bluuebunny/crossref_metadata_2025 huggingface dataset:
Bone Marrow Disseminated Tumor Cell Detection Is Beneficial for the Early Finding of Bone Metastasis and Prognosis
Authors: Wang Yulan , Liu Jun , Gong Yanping , Hu Binjie , Xie Jianzhu , Cheng Jin , Huang Qian
Published: July 2024

Background: Disseminated tumor cells (DTCs) are thought to be the initiators of tumor recurrence and metastasis. However, based on the current imaging examination methods, early detection of DTCs is extremely difficult due to their small number and dormant state. Methods: We used the SE-iFISH approach to detect bone marrow DTCs (mDTCs) in patients with breast or prostate cancer, and compared it with various imaging examination methods to explore its role in predicting metastasis and prognosis. Results: Fifteen patients were enrolled in this study. Among them, 11 patients showed imaging-confirmed bone metastases in different sites of the body, of which seven patients had iliac mDTCs and signs of iliac bone metastases on imaging. For the remaining four patients, imaging confirmed that the bone metastatic foci were far from the ilium, but in one patient, mDTCs were detected in the ilium. Interestedly, iliac mDTCs were also detected in two out of four patients who had no sign of bone metastases on imaging. Furthermore, the epithelial marker, CK18, was ubiquitously expressed in mDTCs, but its expression was very low in peripheral circulating tumor cells (pCTCs). The Kaplan–Meier plot suggested that CK18+ mDTCs ≥ 5 was related to poor overall survival (OS) compared with that of CK18+ mDTCs < 5 in breast cancer patients (median OS: 22.1 vs. 46.9 months; log-rank, p = 0.035). Conclusions: SE-iFISH examination for mDTCs is more sensitive than the conventional methods used for detecting bone metastases. mDTC detection facilitated the early finding of tumor cells in the bone marrow and ≥5 CK18+ mDTCs was associated with a poor prognosis in breast cancer patients.
Exploring the Prevalence of Functional Gastrointestinal Diseases and the Accompanied Differences in Dietary and Lifestyle Patterns: A Two-Generational Study
Authors: Aljaaly Elham A. , Khatib Mai A.
Published: July 2024

Background and Objectives: Generation Z and millennials in Saudi Arabia both experienced the stress of the COVID-19 pandemic and the accompanying factors that may have had an impact on the incidence of functional gastrointestinal diseases (FGIDs) in both generations. This study aims to explore how prevalent FGIDs are among adolescents and their parents. Methods and Study Design: A cross-sectional, school-based study conducted in public high schools for boys and girls in Jeddah, Saudi Arabia. We adapted 37 items from the ROME IV Diagnostic Questionnaires for children and adults, as well as other questionnaires. IBM SPSS Statistics (Version 28.0) was used. Results: Generation Z showed a higher prevalence of FGIDs (33.5%, n = 126) in comparison with millennials (20.0%, n = 28). In both generations, the most prevalent FGID was functional constipation; the least prevalent were irritable bowel syndrome and abdominal migraine, with no significant change in the severity or frequency of symptoms during the pandemic. The type of commonly consumed beverages was a risk factor for FGIDs. Participants in generation Z were less likely to use complementary and alternative medicine (67.4%) to prevent diseases and enhance immunity compared with millennials (82.9%). Conclusions: The study results confirmed disparities in the prevalence of FGIDs between the two generations before and during the COVID-19 pandemic, which requires further research in other areas of Saudi Arabia. Recognizing the differences between the millennial parents and the generation Z high schoolers could assist health professionals in planning individualized, generation-based interventions and educators in designing and tailoring programs based on generational differences.
WHO/ICC Classification for Myelodysplastic Neoplasms/Syndromes Performs Better for Subtype Cytomorphological Diagnosis?
Authors: Vicente Ana Isabel , Luna Irene , Ruiz Juan Carlos , Remigia María José , Jerez Andrés , Lluch Rafael , Llopis Inmaculada , Marco María Josefa , Benet Carmen , Alonso Carmen , Linares María Dolores , Serrano Luis , Orero María Teresa , Ortuño Francisco José , Senent María Leonor
Published: July 2024

The International Consensus Classification of Myeloid Neoplasms and Acute Leukemias (ICC) and the 5th edition of the WHO classification (WHO 2022) have refined the diagnosis of myelodysplastic syndromes (MDS). Both classifications segregate MDS subtypes based on molecular or cytogenetic findings but rely on the subjective assessment of blast cell percentage and dysplasia in hematopoietic cell lineages. This study aimed to evaluate interobserver concordance among 13 cytomorphologists from eight hospitals in assessing blast percentages and dysplastic features in 44 MDS patients. The study found fair interobserver agreement for the PB blast percentage and moderate agreement for the BM blast percentage, with the best concordance in cases with <5% BM blasts and >10% BM blasts. Monocyte count agreement was fair, and dysplasia assessment showed moderate concordance for megakaryocytic lineage but lower concordance for erythroid and granulocytic lineages. Overall, interobserver concordance for MDS subtypes was moderate across all classifications, with slightly better results for WHO 2022. These findings highlight the ongoing need for morphological evaluation in MDS diagnosis despite advances in genetic and molecular techniques. The study supports the blast percentage ranges established by the ICC but suggests refining BM blast cutoffs. Given the moderate interobserver concordance, a unified classification approach for MDS is recommended.
Safety Risk Modelling and Assessment of Civil Unmanned Aircraft System Operations: A Comprehensive Review
Authors: Du Sen , Zhong Gang , Wang Fei , Pang Bizhao , Zhang Honghai , Jiao Qingyu
Published: July 2024

Safety concerns are progressively emerging regarding the adoption of Unmanned Aircraft Systems (UASs) in diverse civil applications, particularly within the booming air transportation system, such as in Advanced Air Mobility. The outcomes of risk assessment determine operation authorization and mitigation strategies. However, civil UAS operations bring novel safety issues distinct from traditional aviation, like ground impact risk, etc. Existing studies vary in their risk definitions, modelling mechanisms, and objectives. There remains an incomplete gap of challenges, opportunities, and future efforts needed to collaboratively address diverse safety risks. This paper undertakes a comprehensive review of the literature in the domain, providing a summative understanding of the risk assessment of civil UAS operations. Specifically, four basic modelling approaches utilized commonly are identified comprising the safety risk management process, causal model, collision risk model, and ground risk model. Then, this paper reviews the state of the art in each category and explores the practical applications they contribute to, the support offered to participants from multiple stakeholders, and the primary technical challenges encountered. Moreover, potential directions for future work are outlined based on the high-level common problems. We believe that this review from a holistic perspective contributes towards better implementation of risk assessment in civil UAS operations, thus facilitating safe integration into the airspace system.
Support for the Time-Varying Drift Rate Model of Perceptual Discrimination in Dynamic and Static Noise Using Bayesian Model-Fitting Methodology
Authors: Deakin Jordan , Schofield Andrew , Heinke Dietmar
Published: July 2024

The drift-diffusion model (DDM) is a common approach to understanding human decision making. It considers decision making as accumulation of evidence about visual stimuli until sufficient evidence is reached to make a decision (decision boundary). Recently, Smith and colleagues proposed an extension of DDM, the time-varying DDM (TV-DDM). Here, the standard simplification that evidence accumulation operates on a fully formed representation of perceptual information is replaced with a perceptual integration stage modulating evidence accumulation. They suggested that this model particularly captures decision making regarding stimuli with dynamic noise. We tested this new model in two studies by using Bayesian parameter estimation and model comparison with marginal likelihoods. The first study replicated Smith and colleagues’ findings by utilizing the classical random-dot kinomatogram (RDK) task, which requires judging the motion direction of randomly moving dots (motion discrimination task). In the second study, we used a novel type of stimulus designed to be like RDKs but with randomized hue of stationary dots (color discrimination task). This study also found TV-DDM to be superior, suggesting that perceptual integration is also relevant for static noise possibly where integration over space is required. We also found support for within-trial changes in decision boundaries (“collapsing boundaries”). Interestingly, and in contrast to most studies, the boundaries increased with increasing task difficulty (amount of noise). Future studies will need to test this finding in a formal model.
Puppet Dynasty Recognition System Based on MobileNetV2
Authors: Xie Xiaona , Liu Zeqian , Wang Yuanshuai , Fu Haoyue , Liu Mengqi , Zhang Yingqin , Xu Jinbo
Published: July 2024

Traditional image classification usually relies on manual feature extraction; however, with the rapid development of artificial intelligence and intelligent vision technology, deep learning models such as CNNs can automatically extract key features from input images to achieve efficient classification. This study focuses on the application of lightweight separable convolutional neural networks in domain-specific image classification tasks. In this paper, we discuss how to use the SSDLite object detection algorithm combined with the MobileNetV2 lightweight convolutional architecture for puppet dynasty recognition from images—a novel and challenging task. By constructing a system that combines object detection and image classification, we aimed to solve the problem of automatic puppet dynasty recognition to reduce manual intervention and improve recognition efficiency and accuracy. We hope that this will have significant implications in the fields of cultural protection and art history research.
Prompt Engineering in Healthcare
Authors: Patil Rajvardhan , Heston Thomas F. , Bhuse Vijay
Published: July 2024

The rapid advancements in artificial intelligence, particularly generative AI and large language models, have unlocked new possibilities for revolutionizing healthcare delivery. However, harnessing the full potential of these technologies requires effective prompt engineering—designing and optimizing input prompts to guide AI systems toward generating clinically relevant and accurate outputs. Despite the importance of prompt engineering, medical education has yet to fully incorporate comprehensive training on this critical skill, leading to a knowledge gap among medical clinicians. This article addresses this educational gap by providing an overview of generative AI prompt engineering, its potential applications in primary care medicine, and best practices for its effective implementation. The role of well-crafted prompts in eliciting accurate, relevant, and valuable responses from AI models is discussed, emphasizing the need for prompts grounded in medical knowledge and aligned with evidence-based guidelines. The article explores various applications of prompt engineering in primary care, including enhancing patient–provider communication, streamlining clinical documentation, supporting medical education, and facilitating personalized care and shared decision-making. Incorporating domain-specific knowledge, engaging in iterative refinement and validation of prompts, and addressing ethical considerations and potential biases are highlighted. Embracing prompt engineering as a core competency in medical education will be crucial for successfully adopting and implementing AI technologies in primary care, ultimately leading to improved patient outcomes and enhanced healthcare delivery.
Dynamic User Tourism Interest Modeling through Domain Information Integration: A Hierarchical Approach
Authors: Todo Hiroyoshi , Zhang Xiliang , Zhang Zhongguo , Todo Yuki
Published: July 2024

With the exponential growth of online review platforms, understanding user preferences and interests in the tourism domain has become increasingly critical for businesses and service providers. However, extracting meaningful insights from the vast amount of available data poses a significant challenge. Traditional methods often struggle to capture the nuanced and hierarchical nature of user interests within the tourism domain. This paper pioneers the integration of domain information modeling technology into the realm of online review information mining, presenting a novel approach to constructing a user tourism interest model. Unlike existing methods, which primarily rely on flat or simplistic representations of user data, our approach leverages the hierarchical structure inherent in tourism domain information modeling. By harnessing big data within the tourism domain, we construct hierarchical tourism attributes and apply a conditional random field model along with an affective dictionary to facilitate the hierarchical mining of user travel interest information. This culminates in the establishment of a comprehensive user travel interest model using advanced information modeling techniques. Building upon this foundation, we further propose a dynamic user travel interest model, showcasing its adaptability and responsiveness to changing user preferences. Finally, we validate the accuracy and effectiveness of our model through simulation experiments within a user travel recommendation system, demonstrating significant improvements over traditional methods.
YOLO-FMDI: A Lightweight YOLOv8 Focusing on a Multi-Scale Feature Diffusion Interaction Neck for Tomato Pest and Disease Detection
Authors: Sun Hao , Nicholaus Isack Thomas , Fu Rui , Kang Dae-Ki
Published: July 2024

At the present stage, the field of detecting vegetable pests and diseases is in dire need of the integration of computer vision technologies. However, the deployment of efficient and lightweight object-detection models on edge devices in vegetable cultivation environments is a key issue. To address the limitations of current target-detection models, we propose a novel lightweight object-detection model based on YOLOv8n while maintaining high accuracy. In this paper, (1) we propose a new neck structure, Focus Multi-scale Feature Diffusion Interaction (FMDI), and inject it into the YOLOv8n architecture, which performs multi-scale fusion across hierarchical features and improves the accuracy of pest target detection. (2) We propose a new efficient Multi-core Focused Network (MFN) for extracting features of different scales and capturing local contextual information, which optimizes the processing power of feature information. (3) We incorporate the novel and efficient Universal Inverted Bottleneck (UIB) block to replace the original bottleneck block, which effectively simplifies the structure of the block and achieves the lightweight model. Finally, the performance of YOLO-FMDI is evaluated through a large number of ablation and comparison experiments. Notably, compared with the original YOLOv8n, our model reduces the parameters, GFLOPs, and model size by 18.2%, 6.1%, and 15.9%, respectively, improving the mean average precision (mAP50) by 1.2%. These findings emphasize the excellent performance of our proposed model for tomato pest and disease detection, which provides a lightweight and high-precision solution for vegetable cultivation applications.
A Blockchain-Based Detection and Control System for Model-Generated False Information
Authors: Liu Chenlei , Xu Yuhua , Hu Bing , Sun Zhixin
Published: July 2024

In the digital age, spreading false information has a far-reaching impact on various areas, such as society, politics, and the economy. With the popularization of applications of text generation models, the cost of producing false information has significantly decreased, making it challenging for human beings to screen it. Therefore, research on detection screening and early warning control for model-generated false information becomes particularly important. In this paper, we propose a model-generated false information detection and control system based on blockchain. Firstly, we design a model-generated false information detection method combining model-generated text discrimination based on a self-attention network and text similarity detection based on a twin network. Secondly, we construct a blockchain-based model-generated false information control and traceability system. It utilizes the proposed detection algorithm to provide early warning and control of model-generated false information involving important and sensitive events before social network release. For information judged to be model-generated false, the stored data on the blockchain is utilized to track and trace the publisher. Ultimately, experimental tests prove that the proposed detection method improves the accuracy of false information detection. In addition, the operational efficiency of the prototype system can meet quality of service requirements.
Implementation of Series Resonance-Based Fault Current Limiter for Enhanced Transient Stability of Grid-Connected Photovoltaic Farm
Authors: Demin Slava , Barbie Eli , Heistrene Leena , Belikov Juri , Petlenkov Eduard , Levron Yoash , Baimel Dmitry
Published: July 2024

This paper presents the implementation of an improved resonance-type FCL, designed to enhance the transient stability of a photovoltaic farm. This FCL overcomes the well-known drawbacks associated with the conventional resonance-based FCLs. This FCL limits the fault during the fault period, quickly achieves stability during the recovery period, independent of the reactor’s charging state, and notably reduces DC-link voltage fluctuations during faults. A power system simulation setup comprising a PV farm, synchronous generator, transformers, circuit breakers, transmission system, and several branches of loads is used for testing the proposed FCL. The simulation results validate that the proposed FCL better improves the system’s stability and leads to improved fault current, PCC voltage, active power, reactive power, and DC-link voltage compared to other common types of resonant FCLs.
Systematic Review on Requirements Engineering in Quantum Computing: Insights and Future Directions
Authors: Sepúlveda Samuel , Cravero Ania , Fonseca Guillermo , Antonelli Leandro
Published: July 2024

Context: Quantum software development is a complex and intricate process that diverges significantly from traditional software development. Quantum computing and quantum software are deeply entangled with quantum mechanics, which introduces a different level of abstraction and a deep dependence on quantum physical properties. The classical requirements engineering methods must be adapted to encompass the essential quantum features in this new paradigm. Aim: This study aims to systematically identify and analyze challenges, opportunities, developments, and new lines of research in requirements engineering for quantum computing. Method: We conducted a systematic literature review, including three research questions. This study included 105 papers published from 2017 to 2024. Results: The main results include the identification of problems associated with defining specific requirements for quantum software and hybrid system requirements. In addition, we identified challenges related to the absence of standards for quantum requirements engineering. Finally, we can see the advances in developing programming languages and simulation tools for developing software in hybrid systems. Conclusions: This study presents the challenges and opportunities in quantum computing requirements engineering, emphasizing the need for new methodologies and tools. It proposes a roadmap for future research to develop a standardized framework, contributing to theoretical foundations and practical applications.
Real-Time Personal Protective Equipment Non-Compliance Recognition on AI Edge Cameras
Authors: Sanjeewani Pubudu , Neuber Glenn , Fitzgerald John , Chandrasena Nadeesha , Potums Stijn , Alavi Azadeh , Lane Christopher
Published: July 2024

Despite advancements in technology, safety equipment, and training within the construction industry over recent decades, the prevalence of fatal and nonfatal injuries and accidents remains a significant concern among construction workers. Hard hats and safety vests are crucial safety gear known to mitigate severe head trauma and other injuries. However, adherence to safety protocols, including the use of such gear, is often inadequate, posing potential risks to workers. Moreover, current manual safety monitoring systems are laborious and time-consuming. To address these challenges and enhance workplace safety, there is a pressing need to automate safety monitoring processes economically, with reduced processing times. This research proposes a deep learning-based pipeline for real-time identification of non-compliance with wearing hard hats and safety vests, enabling safety officers to preempt hazards and mitigate risks at construction sites. We evaluate various neural networks for edge deployment and find that the Single Shot Multibox Detector (SSD) MobileNet V2 model excels in computational efficiency, making it particularly suitable for this application-oriented task. The experiments and comparative analyses demonstrate the pipeline’s effectiveness in accurately identifying instances of non-compliance across different scenarios, underscoring its potential for improving safety outcomes.
Magnetoresistive Shunt as an Alternative to Wheatstone Bridge Sensors in Electrical Current Sensing
Authors: Ramírez-Muñoz Diego , García-Gil Rafael , Soriano-Díaz Sandra , Cardoso Susana , Freitas Paulo P.
Published: July 2024

The main objective of the work is to investigate the capacity of a single magnetoresistance (MR) element to measure AC electrical currents. An instrumentation system is presented to characterize individually the four active elements of an MR bridge current sensor preserving their internal connections. The system suggests the possibility to sense electrical currents using only one element of the bridge opening the way to design new MR sensors based on this concept. Sensitivity, offset and non-linearity deviation were obtained using bridges of tunnel (TMR)- and giant (GMR)-based MR technologies. The single element embedded in a Wheatstone bridge configuration is used for practical current measurements in a 50 Hz line. An electronic circuitry is proposed to measure alternating (AC) currents with a single MR element, including a lock-in amplifier and an interface to properly convert the signal to its root mean square (rms) value with a resolution of 250 mA peak in the 125 A range.
Wideband Eight-Antenna Array Designs for 5G Smartphone Applications
Authors: Huang Guan-Long , Chang Ting-Yu , Sim Chow-Yen-Desmond
Published: July 2024

This paper proposes a broadband eight-antenna array design suitable for Fifth Generation New Radio (5G NR) smartphone applications. To cover the 5G NR bands n77/n78/n79 (3300–5000 MHz) and 5G NR-U n46 band (5150–5925 MHz), the single antenna array unit applied is a modified loop antenna element (MLAE) that can generate three different loop modes. To yield good multi-input multi-output (MIMO) performances, the designed MLAE is further arranged as an eight-antenna array, and the experimental results show that the overlapping 6 dB bandwidth can cover the bands-of-interest (3300–5925 MHz) with good isolation and total efficiency of >10 dB and 51–84%, respectively. Finally, good MIMO performances, such as an envelope correlation coefficient (ECC) of lower than 0.1 and desirable channel capacity (CC) of 37–40 bps/Hz, were calculated across the bands-of-interest.
A Review of End-of-Life Scenarios for Fibre-Reinforced Polymer Materials
Authors: Ziemińska-Stolarska Aleksandra , Sobulska Mariia , Pietrzak Monika , Zbiciński Ireneusz
Published: July 2024

The paper presents a review of End-of-Life scenarios (EoL) (disposal, incineration, chemical, thermal and mechanical recycling) compared to the production stage of Fibre-Reinforced Polymers (FRPs) of composites regarding global warming potential. Innovative FRP manufacturing technologies (vacuum infusion, ultraviolet curved pultrusion, hot stamping, three-dimensional printing and automatic tape placement) commonly used in the shipbuilding industry were environmentally assessed. The materials, energy flows and waste discharged to the environment over the whole life cycle were collected, identified and quantified based on Life Cycle Assessment (LCA) analysis in the frame of the Fibre4Yards project. The results of LCA calculations show that waste management (the EoL scenario) contributes 5 to 39% of the total carbon footprint for FRP technologies. The highest contribution of the EoL scenario was found for technologies where polypropylene was applied, i.e., 33 and 38% of the total CO2 emissions. Our analysis of the literature and information from industrial partners confirm that the standard and most common waste scenario for FRP materials and compounds is still incineration and landfilling.
Fatigue Life of Flywheel Energy Storage Rotors Composed of 30Cr2Ni4MoV Steel
Authors: Hu Dongxu , Dai Xingjian , Xie Bo , Li Wen , Yu Hongyan , Chen Haisheng
Published: July 2024

In supporting the stable operation of high-penetration renewable energy grids, flywheel energy storage systems undergo frequent charge–discharge cycles, resulting in significant stress fluctuations in the rotor core. This paper investigates the fatigue life of flywheel energy storage rotors fabricated from 30Cr2Ni4MoV alloy steel, attempting to elucidate the material’s mechanical properties, crack propagation behavior, and impact of internal defects on fatigue life. Tensile tests reveal that the material exhibited high yield (992 MPa) and tensile strengths (1130 MPa). The Paris formula is used to model crack growth rates, ending in good agreement with the experimental data. Fatigue tests at various stress conditions highlight the material’s significant variability in fatigue life and emphasize the need for reliable design approaches. This paper also demonstrates that internal defect size and location critically affect fatigue life, calling for improvements in forging inspection standards. Overall, the present study provides a comprehensive analysis of 30Cr2Ni4MoV steel’s suitability for flywheel rotors, balancing safety, and operational efficiency.
Evaporative Fractionation as the Important Formation Mechanism of Light Oil Reservoirs in the Dongying Depression, NE China
Authors: Qiao Rongzhen , Li Meijun , Zhang Donglin , Chen Zhonghong , Xiao Hong
Published: July 2024

Light oil, a high-quality energy resource abundant in deep basins, is prevalent in the northern zone of the Dongying Depression. To elucidate the formation mechanism of light oil reservoirs, this study investigates the molecular and stable isotope composition, biomarkers, light hydrocarbons, and diamondoid compositions of petroleum. The results reveal that the gas primarily consists of oil-cracking gas from a late filling event, mixed with oil-associated gas generated during the source rock’s “oil window” maturity phase. Methane exhibits enriched light carbon isotopes, indicating noticeable migration fractionation effects. The crude oil in the same deep strata exhibits high maturity, originating from both terrestrial and aquatic organic matter in the source materials. Molar proportions of n-alkanes and light hydrocarbon indices (Tol/nC7, nC7/MCH) indicate significant evaporative fractionation in the petroleum reservoirs. This fractionation process modified early-formed oil reservoirs due to the late filling of highly mature gas reservoirs. The evaporative fractionation at different stages has varying effects on the diamondoid ratio (1- + 2-MA)/(3- + 4-MD). It is considered a pivotal mechanism in the formation of deep condensate reservoirs and volatile oil reservoirs.
Estimation of Railway Line Impedance at Low Frequency Using Onboard Measurements Only
Authors: Mariscotti Andrea
Published: July 2024

Estimating line impedance is relevant in transmission and distribution networks, in particular for planning and control. The large number of deployed PMUs has fostered the use of passive indirect methods based on network model identification. Electrified railways are a particular example of a distribution network, with moving highly dynamic loads, that would benefit from line impedance information for energy efficiency and optimization purposes, but for which many of the methods used in industrial applications cannot be directly applied. The estimate is carried out onboard using a passive method in a single-point perspective, suitable for implementation with energy metering onboard equipment. A comparison of two methods is carried out based on the non-linear least mean squares (LMS) optimization of an over-determined system of equations and on the auto- and cross-spectra of the pantograph voltage and current. The methods are checked preliminarily with a simulated synthetic network, showing good accuracy, within 5%. They are then applied to measured data over a 20 min run over the Swiss 16.7 Hz railway network. Both methods are suitable to track network impedance in real time during the train journey; but with suitable checks on the significance of the pantograph current and on the values of the coefficient of determination, the LMS method seems more reliable with predictable behavior.
Tree Height Estimation of Chinese Fir Forests Based on Geographically Weighted Regression and Forest Survey Data
Authors: Zheng Xinyu , Wang Hao , Dong Chen , Lou Xiongwei , Wu Dasheng , Fang Luming , Dai Dan , Xu Liuchang , Xue Xingyu
Published: July 2024

Estimating tree height at the national to regional scale is crucial for assessing forest health and forest carbon storage and understanding forest ecosystem processes. It also aids in formulating forest management and restoration policies to mitigate global climate change. Extensive ground-survey data offer a valuable resource for estimating tree height. In tree height estimation modeling, a few comparative studies have examined the effectiveness of global-based versus local-based models, and the spatial heterogeneity of independent variable parameters remains insufficiently explored. This study utilized ~200,000 ground-survey data points covering the entire provincial region to compare the performance of the global-based Ordinary Least Squares (OLS) and Random Forest (RF) model, as well as local-based Geographically Weighted Regression (GWR) model, for predicting the average tree height of Chinese fir forests in Zhejiang Province China. The results showed that the GWR model outperformed both OLS and RF in terms of predictive accuracy, achieving an R-squared (R2) and adjusted R2 of 0.81 and MAE and RMSE of 0.93 and 1.28, respectively. The performance indicated that the local-based GWR held advantages over global-based models, especially in revealing the spatial non-stationarity of forests. Visualization of parameter estimates across independent variables revealed spatial non-stationarity in their impact effects. In mountainous areas with dense forest coverage, the parameter estimates for average age were notably higher, whereas in forests proximate to urban areas, the parameters were comparatively lower. This study demonstrates the effectiveness of large ground-survey data and GWR in tree height estimation modeling at a provincial scale.
Assessing the Flight Potential of the Four-Eyed Fir Bark Beetle Polygraphus proximus Blandford in Natural Conditions
Authors: Demidko Denis A. , Kulakov Sergey S. , Efremenko Anton A. , Babichev Nikita S. , Barchenkov Alexey P. , Mikhailov Pavel V.
Published: July 2024

The four-eyed fir bark beetle Polygraphus proximus Blandford (Coleoptera, Curculionidae, Scolytinae) has become an aggressive invader in Siberia’s fir forests in recent decades. However, its spread in the invaded area is not yet complete; this species is absent so far in most of the Siberian fir Abies sibirica Ledeb. range. To predict this process, appropriate models are needed, including at the local level. One of the essential parameters for modeling is the flight characteristics of imago. To evaluate its flight potential, we placed slabs (sections of the bark with a thin sapwood layer) in an area without forests or with forests with an absence of fir at the end of May. The beetles overwintered under the bark in these slabs. We also placed short fir trap logs at distances ranging from 50 to 1500 m in the four cardinal directions from the release point to attract migrating beetles. After the beetles from the slabs had completed their migration, we evaluated the number of parental pairs (male and female) of the four-eyed fir bark beetle on the trap logs (p) and their number per dm2 (pS). The sole factor that affected the colonization of host objects in our experiment was the distance from the release point. The relationship between p and pS and the distance to the release point can be accurately represented by a negative exponential curve. This experiment established a flight distance of 1500 m, with calculated values of 4919 m for p and 2965 m for pS. However, an analysis of similar experiments and studies using flight mills suggests that these values may not be extreme in an environment with few and sparse host objects. In conditions of abundant food supply, the actual flight distance of P. proximus beetles is significantly less than the theoretically possible maximum.
Observed Vertical Dispersion Patterns of Particulate Matter in Urban Street Canyons and Dominant Influencing Factors
Authors: Wang Xiaoshuang , Chen Xiaoping , Ma Bojun , Zhou Zhixiang , Peng Changhui
Published: July 2024

When developing strategies aimed at mitigating air pollution in densely populated urban areas, it is vital to accurately investigate the vertical distribution of airborne particulate matter (PM) and its primary influencing factors. For this study, field experiments were conducted to quantify the vertical distribution and dispersion processes of PM at five vertical heights related to trees—including at street level near vehicular emission sources (0.3 m), pedestrian breathing height (1.5 m), beneath the canopy (6 m), mid-canopy (9 m), and the top of the canopy (12 m)—within a street-facing building in Wuhan, China. Comparing the vertical dispersion patterns of PM with six particle sizes (PM1, PM2.5, PM4, PM7, PM10, and total suspended particulates—TSPs), larger particles exhibited more pronounced variations with height, notably TSPs (correlation coefficient of −0.95) and PM10 (−0.84). The findings consistently revealed a downward trend in PM concentrations across various particle sizes with increasing height, indicating a negative linear correlation between particle concentrations and altitude within the street canyon. For every 1% increase in vertical height, the PM2.5 concentration decreased by approximately 5.44%, the PM10 concentration decreased by 132.1%, and the TSP concentration decreased by 180.6%. These findings show potential for guiding building designers in developing effective strategies, such as optimal vent placement, in order to mitigate the intrusion of outdoor air pollution—particularly PM2.5—into indoor environments. Furthermore, this research provides novel insights for residents living in street-facing buildings and individuals with respiratory diseases, aiding them in the selection of residential floors to minimize health risks associated with exposure to respirable PM.
Towards Collaborative Edge Intelligence: Blockchain-Based Data Valuation and Scheduling for Improved Quality of Service
Authors: Du Yao , Wang Zehua , Leung Cyril , Leung Victor C. M.
Published: July 2024

Collaborative edge intelligence, a distributed computing paradigm, refers to a system where multiple edge devices work together to process data and perform distributed machine learning (DML) tasks locally. Decentralized Internet of Things (IoT) devices share knowledge and resources to improve the quality of service (QoS) of the system with reduced reliance on centralized cloud infrastructure. However, the paradigm is vulnerable to free-riding attacks, where some devices benefit from the collective intelligence without contributing their fair share, potentially disincentivizing collaboration and undermining the system’s effectiveness. Moreover, data collected from heterogeneous IoT devices may contain biased information that decreases the prediction accuracy of DML models. To address these challenges, we propose a novel incentive mechanism that relies on time-dependent blockchain records and multi-access edge computing (MEC). We formulate the QoS problem as an unbounded multiple knapsack problem at the network edge. Furthermore, a decentralized valuation protocol is introduced atop blockchain to incentivize contributors and disincentivize free-riders. To improve model prediction accuracy within latency requirements, a data scheduling algorithm is given based on a curriculum learning framework. Based on our computer simulations using heterogeneous datasets, we identify two critical factors for enhancing the QoS in collaborative edge intelligence systems: (1) mitigating the impact of information loss and free-riders via decentralized data valuation and (2) optimizing the marginal utility of individual data samples by adaptive data scheduling.
The Effect of Domain Length and Initialization Noise on Direct Numerical Simulation of Shear Stratified Turbulence
Authors: Palma Vashkar , MacDonald Daniel , Raessi Mehdi
Published: July 2024

Direct numerical simulation (DNS) has been employed with success in a variety of oceanographic applications, particularly for investigating the internal dynamics of Kelvin–Helmholtz (KH) billows. However, it is difficult to relate these results directly with observations of ocean turbulence due to the significant scale differences involved (ocean shear layers are typically on the order of tens to hundreds of meters in thickness, compared to DNS studies, with layers on the order of one to tens of centimeters). As efforts continue to inform our understanding of geophysical-scale turbulence by extrapolating DNS results, it is important to understand the impact of model setup and initial conditions on the resulting turbulent quantities. Given that geophysical-scale measurements, whether through microstructures or other techniques, can only provide estimates of averaged TKE quantities (e.g., TKE dissipation or buoyancy flux), it may be necessary to compare mean turbulent quantities derived from DNS (i.e., across one or more complete billow evolutions) with ocean measurements. In this study, we analyze the effect of domain length and initial velocity noise on resulting turbulent quantities. Domain length is important, as dimensions that are not integer multiples of the natural KH billow wavelength may compress or stretch the billows and impact their energetics. The addition of random noise in the initial velocity field is often used to trigger turbulence and suppress secondary instabilities; however, the impact of noise on the resulting turbulent energetics is largely unknown. In this study, we conclude that domain lengths on the order of 1.5 times the natural wavelength or less can affect the resulting turbulent energetics by a factor of two or more. We also conclude that increasing the amplitude of random initial velocity noise decreases the resulting turbulent energetics, but that different realizations of the random noise field may have an even greater impact than amplitude. These results should be considered when designing a DNS experiment.
Numerical Solution of the Newtonian Plane Couette Flow with Linear Dynamic Wall Slip
Authors: Abou Hasan Muner M. , Ahmed Ethar A. A. , Ghaleb Ahmed F. , Abou-Dina Moustafa S. , Georgiou Georgios C.
Published: July 2024

An efficient numerical approach based on weighted-average finite differences is used to solve the Newtonian plane Couette flow with wall slip, obeying a dynamic slip law that generalizes the Navier slip law with the inclusion of a relaxation term. Slip is exhibited only along the fixed lower plate, and the motion is triggered by the motion of the upper plate. Three different cases are considered for the motion of the moving plate, i.e., constant speed, oscillating speed, and a single-period sinusoidal speed. The velocity and the volumetric flow rate are calculated in all cases and comparisons are made with the results of other methods and available results in the literature. The numerical outcomes confirm the damping with time and the lagging effects arising from the Navier and dynamic wall slip conditions and demonstrate the hysteretic behavior of the slip velocity in following the harmonic boundary motion.
Probiotic Functions in Fermented Foods: Anti-Viral, Immunomodulatory, and Anti-Cancer Benefits
Authors: Pyo Yeonhee , Kwon Ki Han , Jung Yeon Ja
Published: July 2024

Fermented foods can provide many benefits to our health. These foods are created by the action of microorganisms and help support our digestive health and immune system. Fermented foods include yogurt, kimchi, pickles, kefir, beer, wine, and more. Fermented foods contain probiotics, lactic acid bacteria (LAB), yeast, organic acids, ethanol, or antimicrobial compounds, which help balance the gut microbiome and improve digestive health. Fermented foods can also benefit your overall health by increasing the diversity of your gut microbiome and reducing inflammation. By routinely consuming fermented foods with these benefits, we can continue to improve our health. Probiotics from fermented foods are beneficial strains of bacteria that are safe for human health and constitute an important component of human health, even for children and the elderly. Probiotics can have a positive impact on your health, especially by helping to balance your gut microbiome and improve digestive health. Probiotics can also boost your immune system and reduce inflammation, which can benefit your overall health. Probiotics, which can be consumed in the diet or in supplement form, are found in many different types of foods and beverages. Research is continuing to investigate the health effects of probiotics and how they can be utilized. The potential mechanisms of probiotics include anti-cancer activity, preventing and treating immune system-related diseases, and slowing the development of Alzheimer’s disease and Huntington’s disease. This is due to the gut–brain axis of probiotics, which provides a range of health benefits beyond the digestive and gastrointestinal systems. Probiotics reduce tumor necrosis factor-α and interleukins through the nuclear factor-kappa B and mitogen-activated protein kinase pathways. They have been shown to protect against colon cancer and colitis by interfering with the adhesion of harmful bacteria in the gut. This article is based on clinical and review studies identified in the electronic databases PubMed, Web of Science, Embase, and Google Scholar, and a systematic review of clinical studies was performed.
Physicochemical and Sensory Properties and Antioxidant Activity of Xylitol Candies Containing Yuja (Citrus junos) Peels or Pulp
Authors: Im Ju-Hye , Lee Mi-Kyung , Lee Hae-In
Published: July 2024

Xylitol candies offer numerous health benefits such as preventing cavities and obesity. However, a preference for them tends to be low due to their distinctive flavor. In this study, we developed xylitol candies containing mature yuja peel (MYP-C), immature yuja peel (IYP-C), and yuja pulp (YP-C). To determine the optimal yuja added to xylitol candy, we compared and analyzed its physicochemical properties, sensory characteristics, and antioxidant activities. IYP-C and MYP-C significantly increased the naringin and hesperidin contents compared to the control and the YP-C. In particular, the IYP-C exhibited the highest content of flavonoids and polyphenols, which contributed to enhancing antioxidant activity such as ferric reducing antioxidant power (FRAP), 1,1 diphenyl-2-picrylhydrazyl (DPPH), and 2,2′-azino-di-2 ethyl-benzothiazoline sulfonate (ABTS+) radical scavenging activities. The IYP-C had the highest crude ash content. The L*, a*, and b* values of MYP-C and IYP-C showed dark red and yellow colors compared to the CON and YP-C groups. The sensory analysis conducted using electronic tongue equipment revealed that IYP-C exhibited high levels of umami, sweetness, and bitterness, while YP-C showed the highest intensity of sourness. In conclusion, these results suggest that IYP-C rather than MYP-C and YP-C provide xylitol candy with good qualities in terms of antioxidant activities and physicochemical characteristics.
Students’ Well-Being and Academic Engagement: A Multivariate Analysis of the Influencing Factors
Authors: Puiu Silvia , Udriștioiu Mihaela Tinca , Petrișor Iulian , Yılmaz Sıdıka Ece , Pfefferová Miriam Spodniaková , Raykova Zhelyazka , Yildizhan Hasan , Marekova Elisaveta
Published: July 2024

This paper aims to identify the factors that are positively or negatively impacting students’ well-being and their academic engagement. We used partial least-squares structural equation modeling (PLS-SEM) using the data collected through a questionnaire from four countries: Romania, Turkey, Slovakia, and Bulgaria. The model includes seven factors that influence the well-being of students and indirectly their academic engagement: stressors in the students’ lives; professors’ support; social support from family and friends; the students’ perceived satisfaction in their lives; engaging in activities during their leisure time; self-exploration regarding their careers; and environmental exploration regarding their careers. The results show that all factors, except for stressors and environmental exploration regarding their careers, positively influence the students’ well-being and thus their academic engagement. These findings are useful for university professors and managers in better organizing activities to increase academic performance.
Analysis of Urinary Incontinence in the Neurogenic Bladder and Its Relationship with the Satisfaction and Lifestyle of People with SCI
Authors: Neves Videira Lorena Gomes , Corbo Letícia Noelle , de Avila Marla Andreia Garcia , Martins Giovana Pelosi , Schoeller Soraia Dornelles , Kappler Christoph , Faleiros Fabiana
Published: July 2024

One of the most common complications of neurogenic bladder secondary to spinal cord injury (SCI) is urinary incontinence, which is possibly related to bladder-emptying methods and changes in quality of life. This study aimed to identify the occurrence of this complication in adults with SCI and analyze its relationship with bladder-emptying methods, satisfaction, and lifestyle. This is a quantitative, exploratory, and cross-sectional study. The variables were collected using the Bowel and Bladder Treatment Index during a telephone interview with 290 participants from February to November 2021. According to the results, 70% of the participants were male and 74.1% performed clean intermediate catheterization (CIC) as the main bladder-emptying method. Moreover, 55.6% were considered incontinent in the last year. Emptying by normal urination and bladder reflex triggering had a statistically significant relationship with urinary incontinence. A statistical association was observed between all the variables of satisfaction and lifestyle with urinary incontinence. Although CIC reduced urine leakage, a considerable number of participants still presented with frequent urine leakage. Urinary incontinence had a negative impact on satisfaction with the bladder-emptying method, effectiveness of bladder management, quality of life, and personal and social relationships.