bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Turbojet Engine with Water Injection for Aircraft Use as Controlled Object
Authors: Tudosie Alexandru-Nicolae , Lungu Mihai
Published: December 2024

This study addresses an under-represented topic in turbojets’ design—the characterizing of this type of engine as an entity subject to automatic control. This study’s subject is a medium-size turbojet, improved with a water injection system for thrust augmentation, and evaluated as a controlled object. The method of coolant injection in the compressor and/or in the combustion chamber of the aviation engine has been intensively studied and applied for the temporary increase in thrust. After a period of abandonment, the method seems to be returning in a version that also produces a reduction in pollutant emissions. Starting from determining turbojet performances on the test rig and establishing the equations that define the turbojet as a system, the mathematical model for both versions (basic and with a water injection) was issued. In order to correlate the basic engine operation with the water injection, a version of control architecture was designed, containing two controllers (for engine’s speed and for the injected water flow rate). An embedded control system was described by its mathematical model; based on its equations, its block diagram with transfer functions was issued. The system’s quality was evaluated by performing studies that concern the turbojet’s main parameters (speed and combustor temperature) and time behavior (system response at step input), which led to some results and conclusions regarding how the water injection changed the properties of the engine as a controlled object: the engine has become slower with bigger static errors for the studied parameters (affecting the stabilization at their values imposed by the new operating regime). The proposed method, based on the characterization of the engine as a controlled object (with and without coolant injection), can be very useful as a method of predicting the behavior of any turbojet when the addition of coolant injection system is desired; obviously, the appropriate modeling of both the turbojet and the injection system is necessary.
Investigation into Shock Control and Drag Reduction Characteristics of Opposing Plasma Synthetic Jet in Mach 8 Flow
Authors: Zhang Xudong , Dong Hao , Cao Xingyu , Zhou Yan , Xie Wei , Song Wei , Wang Yan
Published: December 2024

Plasma synthetic jet (PSJ) is widely employed in flow control due to its advantages of zero-mass and fast-response. A novel measurement method for high-frequency dynamic drag variation was adopted in a Mach 8 wind tunnel experiment, demonstrating that the opposing PSJ can achieve a maximum drag reduction of 40.27% and an average drag reduction of 13.25% within one discharge cycle. Subsequently, the numerical method was verified in detail and the effects of different discharge energies and nozzle diameters on the drag reduction characteristics of the opposing PSJ were studied. The results show that an increase in discharge energy is beneficial for the drag reduction characteristics of the opposing PSJ, although the efficiency remains relatively low. In contrast, increasing the nozzle diameter enhances the average drag reduction but significantly reduces the duration of effective control. The drag reduction mechanism of the opposing PSJ can be attributed to the combined effects of pushing the strong bow shock away to form a weaker oblique shock, followed by the reattachment of the shock downstream.
Effect of Soil Type on Running Performance of Small Lunar Rover
Authors: Watanabe Kimitaka , Horiguchi Tomoki , Tanaka Kazuto
Published: December 2024

It is very easy for a small lunar rover to slip on the regolith of the lunar surface and become stuck. Previous studies have quantitatively evaluated the effects of wheel geometry, such as elliptical or eccentric wheels, on the performance of a rover when climbing up slopes. These studies reported that the rovers were able to run on a 30-degree slope made of silica sand. In this study, a small rover was designed and created, and running tests were conducted using lunar soil simulant and silica sand to predict its performance on the lunar surface. The effects of soil differences on the performance of the rover were clarified through the running tests and the measurement of reaction force on the lug. Although the rover exhibited a greater slip ratio on the lunar soil simulant than on the silica sand, the rover with eccentric wheels was able to climb up to a 30-degree angle on the lunar soil simulant. The results for the sinkage measurement of the rover showed that the eccentric wheels prevented sinkage with their up-and-down motion, enabling the rover to climb steep slopes. Furthermore, the tests for measuring the reaction force on the lug indicated that the density change in the lunar soil simulant did not provide sufficient reaction force, and that the running performance on the lunar soil simulant was lower than that on the silica sand.
The Maintenance System and Profitability of Pig Production in Poland Under Conditions of ASF Occurrence
Authors: Pawłowski Krzysztof Piotr , Firlej Paulina Karolina , Pietrzak Kamila , Bartkowiak Zofia , Sołtysiak Gabriela
Published: December 2024

In the last two decades, the pig market in Poland has been influenced by two key events: accession to the European Union and the spread of African swine fever (ASF). During this time, the pig population in Poland has almost doubled, and the number of farms keeping pigs has fallen almost tenfold. On the other hand, the import of piglets intended for further rearing has increased significantly, which reduces the value added to production retained in the country. The changes taking place in the pig market in Poland in the conditions of ASF have, therefore, prompted the question of which pig-keeping systems are more profitable for pig producers, and identifying this relationship was the main objective of the analysis in this study. This research was conducted using source data from the databases of the Central Statistical Office, the FADN, and the Integrated Agricultural Market Information System of the Ministry of Agriculture and Rural Development. Factors influencing the value of pig production were identified using panel regression, and profitability analysis was performed based on changes in the levels of and relationship between feed costs and live pig prices. Feed consumption was determined based on feed rations for individual utility groups in both products. As the analysis has shown, a closed cycle of pig farming is characterized by better stability and resistance to market shocks compared to an open cycle (over the entire period under review, only the closed system ensured a positive surplus of production value over feed costs), which significantly increases the possibility of obtaining a positive surplus of production value over the value of the main cost, which is the feed cost. However, with the occurrence of extraordinary situations, such as an ASF outbreak in the herd, rebuilding production in a closed cycle may be much more difficult and expensive than production in an open cycle.
Optimizing Biochar Concentration for Mitigating Nutrient Losses in Runoff: An Investigation into Soil Quality Improvement and Non-Point Source Pollution Reduction
Authors: Xing Liqi , Niu Xiaoyin , Yin Xianwei , Duan Zhenhao , Liu Aiju , Ma Yanfei , Gao Peiling
Published: December 2024

Rainfall runoff can lead to a reduced soil quality and non-point source pollution through the removal of nutrients from the topsoil that are not utilized by plants. The use of biochar is an effective method to solve this problem. The aim of this study was to determine the optimal concentration of added biochar to reduce the soil particle, NH4+ -N (AN), NO3− -N (NN), and total phosphorus (TP) losses. Additionally, the inhibitory mechanisms of biochar that mitigate nutrient loss were revealed using FT-IR (Fourier-transform infrared) spectrometry and SEM (scanning electron microscopy). Compared with the control group, the addition of 2% biochar resulted in decreases in the AN, NN, TP, and soil erosion rates of 57.08%, 4.25%, 30.37%, and 22.78%, respectively; the leaching loss rates of AN and NN were reduced by 6.4% and 9.87%, respectively. However, it should be noted that the use of biochar resulted in an increase in the loss of soil particles smaller than 20 μm, while it resulted in a decrease in the loss of soil particles larger than 20 μm. Adsorption processes on the benzene ring may have caused the absorption peak at approximately 1600 cm−1 to disappear after adsorption. The porous structure of biochar and the presence of hydrophilic groups (such as hydroxyl groups) facilitate adsorption reactions. The optimal concentration of added biochar was 2%.
Why Do Consumers Buy Organic? Exploring Motivations and Socio-Economic Patterns
Authors: Jakubowska Dominika , Grzywińska-Rąpca Małgorzata , Grzybowska-Brzezińska Mariola
Published: December 2024

The study explored the relationship between motivations and attitudes for purchasing certified organic food and the socio-economic characteristics of consumers. The research involved 1020 Polish respondents, using in-depth interviews with a structured electronic questionnaire. Participants were selected through non-random, purposive sampling, selecting only consumers of organic food. The findings revealed that altruistic-egoistic motives are prevalent among consumers with higher food expenditure, particularly on organic products. Three main categories of organic food consumers’ attitudes were identified: pro-environmental, driven by concerns for sustainability and environmental protection; pro-health, centered on personal and family health and food safety; and pro-wellbeing, focusing on personal satisfaction, emotional wellbeing, and ethical considerations. Women were more likely to prioritize pro-health attitudes, reflecting a focus on personal and family health. Additionally, higher household income was linked to stronger pro-environmental and pro-health purchasing attitudes. This study contributes to consumer behavior theory and practical applications by offering a framework that integrates altruistic-egoistic and hedonic motives, as well as pro-environmental, pro-health, and hedonic attitudes, illustrating their roles in shaping organic food purchasing decisions and highlighting the interplay between these motivations and socio-economic characteristics.
Multiple Effects of Agricultural Heritage Identity on Residents’ Value Co-Creation—A Host–Guest Interaction Perspective on Tea Culture Tourism in China
Authors: Chen Youcheng , Lin Yuting , Su Xinwei , Chen Pengwei , Song Huiqi
Published: December 2024

The identity of community residents is the basis for passing on the GIAHS (globally important agricultural heritage system) and promoting the sustainable development of heritage sites. However, there is a lack of discussion in the existent literature on its composition and effective mechanisms. Based on the theory of the ABC (Affect–Behavior–Cognition) model, we collected 482 questionnaires from residents of tea communities of the GIAHS. We construct a model of multiple effects of identity on willingness to co-create values based on the perspective of host–guest interaction. The results show that identity has both social identity and self-identity dimensions. It has a valuable effect on the process of host–guest interaction. It stimulates residents’ willingness to co-create value for the GIAHS through the chain-mediating effect of welcoming nature and emotional closeness. The study results reveal the theoretical mechanisms by which the identity of residents influences the multiple behaviors of guests. It also provides an interactive perspective for the study of the participation of the community in the GIAHS. At the same time, it promotes the local practice of value mining and development research in the GIAHS. In addition, it expands the research framework of value co-creation in heritage tourism.
Selenium’s Role in Plant Secondary Metabolism: Regulation and Mechanistic Insights
Authors: Zhou Yan , Nie Kaiqin , Geng Lulu , Wang Yixin , Li Linling , Cheng Hua
Published: December 2024

Selenium (Se) is an indispensable trace element for humans and other animals. Various studies have demonstrated the beneficial effects of Se on plants, including the promotion of growth, accumulation of secondary metabolites, and enhancement of antioxidant capacity, thereby improving plant stress resistance. Consequently, Se biofortification has emerged as an effective strategy to elevate Se content and nutritional quality in plants, attracting widespread attention. The mechanism of selenium (Se) at the plant secondary metabolic level has not yet been fully elucidated, and it remains an unanswered question as to how selenium affects plant secondary metabolic pathways and how these metabolic pathways respond to selenium biofortification. Although it has been shown that selenium can affect the antioxidant system and defense mechanisms in plants, detailed mechanisms of selenium’s action on plant secondary metabolic pathways, including its effects on specific metabolic enzymes and regulatory genes, still need to be revealed by further in-depth studies. The present study aims to elucidate the mechanisms of Se absorption, transport, and metabolism in plants under Se-rich conditions and to investigate the impact of various Se biofortification methods on the content of plant secondary metabolites. By integrating existing research progress, this paper will delve into the potential molecular regulatory mechanisms of Se on plant secondary metabolism, aiming to unravel the interplay between Se and plant secondary metabolism. This study provides a novel perspective and direction for future research on plant secondary metabolism and the biological utilization of Se.
HAD-YOLO: An Accurate and Effective Weed Detection Model Based on Improved YOLOV5 Network
Authors: Deng Long , Miao Zhonghua , Zhao Xueguan , Yang Shuo , Gao Yuanyuan , Zhai Changyuan , Zhao Chunjiang
Published: December 2024

Weeds significantly impact crop yields and quality, necessitating strict control. Effective weed identification is essential to precision weeding in the field. Existing detection methods struggle with the inconsistent size scales of weed targets and the issue of small targets, making it difficult to achieve efficient detection, and they are unable to satisfy both the speed and accuracy requirements for detection at the same time. Therefore, this study, focusing on three common types of weeds in the field—Amaranthus retroflexus, Eleusine indica, and Chenopodium—proposes the HAD-YOLO model. With the purpose of improving the model’s capacity to extract features and making it more lightweight, this algorithm employs the HGNetV2 as its backbone network. The Scale Sequence Feature Fusion Module (SSFF) and Triple Feature Encoding Module (TFE) from the ASF-YOLO are introduced to improve the model’s capacity to extract features across various scales, and on this basis, to improve the model’s capacity to detect small targets, a P2 feature layer is included. Finally, a target detection head with an attention mechanism, Dynamic head (Dyhead), is utilized to improve the detection head’s capacity for representation. Experimental results show that on the dataset collected in the greenhouse, the mAP for weed detection is 94.2%; using this as the pre-trained weight, on the dataset collected in the field environment, the mAP for weed detection is 96.2%, and the detection FPS is 30.6. Overall, the HAD-YOLO model has effectively addressed the requirements for accurate weed identification, offering both theoretical and technical backing for automatic weed control. Future efforts will involve collecting more weed data from various agricultural field scenarios to validate and enhance the generalization capabilities of the HAD-YOLO model.
Effects of Soil Disinfection Using Chlorine Dioxide on Soil Fungal Communities in Apple Replant Fields and the Growth of Malus hupehensis Rehd. Seedlings
Authors: Wang Gongshuai , Xie Yuxin , Lv Jinhui , Zhang Susu , Yin Chengmiao , Liu Yusong , Mao Zhiquan
Published: December 2024

The effectiveness of chlorine dioxide as a soil disinfectant for the prevention and control of apple replant disease (ARD) remains largely unexplored. This study aimed to determine the optimal concentration of chlorine dioxide for soil disinfection and to evaluate its impact on the prevention and control of ARD. The experimental results indicated that a high concentration of 600 mg·L−1 chlorine dioxide exhibited a potent lethal effect on both the hyphae and spores of pathogenic Fusarium. Results from pot experiments demonstrated that, compared with the replant control, the height, ground diameter, fresh weight, and dry weight of Malus hupehensis Rehd. seedlings treated with 600 mg·L−1 chlorine dioxide increased by 27.20%, 15.95%, 100.70%, and 76.28%, respectively. Additionally, the root length, surface area, volume, and number of root tips of the seedlings increased by 31.74%, 96.54%, 257.29%, and 85.29%, respectively. The activities of root-protective enzymes, including superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD), also increased to varying degrees, whereas the malondialdehyde (MDA) content was significantly reduced compared with the control treatment. Furthermore, the number of soil fungi treated with chlorine dioxide and the concentration of phenolic acid compounds in replant soil were significantly reduced. Treatment with 600 mg·L−1 chlorine dioxide significantly decreased the detected copy number of genes associated with soil-borne pathogenic Fusarium, optimized the soil microbial community structure, and reduced the relative abundance of pathogenic fungi. In summary, disinfection of replant soil using 600 mg·L−1 chlorine dioxide can enhance the growth of M. hupehensis Rehd. seedlings, inhibit the growth and reproduction of pathogenic Fusarium fungi, improve the soil environment, and effectively prevent and control ARD.
Sexual Offspring Production by Acorn Ant Temnothorax crassispinus Colonies Is Associated with the Colony Size but Not with the Volume of the Nest Cavity
Authors: Rolski Mateusz , Gruszka Anna , Marczak Mariia , Mitrus Sławomir
Published: December 2024

Nest sites are important for ants, as the nests provide refuge against enemies and ensure optimal conditions for the brood development. As the construction of a nest is a time and energy consuming activity, many ant species dwell in ready-for-use cavities. For them, choosing a good nest site is important, as the quality of nest site could affect factors such as the energy allocation and production of sexual individuals. Ants of genus Temnothorax inhabit cavities in, e.g., acorns and twigs, and these ant colonies prefer larger cavities as nest sites. It was previously shown that colonies inhabiting more durable nest sites invested more resources in sexual production. The aim of this study was to find out whether the volume of a nest cavity influences the sexual offspring production of acorn ant Temnothorax crassispinus colonies. For this purpose, a three-month long laboratory experiment was performed. Colonies of the Temnothorax ant were kept in artificial nest cavities differing in volume. It was found that there was no correlation between the productivity per capita and the colony size. More populous colonies produced more sexual individuals; however, we found no statistical difference in the production of sexual individuals between the colonies that inhabited cavities of different volumes.
Modeling Mastitis Risk Management Effects on Dairy Milk Yield and Global Warming Potential
Authors: Ferronato Giulia , Simonetto Anna , Gilioli Gianni , Zecconi Alfonso
Published: December 2024

Mastitis represents a significant challenge for dairy farming, resulting in economic losses and environmental impacts. This study assesses a model for the evaluation of the impact of mastitis on dairy productivity and Global Warming Potential (GWP) under diverse management scenarios. The model considers a range of factors, including bedding materials, milking systems, health surveillance, and overcrowding. The results of the simulation demonstrate that effective management, encompassing the utilization of sand bedding, and the presence of an annual herd health monitoring plan have the potential to reduce the prevalence of mastitis and enhance milk yield by up to 10% in milking parlors and 7% in automatic milking systems. At the herd level, the GWP ranged from 1.37 to 1.78 kg CO2eq/kg Fat- and Protein-Corrected Milk (FPCM), with the use of sand bedding resulting in a 14% reduction in GWP, while the utilization of non-composted manure-based materials led to an increase of 12%. The occurrence of overcrowding and a lack of adequate cleanliness in resting areas were found to have a markedly detrimental impact on both productivity and the environmental performance of cows. These findings illustrate the dual benefits of enhanced mastitis management, namely improved milk production and reduced environmental impact. They offer valuable insights for farmers and policymakers alike.
Molecular Evolution of the Ovgp1 Gene in the Subfamily Murinae
Authors: Balastegui-Alarcón Miriam , Moros-Nicolás Carla , Ballesta José , Izquierdo-Rico Mª José , Chevret Pascale , Avilés Manuel
Published: December 2024

OGP, encoded by the Ovgp1 gene, is the major non-serum oviductal protein in most mammals. In the genome of Rattus norvegicus, Ovgp1 has been identified as a pseudogene. However, Mus musculus presents a functional gene. As the rat and the mouse belong to the subfamily Murinae, Ovgp1 has probably been lost after their divergence. This study aims to determine when the pseudogenization event occurred and which proteins could replace its function. To attain that, the potential expression of members belonging to the GH18 family is investigated in the rat oviduct by means of molecular and proteomic analyses. Specific Ovgp1 regions are sequenced in different murine rodent species. The analysis reveals the presence of stop codons only in some species of the Rattini tribe, suggesting that the majority of the murine species present a functional gene. Thus, the pseudogenization of Ovgp1 could be dated back to around 10 Mya, after the divergence of the Rattini tribe. The expression of several genes and proteins of the GH18 family, such as Chia, Chit1, Chi3l1, and Chid1, are detected in the rat oviduct. This study opens the door for further research on GH18 family proteins that mimic the OGP functions in species where Ovgp1 is pseudogenized.
MOMAST® Downregulates AQP3 Expression and Function in Human Colon Cells
Authors: Angelini Ines , Centrone Mariangela , Caponio Giusy Rita , Di Mise Annarita , Gerbino Andrea , Ranieri Marianna , Valenti Giovanna , Tamma Grazia
Published: December 2024

The water channel AQP3 is an aquaglyceroporin expressed in villus epithelial cells, and it plays a role in water transport across human colonic surface cells. Beyond water, AQP3 can mediate glycerol and H2O2 transport. Abnormal expression and function of AQP3 have been found in various diseases often characterized by altered cell growth and proliferation. Here, the beneficial effects of MOMAST® have been evaluated. MOMAST® is an antioxidant-patented natural phenolic complex obtained from olive wastewater (OWW) of the Coratina cultivar. Treatment of human colon HCT8 cells with MOMAST® reduced cell viability. Confocal studies and Western Blotting analysis demonstrated that treatment with MOMAST® significantly decreased the staining and the expression of AQP3. Importantly, functional studies revealed that the reduction of AQP3 abundance correlates with a significant decrease in glycerol and H2O2 uptake. Indeed, the H2O2 transport was partially but significantly reduced in the presence of MOMAST® or DFP00173, a selective inhibitor of AQP3. In addition, the MOMAST®-induced AQP3 decrease was associated with reduced epithelial-mesenchymal transition (EMT)-related proteins such as vimentin and β-catenin. Together, these findings propose MOMAST® as a potential adjuvant in colon diseases associated with abnormal cell growth by targeting AQP3.
Recent Trends in the Development and Application of Nano-Antioxidants for Skin-Related Disease
Authors: Xue Yi , Wang Tao , Liu Ji-Peng , Chen Qi , Dai Xiao-Long , Su Min , Cheng Yu-Hang , Chu Cheng-Chao , Ren Yun-Qing
Published: December 2024

Skin is a vital barrier for the human body, protecting against external environmental influences and maintaining internal homeostasis. In addition, an imbalance of oxidative stress and antioxidant mechanisms can lead to skin-related diseases. Thus, for treating skin-related diseases, antioxidant therapy may be an important strategy to alleviate these symptoms. However, traditional drug therapies have limitations in treating these conditions, such as lack of lasting effect and insufficient skin permeability. Recently, nano-antioxidants, with their good permeability, sustained-release ability, multifunctionality, and other beneficial characteristics, have showed their advances in the exploration of skin-related diseases from research on safe therapies to clinical practice. Hereby, we review the latest research and advancements in nano-antioxidants for skin-related diseases. We categorize skin-related diseases into four main groups: skin inflammatory diseases, skin damage caused by ultraviolet rays, skin wound healing, and other skin-related conditions. Additionally, we summarize the prospects and potential future directions for nano-antioxidant drugs in treating skin-related diseases.
Dietary Capsaicin Supplementation Mitigates Calving-Induced Stress and Enhances Antioxidant Capacity, Immune Function, and Gut Microbiota in Periparturient Dairy Cows
Authors: Li Hangfan , Wu Zibin , Yu Baisheng , Chen Jinyuan , Yang Chuang , Guo Yongqing , Sun Baoli
Published: December 2024

This study investigated the effects of dietary capsaicin supplementation on antioxidant capacity, immune function, and gut microbiota in periparturient dairy cows. Twenty Holstein cows with an average parity of 2.5 ± 0.76, milk production of 31.30 ± 2.39 kg, and 36.10 ± 2.38 days to calving were randomly assigned to either a control group fed a basal diet or a treatment group supplemented with 1.2 g/head/day of capsaicin. The supplementation was administered during an evaluation period spanning from 28 days before delivery to 21 days after delivery using a randomized block experimental design. Results showed that capsaicin significantly reduced milk somatic cell count and pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) while enhancing serum antioxidant enzymes (SOD, GSH-Px, and CAT) and immunoglobulin levels (IgG, IgA, and IgM). Moreover, capsaicin altered gut microbiota composition, increasing the relative abundance of beneficial genera. These findings suggest that dietary capsaicin supplementation during the transition period improves lactation performance and supports immune function, as well as alleviates oxidative stress. This study highlights the potential of capsaicin as a practical dietary strategy for enhancing productivity in dairy farming.
Analysis of Wind Speed Characteristics Along a High-Speed Railway
Authors: Chen Xin , Ye Xiaoling , Zhang Yingchao , Xiong Xiong
Published: December 2024

The safe operation of high-speed railways (HSRs) is significantly challenged by strong winds. Accurate wind speed prediction along HSRs is crucial for ensuring the safety of train operations. However, existing research primarily focuses on designing and improving data-driven models, with limited attention given to the characteristics of wind speed specific to HSR environments. To address this gap, this study analyzes the wind speed characteristics of weather stations (WSs) and railway stations (RSs) along an HSR. These characteristics are explored from multiple perspectives, including wind speed variability, amplitude, correlation, wind speed distribution, and turbulence across different time scales. Additionally, the normalized cumulative periodogram (NCP) and Bartlett’s test are employed to quantify wind speed predictability. A wind speed prediction model is then developed based on predictability analysis. The findings reveal that RS wind speeds differ significantly from WS wind speeds, exhibiting higher volatility. The predictability of wind speed is influenced by the sampling interval: as the sampling time increases, the predictability and length of the predictable historical wind speed period decrease. By establishing a prediction model grounded in wind speed predictability analysis, irrelevant historical wind speed data can be excluded, improving the model’s prediction accuracy. Predictability analysis thus provides a robust foundation for forecasting strong winds along HSRs, ultimately enhancing train operation safety.
Imputing Covariance for Meta-Analysis in the Presence of Interaction
Authors: Liu Enwu , Liu Ryan Yan
Published: December 2024

Detecting interactions is a critical aspect of medical research. When interactions are present, it is essential to calculate confidence intervals for both the main effect and the interaction effect. This requires determining the covariance between the two effects. In a two-stage individual patient data (IPD) meta-analysis, the coefficients, as well as their variances and covariances, can be calculated for each study. These coefficients can then be combined into an overall estimate using either a fixed-effect or random-effects meta-analysis model. The overall variance of the combined coefficient is typically derived using the inverse-variance method. The most commonly used method for calculating the overall covariance between the main effect and the interaction effect in meta-analysis is multivariate meta-analysis. In this paper, we propose an alternative, straightforward, and transparent method for calculating this covariance when interactions are considered in a meta-analysis. To facilitate implementation, we have developed an R package, ‘covmeta’.
EduDCM: A Novel Framework for Automatic Educational Dialogue Classification Dataset Construction via Distant Supervision and Large Language Models
Authors: Qi Changyong , Zheng Longwei , Wei Yuang , Xu Haoxin , Chen Peiji , Gu Xiaoqing
Published: December 2024

Educational dialogue classification is a critical task for analyzing classroom interactions and fostering effective teaching strategies. However, the scarcity of annotated data and the high cost of manual labeling pose significant challenges, especially in low-resource educational contexts. This article presents the EduDCM framework for the first time, offering an original approach to addressing these challenges. EduDCM innovatively integrates distant supervision with the capabilities of Large Language Models (LLMs) to automate the construction of high-quality educational dialogue classification datasets. EduDCM reduces the noise typically associated with distant supervision by leveraging LLMs for context-aware label generation and incorporating heuristic alignment techniques. To validate the framework, we constructed the EduTalk dataset, encompassing diverse classroom dialogues labeled with pedagogical categories. Extensive experiments on EduTalk and publicly available datasets, combined with expert evaluations, confirm the superior quality of EduDCM-generated datasets. Models trained on EduDCM data achieved a performance comparable to that of manually annotated datasets. Expert evaluations using a 5-point Likert scale show that EduDCM outperforms Template-Based Generation and Few-Shot GPT in terms of annotation accuracy, category coverage, and consistency. These findings emphasize EduDCM’s novelty and its effectiveness in generating high-quality, scalable datasets for low-resource educational NLP tasks, thus reducing manual annotation efforts.
Comparative Analysis of Phase-Field and Intrinsic Cohesive Zone Models for Fracture Simulations in Multiphase Materials with Interfaces: Investigation of the Influence of the Microstructure on the Fracture Properties
Authors: Najafi Koopas Rasoul , Rezaei Shahed , Rauter Natalie , Ostwald Richard , Lammering Rolf
Published: December 2024

This study evaluates four widely used fracture simulation methods, comparing their computational expenses and implementation complexities within the finite element (FE) framework when employed on heterogeneous solids. Fracture methods considered encompass the intrinsic cohesive zone model (CZM) using zero-thickness cohesive interface elements (CIEs), the standard phase-field fracture (SPFM) approach, the cohesive phase-field fracture (CPFM) approach, and an innovative hybrid model. The hybrid approach combines the CPFM fracture method with the CZM, specifically applying the CZM within the interface zone. The finite element model studied is characterized by three specific phases: inclusions, matrix, and the interface zone. This case study serves as a potential template for meso- or micro-level simulations involving a variety of composite materials. The thorough assessment of these modeling techniques indicates that the CPFM approach stands out as the most effective computational model, provided that the thickness of the interface zone is not significantly smaller than that of the other phases. In materials like concrete, which contain interfaces within their microstructure, the interface thickness is notably small when compared to other phases. This leads to the hybrid model standing as the most authentic finite element model, utilizing CIEs within the interface to simulate interface debonding. A significant finding from this investigation is that within the CPFM method, for a specific interface thickness, convergence with the hybrid model can be observed. This suggests that the CPFM fracture method could serve as a unified fracture approach for multiphase materials when a specific interfacial thickness is used. In addition, this research provides valuable insights that can advance efforts to fine-tune material microstructures. An investigation of the influence of interfacial material properties, voids, and the spatial arrangement of inclusions shows a pronounced effect of these parameters on the fracture toughness of the material.
Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman Filter
Authors: Lu Sihang , Wang Baolin , Dong Zaopeng , Hu Zhihao , Ding Yilun , Liu Wangsheng
Published: December 2024

The accurate nonlinear modeling of an unmanned surface vessel (USV) is essential for advanced control and operational performance. This paper combines the locally weighted regression (LWR) algorithm and the extended Kalman filter (EKF) for parameter identification using state data from full-scale vessel experiments. To mitigate the effects of disturbances and abrupt changes in the full-scale vessel data, LWR filtering is applied for data smoothing before parameter identification. The EKF is then used to estimate the unknown parameters in the second-order nonlinear Nomoto model of the USV. These parameters are incorporated into the Nomoto model, and simulations are conducted by inputting the same rudder inputs as in the experimental data. The predicted heading angle and yaw rate are compared with experimental results, showing that the mean absolute error (MAE) for the heading angle is within 10° and the MAE for the yaw rate is within 1.5°/s. Additionally, the coefficient of determination (R2) values for both predictions are above 0.93. The simulation results demonstrate that the combination of LWR filtering and EKF effectively identifies parameters and models the nonlinear response of the USV, achieving high accuracy in the established second-order model.
Effects of Repolishing Systems on Surface Characteristics of a 3D-Printed Permanent Material
Authors: Vichi Alessandro , Mura Giovanni , Vannini Federico , Baldani Sofia , Paolone Gaetano , Goracci Cecilia
Published: December 2024

Background: 3D-printed (3DP) resins for permanent restorations are increasing in availability and gaining popularity. Aesthetic stability is an issue of dental resins that may compromise the long-term success of restorations. A staining test has been performed to evaluate gloss, roughness, and color staining of a permanent 3DP resin, and the effects of repolishing. Methods: Squared specimens of one 3DP permanent resin (Crown permanent, Formlabs) have been CAD-designed and 3D-printed. After the roughness, gloss, and color measurements, they were immersed in a coffee staining bath at 44 °C for 24 h and then measured again. Subsequently, they were repolished with three different systems: prophy cup and Nupro prophy paste, HiLuster Plus 2-step, and Opti1Step Polisher 1-step polishing systems. Results: Gloss and Roughness were not significantly affected by the staining procedure. No significant changes were observed for gloss after repolishing, while one of the tested systems (prophy cup and paste) produced a worsening effect on roughness. Color was affected by the staining procedure. After repolishing, b* changes were almost completely recovered, while L* changes were recovered only partially. Conclusions: Color stability still represents a challenge, and 3DP resins for permanent use are affected. Repolishing systems should be further developed and tested.
Calibration and Validation of MODIS-Derived Ground-Level Air Temperature Models by Means of Ground Measurements
Authors: Rocca Marica Teresa , Franzini Marica , Casella Vittorio Marco
Published: December 2024

The research initiatives envisaged by the PNRR (Italian National Recovery and Resilience Plan) include the creation of innovation ecosystems to promote collaboration between universities, research centers, and local institutions with a focus on territorial integration and sustainability. The NODES Project (Nord-Ovest Digitale E Sostenibile) is part of this research. In this context, the Laboratory of Geomatics of the University of Pavia, in collaboration with other partners, deals with the study of the suitability maps for the renowned Pinot Noir wine. To achieve this, we considered different thematic input layers: elevation, slope, aspect, soil depth and type, Land Use Land Cover maps, NDVI, and current and forecast climatic aspects. An important thematic layer is concerned with the air temperature, which requires high spatial and temporal resolution. In the selected study area, the Lombardy Region has some accurate and reliable weather stations with high temporal resolution but low spatial resolution (7 stations in 648.5 square kilometers, i.e., one every 92 square kilometers). In addition, we considered Land Surface Temperature (LST) MODIS maps: these maps have good spatial resolution but present some voids and low temporal resolution. From the first evaluations made, the temperatures reported by MODIS are not always in excellent agreement with the ones from monitoring stations. To evaluate MODIS as a data source, we decided to use Kriging spatio-temporal interpolation. Starting from multitemporal MODIS data matrices, we interpolate them to estimate the temperature of the weather stations, in order to compare the estimation with the real weather station data, thus allowing the validation of MODIS data.
Improvements for the Planning Process in the Scrum Method
Authors: Žáček Miroslav , Hamplová Adéla , Tyrychtr Jan , Vrana Ivan
Published: December 2024

In today’s dynamic development environments, agile methodologies like Scrum are essential for effective software project management. Despite its popularity, the Scrum framework’s reliance on subjective intuition during the sprint planning process can lead to inconsistencies and project delays. This study aims to enhance the sprint planning phase by integrating the BeCoMe method, which is a mathematical approach designed to optimize task selection through structured compromise solutions. Utilizing a soft systems methodology, this research identifies and analyzes the existing inefficiencies in Scrum’s planning process. The implementation of the BeCoMe method in a real-world case study demonstrated significant improvements in task completion rates and overall project efficiency. The method’s structured process reduces biases, fosters team consensus, and enhances decision-making accuracy. The findings suggest that incorporating the BeCoMe method into Scrum can substantially mitigate risks, save time, and improve project outcomes by ensuring a more objective and data-driven approach to sprint planning. These insights are crucial for developers managing modern software projects, offering a robust framework for enhancing planning efficiency and success rates.
Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays
Authors: Arce Armando , Arce Fernando , Stevens-Navarro Enrique , Pineda-Rico Ulises , Cardenas-Juarez Marco , Garcia-Barrientos Abel
Published: December 2024

This work proposes and describes a deep learning-based approach utilizing recurrent neural networks (RNNs) for beam pattern synthesis considering uniform linear arrays. In this particular case, the deep neural network (DNN) learns from previously optimized radiation patterns as inputs and generates complex excitations as output. Beam patterns are optimized using a genetic algorithm during the training phase in order to reduce sidelobes and achieve high directivity. Idealized and test beam patterns are employed as inputs for the DNN, demonstrating their effectiveness in scenarios with high prediction complexity and closely spaced elements. Additionally, a comparative analysis is conducted among the three DNN architectures. Numerical experiments reveal improvements in performance when using the long short-term memory network (LSTM) compared to fully connected and convolutional neural networks.
Data-Driven Modular Vehicle Scheduling in Scenic Areas
Authors: Hong Yilin , Xu Min , Jin Yong , Wang Shuaian
Published: December 2024

As tourism demand continues to grow and fluctuate, the problems of increasing empty capacity and high operating costs for tourist shuttle buses have become more acute. Modular vehicles, an emerging transport technology, offer flexible length adjustments and provide innovative solutions to address these challenges. This paper develops a data-driven method to address the problem of scheduling modular vehicles in scenic areas with dynamic passenger demand. The aim is to minimize operating costs and maximize vehicle utilization by exploiting the adjustable capacity of modular vehicles. This approach is applied to tourist shuttle scenarios, and a sensitivity analysis is conducted by varying parameters such as individual vehicle capacity and waiting penalties. Then, we investigate the optimization performance gap between the proposed model and the theoretical global optimum model. The results show that increasing vehicle capacity and varying penalties improve the performance of the data-driven model, and the optimization rate of this model can reach 70.2% of the theoretical optimum, quantifying the effectiveness of the model. The method proposed in this study can effectively reduce the operating cost of shuttle vehicles for scenic areas and meet the challenge of unpredictable passenger demand, which serves as a good reference for fleet management in scenic areas.
Engineering Tomorrow: Optimised Materials and Surfaces for Additive Manufacturing
Authors: Urbas Raša , Vukoje Marina , Cigula Tomislav
Published: December 2024

Focusing on advancements in additive manufacturing, this Special Issue explores the role of novel materials and surface technologies in improving functionality, performance, and sustainability. The research papers highlight innovative approaches to understanding and overcoming material limitations, particularly in the areas of surface engineering, process optimisation, and multi-material applications. Key topics include the tribological behaviour, wear resistance, and surface quality of coated components, the effects of process parameters on mechanical strength and surface roughness, and advanced methods for surface characterisation and quality control. By addressing challenges, such as material efficiency, friction reduction, and structural integrity, the researchers emphasise the crucial role of novel materials and technologies to push the boundaries of additive manufacturing. Looking to the future, integrated strategies that prioritise sustainability, material innovation, and application-driven optimisation will be crucial. Through collaboration and technological advancement, this Special Issue provides valuable insights that will contribute to a more efficient, sustainable, and versatile future of additive manufacturing.
Efficiency Assessment Method for Evoking Cultural Empathy in Symbolic Cultural and Creative Products Based on Fuzzy-FMEA
Authors: Wang Ning , Wang Weiwei , Yu Suihuai , Chen Jian , Yang Xiaoyan
Published: December 2024

To address the issue of user empathy throughout the emotional experience process, this study presents a method to evaluate the efficacy of cultural empathy evoked based on fuzzy-FMEA. The method focuses on symbolic culture and creative products, constructing an evaluation index system and decision-making framework in terms of cultural empathic evoking. It utilizes thematic analysis to discover and categorize the factors that influence cultural empathy, as well as an evaluation index system to improve the Failure Mode and Effects Analysis framework. It effectively solves the limitations of traditional FMEA, such as single weighting and uncertainty. According to the assessment report, cognitive association failure and scenario restoration failure are significant risk factors for cultural empathy-evoking failure. This study’s findings provide designers with realistic proposals for thematic symbolic imagery and serialized design forms, as well as scientific assessment tools and decision-making resources for cultural industries and policymakers.
Development of Automatic Berthing Support Program for Autonomous Ships
Authors: Kang Byung-Sun , Jung Chang-Hyun , Kim Keewon , Kim Hyunwoo , Kim Jin-Soo , Kim Dae-Hae
Published: December 2024

Research on autonomous ships has primarily focused on developing response technologies for navigation from pilot station to pilot station. This study developed an automatic berthing support program that calculates the necessary thruster output values for the bow and stern to achieve the desired berthing speed under varying external force conditions, requiring only essential ship information as input. The program determines the thruster output by analyzing the forces and moments acting on the hull during the berthing process. An experimental setup equipped with the automatic berthing support program was installed on a ship. The outputs of the bow thruster (Thruster(F)) and stern tug (Tug(A)) were 300–544 hp on average, whereas the values calculated by the automatic berthing program (Program(F), Program(A)) were 105–131 hp. The calculation results of the automatic berthing support program of the ship were approximately 3–5 times greater than the horsepower values of the thruster and tug used during actual berthing, probably because the actual berthing speed was 0.25–1.13 m/s, which is more than five times higher than the set speed of 0.05–0.15 m/s. The results indicate that the automatic berthing support program is promising for future applications in automatic berthing systems for autonomous ships.