bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating the Thermal Conductivity of Unsaturated Sand
Authors: Liu Xuejun , Gao Yucong , Li Yanjun
Published: April 2024

A modified parallel model for estimating the thermal conductivity of unsaturated sand was proposed in this study. The heat conduction in the solid phase of sand depends mainly on the form of contacts between solid particles, while water bridges at the particle contacts increase the contact areas and remarkably enlarge the transfer paths of heat conduction in sandy soils. However, the thermal conductivity of the solid particle itself (λs) cannot describe the influence of the form of contacts and water bridges on heat conduction through the solid phase. In this study, the equivalent thermal conductivity of the solid particle (λes) was presented which reflected the influence of the form of contacts and water bridges between particles under dry conditions or a low degree of saturation, respectively. The relationship between λes and degree of saturation was described by hyperbolic expression. The modified model was calibrated using measured values of the thermal conductivity from published datasets, including those for 41 types of sand from 15 studies. Numerical analyses of the temperature field of the energy pile were performed and validated against laboratory measurements. The results illustrated that the modified model was more applicable than the original model for predictions of sand thermal conductivity.
Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art
Authors: Wan Wentong , Huang Runcai
Published: April 2024

The emergence of AI Art has ignited extensive debates on social media platforms. Various online communities have expressed their opinions on different facets of AI Art and participated in discussions with other users, leading to the generation of a substantial volume of data. Analyzing these data can provide useful insights into the public’s opinions on AI Art, enable the investigation of the origins of conflicts in online debates, and contribute to the sustainable development of AI Art. This paper presents a deep learning-driven framework for analyzing the characteristics of public opinion on the Weibo topic of AI Art. To classify the sentiments users expressed in Weibo posts, the linguistic feature-enhanced pre-training model (LERT) was employed to improve text representation via the fusion of syntactic features, followed by a bidirectional Simple Recurrent Unit (SRU) embedded with a soft attention module (BiSRU++) for capturing the long-range dependencies in text features, thus improving the sentiment classification performance. Furthermore, a text clustering analysis was performed across sentiments to capture the nuanced opinions expressed by Weibo users, hence providing useful insights about different online communities. The results indicate that the proposed sentiment analysis model outperforms common baseline models in terms of classification metrics and time efficiency, and the clustering analysis has provided valuable insights for in-depth analyses of AI Art.
A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
Authors: Samborska-Goik Katarzyna , Pogrzeba Marta
Published: April 2024

The pollution of groundwater and soil by hydrocarbons is a significant and growing global problem. Efforts to mitigate and minimise pollution risks are often based on modelling. Modelling-based solutions for prediction and control play a critical role in preserving dwindling water resources and facilitating remediation. The objectives of this article are to: (i) to provide a concise overview of the mechanisms that influence the migration of hydrocarbons in groundwater and to improve the understanding of the processes that affect contamination levels, (ii) to compile the most commonly used models to simulate the migration and fate of hydrocarbons in the subsurface; and (iii) to evaluate these solutions in terms of their functionality, limitations, and requirements. The aim of this article is to enable potential users to make an informed decision regarding the modelling approaches (deterministic, stochastic, and hybrid) and to match their expectations with the characteristics of the models. The review of 11 1D screening models, 18 deterministic models, 7 stochastic tools, and machine learning experiments aimed at modelling hydrocarbon migration in the subsurface should provide a solid basis for understanding the capabilities of each method and their potential applications.
Residual Dense Swin Transformer for Continuous-Scale Super-Resolution Algorithm
Authors: Liu Jinwei , Gui Zihan , Yuan Chenghao , Yang Guangyi , Gao Yi
Published: April 2024

The single-image super-resolution task benefits has a wide range of application scenarios, so has long been a hotspot in the field of computer vision. However, designing a continuous-scale super-resolution algorithm with excellent performance is still a difficult problem to solve. In order to solve this problem, we propose a continuous-scale SR algorithm based on a Transformer, which is called residual dense Swin Transformer (RDST). Firstly, we design a residual dense Transformer block (RDTB) to enhance the information flow before and after the network and extract local fusion features. Then, we use multilevel feature fusion to obtain richer feature information. Finally, we use the upsampling module based on the local implicit image function (LIIF) to obtain continuous-scale super-resolution results. We test RDST on multiple benchmarks. The experimental results show that RDST achieves SOTA performance in the fixed scale of super-resolution tasks in the distribution, and significantly improves (0.1∼0.6 dB) the arbitrary scale of super-resolution tasks out of distribution. Sufficient experiments show that our RDST can use fewer parameters, and its performance is better than the SOTA SR method.
Automatic Literature Mapping Selection: Classification of Papers on Industry Productivity
Authors: Bispo Guilherme Dantas , Vergara Guilherme Fay , Saiki Gabriela Mayumi , Martins Patrícia Helena dos Santos , Coelho Jaqueline Gutierri , Rodrigues Gabriel Arquelau Pimenta , Oliveira Matheus Noschang de , Mosquéra Letícia Rezende , Gonçalves Vinícius Pereira , Neumann Clovis , Serrano André Luiz Marques
Published: April 2024

The academic community has witnessed a notable increase in paper publications, whereby the rapid pace at which modern society seeks information underscores the critical need for literature mapping. This study introduces an innovative automatic model for categorizing articles by subject matter using Machine Learning (ML) algorithms for classification and category labeling, alongside a proposed ranking method called SSS (Scientific Significance Score) and using Z-score to select the finest papers. This paper’s use case concerns industry productivity. The key findings include the following: (1) The Decision Tree model demonstrated superior performance with an accuracy rate of 75% in classifying articles within the productivity and industry theme. (2) Through a ranking methodology based on citation count and publication date, it identified the finest papers. (3) Recent publications with higher citation counts achieved better scores. (4) The model’s sensitivity to outliers underscores the importance of addressing database imbalances, necessitating caution during training by excluding biased categories. These findings not only advance the utilization of ML models for paper classification but also lay a foundation for further research into productivity within the industry, exploring themes such as artificial intelligence, efficiency, industry 4.0, innovation, and sustainability.
Cytotoxicity of Bleaching Products: A Systematic Review
Authors: Montaner Mireia , Sanz José Luis , Llena Carmen , Melo María , Puig-Herreros Clara , Ghilotti James
Published: April 2024

The high demand for aesthetic treatments among the population has resulted in a wide array of bleaching products available on the market. It is imperative to investigate the potential adverse effects these products may have on dental health. The objective of this systematic review is to assess, based on available experimental in vitro studies in the scientific literature, whether bleaching products exhibit cytotoxic properties against pulp stem cells and fibroblasts. A comprehensive literature search was conducted across the Medline, Scopus, and Lilacs databases using the search formula ((Tooth whitening) OR (bleaching agent)) AND cytotoxicity AND ((stem cell*) OR fibroblast). Following the application of inclusion and exclusion criteria, 14 articles were deemed suitable for full analysis. The most utilized assay in the evaluated studies was the MTT cell viability assay. Fibroblasts emerged as the most scrutinized cell type due to the potential adverse effects of bleaching, such as invasive cervical resorption, which primarily affects the periodontal ligament where fibroblasts are located. It was observed that as the concentration of hydrogen peroxide increases, so does the cytotoxicity of the product. Additionally, other factors such as application time, activation methods, and the type of peroxide used also play a significant role. Bleaching products have been shown to exert cytotoxic effects on fibroblasts. Further exploration of the impact of bleaching agents on dental pulp stem cells is warranted to better understand their implications for these cells.
Transforming Landslide Prediction: A Novel Approach Combining Numerical Methods and Advanced Correlation Analysis in Slope Stability Investigation
Authors: Umar Ibrahim , Lin Hang , Hassan Jubril
Published: April 2024

Landslides cause significant economic losses and casualties worldwide. However, robust prediction remains challenging due to the complexity of geological factors contributing to slope stability. Advanced correlation analysis methods can improve prediction capabilities. This study aimed to develop a novel landslide prediction approach that combines numerical modeling and correlation analysis (Spearman rho and Kendall tau) to improve displacement-based failure prediction. Simulations generate multi-location displacement data sets on soil and rock slopes under incremental stability reductions. Targeted monitoring points profile local displacement responses. Statistical analyses, including mean/variance and Spearman/Kendall correlations, quantified displacement-stability relationships. For the homogeneous soil slope, monitoring point 2 of the middle section of the slope showed a mean horizontal displacement of 17.65 mm and a mean vertical displacement of 9.72 mm under stability reduction. Spearman’s rho correlation coefficients ranged from 0.31 to 0.76, while Kendall’s tau values ranged from 0.29 to 0.64, indicating variable displacement–stability relationships. The joint rock slope model had strong positive total displacement correlations (Spearman’s and Kendall’s correlation ranges of +1.0 and −1.0) at most points. Horizontal and vertical displacements reached mean maxima of 44.13 mm and 22.17 mm, respectively, at the unstable point 2 of the center section of the slope. The advanced correlation analysis techniques provided superior identification of parameters affecting slope stability compared to standard methods. The generated predictive model dramatically improves landslide prediction capability, allowing preventive measures to be taken to mitigate future losses through this new approach.
Integrated Power and Thermal Management Systems for Civil Aircraft: Review, Challenges, and Future Opportunities
Authors: Ouyang Zeyu , Nikolaidis Theoklis , Jafari Soheil
Published: April 2024

Projects related to green aviation designed to achieve fuel savings and emission reductions are increasingly being established in response to growing concerns over climate change. Within the aviation industry, there is a growing trend towards the electrification of aircraft, with more-electric aircraft (MEA) and all-electric aircraft (AEA) being proposed. However, increasing electrification causes challenges with conventional thermal management system (TMS) and power management system (PMS) designs in aircraft. As a result, the integrated power and thermal management system (IPTMS) has been developed for energy-optimised aircraft projects. This review paper aims to review recent IPTMS progress and explore potential design solutions for civil aircraft. Firstly, the paper reviews the IPTMS in electrified propulsion aircraft (EPA), presenting the architectures and challenges of the propulsion systems, the TMS cooling strategies, and the power management optimisation. Then, several research topics in IPTMS are reviewed in detail: architecture design, power management optimisation, modelling, and analysis method development. Through the review of state-of-the-art IPTMS research, the challenges and future opportunities and requirements of IPTMS design are discussed. Based on the discussions, two potential solutions for IPTMS to address the challenges of civil EPA are proposed, including the combination of architecture design and power management optimisation and the combination of modelling and analysis methods.
Estimation of the Resultant Expanded Uncertainty of the Output Quantities of the Measurement Chain Using the Discrete Wavelet Transform Algorithm
Authors: Kampik Marian , Roj Jerzy , Dróżdż Łukasz
Published: April 2024

This paper discusses the role of the discrete wavelet transform algorithm in processing error signals present in the input quantities of the algorithm. In considering the error model of the measurement chain, the parameters of the error signals in the input quantities of the wavelet transform algorithm are estimated. Subsequently, in accounting for the algorithm’s properties, the parameters of its output values are determined, and the resulting uncertainty values of the output quantities of the measurement chain are estimated. The interval reduction arithmetic method is employed in the calculations for estimating the expanded uncertainty. All findings were validated through measurements conducted using the implemented measurement chain.
Mathematical Modeling of SOIC Package Dynamics in Dielectric Fluids during High-Voltage Insulation Testing
Authors: Aparicio Yohan A. , Jimenez Manuel
Published: April 2024

The efficient testing and validation of the high-voltage (HV) insulation of small-outline integrated circuit (SOIC) packages presents numerous challenges when trying to achieve faster and more accurate processes. The complex behavior these packages when submerged in diverse physical media with varying densities requires a detailed analysis to understand the factors influencing their behavior. We propose a systematic and scalable mathematical model based on trapezoidal motion patterns and a deterministic analysis of hydrodynamic forces to predict SOIC package misalignment during automated high-voltage testing in a dielectric fluid. Our model incorporates factors known to cause misalignment during the maneuvering of packages, such as surface tension forces, sloshing, cavity formation, surface waves, and bubbles during the insertion, extraction, and displacement of devices while optimizing test speed for minimum testing time. Our model was validated via a full-factorial statistical experiment for different SOIC package sizes on a pick-and-place (PNP) machine with preprogrammed software and a zero-insertion force socket immersed in different dielectric fluids under controlled thermal conditions. Results indicate the model achieves 99.64% reliability with a margin of error of less than 4.78%. Our research deepens the knowledge and understanding of the physical and hydrodynamic factors that impact the automated testing processes of high-voltage insulator SOIC packages of different sizes for different dielectric fluids. It enables improved testing times and higher reliability than traditional trial-and-error methods for high-voltage SOIC packages, leading to more efficient and accurate processes in the electronics industry.
Product Improvement Using Knowledge Mining and Effect Analogy
Authors: Wang Kang , Tan Runhua , Peng Qingjin
Published: April 2024

Different from new product development, design improvement aims to solve the problems of existing products. Although design knowledge and effect tools have been applied in product improvement, the existing methods for design improvement are limited in their specific application areas. A general method of product improvement is proposed in this paper using the knowledge mining and effect analogy. The length–time dimension is introduced to link the problem analysis and problem-solving for the first time. This method includes the effect knowledge base construction, length–time dimension extraction, effect retrieval, effect ranking, analogy object selection, and effect structure mapping. This method integrates a variety of algorithms and software tools in design knowledge mining to improve the efficiency of the effect analogy for product improvement. Through the comparative analysis of three effect retrieval methods and design improvement of a button battery ring device, the superiority and feasibility of the proposed method are verified.
Swarm of Drones in a Simulation Environment—Efficiency and Adaptation
Authors: Marek Dariusz , Paszkuta Marcin , Szyguła Jakub , Biernacki Piotr , Domański Adam , Szczygieł Marta , Król Marcel , Wojciechowski Konrad
Published: April 2024

In the swiftly advancing field of swarm robotics and unmanned aerial vehicles, precise and effective testing methods are essential. This article explores the crucial role of software-in-the-loop (SITL) simulations in developing, testing, and validating drone swarm control algorithms. Such simulations play a crucial role in reproducing real-world operational scenarios. Additionally, they can (regardless of the type of application) accelerate the development process, reduce operational risks, and ensure the consistent performance of drone swarms. Our study demonstrates that different geometrical arrangements of drone swarms require flexible control strategies. The leader-based control model facilitates coherent movement and enhanced coordination. Addressing various issues such as communication delays and inaccuracies in positioning is essential here. These shortcomings underscore the value of improved approaches to collision avoidance. The research described in this article focused on the dynamics of drone swarms in a simulated context and emphasized their operational efficiency and adaptability in various scenarios. Advanced simulation tools were utilized to analyze the interaction, communication, and adaptability of autonomous units. The presented results indicate that the arrangement of drones significantly affects their coordination and collision avoidance capabilities. They also underscore the importance of control systems that can adapt to various situations. The impact of communication delays and errors in positioning systems on the required distance between drones in a grid structure is also presented. This article assesses the impact of different levels of GPS accuracy and communication delays on the coordination of group movement and collision avoidance capabilities.
Identifying Users’ Needs to Design and Manufacture 3D-Printed Upper Limb Sockets: A Survey-Based Study
Authors: Roda-Sales Alba , Llop-Harillo Immaculada
Published: April 2024

The development of prosthetic arms has increased in recent years, particularly with the growth of 3D printing technologies. However, one of the main weaknesses of 3D-printed prosthetics is the prosthetic socket, which commonly presents a generic adjustable design that may produce discomfort. In fact, the socket has always been a part that has frequently caused discomfort in traditionally manufactured prosthetics and, consequently, high rejection rates. Studies about improving the socket component in traditional and 3D-printed upper limb prostheses are scarce. Advancements in 3D printing and 3D scanning will offer a high potential to improve the design and manufacturing of 3D-printed sockets. Thus, to propose better designs and manufacturing protocols, this paper presents a questionnaire to assess the needs of upper limb prosthetics users or potential users, as well as a survey-based study with 18 respondents. The results reveal that users prioritize breathability and low cost, a stable fixing system, products without the need for shape adjustments, a light weight and comfort regarding the products they require. The results of this study provide insights into the key characteristics that sockets should accomplish according to users’ needs that are applicable to 3D-printed sockets and traditionally manufactured sockets, and they contribute to improving their design and manufacturing.
Usability Analysis of Smart Speakers from a Learnability Perspective for Novel Users
Authors: Doi Toshihisa , Nishikawa Yuki
Published: April 2024

Although commercial smart speakers are becoming increasingly popular, there is still much potential for investigation into their usability. In this study, we analyzed the usability of commercial smart speakers by focusing on the learnability of young users who are not yet familiar with voice user interface (VUI) operation. In the experiment, we conducted a task in which users repeatedly operated a smart speaker 10 times under four conditions, combining two experimental factors: the presence or absence of a screen on the smart speaker and the operation method (voice control only or in conjunction with remote-control operation). The usability of the smart speaker was analyzed in terms of task-completion time, task-completion rate, number of errors, subjective evaluation, and retrospective protocol analysis. In particular, we confirmed and compared the learning curves for each condition in terms of the performance metrics. The experimental results showed that there were no substantial differences in the learning curves between the presence and absence of a screen. In addition, the “lack of feedback” and “system response error” were identified as usability problems, and it was suggested that these problems led to “distrust of the system”.
MTS Decomposition and Recombining Significantly Improves Training Efficiency in Deep Learning: A Case Study in Air Quality Prediction over Sub-Tropical Area
Authors: Tam Benedito Chi Man , Tang Su-Kit , Cardoso Alberto
Published: April 2024

It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining (MTS-DR) is proposed for this purpose with better accuracy. A proposed MTS-DR model was built to prove that not only the training time is shortened but also the error loss is slightly reduced. A case study is for demonstrating air quality forecasting in sub-tropical urban cities. Since MTS decomposition reduces complexity and makes the features to be explored easier, the speed of deep learning models as well as their accuracy are improved. The experiments show it is easier to train the trend component, and there is no need to train the seasonal component with zero MSE. All forecast results are visualized to show that the total training time has been shortened greatly and that the forecast is ideal for changing trends. The proposed method is also suitable for other time series MTS with seasonal oscillations since it was applied to the datasets of six different kinds of air pollutants individually. Thus, this proposed method has some commonality and could be applied to other datasets with obvious seasonality.
Correction: Dong et al. Computerized Ionospheric Tomography Based on the ADS-B System. Atmosphere 2023, 14, 1091
Authors: Dong Xiang , Yuan Zhigang , Zhu Qinglin , Wang Haining , Sun Fang , Zhu Jiawei , Liu Yi , Zhou Chen
Published: April 2024

In the original publication [...]
Net Radiation Drives Evapotranspiration Dynamics in a Bottomland Hardwood Forest in the Southeastern United States: Insights from Multi-Modeling Approaches
Authors: Kandel Bibek , Bhattacharjee Joydeep
Published: April 2024

Evapotranspiration (ET) is a major component of the water budget in Bottomland Hardwood Forests (BHFs) and is driven by a complex intertwined suite of meteorological variables. The understanding of these interdependencies leading to seasonal variations in ET is crucial in better informing water resource management in the region. We used structural equation modeling and AIC modeling to analyze drivers of ET using Eddy covariance water flux data collected from a BHF located in the Russel Sage Wildlife Management Area (RSWMA). It consists of mature closed-canopy deciduous hardwood trees with an average canopy height of 27 m. A factor analysis was used to characterize the shared variance among drivers, and a path analysis was used to quantify the independent contributions of individual drivers. In our results, ET and net radiation (Rn) showed similar variability patterns with Vapor Pressure Deficit (VPD) and temperature in the spring, summer, and autumn seasons, while they differed in the winter season. The path analysis showed that Rn has the strongest influence on ET variations via direct and indirect pathways. In deciduous forests like BHFs, our results suggest that ET is more energy dependent during the growing season (spring and summer) and early non-growing season (autumn) and more temperature dependent during the winter season.
Multi-Source Dataset Assessment and Variation Characteristics of Snow Depth in Eurasia from 1980 to 2018
Authors: Cheng Kaili , Wei Zhigang , Li Xianru , Ma Li
Published: April 2024

Snow is an indicator of climate change. Its variation can affect surface energy, water balance, and atmospheric circulation, providing important feedback on climate change. There is a lack of assessment of the spatial characteristics of multi-source snow data in Eurasia, and these data exhibit high spatial variability and other differences. Therefore, using data obtained from the Global Historical Climatology Network Daily (GHCND) from 1980 to 2018, snow depth information from ERA5, MERRA2, and GlobSnow is assessed in this study. The spatiotemporal variation characteristics and the primary spatial modes of seasonal variations in snow depth are analyzed. The results show that the snow depth, according to GlobSnow data, is closer to that of the measured site data, while the ERA5_Land and MERRA2 data are overestimated. The annual variations in snow depth are consistent with seasonal variations in winter and spring, with an increasing trend in the mountains of Central Asia and Siberia and a decreasing trend in most of the rest of Eurasia. The dominant patterns of snow depth in late autumn, winter, and spring are all north–south dipole patterns, and there is overall consistency in summer.
Evaluation of a High Resolution WRF Model for Southeast Brazilian Coast: The Importance of Physical Parameterization to Wind Representation
Authors: Gonçalves Layrson de Jesus Menezes , Kaiser Júlia , Palmeira Ronaldo Maia de Jesus , Gallo Marcos Nicolás , Parente Carlos Eduardo
Published: April 2024

This study assesses the performance of the Weather Research and Forecasting (WRF) model using a high-resolution spatial grid (1 km) with various combinations of physical parameterization packages to simulate a severe event in August 2021 in the southeastern Brazilian coast. After determining the optimal set of physical parameterizations for representing wind patterns during this event, a year-long evaluation was conducted, covering forecast horizons of 24, 48, and 72 h. The simulation results were compared with observational wind data from four weather stations. The findings highlight variations in the efficacy of different physical parameterization sets, with certain sets encountering challenges in accurately depicting the peak of the severe event. The most favorable results were achieved using a combination of Tiedtke (cumulus), Thompson (microphysics), TKE (boundary layer), Monin-Obukhov (surface layer), Unified-NOAH (land surface), and RRTMG (shortwave and longwave radiation). Over the one-year forecasting period, the WRF model effectively represented the overall wind pattern, including forecasts up to three days in advance (72-h forecast horizon). Generally, the statistical metrics indicate robust model performance, even for the 72-h forecast horizon, with correlation coefficients consistently exceeding 0.60 at all analyzed points. While the model proficiently captured wind distribution, it tended to overestimate northeast wind speed and gust intensities. Notably, forecast accuracy decreased as stations approached the ocean, exemplified by the ATPM station.
Study of the Spatiotemporal Distribution Characteristics of Rainfall Using Hybrid Dimensionality Reduction-Clustering Model: A Case Study of Kunming City, China
Authors: Lin Weijie , Liu Yuanyuan , Li Na , Wang Jing , Zhang Nianqiang , Wang Yanyan , Wang Mingyang , Ren Hancheng , Li Min
Published: April 2024

In recent years, the frequency and intensity of global extreme weather events have gradually increased, leading to significant changes in urban rainfall patterns. The uneven distribution of rainfall has caused varying degrees of water security issues in different regions. Accurately grasping the spatiotemporal distribution patterns of rainfall is crucial for understanding the hydrological cycle and predicting the availability of water resources. This study collected rainfall data every five minutes from 62 rain gauge stations in the main urban area of Kunming City from 2019 to 2021, constructing an unsupervised hybrid dimensionality reduction-clustering (HDRC) model. The model employs the Locally Linear Embedding (LLE) algorithm from manifold learning for dimensionality reduction of the data samples and uses the dynamic clustering K-Means algorithm for cluster analysis. The results show that the model categorizes the rainfall in the Kunming area into three types: The first type has its rainfall center distributed on the north shore of Dian Lake and the southern part of Kunming’s main urban area, with spatial dynamics showing the rainfall distribution gradually developing from the Dian Lake water body towards the land. The second type’s rainfall center is located in the northern mountainous area of Kunming, with a smaller spatial dynamic change trend. The water vapor has a relatively fixed and concentrated rainfall center due to the orographic uplift effect of the mountains. The third type’s rainfall center is located in the main urban area of Kunming, with this type of rainfall showing smaller variations in all indicators, mainly occurring in May and September when the temperature is lower, related to the urban heat island effect. This research provides a general workflow for spatial rainfall classification, capable of mining the spatiotemporal distribution patterns of regional rainfall based on extensive data and generating typical samples of rainfall types.
Diagnosis of Forme Fruste Keratoconus Using Corvis ST Sequences with Digital Image Correlation and Machine Learning
Authors: Yang Lanting , Qi Kehan , Zhang Peipei , Cheng Jiaxuan , Soha Hera , Jin Yun , Ci Haochen , Zheng Xianling , Wang Bo , Mei Yue , Chen Shihao , Wang Junjie
Published: April 2024

Purpose: This study aimed to employ the incremental digital image correlation (DIC) method to obtain displacement and strain field data of the cornea from Corvis ST (CVS) sequences and access the performance of embedding these biomechanical data with machine learning models to distinguish forme fruste keratoconus (FFKC) from normal corneas. Methods: 100 subjects were categorized into normal (N = 50) and FFKC (N = 50) groups. Image sequences depicting the horizontal cross-section of the human cornea under air puff were captured using the Corvis ST tonometer. The high-speed evolution of full-field corneal displacement, strain, velocity, and strain rate was reconstructed utilizing the incremental DIC approach. Maximum (max-) and average (ave-) values of full-field displacement V, shear strain γxy, velocity VR, and shear strain rate γxyR were determined over time, generating eight evolution curves denoting max-V, max-γxy, max-VR, max-γxyR, ave-V, ave-γxy, ave-VR, and ave-γxyR, respectively. These evolution data were inputted into two machine learning (ML) models, specifically Naïve Bayes (NB) and Random Forest (RF) models, which were subsequently employed to construct a voting classifier. The performance of the models in diagnosing FFKC from normal corneas was compared to existing CVS parameters. Results: The Normal group and the FFKC group each included 50 eyes. The FFKC group did not differ from healthy controls for age (p = 0.26) and gender (p = 0.36) at baseline, but they had significantly lower bIOP (p < 0.001) and thinner central cornea thickness (CCT) (p < 0.001). The results demonstrated that the proposed voting ensemble model yielded the highest performance with an AUC of 1.00, followed by the RF model with an AUC of 0.99. Radius and A2 Time emerged as the best-performing CVS parameters with AUC values of 0.948 and 0.938, respectively. Nonetheless, no existing Corvis ST parameters outperformed the ML models. A progressive enhancement in performance of the ML models was observed with incremental time points during the corneal deformation. Conclusion: This study represents the first instance where displacement and strain data following incremental DIC analysis of Corvis ST images were integrated with machine learning models to effectively differentiate FFKC corneas from normal ones, achieving superior accuracy compared to existing CVS parameters. Considering biomechanical responses of the inner cornea and their temporal pattern changes may significantly improve the early detection of keratoconus.
Toward the Exploitation of Sustainable Green Factory: Biotechnology Use of Nannochloropsis spp.
Authors: Canini Davide , Ceschi Edoardo , Perozeni Federico
Published: April 2024

Securing food, energy, and raw materials for a growing population is one of the most significant challenges of our century. Algae play a central role as an alternative to plants. Wastewater and flue gas can secure nutrients and CO2 for carbon fixation. Unfortunately, algae domestication is necessary to enhance biomass production and reduce cultivation costs. Nannochloropsis spp. have increased in popularity among microalgae due to their ability to accumulate high amounts of lipids, including PUFAs. Recently, the interest in the use of Nannochloropsis spp. as a green bio-factory for producing high-value products increased proportionally to the advances of synthetic biology and genetic tools in these species. In this review, we summarized the state of the art of current nuclear genetic manipulation techniques and a few examples of their application. The industrial use of Nannochloropsis spp. has not been feasible yet, but genetic tools can finally lead to exploiting this full-of-potential microalga.
The Influence of Whole-Body Cryotherapy or Winter Swimming on the Activity of Antioxidant Enzymes
Authors: Ptaszek Bartłomiej , Podsiadło Szymon , Czerwińska-Ledwig Olga , Teległów Aneta , Pilch Wanda , Sadowska-Krępa Ewa
Published: April 2024

The aim of this study was to investigate the effect of a series of 20 whole-body cryotherapy sessions and 20 winter swimming sessions on the level of catalase, glutathione peroxidase and superoxide dismutase. The experimental group consisted of 60 people (30 people received cryotherapy and 30 people swam in cold water). The control group—without intervention: 30 people. Each of the three groups was tested twice. Analyzing the changes in the examined indicators, a statistical increase of CAT was observed in men after the use of WBC, as well as an increase of SOD in women and a decrease of SOD in men after the winter swimming season. Regular WS does not seem to place an excessive burden on the body in terms of intensifying oxidative processes. WS sessions once a week can be recommended as an effective method of improving health by causing positive adaptive changes and strengthening the body’s antioxidant barrier. WBC may be used as an adjunct therapy in the treatment of diseases caused by oxidative stress, as it improves the body’s antioxidant capacity. Further research is needed to elucidate antioxidant mechanisms in humans and to determine the short- and long-term effects of exposure to WS and WBC.
Aquaculture Performance and Genetic Diversity of a New [(Crassostrea hongkongensis ♀ × C. gigas ♂) ♂ × C. hongkongensis ♀] Variety of the Oyster “South China No. 1” in Beibu Gulf, China
Authors: Wei Zonglu , Qin Yanping , Liu Haoxiang , Xing Qinggan , Yu Ziniu , Zhang Yuehuan , Pan Ying
Published: April 2024

Crassostrea hongkongensis is an economically important bivalve found in various parts of the South China Sea. A new interspecific backcross ([(Crassostrea hongkongensis ♀ × C. gigas ♂) ♂ × C. hongkongensis ♀]) variety was bred by the South China Sea Institute of Oceanology which named “South China No. 1”. This study aims to explore the effects of stocking density on the growth performance of “South China No. 1”, compared their growth performance and genetic diversity to C. hongkongensis, and found the best place breeding site for “South China No. 1” in Beibu Gulf. The results showed that stocking a density of 20 oysters/substrate can significantly increase the shell height, shell width, total weight, survival rate, daily shell height gain and daily body mass gain. It was found that the shell height and total weight of “South China No. 1” cultured in Fangchenggang were significantly higher than that of those in Beihai and Qinzhou from September 2018 to November 2018. Similarly, the shell width of oysters in Fangchenggang and Qinzhou was also significantly higher in September 2018, and the interaction between site and stocking density had significant effects on the shell width in March 2018 and November 2018. In addition, the shell height and shell width of “South China No. 1” were significantly higher than that of C. hongkongensis in all three sites. At all three sites, the phytoplankton community structure was mostly dominated by Bacillariophyta. In the Hardy–Weinberg equilibrium test, for the seven populations and ten microsatellites, in 10 of the 70 groups, the segregation distortion was significant. These results suggest that a stocking density of 20 oysters/substrate can promote the shell height, shell width and total weight of “South China No. 1” in Beibu Gulf, China. “South China No. 1” has better growth performance compared with C. hongkongensis. Fangchenggang is a suitable place to cultivate the “South China No. 1” breed according to the total weight and sum of all algal genus abundances. The results of this study can be used as a reference to further understand the stocking density and genetic diversity of the “South China No. 1” breed in Beibu Gulf, China.
GateView: A Multi-Omics Platform for Gene Feature Analysis of Virus Receptors within Human Normal Tissues and Tumors
Authors: Sun Yang , Huang Zi-Liang , Chen Wen-Xin , Zhang Yi-Feng , Lei Hao-Tian , Huang Qiao-Juan , Lun Zhao-Rong , Qu Liang-Hu , Zheng Ling-Ling
Published: April 2024

Viruses are obligate intracellular parasites that rely on cell surface receptor molecules to complete the first step of invading host cells. The experimental method for virus receptor screening is time-consuming, and receptor molecules have been identified for less than half of known viruses. This study collected known human viruses and their receptor molecules. Through bioinformatics analysis, common characteristics of virus receptor molecules (including sequence, expression, mutation, etc.) were obtained to study why these membrane proteins are more likely to become virus receptors. An in-depth analysis of the cataloged virus receptors revealed several noteworthy findings. Compared to other membrane proteins, human virus receptors generally exhibited higher expression levels and lower sequence conservation. These receptors were found in multiple tissues, with certain tissues and cell types displaying significantly higher expression levels. While most receptor molecules showed noticeable age-related variations in expression across different tissues, only a limited number of them exhibited gender-related differences in specific tissues. Interestingly, in contrast to normal tissues, virus receptors showed significant dysregulation in various types of tumors, particularly those associated with dsRNA and retrovirus receptors. Finally, GateView, a multi-omics platform, was established to analyze the gene features of virus receptors in human normal tissues and tumors. Serving as a valuable resource, it enables the exploration of common patterns among virus receptors and the investigation of virus tropism across different tissues, population preferences, virus pathogenicity, and oncolytic virus mechanisms.
Caspase-5: Structure, Pro-Inflammatory Activity and Evolution
Authors: Eckhart Leopold , Fischer Heinz
Published: April 2024

Caspase-5 is a protease that induces inflammation in response to lipopolysaccharide (LPS), a component of the cell envelope of Gram-negative bacteria. The expression level of the CASP5 gene is very low in the basal state, but strongly increases in the presence of LPS. Intracellular LPS binds to the caspase activation and recruitment domain (CARD) of caspase-5, leading to the formation of a non-canonical inflammasome. Subsequently, the catalytic domain of caspase-5 cleaves gasdermin D and thereby facilitates the formation of cell membrane pores through which pro-inflammatory cytokines of the interleukin-1 family are released. Caspase-4 is also able to form a non-canonical inflammasome upon binding to LPS, but its expression is less dependent on LPS than the expression of caspase-5. Caspase-4 and caspase-5 have evolved via the duplication of a single ancestral gene in a subclade of primates, including humans. Notably, the main biomedical model species, the mouse, has only one ortholog, namely caspase-11. Here, we review the structural features and the mechanisms of regulation that are important for the pro-inflammatory roles of caspase-5. We summarize the interspecies differences and the evolution of pro-inflammatory caspases in mammals and discuss the potential roles of caspase-5 in the defense against Gram-negative bacteria and in sepsis.
Comparative Study of Field-Effect Transistors Based on Graphene Oxide and CVD Graphene in Highly Sensitive NT-proBNP Aptasensors
Authors: Kudriavtseva Anastasiia , Jarić Stefan , Nekrasov Nikita , Orlov Alexey V. , Gadjanski Ivana , Bobrinetskiy Ivan , Nikitin Petr I. , Knežević Nikola
Published: April 2024

Graphene-based materials are actively being investigated as sensing elements for the detection of different analytes. Both graphene grown by chemical vapor deposition (CVD) and graphene oxide (GO) produced by the modified Hummers’ method are actively used in the development of biosensors. The production costs of CVD graphene- and GO-based sensors are similar; however, the question remains regarding the most efficient graphene-based material for the construction of point-of-care diagnostic devices. To this end, in this work, we compare CVD graphene aptasensors with the aptasensors based on reduced GO (rGO) for their capabilities in the detection of NT-proBNP, which serves as the gold standard biomarker for heart failure. Both types of aptasensors were developed using commercial gold interdigitated electrodes (IDEs) with either CVD graphene or GO formed on top as a channel of liquid-gated field-effect transistor (FET), yielding GFET and rGO-FET sensors, respectively. The functional properties of the two types of aptasensors were compared. Both demonstrate good dynamic range from 10 fg/mL to 100 pg/mL. The limit of detection for NT-proBNP in artificial saliva was 100 fg/mL and 1 pg/mL for rGO-FET- and GFET-based aptasensors, respectively. While CVD GFET demonstrates less variations in parameters, higher sensitivity was demonstrated by the rGO-FET due to its higher roughness and larger bandgap. The demonstrated low cost and scalability of technology for both types of graphene-based aptasensors may be applicable for the development of different graphene-based biosensors for rapid, stable, on-site, and highly sensitive detection of diverse biochemical markers.
Biplane Enhancement Coil for Magnetic Induction Tomography of Cerebral Hemorrhage
Authors: Cao Zhongkai , Ye Bo , Cao Honggui , Zou Yangkun , Zhu Zhizhen , Xing Hongbin
Published: April 2024

Magnetic Induction Tomography (MIT) is a non-invasive imaging technique used for dynamic monitoring and early screening of cerebral hemorrhage. Currently, there is a significant challenge in cerebral hemorrhage MIT due to weak detection signals, which seriously affects the accuracy of the detection results. To address this issue, a dual-plane enhanced coil was proposed by combining the target field method with consideration of the spatial magnetic field attenuation pattern within the imaging target region. Simulated detection models were constructed using the proposed coil and cylindrical coil as excitation coils, respectively, and simulation imaging tests were conducted using the detection results. The simulation results indicate that compared to the cylindrical coil, the proposed coil enhances the linearity of the magnetic field within the imaging target region by 60.43%. Additionally, it effectively enhances the detection voltage and phase values. The simulation results of hemorrhage detection show that the proposed coil improves the accuracy of hemorrhage detection by 18.26%. It provides more precise detection results, offering a more reliable solution for cerebral hemorrhage localization and detection.
A Label-Free Droplet Sorting Platform Integrating Dielectrophoretic Separation for Estimating Bacterial Antimicrobial Resistance
Authors: Yan Jia-De , Yang Chiou-Ying , Han Arum , Wu Ching-Chou
Published: April 2024

Antimicrobial resistance (AMR) has become a crucial global health issue. Antibiotic-resistant bacteria can survive after antibiotic treatments, lowering drug efficacy and increasing lethal risks. A microfluidic water-in-oil emulsion droplet system can entrap microorganisms and antibiotics within the tiny bioreactor, separate from the surroundings, enabling independent assays that can be performed in a high-throughput manner. This study presents the development of a label-free dielectrophoresis (DEP)-based microfluidic platform to sort droplets that co-encapsulate Escherichia coli (E. coli) and ampicillin (Amp) and droplets that co-encapsulate Amp-resistant (AmpR) E. coli with Amp only based on the conductivity-dependent DEP force (FDEP) without the assistance of optical analyses. The 9.4% low conductivity (LC) Luria–Bertani (LB) broth diluted with 170 mM mannitol can maintain E. coli and AmpR E. coli growth for 3 h and allow Amp to kill almost all E. coli, which can significantly increase the LCLB conductivity by about 100 μS/cm. Therefore, the AmpR E. coli/9.4%LCLB/Amp where no cells are killed and the E. coli/9.4%LCLB/Amp-containing droplets where most of the cells are killed can be sorted based on this conductivity difference at an applied electric field of 2 MHz and 100 Vpp that generates positive FDEP. Moreover, the sorting ratio significantly decreased to about 50% when the population of AmpR E. coli was equal to or higher than 50% in droplets. The conductivity-dependent DEP-based sorting platform exhibits promising potential to probe the ratio of AmpR E. coli in an unknown bacterial sample by using the sorting ratio as an index.