bluuebunny/crossref_metadata_2025 huggingface dataset:
Winter Carbon Dioxide Measurement in Honeybee Hives
Authors: Newton Michael I. , Chamberlain Luke , McVeigh Adam , Bencsik Martin
Published: February 2024

Sensor technologies have sufficiently advanced to provide low-cost devices that can quantify carbon dioxide levels in honeybee hives with high temporal resolution and in a small enough package for hive deployment. Recent publications have shown that summer carbon dioxide levels vary throughout the day and night over ranges that typically exceed 5000 ppm. Such dramatic changes in a measurable parameter associated with bee physiology are likely to convey information about the colony health. In this work, we present data from four UK-based hives collected through the winter of 2022/2023, with a focus on seeing if carbon dioxide can indicate when colonies are at risk of failure. These hives have been fitted with two Sensirion SCD41 photoacoustic non-dispersive infrared (NDIR) carbon dioxide sensors, one in the queen excluder, at the top of the brood box, and one in the crown board, at the top of the hive. Hive scales have been used to monitor the hive mass, and internal and external temperature sensors have been included. Embedded accelerometers in the central frame of the brood box have been used to measure vibrations. Data showed that the high daily variation in carbon dioxide continued throughout the coldest days of winter, and the vibrational data suggested that daily fanning may be responsible for restoring lower carbon dioxide levels. The process of fanning will draw in colder air to the hive at a time when the bees should be using their energy to maintain the colony temperature. Monitoring carbon dioxide may provide feedback, prompting human intervention when the colony is close to collapse, and a better understanding may contribute to discussions on future hive design.
Attempts to Use Hemp (Cannabis sativa L. var. sativa) Inflorescence Extract to Limit the Growth of Fungi Occurring in Agricultural Crops
Authors: Kursa Weronika , Jamiołkowska Agnieszka , Wyrostek Jakub , Kowalski Radosław
Published: February 2024

The primary objective of this investigation was to assess the potential applicability of hemp (Cannabis sativa L. var. sativa) lateral inflorescence extract in mitigating the growth of fungi, including phytopathogens, on agricultural plants. The extract, comprising a blend of biologically active compounds, holds promise for integration into contemporary plant protection methodologies. The research involved a comprehensive analysis of the extract’s chemical composition, encompassing the determination of total polyphenol and flavonoid content (utilizing spectrophotometric methods), antioxidant activity (evaluated through the DPPH method employing synthetic 2,2-diphenyl-1-picrylhydrazyl radical), and cannabinoid content (analyzed using HPLC techniques). Additionally, this study employed the poisoned substrate method to gauge the impact of 5, 10, and 20% extract concentrations on the growth of various microfungi, including Alternaria alternata, Botrytis cinerea, Colletotrichum coccodes, Fusarium avenaceum, F. culmorum, F. graminearum, F. oxysporum, F. sporotrichioides, and Trichoderma koningii. The hemp extract demonstrated a substantial presence of total polyphenolic compounds, with polyphenol and flavonoid concentrations measuring 149.65 mg/mL and 1.55 mg/mL, respectively. Furthermore, the extract contained cannabinoids at a concentration of 0.12%. The most pronounced antifungal activity was observed with the 20% extract, particularly against T. koningii (62.22–84.79%), C. coccodes (61.65–81.82%), and B. cinerea (45.00–75.42%). However, the efficacy of hemp extracts exhibited large differences against Fusarium spp. (3.10–72.95%), dependent on the specific extract and fungus strain. Introduction of hemp extracts to the substrate induced a reduction in substrate pigment and a discernible color alteration in the mycelium to a lighter shade compared to the control. These findings mark the initial phase in the exploration of practical applications for plant extracts, setting the groundwork for subsequent field trials to ascertain the extract’s impact on phytotoxicity and the health status of agricultural plants.
PLA-Sago Starch Implants: The Optimization of Injection Molding Parameter and Plasticizer Material Compositions
Authors: Whulanza Yudan , Taufiqurrakhman Mohamad , Supriadi Sugeng , Chalid Mochamad , Kreshanti Prasetyanugraheni , Azadi Athoillah
Published: February 2024

Previous research extensively characterized PLA blends for various biomedical applications, especially in polymer-based biodegradable implant fixations, offering advantages over metallic counterparts. Nevertheless, achieving an optimal PLA mixture with both mechanical resistance and fast biodegradability remains a challenge. Currently, literature still lacks insights into the manufacturing parameter impact on sago starch/PLA in combination with PEG plasticizer. The objective of this study is to assess variations in injection molding temperatures and sago/PLA/PEG weight compositions to identify the optimal combination enhancing miniplate mechanical properties and biodegradation behavior. Mechanical tests reveal that incorporating PEG into pure PLA yields high mechanical performance, correlating linearly with increasing injection temperature. However, the interaction once the three materials are mixed decreases mechanical performance across tested temperatures. Higher biodegradation rates are observed with a larger weight composition of the hydrophilic behavior attributed to sago starch presence. The observed novelty in PLA mixed with 20% sago starch and 10% PEG at 170 °C indicates a better performance in elastic modulus and elongation at break also the degradation rate, emphasizing the role of injection temperature in molding miniplate implants. In conclusion, the interplay of injection molding parameters and material compositions is crucial for optimizing PLA-based miniplate implants, with potential contributions to tissue implants rather than bone implants due to their mechanical limitation.
Evaluation of User-Centric Cell-Free Massive Multiple-Input Multiple-Output Networks Considering Realistic Channels and Frontend Nonlinear Distortion
Authors: Hoffmann Marcin , Kryszkiewicz Paweł
Published: February 2024

Future 6G networks are expected to utilize Massive Multiple-Input Multiple-Output (M-MIMO) and follow a user-centric cell-free (UCCF) architecture. In a UCCF M-MIMO network, the user can be potentially served by multiple surrounding Radio Units (RUs) and Distributed Units (DUs) controlled and coordinated by a single virtualized Centralized Unit (CU). Moreover, in an M-MIMO network, each transmit frontend is equipped with a Power Amplifier (PA), typically with nonlinear characteristics, that can have a significant impact on the throughput achieved by network users. This work evaluates a UCCF M-MIMO network within an advanced system-level simulator considering multicarrier transmission, using Orthogonal Frequency-Division Multiplexing (OFDM), realistic signal-processing steps, e.g., per resource block scheduling, and a nonlinear radio frontend. Moreover, both idealistic independent and identically distributed (i.i.d.) Rayleigh and 3D ray-tracing-based radio channels are evaluated. The results show that under the realistic radio channel, the novel user-centric network architecture can lead to an almost uniform distribution of user throughput and improve the rate of the users characterized by the worst radio conditions by over 3 times in comparison to a classical, network-centric design. At the same time, the nonlinear characteristics of the PA can cause significant degradation of the UCCF M-MIMO network’s performance when operating close to its saturation power.
Determination of Nonylphenol in a Highly Sensitive Chemiluminescent Immunoenzyme Assay of Natural Waters
Authors: Berlina Anna N. , Komova Nadezhda S. , Serebrennikova Kseniya V. , Zherdev Anatoly V. , Dzantiev Boris B.
Published: February 2024

A competitive chemiluminescent immunoenzyme assay (CL-EIA) technique is proposed for the sensitive determination of one of the environmentally significant toxicants of anthropogenic origin-nonylphenol—in natural waters. The chosen chemiluminescent detection is characterized by a higher sensitivity compared to the colorimetric. The limit of nonylphenol detection was 9 ng/mL compared to 55 ng/mL for colorimetric one in optimal conditions. The developed analysis can be used for two purposes; it is highly sensitive for the possibility of toxicological analysis and dilution of complex matrices with raw buffer solution, as well as for the analysis of water samples without pretreatment and dilution. The method has a working range from 28 to 1800 ng/mL. The degree of nonylphenol revealing in the spiked samples of river, spring, and waterfall water was 82–119%.
Reconstructing 3D De-Blurred Structures from Limited Angles of View through Turbid Media Using Deep Learning
Authors: Dang Nguyen Ngoc An , Huynh Hoang Nhut , Tran Trung Nghia , Shimizu Koichi
Published: February 2024

Recent studies in transillumination imaging for developing an optical computed tomography device for small animal and human body parts have used deep learning networks to suppress the scattering effect, estimate depth information of light-absorbing structures, and reconstruct three-dimensional images of de-blurred structures. However, they still have limitations, such as knowing the information of the structure in advance, only processing simple structures, limited effectiveness for structures with a depth of about 15 mm, and the need to use separated deep learning networks for de-blurring and estimating information. Furthermore, the current technique cannot handle multiple structures distributed at different depths next to each other in the same image. To overcome the mentioned limitations in transillumination imaging, this study proposed a pixel-by-pixel scanning technique in combination with deep learning networks (Attention Res-UNet for scattering suppression and DenseNet-169 for depth estimation) to estimate the existence of each pixel and the relative structural depth information. The efficacy of the proposed method was evaluated through experiments that involved a complex model within a tissue-equivalent phantom and a mouse, achieving a reconstruction error of 2.18% compared to the dimensions of the ground truth when using the fully convolutional network. Furthermore, we could use the depth matrix obtained from the convolutional neural network (DenseNet-169) to reconstruct the absorbing structures using a binary thresholding method, which produced a reconstruction error of 6.82%. Therefore, only one convolutional neural network (DenseNet-169) must be used for depth estimation and explicit image reconstruction. Therefore, it reduces time and computational resources. With depth information at each pixel, reconstruction of 3D image of the de-blurred structures could be performed even from a single blurred image. These results confirm the feasibility and robustness of the proposed pixel-by-pixel scanning technique to restore the internal structure of the body, including intricate networks such as blood vessels or abnormal tissues.
Analysis of a Novel Fluidic Oscillator under Several Dimensional Modifications
Authors: Karimzadegan Kavoos , Mirzaei Masoud , Bergada Josep M.
Published: February 2024

To activate the boundary layer in Active Flow Control (AFC) applications, the use of pulsating flow has notable energy advantages over constant blowing/suction jet injections. For a given AFC application, five parameters, jet location and width, inclination angle, frequency of injection, and the momentum coefficient, need to be tuned. Presently, two main devices are capable of injecting pulsating flow with a momentum coefficient sufficient to delay the boundary layer separation: these are zero-net-mass-flow Actuators (ZNMFAs) and fluidic oscillators (FOs). In the present study, a novel FO configuration is analyzed for the first time at relatively high Reynolds numbers, and fluid is considered to be incompressible. After obtaining the typical linear correlation between the incoming Reynolds number and the outlet flow oscillating frequency, the effects of dimensional modifications on outlet width and mixing chamber wedge inclination angle are addressed. Modifications of the outlet width were observed to create large variations in FO performance. The origin of self-sustained oscillations is also analyzed in the present manuscript and greatly helps in clarifying the forces acting on the jet inside the mixing chamber. In fact, we can conclude by saying that the current FO configuration is pressure-driven, although the mass flow forces appear to be much more relevant than in previously studied FO configurations.
A Method for Calculating the Instability Probability in a Deeply Buried Tunnel Construction with a Hidden Karst Cave at the Bottom
Authors: Wu Bo , Sun Wentao
Published: February 2024

In order to quantitatively assess the risk of deeply buried tunnel construction in the case of a hidden karst cave at the bottom of the tunnel, the first deeply buried tunnel construction failure model (PH−1) is firstly proposed based on the upper bound theorem of limit analysis and Protodyakonov’s theory. Thereafter, based on the upper bound theorem of limit analysis and the energy dissipation theory, the second deeply buried tunnel construction failure model (PH−2) is proposed. Furthermore, on the basis of the reliability theory, the dimensionless performance functions of each damage region under these two deeply buried tunnel construction damage modes are constructed. Finally, the instability probability of each damage region under the two damage modes is calculated using the Monte Carlo sampling method. This study suggests that the PH−1 failure model is not suitable for analysing the probability of instability in tunnel construction in hard plastic red clay strata, while the PH−2 failure model is appropriate for analysing the probability of instability in tunnel construction in hard plastic red clay strata. The worse the geological conditions, the larger the diameter of the cavern, the smaller the clearance between the cavern and the tunnel, and the greater the threat to safe tunnel construction.
A Review of Industry 4.0 Assessment Instruments for Digital Transformation
Authors: Zamora Iribarren Miranda , Garay-Rondero Claudia Lizette , Lemus-Aguilar Isaac , Peimbert-García Rodrigo E.
Published: February 2024

Nowadays, different Industry 4.0 technologies have been implemented into diverse industries. However, these implementations are not standardized across similar industries and countries. Consequently, companies are actively looking for assessment instruments—maturity models, roadmaps, frameworks, readiness assessments—to assist in their digital transformation, to determine their Industry 4.0 level, and to identify technologies and strategies that should be implemented in specific areas, thus developing a feasible implementation plan. A review is conducted following the PRISMA (Preferred Reporting Items for Systematic Literature Reviews and Meta-Analyses) methodology to analyze the different research works on assessment instruments focused on Industry 4.0. A total of 538 articles, book chapters, conference proceedings, editorial material, reviews, and reports written in the English language were retrieved. Of these, 132 research papers were examined using a mixed analysis format to generate bibliometrics, and 36 articles were then deeply studied for a complete meta-analysis. The findings and insights of this meta-analysis led to a compilation and summary of dimensions, outcomes, enablers, and key components typically involved in Industry 4.0, which are comprehensively integrated to present the best practices for assessing Industry 4.0 and deploying a digital transformation that can impact productivity, flexibility, sustainability, quality, costs, and time.
Research on the Anchor-Rod Recognition and Positioning Method of a Coal-Mine Roadway Based on Image Enhancement and Multiattention Mechanism Fusion-Improved YOLOv7 Model
Authors: Xue Xusheng , Yue Jianing , Yang Xingyun , Mao Qinghua , Qin Yihan , Zhang Enqiao , Wang Chuanwei
Published: February 2024

A drill-anchor robot is an essential means of efficient drilling and anchoring in coal-mine roadways. It is significant to calculate the position of the drill-anchor robot based on the positioning information of the supported anchor rod to improve tunneling efficiency. Therefore, identifying and positioning the supported anchor rod has become a critical problem that needs to be solved urgently. Aiming at the problem that the target in the image is blurred and cannot be accurately identified due to the low and uneven illumination environment, we proposed an improved YOLOv7 (the seventh version of the You Only Look Once) model based on the fusion of image enhancement and multiattention mechanism, and the self-made dataset is used for testing and training. Aiming at the problem that the traditional positioning method cannot guarantee accuracy and efficiency simultaneously, an anchor-rod positioning method using depth image and RGB image alignment combined with least squares linear fitting is proposed, and the positioning accuracy is improved by processing the depth map. The results show that the improved model improves the mAP by 5.7% compared with YOLOv7 and can accurately identify the target. Through the positioning method proposed in this paper, the error between the positioning coordinate and the measurement coordinate of the target point on each axis does not exceed 11 mm, which has high positioning accuracy and improves the positioning accuracy and robustness of the anchor rod in the coal-mine roadway.
Nature-Based Management of Lawns—Enhancing Biodiversity in Urban Green Infrastructure
Authors: Winkler Jan , Pasternak Grzegorz , Sas Wojciech , Hurajová Erika , Koda Eugeniusz , Vaverková Magdalena Daria
Published: February 2024

Urban green areas have multifunctional benefits that may mitigate unfavorable health and ecological effects. Green areas represent important biodiversity hideouts in anthropogenic regions. Lawns are the most common elements of urban greenery, covering a considerable number of green areas in cities. The aim of this analysis was to gain knowledge on urban greenery and elaborate recommendations related to management that favors grass lawn biodiversity. The established working hypothesis is that the limited intensity of management in urban grass areas is reflected in the modification of their species composition, as well as their potential ecological functions. An experiment on the management of city lawns was conducted in 2010 (still ongoing). There are three lawn variants under different management methods: ornamental, city, and permaculture lawns. Vegetation was assessed using the method of phytocoenological relevés. The coverage values of the individual plant species were processed using multidimensional analysis of ecological data. The results showed that human decisions and activities affected the species composition of these grassy areas. There were 46 plant taxa found during the monitoring: 12 in ornamental lawn variants, 24 in city lawn variants, and 31 in permaculture lawn variants. Permaculture lawns with extensive management represent the most environmentally friendly variant with respect to biodiversity and soil moisture content. However, changes in species composition have raised questions regarding the extent to which they may perform other ecosystem functions. Increasing the intensity of lawn management has resulted in lower plant diversity. Extensive management alters the aesthetic value of lawns and creates spaces for species that may spread in urban environments.
Non-Iterative Cluster Routing: Analysis and Implementation Strategies
Authors: Pham Huong , Cheng Samuel
Published: February 2024

In conventional routing, a capsule network employs routing algorithms for bidirectional information flow between layers through iterative processes. In contrast, the cluster routingtechnique utilizes a non-iterative process and can outperform state-of-the-art models with fewer parameters, while preserving the part–whole relationship and demonstrating robust generalization to novel viewpoints. This paper aims to further analyze and clarify this concept, providing insights that allow users to implement the cluster routing technique efficiently. Additionally, we expand the technique and propose variations based on the routing principle of achieving consensus among votes in distinct clusters. In some cases, these variations have the potential to enhance and boost the cluster routing performance while utilizing similar memory and computing resources.
Harnessing Digital Twins for Agriculture 5.0: A Comparative Analysis of 3D Point Cloud Tools
Authors: Catala-Roman Paula , Navarro Enrique A. , Segura-Garcia Jaume , Garcia-Pineda Miguel
Published: February 2024

Digital twins are essential in Agriculture 5.0, providing an accurate digital representation of agricultural objects and processes, enabling data-driven decision-making, the simulation of future scenarios, and innovation for a more efficient and sustainable agriculture. The main objective of this article is to review and compare the main tools for the development of digital twins for Agriculture 5.0 applications using 3D point cloud models created from photogrammetry techniques. For this purpose, the most commonly used tools for the development of these 3D models are presented. As a methodological approach, a qualitative comparison of the main characteristics of these tools was carried out. Then, based on some images taken in an orange grove, a quality analysis of the 3D point cloud models obtained by each of the analyzed tools was carried out. We also obtained a synthetic quality index in order to have a way to categorize the different pieces of software. Finally, as a conclusion, we compared the performance of the different software tools and the point clouds obtained by considering objective metrics (from the 3D quality assessment) and qualitative metrics in the synthetic quality index. With this index, we found that OpenDroneMap was the best software in terms of quality-cost ratio. Also, the paper introduces the concept of Agriculture 6.0, exploring the integration of advancements from Agriculture 5.0 to envision the potential evolution of agricultural practices and technologies, considering their impact on social and economic aspects.
The Influence of Rainfall and Evaporation Wetting–Drying Cycles on the Open-Pit Coal Mine Dumps in Cam Pha, Quang Ninh Region of Vietnam
Authors: Bang Van Son , Wang Yi , Vu Trong , Zhou Wei , Liu Xin , Ao Zhongchen , Nguyen Duc , Pham Hien , Nguyen Hoai
Published: February 2024

Among the slope hazards caused by rainfall, not all of them occur directly during storm washout, and the wetting–drying cycles’ effect on the rainfall–evaporation process is an important cause of shallow slope instability. In this study, taking the slope of the open-pit coal mine dumps in Cam Pha, in the Quang Ninh region of Vietnam, as the research object, we carry out experiments on the physical properties of the rock body under different wetting–drying cycles, as well as numerical analyses. The results show that the wetting–drying cycles significantly affect the physical and mechanical parameters and permeability of the rock body. In the process of the wetting–drying cycle, a transient saturated zone occurs on the surface of the slope, and the range of the unsaturated zone inside the slope body decreases with the increase in the number of wetting–drying cycles. Moreover, the infiltration line keeps moving downward, but the rate of downward movement is slowed down by the decrease in the gradient of matrix suction affected by rainfall. Under the influence of the wetting–drying cycles, the slope displacement, plastic zone, and maximum shear strain increment range gradually approach the slope surface with the wetting–drying cycles, and the displacement peak gradually increases. A dump is a site for the centralized discharge of mining waste, formed by the crushing and stockpiling of the original rock formation. Bang Nau is the name of the dump considered in this study. After multiple rainfall events, the slope stability under five wetting–drying cycles decreases from 1.721 to 1.055, and the landslide mode changes from a whole landslide to a single-step shallow landslide, with a certain landslide risk. It is necessary to strengthen the slope stability as the landslide risk is very high, and it is necessary to strengthen the monitoring and inspection of the slope.
Fault Controls on Hydrocarbon Migration—An Example from the Southwestern Pearl River Mouth Basin
Authors: Xu Bin , Miocic Johannes M. , Cheng Yanjun , Xu Lili , Ma Saiting , Sun Wenjie , Chu Yichen , Wu Zhiping
Published: February 2024

Faults play a pivotal role in controlling fluid migration and retention within sedimentary basins, particularly in the context of fault-bound hydrocarbon reservoirs. Assessing the stability and sealing capabilities of faults enhances our comprehension of these systems and aids in the identification of pathways for fluid migration. In this study, we focus on a series of fault-bound hydrocarbon accumulations located in the southern Wenchang A subbasin within the Pearl River Mouth Basin. We emphasize the significant influence of faults in governing the processes of hydrocarbon migration and accumulation. By leveraging 3D seismic data and well information, we have assessed the sealing potential of ten faults that either currently retain hydrocarbon columns or have the potential to do so. Our analysis reveals that even faults with a relatively low Shale Gouge Ratio (as low as 15%) can effectively support substantial column heights. Taking into account factors, such as the source rock maturity, fault activity, geometry, sealing potential, and the distribution of hydrocarbon accumulations, we have formulated a conceptual model for hydrocarbon migration and accumulation within the study area. This model underscores potential fluid traps within the rift basin, shedding light on the complex dynamics of hydrocarbon movement in this region.
Insight into the Role of the Pore Structure and Surface Functional Groups in Biochar on the Adsorption of Sulfamethoxazole from Synthetic Urine
Authors: He Rui , Hui Kai , Zhang Xu , Yao Hong
Published: February 2024

The study assessed the influence of pyrolysis temperature on the properties of hickory sawdust and peanut shells based biochar, particularly its pore structure, surface functional groups, and adsorption capacity. Results from SEM analysis demonstrated that higher pyrolysis temperatures led to an enhanced pore structure and surface roughness in biochars, providing increased adsorption capacity. Raman spectrum analysis revealed higher levels of disorder and graphitization in biochars pyrolyzed at elevated temperatures. Quantification of surface functional groups using the Boehm method indicated a shift in the abundance of basic and acidic groups under high pyrolysis conditions. Employing the FHH model, fractal characteristics were observed in the pore structure of different biochars, with high-temperature biochars displaying increased disorder. The study also explored the mechanism of SMX adsorption onto biochars, revealing higher adsorption capacity for biochars with richer pore structures and rougher surfaces. The Elovich model proved to be the best fit for describing the chemisorption process of SMX onto the biochars. Moreover, the study demonstrated the impact of urine ions on SMX adsorption onto the biochars. These findings provide valuable insights into the properties and potential applications of biochars in environmental remediation.
Plant Diseased Lesion Image Segmentation and Recognition Based on Improved Multi-Scale Attention Net
Authors: Yang Tao , Wang Yannian , Lian Jihong
Published: February 2024

Fallen leaf disease can lead to a decrease in leaf area, a decrease in photosynthetic products, insufficient accumulation of fruit sugar, poor coloring and flavor, and a large number of fruits developing sunburn. To address the aforementioned issue, this article introduces a deep learning algorithm designed for the segmentation and recognition of agricultural disease images, particularly those involving leaf lesions. The essence of this algorithm lies in enhancing the Multi-scale Attention Net (MA-Net) encoder and attention mechanism to improve the model’s performance when processing agricultural disease images. Firstly, an analysis was conducted on MA-Net, and its limitations were identified. Compared to res-block, Mix Vision Transformer (MiT) consumes relatively less time during the training process, can better capture global and contextual information in images, and has better robustness and scalability. Then, the feature extraction parts of different networks were used as encoders to join the MA-Net network. Compared to a Position-wise Attention Block (PAB), which has higher computational complexity and requires a larger amount of computing resources, Effective Channel Attention net (ECANet) reduces the number of model parameters and computation by learning the correlation between channels, as well as having a better denoising ability. The experimental results show that the proposed solution has high accuracy and stability in agricultural disease image segmentation and recognition. The mean Intersection over Union (mIoU) is 98.1%, which is 0.2% higher than traditional MA-Net; Dice Loss is 0.9%, which is 0.1% lower than traditional MA-Net.
PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition
Authors: Yang Hongjian , Zhang Qinghao , Kwon Hyuk-Chul
Published: February 2024

Named entity recognition (NER) in natural language processing encompasses three primary types: flat, nested, and discontinuous. While the flat type often garners attention from researchers, nested NER poses a significant challenge. Current approaches to addressing nested NER involve sequence labeling methods with merged label layers, cascaded models, and those rooted in reading comprehension. Among these, sequence labeling with merged label layers stands out for its simplicity and ease of implementation. Yet, highlighted issues persist within this method, prompting our aim to enhance its efficacy. In this study, we propose augmentations to the sequence labeling approach by employing a pipeline model bifurcated into sequence labeling and text classification tasks. Departing from annotating specific entity categories, we amalgamated types into main and sub-categories for a unified treatment. These categories were subsequently embedded as identifiers in the recognition text for the text categorization task. Our choice of resolution involved BERT+BiLSTM+CRF for sequence labeling and the BERT model for text classification. Experiments were conducted across three nested NER datasets: GENIA, CMeEE, and GermEval 2014, featuring annotations varying from four to two levels. Before model training, we conducted separate statistical analyses on nested entities within the medical dataset CMeEE and the everyday life dataset GermEval 2014. Our research unveiled a consistent dominance of a particular entity category within nested entities across both datasets. This observation suggests the potential utility of labeling primary and subsidiary entities for effective category recognition. Model performance was evaluated based on F1 scores, considering correct recognition only when both the complete entity name and category were identified. Results showcased substantial performance enhancement after our proposed modifications compared to the original method. Additionally, our improved model exhibited strong competitiveness against existing models. F1 scores on the GENIA, CMeEE, and GermEval 2014 datasets reached 79.21, 66.71, and 87.81, respectively. Our research highlights that, while preserving the original method’s simplicity and implementation ease, our enhanced model achieves heightened performance and competitive prowess compared to other methodologies.
A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
Authors: Su Chunsheng , Hu Qijun , Yang Zifan , Huo Runke
Published: February 2024

With the advent of the era of big data and information technology, deep learning (DL) has become a hot trend in the research field of artificial intelligence (AI). The use of deep learning methods for parameter inversion, disease identification, detection, surrounding rock classification, disaster prediction, and other tunnel engineering problems has also become a new trend in recent years, both domestically and internationally. This paper briefly introduces the development process of deep learning. By reviewing a number of published papers on the application of deep learning in tunnel engineering over the past 20 years, this paper discusses the intelligent application of deep learning algorithms in tunnel engineering, including collapse risk assessment, water inrush prediction, crack identification, structural stability evaluation, and seepage erosion in mountain tunnels, urban subway tunnels, and subsea tunnels. Finally, it explores the future challenges and development prospects of deep learning in tunnel engineering.
Effectiveness of Nanotechnology Treatments in Composite Aircraft Applications
Authors: Cestino Enrico , Catapano Juri , Galvano Francesco , Felis Andrea , Zuccalà Sabrina , Martilla Valentina , Sapienza Vito , Chesta Lorenzo
Published: February 2024

This paper aims to verify the effectiveness of a process of superficial protection based on nanotechnologies produced by 4Ward360 and specifically developed for aeronautical applications on composite material aircraft. The Dardo aircraft, a composite VLA category manufactured by CFM Air, was taken as a reference case and two application/investigation areas were identified. The potential anticorrosive behavior of the nanotechnology treatment was investigated when applied to the metal joints of the aircraft, such as the wing–fuselage attachments usually made of Al-2024-T3 aluminum alloy. Furthermore, the potential increased effectiveness in cleaning was investigated as another possible application concerning the parts made of composite material both solid and in a sandwich configuration and the plexiglass parts of the canopy.
Enhancing Soft Tissue Differentiation with Different Dual-Energy CT Systems: A Phantom Study
Authors: Gallo Pasqualina , D’Alessio Andrea , Pascuzzo Riccardo , Gallo Salvatore , Fumagalli Maria Luisa , Ortenzia Ornella , Tenconi Chiara , Cavatorta Claudia , Pignoli Emanuele , Ghetti Caterina , Bruzzone Maria Grazia , De Martin Elena
Published: February 2024

To quantitatively evaluate the possible advantages of quantifying and differentiating various soft tissues using virtual monochromatic images (VMI) derived from different dual-energy computed tomography (DECT) technologies. This study involved four DECT scanners with different technologies. CIRS phantom images were acquired in single-energy (SECT) and DECT modes with each scanner. The analysis focused on five equivalent soft-tissue inserts: adipose, breast, liver, muscle, and bone (200 mg). The signal-to-noise ratio (SNR) was calculated for each equivalent soft-tissue insert. Finally, the contrasts of tissue pairs between DECT and SECT images were compared using Wilcoxon signed-rank tests adjusted for multiple comparisons. Average CT numbers and noise showed a significant difference pattern between DECT with respect to SECT for each CT scanner. Generally, energy levels of 70 keV or higher led to improved SNR in VMI for most of the equivalent soft-tissue inserts. However, energy levels of 40–50 keV showed significantly higher contrasts in most of the equivalent soft-tissue insert pairs. DECT images at low energies, especially at 40–50 keV, outperform SECT images in discriminating soft tissues across all four DECT technologies. The combined use of DECT images reconstructed at different energy levels provides a more comprehensive set of information for diagnostic and/or radiotherapy evaluation compared to SECT. Some differences between scanners are evident, depending on the DECT acquisition technique and reconstruction method.
Szász–Durrmeyer Operators Involving Confluent Appell Polynomials
Authors: Kanat Kadir , Erdal Selin
Published: February 2024

This article is concerned with the Durrmeyer-type generalization of Szász operators, including confluent Appell polynomials and their approximation properties. Also, the rate of convergence of the confluent Durrmeyer operators is found by using the modulus of continuity and Peetre’s K-functional. Then, we show that, under special choices of A(t), the newly constructed operators reduce confluent Hermite polynomials and confluent Bernoulli polynomials, respectively. Finally, we present a comparison of newly constructed operators with the Durrmeyer-type Szász operators graphically.
Bimetal-Initiated Concerted Zn Regulation Enabling Highly Stable Aqueous Zn-Ion Batteries
Authors: Yin Hong , Liu Yuliang , Zhu Yifeng , Ye Fengxiang , Xu Guangliang , Lin Mengfang , Kang Wenbin
Published: February 2024

Aqueous zinc ion batteries are highly sought after for the next generation of sustainable energy storage systems. However, their development is significantly impeded by the presence of undesired zinc dendrites, which greatly reduce their cycle life. It is well-received that surface passivation by introducing foreign metals represents a compelling measure to enhance the stability of Zn anodes. Nevertheless, the vast potential of effecting concerted interplay between multiple metal elements for enhanced overall performance in Zn ion batteries remains elusive, due to the overwhelming challenge in creating uniform textures from hetero-units and understanding the mechanism underlying the synergistic performance gain. In this work, an innovative bimetallic overlaying strategy is proposed that renders possible the synergy between AgZn3 and CuZn5 in effecting uniform Zn deposition in a laterally confined and compact manner. The seeded growth of Zn on the bimetal-modulated interface effectively reduces the nucleation potential barrier, yielding a low nucleation overpotential (25 mV). In full cell testing with a commercial MnO2 applied as the cathode, superb cycling stability, surpassing the results reported in previous works, is achieved. The cell delivers an outstanding remaining capacity of 215 mA h g−1 after 300 cycles with almost no capacity degradation observed. The simple and highly efficient bimetal design, which synergizes the strengths of distinct metals, has the potential to drive innovations in the development of multicomponent aqueous Zn batteries with exceptional performance.
A Comparison of the Impact of Pharmacological Treatments on Cardioversion, Rate Control, and Mortality in Data-Driven Atrial Fibrillation Phenotypes in Critical Care
Authors: Lacki Alexander , Martinez-Millana Antonio
Published: February 2024

Critical care physicians are commonly faced with patients exhibiting atrial fibrillation (AF), a cardiac arrhythmia with multifaceted origins. Recent investigations shed light on the heterogeneity among AF patients by uncovering unique AF phenotypes, characterized by differing treatment strategies and clinical outcomes. In this retrospective study encompassing 9401 AF patients in an intensive care cohort, we sought to identify differences in average treatment effects (ATEs) across different patient groups. We extract data from the MIMIC-III database, use hierarchical agglomerative clustering to identify patients’ phenotypes, and assign them to treatment groups based on their initial drug administration during AF episodes. The treatment options examined included beta blockers (BBs), potassium channel blockers (PCBs), calcium channel blockers (CCBs), and magnesium sulfate (MgS). Utilizing multiple imputation and inverse probability of treatment weighting, we estimate ATEs related to rhythm control, rate control, and mortality, approximated as hourly and daily rates (%/h, %/d). Our analysis unveiled four distinctive AF phenotypes: (1) postoperative hypertensive, (2) non-cardiovascular mutlimorbid, (3) cardiovascular multimorbid, and (4) valvulopathy atrial dilation. PCBs showed the highest cardioversion rates across phenotypes, ranging from 11.6%/h (9.35–13.3) to 7.69%/h (5.80–9.22). While CCBs demonstrated the highest effectiveness in controlling ventricular rates within the overall patient cohort, PCBs and MgS outperformed them in specific phenotypes. PCBs exhibited the most favorable mortality outcomes overall, except for the non-cardiovascular multimorbid cluster, where BBs displayed a lower mortality rate of 1.33%/d [1.04–1.93] compared to PCBs’ 1.68%/d [1.10–2.24]. The results of this study underscore the significant diversity in ATEs among individuals with AF and suggest that phenotype-based classification could be a valuable tool for physicians, providing personalized insights to inform clinical decision making.
HIIT Promotes M2 Macrophage Polarization and Sympathetic Nerve Density to Induce Adipose Tissue Browning in T2DM Mice
Authors: Guo Yifan , Zhang Qilong , Yang Dan , Chen Peijie , Xiao Weihua
Published: February 2024

Browning of white adipose tissue (WAT) is a focus of research in type 2 diabetes mellitus (T2DM) and metabolism, which may be a potential molecular mechanism for high-intensity interval training (HIIT) to improve T2DM. In this study, male C57BL/6J wild-type mice were subjected to an 8-week HIIT regimen following T2DM induction through a high-fat diet (HFD) combined with streptozotocin (STZ) injection. We found that HIIT improved glucose metabolism, body weight, and fat mass in T2DM mice. HIIT also decreased adipocyte size and induced browning of WAT. Our data revealed a decrease in TNFα and an increase in IL-10 with HIIT, although the expression of chemokines MCP-1 and CXCL14 was increased. We observed increased pan-macrophage infiltration induced by HIIT, along with a simultaneous decrease in the expression of M1 macrophage markers (iNOS and CD11c) and an increase in M2 macrophage markers (Arg1 and CD206), suggesting that HIIT promotes M2 macrophage polarization. Additionally, HIIT upregulated the expression of Slit3 and neurotrophic factors (BDNF and NGF). The expression of the sympathetic marker tyrosine hydroxylase (TH) and the nerve growth marker GAP43 was also increased, demonstrating the promotion of sympathetic nerve growth and density by HIIT. Notably, we observed macrophages co-localizing with TH, and HIIT induced the accumulation of M2 macrophages around sympathetic nerves, suggesting a potential association between M2 macrophages and increased density of sympathetic nerves. In conclusion, HIIT induces adipose tissue browning and improves glucose metabolism in T2DM mice by enhancing M2 macrophage polarization and promoting sympathetic nerve growth and density.
The Role of Lung Microbiome in Fibrotic Interstitial Lung Disease—A Systematic Review
Authors: Puiu Ruxandra , Motoc Nicoleta Stefania , Lucaciu Sergiu , Ruta Maria Victoria , Rajnoveanu Ruxandra-Mioara , Todea Doina Adina , Man Milena Adina
Published: February 2024

Interstitial Lung Disease (ILD) involves lung disorders marked by chronic inflammation and fibrosis. ILDs include pathologies like idiopathic pulmonary fibrosis (IPF), connective tissue disease-associated ILD (CTD-ILD), hypersensitivity pneumonitis (HP) or sarcoidosis. Existing data covers pathogenesis, diagnosis (especially using high-resolution computed tomography), and treatments like antifibrotic agents. Despite progress, ILD diagnosis and management remains challenging with significant morbidity and mortality. Recent focus is on Progressive Fibrosing ILD (PF-ILD), characterized by worsening symptoms and fibrosis on HRCT. Prevalence is around 30%, excluding IPF, with a poor prognosis. Early diagnosis is crucial for optimizing outcomes in PF-ILD individuals. The lung microbiome comprises all the microorganisms that are in the respiratory tract. Relatively recent research try to evaluate its role in respiratory disease. Healthy lungs have a diverse microbial community. An imbalance in bacterial composition, changes in bacterial metabolic activities, or changes in bacterial distribution within the lung termed dysbiosis is linked to conditions like COPD, asthma and ILDs. We conducted a systematic review of three important scientific data base using a focused search strategy to see how the lung microbiome is involved in the progression of ILDs. Results showed that some differences in the composition and quality of the lung microbiome exist in ILDs that show progressive fibrosing phenotype. The results seem to suggest that the lung microbiota could be involved in ILD progression, but more studies showing its exact pathophysiological mechanisms are needed.
Hierarchical Capability in Distinguishing Severities of Sepsis via Serum Lactate: A Network Meta-Analysis
Authors: Zhu Binlu , Zhou Ruixi , Qin Jiangwei , Li Yifei
Published: February 2024

Background: Blood lactate is a potentially useful biomarker to predict the mortality and severity of sepsis. The purpose of this study is to systematically review the ability of lactate to predict hierarchical sepsis clinical outcomes and distinguish sepsis, severe sepsis and septic shock. Methods: We conducted an exhaustive search of the PubMed, Embase and Cochrane Library databases for studies published before 1 October 2022. Inclusion criteria mandated the presence of case–control, cohort studies and randomized controlled trials that established the association between before-treatment blood lactate levels and the mortality of individuals with sepsis, severe sepsis or septic shock. Data was analyzed using STATA Version 16.0. Results: A total of 127 studies, encompassing 107,445 patients, were ultimately incorporated into our analysis. Meta-analysis of blood lactate levels at varying thresholds revealed a statistically significant elevation in blood lactate levels predicting mortality (OR = 1.57, 95% CI 1.48–1.65, I2 = 92.8%, p < 0.00001). Blood lactate levels were significantly higher in non-survivors compared to survivors in sepsis patients (SMD = 0.77, 95% CI 0.74–0.79, I2 = 83.7%, p = 0.000). The prognostic utility of blood lactate in sepsis mortality was validated through hierarchical summary receiver operating characteristic curve (HSROC) analysis, yielding an area under the curve (AUC) of 0.72 (95% CI 0.68–0.76), accompanied by a summary sensitivity of 0.65 (95% CI 0.59–0.7) and a summary specificity of 0.7 (95% CI 0.64–0.75). Unfortunately, the network meta-analysis could not identify any significant differences in average blood lactate values’ assessments among sepsis, severe sepsis and septic shock patients. Conclusions: This meta-analysis demonstrated that high-level blood lactate was associated with a higher risk of sepsis mortality. Lactate has a relatively accurate predictive ability for the mortality risk of sepsis. However, the network analysis found that the levels of blood lactate were not effective in distinguishing between patients with sepsis, severe sepsis and septic shock.