to emphasize the clinical and biochemical complexity of this disease, we have decided to describe the case of a patient affected by relapsed-refractory (RR) EMD, which presented as para-osseous plasmacytoma with a bi-phenotypical immunoglobulin (Ig) component and lately relapsed as soft-tissue plasmacytoma with a total immunophenotype switch. We have also hypothesized a correlation between Ig patterns and prognosis and suggested the possible inclusion of these biochemical features in the general risk assessment.
Emergency Response for Architectural Heritage in Seismic Areas: An Integrated Approach to Safety and Conservation
Authors: Ferrari Lia
Published: August 2024

In 2015, hazard mitigation became a top priority on the international agenda, according to the United Nations Office for Disaster Risk Reduction. When it comes to architectural heritage, it is crucial to develop tools and site-specific response plans that can help the prompt and effective management of seismic events. The paper presents part of a research study carried out at the University of Parma, aimed at improving emergency strategies for the protection of cultural heritage damaged by earthquakes. Specifically, it analyses first aid and recovery reinforcements, with a specific focus on masonry churches affected by the 2012 quake in the Emilia Romagna region (Italy). The study highlights criticalities and good practices of a site-specific response. It shows that recovery with a sharp separation between emergency and reconstruction activities leads to wasted resources in terms of cost, material, and time. On the other hand, the most effective strategies for the conservation of architectural heritage in earthquake-prone areas have proved to be based on an integrated and shared approach, aimed at balancing safety, conservation, and economic issues. This leads to a broadening of the concept of emergency interventions and, more generally, of structural reinforcement in the field of architectural conservation.
The Worldwide Prevalence of Internet Addiction among Medical Students: A Systematic Review and Meta-Analysis
Authors: Salpynov Zhandos , Kosherova Zhanar , Sarría-Santamera Antonio , Nurkatov Yerbol , Gusmanov Arnur , Semenova Yuliya
Published: August 2024

Background: The internet helps us obtain necessary information, facilitates social communication, and provides access to entertainment content. The internet can also lead to the behavioral addictive condition termed internet addiction (IA) if used excessively. As active internet users, medical students are susceptible to IA, which is known to lead to depression and improper medical care delivery, poor academic performance, worse sleep quality, and undesirable financial issues. This systematic review and meta-analysis aimed to assess medical students’ pooled IA prevalence. Methods: The analysis included thirteen cross-sectional studies involving 4787 medical students. Cumulative, subgroup, and meta-regression meta-analyses were applied, using the random-effects model and the restricted maximum likelihood method. Results: The cumulative meta-analysis revealed a rise in the proportion of IA from 0.08 to 0.29, with minor fluctuations between 2015 and 2022. The IA prevalence in lower-middle-income countries was approximately three times higher than in high-income ones. Age and gender were not associated with IA among medical students. Conclusions: The worldwide prevalence of IA was 0.29, with a 95% CI between 0.19 and 0.41. Considering negative IA implications for medical students’ well-being, policymakers and all stakeholders should pay special attention to addressing IA within the medical student community.
The Impact of Positive and Adverse Experiences in Adolescence on Health and Wellbeing Outcomes in Early Adulthood
Authors: Kemp Lynn , Elcombe Emma , Blythe Stacy , Grace Rebekah , Donohoe Kathy , Sege Robert
Published: August 2024

This study evaluated the associations between positive and adverse experiences and environments in adolescence and health, education and employment outcomes in early adulthood. Data were extracted from the Longitudinal Studies of Australian Youth cohort that commenced in 2003. The items were conceptually mapped to Positive and Adverse Youth Experiences and environments (PYEs and AYEs) at 15, 16 and 17 years old and outcomes at 25 years old. The associations between PYEs, AYEs and general health, mental health, education and employment were examined, including testing whether PYEs mitigated the association between AYEs and outcomes. A higher number of AYEs was associated with poorer health, education, and employment outcomes. Conversely, a higher number of PYEs was correlated with positive outcomes. The participants with higher PYEs had significantly greater odds of better general and mental health outcomes, even after accounting for AYEs. This relationship was not observed for employment or education outcomes. Adolescence and the transition to adulthood are critical developmental stages. Reducing adverse experiences and environments and increasing positive ones during adolescence could enhance adult wellbeing.
What Women with HIV Know about Heart Health and Cardiovascular Risk and Intervention Preferences
Authors: Duthely Lunthita M. , Satish Sanjana , Kedia Sapna A. , Vilchez Lilliana , Valls Priscilla T. , Larson Michaela E. , O’Reilly Carolina Cruzval , Hurtado Vanessa , Bernal Maria Camila , Inestroza Karla , Nogueira Nicholas F. , Glynn Tiffany R. , Kanamori Mariano J. , Martinez Claudia A.
Published: August 2024

Cardiovascular disease (CVD) is a significant health concern influenced by various determinants. Stigma and resilience have emerged as factors in CVD development and management. Women with HIV (WWH) have higher CVD rates than women without HIV. To improve cardiovascular health for WWH, a comprehensive understanding of how these factors interact, the understanding about individual awareness and willingness to engage in risk-reduction interventions are needed. Methods: As part of a study examining CVD risk among WWH aged >35 years old, 90-min focus groups were conducted (May 2022) in the English language. Focus groups aimed to elicit participants’ CVD risk knowledge and potential prevention strategies. Transcripts underwent a qualitative analysis. Results: Nineteen WWH participated in three focus groups. Participants experienced the following: (a) enacted stigma related to their HIV diagnosis (e.g., family, church member, healthcare staff); (b) a recent event (e.g., hospitalization of self/family, death in family, chest pain) triggered both heart health-promoting lifestyle changes and suboptimal health behaviors (e.g., COVID-19 pandemic: unhealthy snacking). Participants wanted to obtain more knowledge (“on a mission”) about CVD risk. In total, 100% expressed willingness to take medication or embark on other lifestyle changes to prevent future CVD events. Although participants identified preventative heart health behaviors (e.g., eating healthy foods; exercising; limiting stress, substances, and smoking), misconceptions were also identified (e.g., “catching” heart disease). Conclusions: Understanding the interplay of the different factors related to heart health is needed both at the provider and the patient level to inform interventions that reduce CVD risk amongst racial/ethnic minoritized women with HIV, living in the Southern region of the US.
Mechanistic Pathways and Clinical Implications of GLP-1 Receptor Agonists in Type 1 Diabetes Management
Authors: Delrue Charlotte , Speeckaert Marijn M.
Published: August 2024

GLP-1 receptor agonists, which were initially intended to treat type 2 diabetes patients, have demonstrated promise as an adjuvant therapy for type 1 diabetes (T1D). These medications can manage T1D by improving β-cell function, reducing glucose fluctuation, and providing cardioprotective effects. Recent research suggests that boosting cell proliferation and lowering apoptosis can help maintain the bulk of β-cells. Furthermore, GLP-1 receptor agonists have potent anti-inflammatory characteristics, improving immunological control and lowering systemic inflammation, both of which are critical for reducing autoimmune damage in T1D. Beyond glucose control, these agonists have neuroprotective qualities and aid in weight management. Combining these medications with insulin could significantly change how T1D is managed. The clinical data and biological mechanisms discussed in this review support the potential use of GLP-1 receptor agonists in T1D.
Impact of a Functional Dairy Powder and Its Primary Component on the Growth of Pathogenic and Probiotic Gut Bacteria and Human Coronavirus 229E
Authors: Dang Vu Bao , Alsherbiny Muhammad A. , Lin Ruohui , Gao Yumei , Li Chunguang , Bhuyan Deep Jyoti
Published: August 2024

Milk boasts an array of potent bioactive compounds, such as lactoferrin (Lf), immunoglobulins, and functional proteins, all delivering substantial therapeutic benefits. In this study, Immune Powder (a functional dairy formulation) and its primary component called Fractionated Milk Protein (FMP) containing Lf, zinc, and immunoglobulins and formulated by Ausnutria Pty Ltd. were evaluated for their potential broad-spectrum pharmacological activity. In particular, this study investigated the antibacterial (against pathogenic Escherichia coli), prebiotic (promoting Lactobacillus delbrueckii growth), anti-inflammatory (inhibition of NO production in RAW264.7 macrophages), and antiviral (against human coronavirus 229E) effects of the samples. In addition, the impact of simulated gastric digestion on the efficacy of the samples was explored. LCMS-based proteomics was implemented to unveil cellular and molecular mechanisms underlying antiviral activity. The Immune Powder demonstrated antibacterial activity against E. coli (up to 99.74 ± 11.47% inhibition), coupled with prebiotic action (10.84 ± 2.2 viability fold-change), albeit these activities diminished post-digestion (p < 0.01). The Immune Powder effectively mitigated NO production in lipopolysaccharide-stimulated RAW264.7 macrophages, with declining efficacy post-digestion (p < 0.0001). The Immune Powder showed similar antiviral activity before and after digestion (p > 0.05) with up to 3-fold improvement. Likewise, FMP exhibited antibacterial potency pre-digestion at high concentrations (95.56 ± 1.23% inhibition at 125 mg/mL) and post-digestion at lower doses (61.82 ± 5.58% inhibition at 3906.25 µg/mL). FMP also showed enhanced prebiotic activity post-digestion (p < 0.0001), NO inhibition pre-digestion, and significant antiviral activity. The proteomics study suggested that the formulation and its primary component shared similar antiviral mechanisms by inhibiting scavenger receptor binding and extracellular matrix interaction.
MicroRNA Profiling in Papillary Thyroid Cancer
Authors: Armos Richard , Bojtor Bence , Papp Marton , Illyes Ildiko , Lengyel Balazs , Kiss Andras , Szili Balazs , Tobias Balint , Balla Bernadett , Piko Henriett , Illes Anett , Putz Zsuzsanna , Kiss Andras , Toth Erika , Takacs Istvan , Kosa Janos P. , Lakatos Peter
Published: August 2024

Genetic alterations are well known to be related to the pathogenesis and prognosis of papillary thyroid carcinoma (PTC). Some miRNA expression dysregulations have previously been described in the context of cancer development including thyroid carcinoma. In our study, we performed original molecular diagnostics on tissue samples related to our own patients. We aimed to identify all dysregulated miRNAs in potential association with PTC development via sequencing much higher numbers of control-matched PTC tissue samples and analyzing a wider variety of miRNA types than previous studies. We analyzed the expression levels of 2656 different human miRNAs in the context of 236 thyroid tissue samples (118 tumor and control pairs) related to anonymized PTC cases. Also, KEGG pathway enrichment analysis and GO framework analysis were used to establish the links between miRNA dysregulation and certain biological processes, pathways of signaling, molecular functions, and cellular components. A total of 30 significant differential miRNA expressions with at least ±1 log2 fold change were found related to PTC including, e.g., miR-551b, miR-146b, miR-221, miR-222, and miR-375, among others, being highly upregulated, as well as miR-873 and miR-204 being downregulated. In addition, we identified miRNA patterns in vast databases (KEGG and GO) closely similar to that of PTC including, e.g., miRNA patterns of prostate cancer, HTLV infection, HIF-1 signaling, cellular responses to growth factor stimulus and organic substance, and negative regulation of gene expression. We also found 352 potential associations between certain miRNA expressions and states of clinicopathological variables. Our findings—supported by the largest case number of original matched-control PTC–miRNA relation research—suggest a distinct miRNA expression profile in PTC that could contribute to a deeper understanding of the underlying molecular mechanisms promoting the pathogenesis of the disease. Moreover, significant miRNA expression deviations and their signaling pathways in PTC presented in our study may serve as potential biomarkers for PTC diagnosis and prognosis or even therapeutic targets in the future.