bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of three-dimensional printing in plastic surgery: a bibliometric analysis
Authors: Tian Jie , Jin Ming-Jia , Gao Yang
Published: August 2024

Recent years have seen the publication of numerous papers on the application of three-dimensional (3D) printing in plastic surgery. Despite this growing interest, a comprehensive bibliometric analysis of the field has yet to be conducted. To address this gap, we undertook a bibliometric study to map out the knowledge structure and identify research hotspots related to 3D printing in plastic surgery. We analyzed publications from 1995 to 2024, found in the Web of Science Core Collection (WoSCC), utilizing tools such as VOSviewer, CiteSpace, and the R package “bibliometrix”. Our analysis included 1,057 documents contributed by 5,545 authors from 1,620 organizations across 71 regions, and these were published in 400 journals. We observed a steady growth in annual publications, with Europe, Asia, North America, and Oceania leading in research output. Notably, Shanghai Jiao Tong University emerged as a primary research institution in this domain. The Journal of Craniofacial Surgery and Journal of Oral and Maxillofacial Surgery have made significant contributions to the field, with Thieringer, Florian M being the most prolific and frequently cited author. Key areas of focus include medical education and surgical procedures, with “3D printing”, “virtual surgical planning” and “reconstructive/orthognathic surgery” highlighted as future research hotspots. Our study provides a detailed bibliometric analysis, revealing the evolution and progress of 3D printing technologies in plastic surgery. As these technologies continue to advance, their impact on clinical practice and patient lives is expected to be profound.
The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology
Authors: Noordijk Ben , Garcia Gomez Monica L. , ten Tusscher Kirsten H. W. J. , de Ridder Dick , van Dijk Aalt D. J. , Smith Robert W.
Published: August 2024

Both machine learning and mechanistic modelling approaches have been used independently with great success in systems biology. Machine learning excels in deriving statistical relationships and quantitative prediction from data, while mechanistic modelling is a powerful approach to capture knowledge and infer causal mechanisms underpinning biological phenomena. Importantly, the strengths of one are the weaknesses of the other, which suggests that substantial gains can be made by combining machine learning with mechanistic modelling, a field referred to as Scientific Machine Learning (SciML). In this review we discuss recent advances in combining these two approaches for systems biology, and point out future avenues for its application in the biological sciences.
Prophylactic Peri-Nephric Drain Placement in Renal Transplant Surgery: A Systematic Review and Meta-Analysis
Authors: Lakha Adil S. , Ahmed Shahzaib , Hunter James , O’Callaghan John
Published: August 2024

Renal transplantation is common worldwide, with >25,000 procedures performed in 2022. Usage of prophylactic perinephric drains is variable in renal transplantation; drains are associated with risks, and there is a lack of consensus regarding benefit of routine drain placement in these patients. This meta-analysis assessed whether prophylactic drainage reduced need for reintervention postoperatively. This systematic review and meta-analysis was carried out using the Preferred Reporting Items in Systematic Reviews and Meta-Analysis, and prospectively registered on PROSPERO. Summary statistics for outcomes of interest underwent meta-analyses to a confidence interval (CI) of 95% and are presented as Forest Plots for Odds Ratio (OR). A systematic literature search in June 2023 revealed 1,540 unique articles across four databases. Of these, four retrospective cohort studies were selected. Meta-analysis of three studies showed no significant reduction in reintervention rate with pre-emptive drain placement, OR = 0.59 (95% CI: 0.16–2.23), p = 0.44. Meta-analysis did not show a significant reduction in perinephric collections with prophylactic drain insertion OR = 0.55 (95% CI: 0.13–2.37), p = 0.42. Finally, there is not good evidence that drain placement reduces superficial wound complications or improves 12-month graft survival. Further work is needed, including well-designed, prospective studies to assess the risks and benefits of drain placement in these patients.

Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023422685, Identifier PROSPERO CRD42021255795.
The Use of Artificial Intelligence Systems in Tourism and Hospitality: The Tourists’ Perspective
Authors: Sousa Ana Elisa , Cardoso Paula , Dias Francisco
Published: August 2024

A myriad of types of artificial intelligence (AI) systems—namely AI-powered site search, augmented reality, biometric data recognition, booking systems, chatbots, drones, kiosks/self-service screens, machine translation, QR codes, robots, virtual reality, and voice assistants—are being used by companies in the tourism and hospitality industry. How are consumers reacting to these profound changes? This study aims to address this issue by identifying the types of AI systems that are used by tourists, the purposes they are used for in the present, and how likely they are to be used in the future. This study also aims to identify the types of emotions (positive vs. negative) that tourists associate with the use of AI systems, as well as the advantages and disadvantages they attribute to them. Considering the exploratory nature of the research, data were collected through an online survey shared on social media, which was available from September to December 2023. Results show that most respondents have already used several AI systems, assign more advantages than disadvantages to their use, and that the emotions they associate with their use are significantly positive. Moreover, compared to the small number of respondents (13.7%) who associate negative emotions with the use of AI systems, respondents who claim to feel positive emotions when using AI systems also evaluate them more positively in terms of their usefulness for tourism and hospitality. They identify more advantages, use a greater diversity of AI systems, and admit that they would use a more diverse range of AI systems in tourism contexts in the future.
Comprehensive Genome-Wide Identification and Characterization of the AP2 Subfamily in Beta vulgaris L. in Response to Exogenous Abscisic Acid
Authors: Zhai Yan , Ni Yuanrong , Wang Hao , Zhou Yuanhang , Xing Wang
Published: August 2024

APETALA2 (AP2) belongs to transcription factor (TF) families, with crucial roles in regulating plant growth, development, and stress responses. In order to explore the characteristics of sugar beet (Beta vulgaris L.) AP2s (BvAP2s) in response to drought stress hormone abscisic acid (ABA), genome-wide identification, and the phylogeny, gene structure and promoter precursor analysis of the BvAP2s were employed to predict their potential functions. It is shown that there are a total of 13 BvAP2 genes in the Beta vulgaris. Based on the primary amino acid sequence, the BvAP2s can be further subdivided into euAP2, euANT and basalANT. In addition, cis-acting element analysis showed that BvAP2s contained several abiotic stress-related elements, including those associated with ABA and drought stress. Roots are the first to perceive stress signals, and ABA-treated beetroot transcriptome and downstream gene prediction of BvAP2s revealed that BVRB_4g074790, BVRB_6g128480 and BVRB_7g179610 may play an important role involved in ABA signaling pathways during the stress response by regulating downstream GRAM genes, LEAs and U-boxes. Additionally, quantitative real-time polymerase chain reaction (qRT-PCR) further confirmed the downregulation of these three BvAP2s in response to ABA induction in sugar beet roots. These findings provide a basis for future utilization of BvAP2s in developing drought-tolerant Beta vulgaris varieties.
Effects of Agricultural Trade on Reducing Carbon Emissions under the “Dual Carbon” Target: Evidence from China
Authors: Li Qiangyi , Zhang Xiaohui
Published: August 2024

Within the “dual carbon” framework, sustainable agriculture is pivotal for climate change mitigation and long-term agricultural health. To explore the environmental effects of agricultural trade, this study assesses the carbon emissions from agriculture using information from 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan due to a lack of available data) from 2001 to 2021. Then, we analyzed the impact of agricultural trade on agricultural carbon emissions and tested for the possible existence of mechanisms. Finally, we validate the synergistic effects of agricultural trade on pollution and carbon abatement. The findings were as follows: (1) Agricultural trade significantly suppresses agricultural carbon emissions. (2) Agricultural technological progress and the rationalization of the structure of the agricultural industry are the two influencing mechanisms. (3) The inhibitory effect of agricultural trade on agricultural carbon emissions is more pronounced in southern regions and regions with lower degree of topographic relief and higher agricultural industrial agglomeration. (4) Agricultural trade fosters the collaborative advancement of mitigating agricultural pollution and reducing carbon emissions. As a result, this study confirms that agricultural trade has a role in reducing carbon emissions and that giving full play to the environmental effects of trade in agricultural products contributes to the high-quality development of agriculture.
Impact of Building Integrated Rooftop Greenhouse (BiRTG) on Heating and Cooling Energy Load: A Study Based on a Container with Rooftop Greenhouse
Authors: Choi Eun-Jung , Lee Doyun , Lee Sang-Min
Published: August 2024

Recently, there has been a growing interest in sustainable agricultural methods aimed at reducing energy consumption and mitigating environmental impacts. Among these methods, the integration of rooftop greenhouses into buildings has emerged as a promising solution for urban agriculture, offering various advantages such as energy-saving effects on both the host building and the rooftop greenhouse, improved resource use efficiency, enhanced food security, and reduced environmental impacts. However, there has been limited research on the energy-saving impact of rooftop greenhouses under different conditions and detailed heat transfer analysis based on actual buildings and rooftop greenhouses. The objective of this study is to investigate the energy benefits of building-integrated rooftop greenhouses by evaluating various operating scenarios for both the building and the greenhouse. A container and upper rooftop greenhouse structure were constructed, and building energy simulation models for the target system were developed. Four different operating scenarios for the greenhouse and three different operating scenarios for the building were compared. Depending on the operating scenarios, the total energy loads of the container and RTG were reduced by 18.4% to 24.7% and 0.7% to 6.3%, respectively. The findings of this study can be utilized for the development of optimized greenhouse control strategies.
Research on Real-Time Groundwater Quality Monitoring System Using Sensors around Livestock Burial Sites
Authors: Yoon Jonghyun , Park Sunhwa , Han Kyungjin
Published: August 2024

This study aimed to establish an economical and rapid response system for carcass leachate leakage using a real-time groundwater monitoring system with sensors. In this work, four parameters, namely electrical conductivity (EC), chloride (Cl), nitrate nitrogen (NO3-N), and ammonia nitrogen (NH4-N), were monitored. Three actual livestock burial sites were selected as pilot areas and monitored for three years, from 2019 to 2021, using these four parameters. As a result of sensor quality control, the accuracy and precision range of the four parameters were found to be acceptable, within 75~125% and ±25%, respectively. When compared to the laboratory measurement value, the field measurement value recorded by the sensors was 1.1 times higher for EC, 1.6 times higher for Cl, and 2.5 times higher for NO3-N. The correlation analysis between the lab measurement and sensor measurement results showed that the EC had the highest correlation coefficient of 0.3837. Additionally, the factor extraction results showed that the EC showed a relatively significant correlation compared to the other parameters. In summary, based on the results of this study, EC may be considered a key sensor parameter for evaluating leachate leakage from groundwater near disposal sites.
A General Image Super-Resolution Reconstruction Technique for Walnut Object Detection Model
Authors: Wu Mingjie , Yang Xuanxi , Yun Lijun , Yang Chenggui , Chen Zaiqing , Xia Yuelong
Published: August 2024

Object detection models are commonly used in yield estimation processes in intelligent walnut production. The accuracy of these models in capturing walnut features largely depends on the quality of the input images. Without changing the existing image acquisition devices, this study proposes a super-resolution reconstruction module for drone-acquired walnut images, named Walnut-SR, to enhance the detailed features of walnut fruits in images, thereby improving the detection accuracy of the object detection model. In Walnut-SR, a deep feature extraction backbone network called MDAARB (multilevel depth adaptive attention residual block) is designed to capture multiscale information through multilevel channel connections. Additionally, Walnut-SR incorporates an RRDB (residual-in-residual dense block) branch, enabling the module to focus on important feature information and reconstruct images with rich details. Finally, the CBAM (convolutional block attention module) attention mechanism is integrated into the shallow feature extraction residual branch to mitigate noise in shallow features. In 2× and 4× reconstruction experiments, objective evaluation results show that the PSNR and SSIM for 2× and 4× reconstruction reached 24.66 dB and 0.8031, and 19.26 dB and 0.4991, respectively. Subjective evaluation results indicate that Walnut-SR can reconstruct images with richer detail information and clearer texture features. Comparative experimental results of the integrated Walnut-SR module show significant improvements in mAP50 and mAP50:95 for object detection models compared to detection results using the original low-resolution images.
Spatial-Temporal Dynamics of Water Resources in Seasonally Dry Tropical Forest: Causes and Vegetation Response
Authors: Ferreira Maria Beatriz , Ferreira Rinaldo Luiz Caraciolo , da Silva Jose Antonio Aleixo , de Lima Robson Borges , Silva Emanuel Araújo , de Sousa Alex Nascimento , De La Cruz Doris Bianca Crispin , da Silva Marcos Vinícius
Published: August 2024

Seasonally Dry Tropical Forests (SDTFs) are situated in regions prone to significant water deficits. This study aimed to evaluate and quantify the dynamics and spatial patterns of vegetation and water bodies through the analysis of physical–hydrological indices for a remnant of FTSD between 2013 and 2021. Basal area, biomass, and tree number were monitored in 80 permanent plots located in two areas of an SDTF remnant with different usage histories. To assess vegetation and water resource conditions, geospatial parameters NDVI, NDWIveg, NDWI, and MNDWI were estimated for the period from 2013 to 2021. The observed patterns were evaluated by simple linear regression, principal component analysis (PCA), and principal component regression (PCR). Area 2 presented higher values of basal area, biomass, and number of trees. In area 1, there was an annual increase in basal area and biomass, even during drought years. The NDVI and NDWIveg indicated the vulnerability of vegetation to the effects of droughts, with higher values recorded in 2020. NDWI and MNDWI detected the water availability pattern in the study area. Physical–hydrological indices in the dynamics of tree vegetation in dry forests are influenced by various factors, including disturbances, soil characteristics, and precipitation patterns. However, their predictive capacity for basal area, biomass, and tree number is limited, highlighting the importance of future research incorporating seasonal variability and specific local conditions into their analyses.
Prediction of Soil Field Capacity and Permanent Wilting Point Using Accessible Parameters by Machine Learning
Authors: Liu Liwei , Ma Xingmao
Published: August 2024

The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. The best-fit variable combination for FC (longitude, latitude, altitude, sand content, silt content, clay content, and electrical conductivity) and PWP (best-fit FC combination plus pH) modeling was determined. Both ANN and GEP showed greater accuracy than linear-based models in simulating the FC and PWP from the best-fit variables. The mean absolute error (MAE) was reduced by 51.54% for the FC and 56.38% for the PWP by the ANN model, compared with the linear model used in the previous literature. The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). Between the ANN and GEP models, the ANN model showed a slightly higher model of interpretability; however, the GEP model exhibited a similar or better ability to avoid large error, based on the error distribution. Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling.
Intelligent Rice Field Weed Control in Precision Agriculture: From Weed Recognition to Variable Rate Spraying
Authors: Guo Zhonghui , Cai Dongdong , Bai Juchi , Xu Tongyu , Yu Fenghua
Published: August 2024

A precision agriculture approach that uses drones for crop protection and variable rate application has become the main method of rice weed control, but it suffers from excessive spraying issues, which can pollute soil and water environments and harm ecosystems. This study proposes a method to generate variable spray prescription maps based on the actual distribution of weeds in rice fields and utilize DJI plant protection UAVs to perform automatic variable spraying operations according to the prescription maps, achieving precise pesticide application. We first construct the YOLOv8n DT model by transferring the “knowledge features” learned by the larger YOLOv8l model with strong feature extraction capabilities to the smaller YOLOv8n model through knowledge distillation. We use this model to identify weeds in the field and generate an actual distribution map of rice field weeds based on the recognition results. The number of weeds in each experimental plot is counted, and the specific amount of pesticide for each plot is determined based on the amount of weeds and the spraying strategy proposed in this study. Variable spray prescription maps are then generated accordingly. DJI plant protection UAVs are used to perform automatic variable spraying operations based on prescription maps. Water-sensitive papers are used to collect droplets during the automatic variable operation process of UAVs, and the variable spraying effect is evaluated through droplet analysis. YOLOv8n-DT improved the accuracy of the model by 3.1% while keeping the model parameters constant, and the accuracy of identifying weeds in rice fields reached 0.82, which is close to the accuracy of the teacher network. Compared to the traditional extensive spraying method, the approach in this study saves approximately 15.28% of herbicides. This study demonstrates a complete workflow from UAV image acquisition to the evaluation of the variable spraying effect of plant protection UAVs. The method proposed in this research may provide an effective solution to balance the use of chemical herbicides and protect ecological safety.
Optimization of Harvesting and Drying Techniques for Quality Seed Production in Specialty Crops: A Systematic Review and Meta-Analysis
Authors: Pedrosa Laura Monteiro , de Almeida Moreira Bruno Rafael , Martins Cibele Chalita
Published: August 2024

Specialty crops enhance food security, biodiversity, and economic resilience, relying on high-quality seeds. However, there is a gap in understanding how to enhance seed quality under specific conditions. This study addresses this gap by proposing that optimizing harvesting and drying can improve specialty crop seed quality. A literature review spanning 2000–2023 was conducted, followed by a meta-analysis to measure effect sizes. From an initial pool of 1589 documents, 45 met the criteria for further analysis. The results indicated that harvesting seeds at intermediate times significantly increased the logarithmic response ratio (LRR), with LRRs of 1.7 for germination and 2 for seedling count compared with early harvesting. Regarding drying methods, no significant differences were found between artificial and natural methods. However, optimal drying temperatures (30–60 °C) were identified, ensuring adequate moisture levels (10–20%) for a germination percentage of 50–100%. The variability in these findings was attributed to the twelve diverse species analyzed. Although the results supported the study’s hypothesis, limited and inconsistent data prevented the determination of optimal conditions for each species, indicating a need for further research. Despite these limitations, the study provides insights into optimizing harvesting and drying to enhance seed quality in specialty crops, contributing to emerging science in this domain.
Physicochemical, Carotenoid, Metabolite, and Volatile Organic Compound Profiling of Okinawan Shiikuwasha (Citrus depressa Hayata) and Calamansi (C. microcarpa Bunge)
Authors: Oe Moena , Asikin Yonathan , Mitsube Fumimasa , Sekiyama Yasuyo , Takara Kensaku , Wada Koji
Published: August 2024

Shiikuwasha (Citrus depressa Hayata) and calamansi (C. microcarpa Bunge) are distinct citrus species with a strong sour taste that grow naturally in Okinawa, Japan. The present study aimed to characterize the physicochemical traits, total carotenoid content, and taste/nutrient-related metabolite and volatile organic compound (VOC) profiles of the mature fruits from three shiikuwasha cultivation lines (“Izumi kugani”, “Izumi kugani-like”, and “Ogimi kugani”) and calamansi. The shiikuwasha lines had higher ratios of soluble solids/titratable acidity but a lower total carotenoid content than calamansi. There were positive statistical correlations between total carotenoids with red, yellow, and orange indices (a*, b*, and a*/b* color spaces, respectively). Principal component analysis (PCA) revealed that shiikuwasha had higher sucrose and choline contents, while calamansi was richer in sourness-related metabolites such as citrate and malate. PCA plots displayed differences in the VOCs between the two citrus cultivars, wherein volatile accumulation was much richer in juices prepared from the whole fruits than in juices from the edible flesh parts. The plots also showed that monoterpenes and sesquiterpenes differentiated the VOC profiles of shiikuwasha and calamansi. This study reveals differences in the flavor components of the two citrus cultivars and highlights the potential uses of each in local and regional agro-business.
Common-Reflection-Surface Stack with Global Simultaneous Multi-Parameter Velocity Analysis—A Fit for Shallow Seismics
Authors: Heilmann Zeno , Deidda Gian Piero
Published: August 2024

In many regions, particularly coastal areas, population growth, overuse of water, and climate change have put quality and availability of water under threat. While accurate, predictive groundwater flow models are essential for effective water resource management, the precision of these models relies on the ability to determine the geometries of geological structures and hydrogeologic systems accurately. In complex geological settings or with deep aquifers, the drilling of observation wells becomes too costly and shallow seismic surveys become the method of choice. Common-Reflection-Surface stacking is being used by major oil companies for hydrocarbon exploration but can serve also as an advanced imaging method for near-surface seismic data. Its spatial stacking aperture covers a whole group of neighboring common midpoint gathers and, as such, a multitude of traces contribute to every single stacking process. Since the level of noise suppression is proportional to the number of contributing traces, Common-Reflection-Surface stacking generates a large increase in signal-to-noise ratio. In addition, the data-driven velocity analysis is a statistical process and is, as such, the more stable the more input traces it has. At the beginning, however, the spatial operator was only used for stacking, not for velocity analysis, since limiting computational demand was mandatory to obtain results within a reasonable time frame. Today’s computing facilities are thousands of times faster and even large efficiency gains do not justify the loss of effectiveness anymore that comes with a truncated velocity analysis. We show that this is particularly true for near-surface data with low signal-to-noise ratio and modest common midpoint fold. For the spatial velocity analysis, we present two options: (1) as reference, a global search of all three parameters of the Common-Reflection-Surface operator, and (2) as a quicker solution, a strategy that uses the two-parameter Common-Diffraction-Surface operator to obtain initial values for a local three-parameter optimization. For shallow P-wave data from a hydrogeological survey, we show that the computational cost of option (2) is one order of magnitude smaller than the cost of option (1), while the stack and corresponding normal-moveout velocities are very similar. Comparing the results of the spatial velocity analysis to those of preceding, computationally lighter, strategies, we find a significant improvement, both in stack section resolution and stacking parameter accuracy.
Reconstruction of High-Frequency Bridge Responses Based on Physical Characteristics of VBI System with BP-ANN
Authors: Lu Xuzhao , Sun Limin , Xia Ye
Published: August 2024

Response reconstruction is essential in bridge health monitoring for recovering missing data and evaluating service status. Previous studies have focused on reconstructing responses at specific cross-sections using data from adjacent sections. To address this challenge, time-series prediction methods have been employed for response reconstruction. However, these methods often struggle with the inherent complexities of long-term time-varying traffic conditions, posing practical challenges. In this study, we analyzed the theoretical physical characteristics of high-frequency bridge dynamics within a simplified vehicle–bridge interaction (VBI) system. Our analysis revealed that the relationship between high-frequency bridge responses across different cross-sections is time-invariant and only dependent on the bridge’s mode shape. This relationship remains unaffected by time-varying factors such as traffic loading and environmental conditions like air temperature. Based on these physical characteristics, we propose the backpropagation artificial neural network (BP-ANN) method for response reconstruction. The validity of these physical characteristics was confirmed through finite element models, and the effectiveness of the proposed method was demonstrated using field test data from a continuous bridge. Our verification results show that the BP-ANN method enables effective utilization of short-term monitoring data for long-term bridge health monitoring, without necessitating real-time adjustments for factors such as traffic conditions or air temperature.
Research on the Dynamic Response Characteristics of the Propulsion Shaft System with an On-Shaft Generator in Ships
Authors: Guo Yukuo , Zhou Ruiping , Ma Zhaozhao , Wang Jianzheng , Ding Longqi
Published: August 2024

The propulsion shaft system, as the core of the ship’s power system, has attracted widespread attention in terms of vibration. This paper is dedicated to the study of the dynamic response characteristics of the ship propulsion shafting system, with a particular focus on the impact of the shaft-driven generator supported by bearings on the dynamic torque of the shafting system. A classic lumped-parameter equivalent system model is adopted, and the time domain transient response simulation calculation is conducted based on the Newmark-β method. A comprehensive analysis is made of the vibration torque and vibration stress in the propulsion shaft system under different rotational speeds and working conditions, with or without the shaft-driven generator connected to the load. Dynamic vibration torque measurements are also taken on a 16100TEU ship for the propulsion shaft system to analyze the consistency between simulation results and experimental results. The results show that at the rated speed of the main engine at 80 rpm and adjacent speeds, the error between the simulation calculation results and the actual measured torque results at the pre- and post-motor measurement points is less than 10% and is much less than the continuous allowable torque of 4.01 × 106 N·m and the instantaneous torque. This installation state, including the propulsion shaft system with the shaft-driven generator supported by bearings, can safely navigate during normal operation. This provides theoretical and experimental support for the future installation of the propulsion shaft system with the shaft-driven generator supported by bearings. In addition, the actual ship experiment enhances the universality and reliability of the research.
Prediction of Boiling Heat Transfer Coefficient for Micro-Fin Using Mini-Channel
Authors: Kinjo Tomihiro , Sei Yuichi , Giannetti Niccolo , Saito Kiyoshi , Enoki Koji
Published: August 2024

The prediction of the heat transfer coefficient commonly relies on the development of new empirical prediction equations when operating conditions and refrigerants change from the existing equations. Creating new prediction equations is expensive and time-consuming; therefore, recent attention has been given to machine learning approaches. However, machine learning requires a large amount of data, and insufficient data can result in inadequate accuracy and applicability. This study showed that using mini-channel data as highly relevant data for the micro-fin heat transfer coefficient yields high prediction accuracy, even when the experimental dataset of interest is limited. In the proposed method, we added dimensionless numbers assumed to significantly influence heat transfer coefficients calculated from experimental data to the training dataset. This allowed efficient learning of the characteristics of thin liquid films present in mini-channels and micro-fins. By combining distinctive physical mechanisms related to heat transfer coefficients with DNN/GPR/Fine-tuning, the proposed method can predict 96.7% of the data points within ±30% deviation. In addition, it has been confirmed that the dryout quality and post-dryout heat transfer coefficients were predicted with high accuracy. Additionally, we utilized visualization techniques to investigate the contents of the black-box machine learning models.
Air Quality Prediction and Ranking Assessment Based on Bootstrap-XGBoost Algorithm and Ordinal Classification Models
Authors: Yang Jingnan , Tian Yuzhu , Wu Chun Ho
Published: August 2024

Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality and realising green economic development. Machine learning algorithms and statistical models have been widely used in air quality prediction and ranking assessment. In this paper, based on daily air quality data for the city of Xi’an, China, from 1 October 2022 to 30 September 2023, we construct support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forests (RF), neural network (NN) and long short-term memory (LSTM) models to analyse the influence of the air quality index for Xi’an and to conduct comparative tests. The predicted values and 95% prediction intervals of the AQI for the next 15 days for Xi’an, China, are given based on the Bootstrap-XGBoost algorithm. Further, the ordinal logit regression and ordinal probit regression models are constructed to evaluate and accurately predict the AQI ranks of the data from 1 October 2023 to 15 October 2023 for Xi’an. Finally, this paper proposes some suggestions and policy measures based on the findings of this paper.
Analysis of a Normalized Structure of a Complex Fractal–Fractional Integral Transform Using Special Functions
Authors: Ibrahim Rabha W. , Salahshour Soheil , Páll-Szabó Ágnes Orsolya
Published: August 2024

By using the most generalized gamma function (parametric gamma function, or p-gamma function), we present the most generalized Rabotnov function, called the p-Rabotnov function. Consequently, new fractal–fractional differential and integral operators of a complex variable in an open unit disk are defined and investigated analytically and geometrically. We address some inequalities involving the generalized fractal–fractional integral operator in some spaces of analytic functions. A novel complex fractal–fractional integral transform (CFFIT) is presented. A normalization of the proposed CFFIT is observed in the open unit disk. Examples are illustrated for power series of analytic functions.
A Multi-Modal Machine Learning Methodology for Predicting Solitary Pulmonary Nodule Malignancy in Patients Undergoing PET/CT Examination
Authors: Apostolopoulos Ioannis D. , Papathanasiou Nikolaos D. , Apostolopoulos Dimitris J. , Papandrianos Nikolaos , Papageorgiou Elpiniki I.
Published: August 2024

This study explores a multi-modal machine-learning-based approach to classify solitary pulmonary nodules (SPNs). Non-small cell lung cancer (NSCLC), presenting primarily as SPNs, is the leading cause of cancer-related deaths worldwide. Early detection and appropriate management of SPNs are critical to improving patient outcomes, necessitating efficient diagnostic methodologies. While CT and PET scans are pivotal in the diagnostic process, their interpretation remains prone to human error and delays in treatment implementation. This study proposes a machine-learning-based network to mitigate these concerns, integrating CT, PET, and manually extracted features in a multi-modal manner by integrating multiple image modalities and tabular features). CT and PET images are classified by a VGG19 network, while additional SPN features in combination with the outputs of VGG19 are processed by an XGBoost model to perform the ultimate diagnosis. The proposed methodology is evaluated using patient data from the Department of Nuclear Medicine of the University Hospital of Patras in Greece. We used 402 patient cases with human annotations to internally validate the model and 96 histopathological-confirmed cases for external evaluation. The model exhibited 97% agreement with the human readers and 85% diagnostic performance in the external set. It also identified the VGG19 predictions from CT and PET images, SUVmax, and diameter as key malignancy predictors. The study suggests that combining all available image modalities and SPN characteristics improves the agreement of the model with the human readers and the diagnostic efficiency.
MedNER: A Service-Oriented Framework for Chinese Medical Named-Entity Recognition with Real-World Application
Authors: Chen Weisi , Qiu Pengxiang , Cauteruccio Francesco
Published: August 2024

Named-entity recognition (NER) is a crucial task in natural language processing, especially for extracting meaningful information from unstructured text data. In the healthcare domain, accurate NER can significantly enhance patient care by enabling efficient extraction and analysis of clinical information. This paper presents MedNER, a novel service-oriented framework designed specifically for medical NER in Chinese medical texts. MedNER leverages advanced deep learning techniques and domain-specific linguistic resources to achieve good performance in identifying diabetes-related entities such as symptoms, tests, and drugs. The framework integrates seamlessly with real-world healthcare systems, offering scalable and efficient solutions for processing large volumes of clinical data. This paper provides an in-depth discussion on the architecture and implementation of MedNER, featuring the concept of Deep Learning as a Service (DLaaS). A prototype has encapsulated BiLSTM-CRF and BERT-BiLSTM-CRF models into the core service, demonstrating its flexibility, usability, and effectiveness in addressing the unique challenges of Chinese medical text processing.
Effects of Ginger (Zingiber officinale) on the Hallmarks of Aging
Authors: Matin Maima , Joshi Tanuj , Wang Dongdong , Tzvetkov Nikolay T. , Matin Farhan Bin , Wierzbicka Agnieszka , Jóźwik Artur , Horbańczuk Jarosław Olav , Atanasov Atanas G.
Published: August 2024

Ginger (Zingiber officinale Roscoe) is broadly used as a traditional remedy and food ingredient, and numerous preclinical and clinical studies have demonstrated health benefits in a range of age-related disorders. Moreover, longevity-promoting effects have been demonstrated in several (preclinical) research models. With this work, we aimed to comprehensively review the reported effects of ginger and its bioactive constituents on the twelve established hallmarks of aging, with the ultimate goal of gaining a deeper understanding of the potential for future interventions in the area of longevity-extension and counteracting of aging-related diseases. The reviewed literature supports the favorable effects of ginger and some of its constituents on all twelve hallmarks of aging, with a particularly high number of animal research studies indicating counteraction of nutrient-sensing dysregulations, mitochondrial dysfunction, chronic inflammation, and dysbiosis. On this background, validation in human clinical trials is still insufficient or is entirely missing, with the exception of some studies indicating positive effects on deregulated nutrient-sensing, chronic inflammation, and dysbiosis. Thus, the existing body of literature clearly supports the potential of ginger to be further studied in clinical trials as a supplement for the promotion of both lifespan and health span.
Role of Neuroactive Steroids in Health and Disease
Authors: Melcangi Roberto Cosimo
Published: August 2024

Steroidogenesis occurs not only in endocrine peripheral glands (i [...]
ELIPF: Explicit Learning Framework for Pre-Emptive Forecasting, Early Detection and Curtailment of Idiopathic Pulmonary Fibrosis Disease
Authors: Theodore Armand Tagne Poupi , Mozumder Md Ariful Islam , Carole Kouayep Sonia , Deji-Oloruntoba Opeyemi , Kim Hee-Cheol , Ajakwe Simeon Okechukwu
Published: August 2024

(1) Background: Among lung diseases, idiopathic pulmonary fibrosis (IPF) appears to be the most common type and causes scarring (fibrosis) of the lungs. IPF disease patients are recommended to undergo lung transplants, or they may witness progressive and irreversible lung damage that will subsequently lead to death. In cases of irreversible damage, it becomes important to predict the patient’s mortality status. Traditional healthcare does not provide sophisticated tools for such predictions. Still, because artificial intelligence has effectively shown its capability to manage crucial healthcare situations, it is possible to predict patients’ mortality using machine learning techniques. (2) Methods: This research proposed a soft voting ensemble model applied to the top 30 best-fit clinical features to predict mortality risk for patients with idiopathic pulmonary fibrosis. Five machine learning algorithms were used for it, namely random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and multi-layer perceptron (MLP). (3) Results: A soft voting ensemble method applied with the combined results of the classifiers showed an accuracy of 79.58%, sensitivity of 86%, F1-score of 84%, prediction error of 0.19, and responsiveness of 0.47. (4) Conclusions: Our proposed model will be helpful for physicians to make the right decision and keep track of the disease, thus reducing the mortality risk, improving the overall health condition of patients, and managing patient stratification.
Pathways from Family Functioning to Internet Gaming Disorder: The Mediating Role of the Dark Triad
Authors: Calaresi Danilo , Verrastro Valeria , Giordano Fiorenza , Gullo Janine , Saladino Valeria
Published: August 2024

With internet gaming disorder (IGD) becoming more common, there are growing worries about the health of those it affects. This study examines how traits like Machiavellianism, psychopathy, and narcissism might connect family functioning to IGD. The research involved 1190 young adults who answered an online survey, sharing their personal experiences. To examine the mediation effects, latent variable structural equation modeling (SEM) was used, revealing complex relationships among the variables under investigation. Although all direct and indirect paths were statistically significant, the mediation effects of narcissism were positive only when Machiavellianism and psychopathy were not included as parallel mediators, but negative otherwise. The findings suggest that individuals with strong family functioning could be less likely to internalize manipulative behaviors and show a lack of empathy, traits that could contribute to their involvement in IGD. The results underscore the importance of recognizing the multifaceted nature of this phenomenon and provide valuable insights for developing comprehensive strategies to prevent and tackle IGD. Therefore, prevention and intervention efforts should consider the combined influences of family functioning, personality traits, and individual and contextual factors in the online environment to effectively address this problem.
Barriers to the Application of Digital Technologies in Construction Health and Safety: A Systematic Review
Authors: Daniel Emmanuel Itodo , Oshodi Olalekan S. , Nwankwo Nnaemeka , Emuze Fidelis A. , Chinyio Ezekiel
Published: August 2024

Construction is one of the most dangerous industries, with workers frequently exposed to hazardous environments, resulting in numerous occupational injuries and illnesses globally. While digital technology (DT) can improve construction health and safety management, there are barriers to its global adoption. This research examines these barriers in both developed and developing countries. A systematic review of 88 articles identified critical barriers, including technical issues, training and knowledge gaps, implementation challenges, data analysis limitations, and system efficiency problems. Standardising the use of new technology is challenging due to the constantly changing nature of construction projects. There is a lack of knowledge on increasing the use of DT in construction. Future research should focus on targeted strategies, pilot studies, and prioritising workers’ health to overcome context-specific barriers and maximise the benefits of these innovative tools to prevent injuries and improve health and safety management.
Contribution and Marginal Effects of Landscape Patterns on Thermal Environment: A Study Based on the BRT Model
Authors: Li Taojun , Huang Xiaohui , Guo Hao , Hong Tingting
Published: August 2024

Urban landscape patterns significantly impact land surface temperature (LST) and the urban heat island (UHI) effect. This study employs the boosted regression tree (BRT) model and variance partitioning analysis to examine the contributions and relationships of two-dimensional and three-dimensional building and vegetation patterns to LST, and their marginal effects at different heights. The results show that the dominant indicators affecting LST differ between buildings and vegetation, with three-dimensional building features being slightly more important than two-dimensional features (percentage of landscape of buildings) and two-dimensional vegetation features (three-dimensional green index) having a greater impact than three-dimensional features. When both buildings and vegetation are considered, building patterns still have significant explanatory power. Building height differences influence each indicator’s contribution and marginal effects on LST, with lower-height areas seeing a joint dominance of buildings and vegetation on LST changes, and higher-height areas showing greater impact from vegetation indicators. Increasing the percentage of landscape of vegetation (PLAND_V) provides the best cooling effect in lower-building-height areas, but in higher-building-height areas, the cooling effect weakens, requiring additional vegetation indicators to assist in cooling.
The Cognitive Load Limits of Multiple Safety Signs
Authors: Kwon Yong Hwa , Kwon Young Beom , Nwagbala Daniel Chukwunonso , Park Jong Yil
Published: August 2024

Current construction safety practices heavily rely on the use of multiple safety signs to mitigate potential risks. This study investigates the cognitive load imposed on construction workers by various design characteristics of safety signs, including text-only, pictogram with text, and pictogram-only signs. A comprehensive cognitive assessment of 513 construction workers was conducted to measure recall rates and visual attention. The results revealed that text-only signs led to lower cognitive load compared with pictogram-based signs, with no significant split-attention effect observed when text and pictograms were combined. The average recall rate across all sign types was 3.32 items, with over 95% of participants recalling six or fewer items. Additionally, recall rates for pictogram-based signs decreased significantly when more than nine items were displayed. Visual attention analysis indicated that while color had no significant impact, the order of placement did. These findings highlight the importance of limiting the number of safety signs and carefully designing them to optimize recall and reduce cognitive load. Future research should explore additional factors such as sex, complexity of work, and environmental conditions to develop a more comprehensive understanding of cognitive load in construction settings.