bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on Flow Characteristics of Venturi Accelerated Vortex Drainage Tool in Horizontal Gas Well
Authors: Zhang Hongtao , Xu Yan , Cai Meng , Li Junliang , Feng Mingxi , Zhang Xiaochuan
Published: March 2024

Vortex drainage gas recovery has been used to carry liquid from gas wells. However, the traditional vortex tools in gas wells cannot produce long effective distance spiral flow at a low gas flow rate, and their operating mechanism has not been thoroughly analyzed. In this paper, the venturi acceleration vortex tool for a horizontal gas well is designed to improve drainage performance. The tube drainage, the vortex tool, and the venturi accelerated vortex tool were applied in a horizontal tube to investigate their drainage capacities by a horizontal well multiphase flow experimental device. The influence of different gas flow rates and liquid flow rates on the length of the spiral flow and pressure drop produced by the three tools was analyzed. The results show that the vortex tool can convert the gas–liquid mixing flow into the gas–liquid separation flow, that is, the liquid flows spirally along the wall and the gas flows in the center of the horizontal tube. Compared with the vortex tool, the venturi accelerated vortex tool can form a longer and more stable spiral flow. The laminar spiral flow reduces the total pressure drop in the tube. The length of the spiral flow increases with the increase in the gas flow rate. With the increase in the liquid flow rate, the spiral flow is not clear because of the turbulent flow. The length of the spiral flow and the pressure drop for the venturi accelerated vortex tool with different gas and liquid flow rates are analyzed to guide the application of the tool. This study provides a new means for the drainage of a horizontal gas well and further clarifies the working mechanism of the vortex drainage tool.
Pr3+-Doped Lithium Niobate and Sodium Niobate with Persistent Luminescence and Mechano-Luminescence Properties
Authors: Hua Yang , Jing Zhenfeng , Ge Pinghui
Published: March 2024

In this research, a comprehensive series of Pr3+-doped lithium niobate and sodium niobate materials were obtained at different temperatures via solid-state sintering, and their structures and properties were compared. NaNbO3: 0.75% Pr3+ phosphors were synthesized by sintering at 1150 °C for 2 h and emitted red persistent luminescence for more than 1200 s, peaking at 612 nm under UV excitation, which was a typical long persistent luminescence phenomenon. Furthermore, the sample glowed when pressurized, and a red bright luminescence which lasted for several seconds was visible to the naked eye. This was a typical mechanical luminescence phenomenon of samples under mechanical stress, directly converting mechanical energy into light energy. It was determined that NaNbO3:Pr3+ and LiNbO3:Pr3+ both possess multimode luminescence. Owing to their red long persistent luminescence (LPL) and mechano-luminescence (ML) properties, Pr3+ phosphors can be employed in fields, such as display technologies, stress sensing, structural damage detection, and other complex applications.
Optimization of Cabin Virus Transmission Suppression Technology Based on Hanging Curtain Physical Isolation
Authors: Cheng Mengmeng , Kong Benben , Song Caiyue , Li Yu , Shi Hong
Published: March 2024

This study presents an innovative physical isolation measure for commercial scenarios, namely, hanging curtains, to prevent the spread of respiratory infections. Using computational fluid dynamics simulation techniques, the closed spaces within cruise cabins were modeled and numerically analyzed, focusing on the dispersion characteristics of droplets. Additionally, orthogonal methods were employed to investigate various arrangements of hanging curtains and their effects on droplet dispersion based on spatial positioning. The research findings indicated that hanging curtains can effectively alter the airflow within a space, realizing the innovative concept of localized pollutant containment. It was found that the spatial partitioning method based on the location of individuals contributes more to reducing droplet dispersion than other methods. Moreover, the sag height of curtains emerges as the most influential factor on individual infection risk, while the scheme for hanging curtain positions has the least impact. Finally, the optimal configuration recommendation is provided: a curtain bottom coordinate of Z = 2.3 m and a top coordinate of Z = 2.8 m when the infection source was positioned at the center of the space. This configuration has also been validated by varying the location of the infection source. The research findings provide valuable insights for formulating preventive measures for passengers on cruise ships and for pandemic control in similar scenarios.
Material Properties and Structure of Al-Mg-Si Alloy Thin-Walled Profiles with Different Alloy Compositions and Aging Processes
Authors: Guo Hui , Li Zhen , Tan Jianping
Published: March 2024

Thin-walled Al-Mg-Si alloy profiles with different compositions and aging states were prepared using the heating and extrusion process. The properties and structure of the profiles were then investigated using a metallographic microscope, scanning electron microscope, projection electron microscope, and universal testing machine. The results show that the yield strength and tensile strength of the profile increases with the increase in total Mg + Si content, and ductility is reduced. If the total Mg + Si content is too high or too low, the crush performance of the material would decrease. Compared with the under-aged and near-peak-aged states, the three types of AI-Mg-Si alloy thin-walled profiles at the over-aged state have better effective energy absorption during crushing and higher bending angle; however, the tensile strength of the profile is optimal at the near-peak-aged state. The effects of alloy composition and aging process on material strength and crushing energy absorption are mainly attributed to the grain structure and differences in precipitation. For coarse grain structures, the grain boundary precipitate free zones are wider, which decreases the profile ductility. Simultaneously, an increase in primary strengthening phases in the grains would increase the profile strength.
Improving Weather Forecasts for Sailing Events Using a Combination of a Numerical Forecast Model and Machine Learning Postprocessing
Authors: Beimel Stav , Suari Yair , Gabbay Freddy
Published: March 2024

Accurate predictions of wind and other weather phenomena are essential for making informed strategic and tactical decisions in sailing. Sailors worldwide utilize current state-of-the-art forecasts, yet such forecasts are often insufficient because they do not offer the high temporal and geographic resolution required by sailors. This paper examines wind forecasting in competitive sailing and demonstrates that traditional wind forecasts can be improved for sailing events by using an integration of traditional numerical modeling and machine learning (ML) methods. Our primary objective is to provide practical and more precise wind forecasts that will give sailors a competitive edge. As a case study, we demonstrate the capabilities of our proposed methods to improve wind forecasting at Lake Kinneret, a popular sailing site. The lake wind pattern is highly influenced by the area’s topographic features and is characterized by unique local and mesoscale phenomena at different times of the day. In this research, we simulate the Kinneret wind during the summers of 2015–2021 in up to one-kilometer resolution using the Weather Research and Forecasting (WRF) atmospheric model. The results are used as input for convolutional neural network (CNN) and multilayer perceptron (MLP) ML models to postprocess and improve the WRF model accuracy. These advanced ML models are trained using training datasets based on the WRF data as well as real data measured by the meteorological service, and subsequently, a validation process of the trained ML model is performed on unseen datasets against site-specific meteorological service observations. Through our experimental analysis, we demonstrate the limitations of the WRF model. It uncovers notable biases in wind direction and velocity, particularly a persistent northern bias in direction and an overestimation of wind strength. Despite its inherent limitations, this study demonstrates that the integration of ML models can potentially improve wind forecasting due to the remarkable prediction accuracy rate achieved by the CNN model, surpassing 95%, while achieving partial success for the MLP model. Furthermore, a successful CNN-based preliminary forecast was effectively generated, suggesting its potential contribution to the future development of a user-friendly tool for sailors.
Preliminary Safety Analysis of Megaconstellations in Low Earth Orbit: Assessing Short-Term and Long-Term Collision Risks
Authors: Ruan Yongjing , Hu Min , Yun Chaoming , Xue Wen
Published: March 2024

The deployment of megaconstellations in low Earth orbit (LEO) poses significant collision risks with space debris. This paper focuses on analyzing the short-term and long-term collision probabilities of megaconstellations to assess their collision risk. Firstly, a short-term collision risk evolution model is developed to accurately address rendezvous collisions. Secondly, a long-term collision risk evolution model is established by considering space object density, space debris attenuation, space target disintegration, and the distribution of disintegration targets. Through simulations conducted on the Starlink Phase I constellation, the results demonstrate a 30–40% increase in short-term collision probability within the constellation shell, a 70.2% probability of at least one collision during the constellation’s lifetime, and a 25.3% increase in secondary collisions following a collision event. This study provides a reference and application for analyzing the orbital safety of LEO megaconstellations and for promoting the sustainable development and utilization of space resources.
Investigating the Effects of Environmentally Friendly Additives on the Exhaust Gas Composition and Fuel Consumption of an Internal Combustion Engine
Authors: Krakowski Rafał , Witkowski Kazimierz
Published: March 2024

This article shows the effect of the addition of effective microorganisms and silver on the exhaust gas composition and fuel consumption. Exhaust emission standards are becoming increasingly stringent, which makes it difficult for engine manufacturers to meet them. For this reason, intensive work is underway to use alternative propulsion systems on ships, and for diesel engines, alternative fuels. Among other things, this applies to mixtures of petroleum-based fuels with vegetable oils and their esters. Unfortunately, their use, due to their physicochemical properties, can negatively affect the performance of the engine and the wear of its components. Therefore, the aim of this study was to see how additives of effective microorganisms in the form of ceramic liquid and tubes, and a silver solution and colloidal silver would affect some engine parameters, including the exhaust gas composition and fuel consumption. The authors are not aware of the results of previous research on this issue. The tests were carried out on a diesel engine for four types of green additives at concentrations of 2% and 5%, at different ranges of its load. The additives added to the diesel fuel were characterised, and the test stand was presented, along with the parameters of the tested fuel. The effect of additives on selected engine parameters, including fuel consumption, was presented. The characteristics of hourly fuel consumption and selected components of the exhaust gas, including nitrogen oxides, carbon monoxide and carbon dioxide as a function of the concentration of ecological additives are shown and analysed. It was found that the most beneficial additive that had a positive effect on the exhaust gas composition and fuel consumption was a silver solution in a 2% concentration. There was a decrease of up to 4% in the NOx content of the exhaust gas, a decrease in carbon monoxide of more than 28%, a decrease in carbon dioxide of 4.6% and a decrease in fuel consumption of around 3% was achieved under the tested conditions. The use of these additives is an innovative solution that has a positive impact on reducing the emissions of harmful compounds into the atmosphere. In further research, it will be necessary to study the effect of this additive on the combustion process in the engine and the wear of its components, as well as to confirm the results obtained in real operating conditions.
Experimental Study of Vegetative Properties in Zeolite–Biochar-Improved Ecological Revetment Substrates
Authors: Shi Yunfeng , Zhou Xinlong , Xiao Henglin , Gui Lin , Hu Kaimeng , Liu Zebang
Published: March 2024

The vegetation of the ecological substrate plays a crucial role in restoring shoreline ecology. This study focused on using zeolite and biochar as substrate modifiers, specifically utilizing the Cynodon dactylon from Central China for vegetation. A pot vegetation experiment was carried out to compare the effects of different ratios of zeolite and biochar. The vegetation indices, including germination index, plant height, and coverage rate, were analyzed and discussed. The results revealed that zeolite primarily influenced the germination index of Cynodon dactylon, while biochar had a more significant impact on germination percentage, germination energy, plant height, and coverage rate. This study discovered that the seed germination effect of the improved substrate initially increased with zeolite content and then decreased. The average germination percentage was 63.96%. Conversely, it decreased with an increase in biochar content, resulting in an average germination percentage of 55.45%. Zeolite and biochar caused a decrease and increase in substrate pH by −0.11 and 0.4 on average, respectively. The germination of each substrate showed a negative correlation with pH. Additionally, the average coverage and plant height decreased with an increase in biochar content. However, the inclusion of 6% zeolite led to an increase in coverage and plant height. Specifically, the average plant height increased by 3.92 cm and the coverage by 7.48%. Our research identified the optimal ratio of zeolite and biochar as 6% zeolite and 0% biochar, showcasing good overall vegetative properties. These findings offer insights for further understanding the vegetative effects of zeolite–biochar-modified substrates and optimizing substrate schemes for ecological vegetation projects.
MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection
Authors: Guo Chang , Zhu Jianfeng , Wang Xiaoming
Published: March 2024

In recent years, the rapid growth of vehicles has imposed a significant burden on urban road resources. To alleviate urban traffic congestion in intelligent transportation systems (ITS), real-time and accurate traffic flow prediction has emerged as an effective approach. However, selecting relevant parameters from traffic flow information and adjusting hyperparameters in intelligent algorithms to achieve high prediction accuracy is a time-consuming process, posing practical challenges in dynamically changing traffic conditions. To address these challenges, this paper introduces a novel prediction architecture called Multiple Variables Heuristic Selection Long Short-Term Memory (MVHS-LSTM). The key innovation lies in its ability to select informative parameters, eliminating unnecessary factors to reduce computational costs while achieving a balance between prediction performance and computing efficiency. The MVHS-LSTM model employs the Ordinary Least Squares (OLS) method to intelligently reduce factors and optimize cost efficiency. Additionally, it dynamically selects hyperparameters through a heuristic iteration process involving epoch, learning rate, and window length, ensuring adaptability and improved accuracy. Extensive simulations were conducted using real traffic flow data from Shanghai to evaluate the enhanced performance of MVHS-LSTM. The prediction results were compared with those of the ARIMA, SVM, and PSO-LSTM models, demonstrating the innovative capabilities and advantages of the proposed model.
Prediction of Grain Porosity Based on WOA–BPNN and Grain Compression Experiment
Authors: Chen Jiahao , Li Jiaxin , Zheng Deqian , Zheng Qianru , Zhang Jiayi , Wu Meimei , Liu Chaosai
Published: March 2024

The multi-field coupling of grain piles in grain silos is a focal point of research in the field of grain storage. The porosity of grain piles is a critical parameter that affects the heat and moisture transfer in grain piles. To investigate the distribution law of the bulk grain pile porosity in grain silos, machine learning algorithms were incorporated into the prediction model for grain porosity. Firstly, this study acquired the database by conducting compression experiments on grain specimens and collecting data from the literature. The back propagation neural network (BPNN) algorithm was optimized using three metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA)). Five machine learning models (GA–BPNN, PSO–BPNN, WOA–BPNN, BPNN, and random forest (RF)) were developed to predict the grain porosity using three input parameters (vertical pressure, grain type, and moisture content). The five models were assessed using four evaluation metrics: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), to determine the best porosity prediction model. Finally, the generalization ability of the best prediction model was verified using the results of the grain cell box experiment on wheat piles. The results indicated that the WOA–BPNN model was the best prediction model with an R2 value of 0.9542, an RMSE value of 0.0079, an MAE value of 0.0044, and an MAPE value of 1.1467%. The WOA–BPNN model demonstrated strong generalization ability, confirming the feasibility of using this model to predict grain porosity. It also established an expression for the relationship between wheat porosity and the vertical pressure of the grain pile. This study presents a machine learning prediction method for determining the porosity of grain piles. The obtained porosity distribution law serves as a crucial basis for conducting comprehensive multi-field coupling analysis of grain piles and offers theoretical support for safe grain storage.
Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum
Authors: Ma Xueting , Wang Congying , Luo Huaping , Guo Ganggang
Published: March 2024

To enhance the accuracy of multispectral detection using unmanned aerial vehicles (UAVs), multispectral data of jujube fruit with different soluble solids content (SSC) and moisture content (MC) were obtained under different relative azimuth angles. Prediction models for SSC and MC of jujube fruit were established using partial least squares regression (PLSR) and support vector machines (SVM), respectively. The findings revealed that the MC of jujube fruit had the best prediction effect when the relative azimuth angle was 90°, while the SSC of the jujube fruit had the best prediction effect at an azimuth angle of 180°. Then, the spectral reflectance data corresponding to the eight relative azimuth angles were used as input variables to establish a jujube fruit quality detection model. The results showed that the prediction model for MC and SSC, established using the angle fusion method, had higher detection accuracy compared to the prediction model established at a single angle. This research provides a technical reference for improving the accuracy of outdoor jujube fruit quality detection using spectral technology.
A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
Authors: Wang Xin , Liu Deyou , Zhou Ling , Li Chao
Published: March 2024

The performance of wind turbines directly determines the profitability of wind farms. However, the complex environmental conditions and influences of various uncertain factors make it difficult to accurately assess and monitor the actual power generation performance of wind turbines. A data-driven approach is proposed to intelligently monitor the power generation performance evolution of wind turbines based on operational data. Considering the inherent nonlinearity and structural complexity of wind turbine systems, a data-derived characteristic construction and dimensionality reduction method based on KPCA is adopted as a prerequisite. Additionally, an AdaBoost-enhanced regressor is applied to wind power prediction with adequate inputs, and day-oriented deviation indicators are further constructed for quantifying performance fluctuations. The final validation phase includes two application cases: In the first case, the results show that the proposed method is sensitive enough to capture the early characteristics of blade damage faults. In the second case, an uncertainty error within ±0.5% demonstrates that the proposed method has high-level accuracy in the quantitative assessment of the power performance and good practical effectiveness in real engineering applications.
Influence of Printing Angulation on the Accuracy (Trueness and Precision) of the Position of Implant Analogs in 3D Models: An In Vitro Pilot Study
Authors: García Noelia , Gómez-Polo Miguel , Fernández Miriam , Antonaya-Martín José Luis , Ortega Rocío , Gómez-Polo Cristina , Revilla-León Marta , Cascos Rocío
Published: March 2024

Background: With CAD-CAM, dental models are often fabricated by additive manufacturing (AM) methods. Dental models for implant prostheses involve the manual placement of analogs, which could affect their final fit. Therefore, in this experimental in vitro study, the accuracy of the analogs’ position in models printed using AM methods was examined by comparing three different printing orientations. Methods: An STL file was obtained by scanning a master model with an implant placed in the maxillary left central incisor position. Three study groups (n = 10) with varying printing orientation parameters were obtained (0, 45, and 90 degrees). They were digitalized with a laboratory scanner and evaluated with an analysis metrology program. Differences between 3D datasets were measured using the RMS for trueness and SD RMS for precision. The data were statistically analyzed using the ANOVA test at a significance level of p < 0.05, followed by the Bonferroni post hoc test. Results: The 45-degrees group showed the best results. Regarding trueness, statistically significant differences were found between the 45- and 90-degrees groups (p < 0.005). In terms of precision, statistically significant differences appeared between the 45- and 0-degrees groups (p < 0.011) and between the 45- and 90-degrees groups (0.003). Conclusions: The printing angulation parameter affects the accuracy of 3D-printed models. Implant models manufactured at 45 degrees of printing angulation are more accurate than those printed at 0 or 90 degrees.
Clutter Map Constant False Alarm Rate Mixed with the Gabor Transform for Target Detection via Monte Carlo Simulation
Authors: Mbouombouo Mboungam Abdel Hamid , Zhi Yongfeng , Fonzeu Monguen Cedric Karel
Published: March 2024

Radar detection is a technology frequently used to detect objects and measure the range, angle, or velocity of those objects. Several studies have been performed to improve the accuracy and performance of detection methods, but they encountered a strong challenge, which was the minimization of false alarms and the distinguishing of real targets from false alarms, especially in nonhomogeneous environments. We propose a new detection method that uses time-frequency analysis tools to improve detection performance and maintain a low constant false alarm rate. Different from existing works, this paper combines the clutter map constant false alarm rate technique with the Gabor transform for accurate target detection in cluttered environments. We suggest the combination of a CFAR detector with a time-frequency method that enables us to tackle challenging scenarios involving near targets. The proposed method allows for locating the exact position of the target by reducing the impact of clutter and maintaining a low rate of false alarms, while the Gabor transform facilitates the extraction of pertinent target characteristics and improves differentiation from clutter. Through experiments and simulations in different scenarios and clutter models, we demonstrate that the method is efficient in measurements and performs well in cluttered environments. This research has a major impact on signal processing and significantly improves target detection in cluttered environments, allowing this method to be deeply developed and implemented.
Research on Product Conceptual Design Scheme Configurations from a Designer–User Conflict Perspective
Authors: Shao Hongyu , Pan Sizhe , Song Yufei , Li Quanfu
Published: March 2024

In the context of rapid product iteration, design conflicts arise from discrepancies in designers’ understanding of user needs, influenced by subjective preferences, behavioural stances, and other factors. This paper proposes a product conceptual design approach based on the design conflict perspective. First, user comments and design documents are collected. Natural language processing (NLP) methods, including cleaning, filtering, lexical segmentation, feature clustering, and sentiment analysis, are employed to identify design themes. The intuitionistic fuzzy sets (IFSs) and term frequency–inverse document frequency (TF-IDF) algorithms are then utilized to obtain evaluation matrices for the products from both users and designers. Subsequently, design conflicts between users and designers are calculated, and an optimal configuration for product conceptual design is determined through regression analysis and planning methods. Finally, the proposed method is validated using a mobile phone as a product example, and suggestions for product improvement are presented. The results indicate that considering design conflicts as a factor in product design and synthesizing designer and user product concepts enhance the accuracy and reliability of product conceptual design generation. The findings of this study offer new insights into the conceptual design configuration for product iteration.
Introducing an Artificial Neural Network for Virtually Increasing the Sample Size of Bioequivalence Studies
Authors: Papadopoulos Dimitris , Karalis Vangelis D.
Published: March 2024

Sample size is a key factor in bioequivalence and clinical trials. An appropriately large sample is necessary to gain valuable insights into a designated population. However, large sample sizes lead to increased human exposure, costs, and a longer time for completion. In a previous study, we introduced the idea of using variational autoencoders (VAEs), a type of artificial neural network, to synthetically create in clinical studies. In this work, we further elaborate on this idea and expand it in the field of bioequivalence (BE) studies. A computational methodology was developed, combining Monte Carlo simulations of 2 × 2 crossover BE trials with deep learning algorithms, specifically VAEs. Various scenarios, including variability levels, the actual sample size, the VAE-generated sample size, and the difference in performance between the two pharmaceutical products under comparison, were explored. All simulations showed that incorporating AI generative algorithms for creating virtual populations in BE trials has many advantages, as less actual human data can be used to achieve similar, and even better, results. Overall, this work shows how the application of generative AI algorithms, like VAEs, in clinical/bioequivalence studies can be a modern tool to significantly reduce human exposure, costs, and trial completion time.
Comparison of the Antioxidant Properties of Extracts Obtained from Walnut Husks as well as the Influence of Juglone on Their Evaluation
Authors: Olszowy-Tomczyk Małgorzata , Wianowska Dorota
Published: March 2024

Concern for the future of the next generation leads to the search for alternative solutions for the proper management of materials considered as useless waste. This study fits into this research trend. Its aim is to demonstrate the potential of walnut husks as a source of compounds with antioxidant properties that can be used in non-food industries. Pressurized liquid extraction, i.e., one of the modern green extraction techniques used on an industrial scale, as well as conventional extraction in Soxhlet and maceration were applied to prepare the extracts. In order to assess in depth their antioxidant activity in relation to the content of characteristic components, various activity assessment methods were used in this research. The results proved that the husk components have such antioxidant properties that they can be of interest to the cosmetics and pharmaceutical industries regarding the management of this waste. The results confirmed the usefulness of assisted extraction in increasing the ecological and economic values of the proposed waste disposal. Moreover, they showed that juglonehas very weak antioxidant properties, and the antioxidant effect of the mixture containing husk extract and juglone solution is mainly additive.
Taking the Deer by the Antlers: Deer in Material Culture in the Balkan Neolithic
Authors: Vitezović Selena
Published: March 2024

Prehistoric communities had strong ties with the animal world that surrounded them—animals were prey, sources of food, and raw materials, but also threats and mysteries, and certain animals often had an important place in the symbolic realm. With the process of domestication and the switch to animal husbandry as the main source of animal food, these relations changed considerably, and a certain dichotomy between “the domestic” and “the wild” may be noted in numerous past communities. When it comes to the Neolithic period in the Balkans, domestic animals had an important place in subsistence and economy, and it seems that cattle had a particularly prominent symbolic role. Wild species preserved some of their significance in both subsistence and symbolic realms, especially cervids (red deer, roe deer, and fallow deer). In this paper, the place of deer in the material culture of the Neolithic communities in the Balkans will be analysed: skeletal elements of deer were used for the production of diverse items, including non-utilitarian ones, or were part of ritual depositions, and deer representations are encountered in other materials, such as clay figurines. The symbolic meaning of deer cannot be reconstructed with certainty; however, it is probable that deer were tied with territoriality and the landscape.
The Protection of Monuments and Immoveable Works of Art from War Damage: A Comparison of Italy in World War II and Ukraine during the Russian Invasion
Authors: Hoeniger Cathleen
Published: March 2024

This article compares the safeguarding of monuments and immoveable works of art in Italy in the first years of World War II to the on-site protection undertaken in Ukraine during the Russian invasion and explores whether traditional or more innovative methods are being employed in Ukraine. Both the planning in advance of war and the implementation of protective measures amidst substantial obstacles are considered. The focus is placed on fixed works of art in churches and public statues. Special attention is given to the vulnerability of churches and their ornamentation during war.
Indoor Radon Surveying and Mitigation in the Case-Study of Celleno Town (Central Italy) Located in a Medium Geogenic Radon Potential Area
Authors: Portaro Manuela , Rocchetti Ilaria , Tuccimei Paola , Galli Gianfranco , Soligo Michele , Ciotoli Giancarlo , Longoni Cristina , Vasquez Dino , Sola Federica
Published: March 2024

Indoor radon surveying and remediation were implemented in a single-family home affected by high levels of indoor radon in the Celleno municipality (central Italy) with the aim of identifying the contribution of radon sources, evaluating the factors affecting radon entry into the building, and reducing radon risk. Average radon levels were relatively low at the ground floor (286 ± 202 Bq m−3) and first floor (167 ± 84 Bq m−3) in autumn when the temperature was still warm and the windows were open, but increased up to 2776 ± 1768 Bq m−3 and 970 ± 202 Bq m−3 in the first half of December, when the heating system was on and the windows were closed. The inner walls of the pilot room at the ground floor, semi buried on one side, were then treated with a waterproof product (a silane terminated polymer) and the average radon was halved (1475 ± 1092 Bq m−3) in the following month, which was still characterised by winter conditions. Radon entry in the room was identified and sealed with the same product, and a radon accumulation space behind a NE-SW oriented wall was naturally ventilated, reducing radon below the reference level in April with northerly winds conditions.
Lake Surface Temperature Retrieval Study Based on Landsat 8 Satellite Imagery—A Case Study of Poyang Lake
Authors: Kong Xudong , Li Yajun , Wang Lingli , Liu Huijie
Published: March 2024

Poyang Lake is the largest freshwater lake in China and forms an essential component of the hydrological, nutrient, and carbon cycles, providing various ecosystem services to the local environment. Since changes in Poyang Lake’s water temperature can significantly affect the surrounding environment and social development, continuous monitoring of lake temperature changes is required. Traditional water monitoring methods are resource intensive and cannot simultaneously conduct extensive water monitoring. Remote sensing of temperature inversion has the advantages of all-weather, efficient, and large-scale real-time monitoring. Six Landsat 8 images from August to October in 2020 and 2021 were utilized to extract lake surface temperature (LST), and the variations in LST over the two years were analyzed to determine the impact of global climate anomalies on inland lakes. The results indicate that the LST in August and October 2021 was significantly higher than that in the same periods of the previous year, and the temperature difference in October reached 8 °C. In contrast to the overall normal distribution pattern of the water temperature in 2020, 2021 exhibited a relatively concentrated, unimodal distribution pattern. A trend analysis of the driving factors suggests that the LST of Poyang Lake is influenced by the global climate, and the artificial heat sources around the lake clearly alter the distribution characteristics of the LST simultaneously.
Distribution and Storage Characteristics of Soil Organic Carbon in Tidal Wetland of Dandou Sea, Guangxi
Authors: Wang Mengsi , Yao Huanmei , Huang Zengshiqi , Liu Yin , Chen Meijun , Zhong Maoyuan , Qiao Junchao
Published: March 2024

In order to study the distribution characteristics of soil organic carbon (SOC) and soil organic carbon storage (SOCS) among different wetland types in Dandou Sea tidal wetland in Guangxi, firstly, based on Sentinel–2 imaging and random forest algorithm, combined with the existing tidal wetland data, a 10 m resolution tidal wetland dataset in Guangxi from 2019 to 2023 was generated, covering mangroves, salt marshes and tidal flats. The results show that the overall accuracy of the recognition results is higher than 96%, and the Kappa coefficient is higher than 0.95, which indicates high accuracy. Subsequently, the distribution characteristics and influencing factors of SOC and SOCS in different habitats were analyzed. The results showed that the SOC content of mangroves and salt marshes was higher than that of tidal flats. The SOC content of mangrove, salt marshes and tidal flats in 0–60 cm soil layer was 5.30–10.42 g/kg, 7.60–9.84 g/kg, and 1.29–2.25 g/kg, respectively. The changes of SOCS were 12.41–26.48 t/ha, 19.58–24.15 t/ha, and 3.61–6.86 t/ha, respectively. With the increase of soil depth, the SOC and SOCS of mangroves decreased gradually, and the SOC and SOCS of salt marshes increased gradually, and SOC and SOCS were mainly affected by soil bulk density (BD), soil moisture content (MC) and pH.
Connection between Barents Sea Ice in May and Early Summer Monsoon Rainfall in the South China Sea and Its Possible Mechanism
Authors: Li Fangyu , Zeng Gang , Zhang Shiyue , Hamadlnel Monzer
Published: March 2024

The impacts of Arctic sea ice on climate in middle and high latitudes have been extensively studied. However, its effects on climate in low latitudes, particularly on summer monsoon rainfall in the South China Sea (SCS), have received limited attention. Thus, this study investigates the connection between the Arctic sea ice concentration (SIC) anomaly and the early summer monsoon rainfall (ESMR) in the SCS and its underlying physical mechanism. The results reveal a significant positive correlation between the Barents Sea (BS) SIC in May and the ESMR in the SCS. When there is more (less) SIC in the Barents Sea (BS) during May, this results in a positive (negative) anomaly of the local turbulent heat flux, which lasts until June. This, in turn, excites an upward (downward) air motion anomaly in the vicinity of the BS, causing a corresponding downward (upward) motion anomaly over the Black Sea. Consequently, this triggers a wave train similar to the Eurasian (SEU) teleconnection, propagating eastward towards East Asia. The SEU further leads to an (a) upward (downward) motion anomaly and weakens (strengthens) the western Pacific subtropical high (WPSH) over the SCS, which is accompanied by a southwest adequate (scarce) water vapor anomaly transporting from the Indian Ocean, resulting in more (less) precipitation in the SCS. Furthermore, the response of ESMR in the SCS to the SIC in the BS is further verified by using the Community Atmosphere Model version 5.3 (CAM5.3). This study introduces novel precursor factors that influence the South China Sea summer monsoon (SCSSM), presenting a new insight for climate prediction in this region, which holds significant implications.
Important Contribution to Aerosol Oxidative Potential from Residential Solid Fuel Burning in Central Ireland
Authors: Rinaldi Matteo , Manarini Francesco , Lucertini Mattia , Rapuano Marco , Decesari Stefano , Paglione Marco , Facchini Maria Cristina , Lin Chunshui , Ceburnis Darius , D. O’Dowd Colin , Buckley Paul , Hellebust Stig , Wenger John , Ovadnevaite Jurgita
Published: March 2024

Numerous studies have shown negative health effects related to exposure to ambient particulate matter (PM), likely due to induced oxidative stress. In this study, we have examined ambient PM samples from Birr, a small (~5000 inhabitants) town in central Ireland, for their water-soluble DTT-based oxidative potential (OP_DTT) with a resolution of 6 h, together with online chemical characterization measurements, to assess the OP of organic aerosols, in particular from residential solid fuel burning. The OP_DTT normalized by the volume of sampled air shows a high variability, ranging from <0.1 to 3.8 nmol min−1 m−3, and a high correlation with PM mass. A lower variability was associated with the mass-normalized OP. Nevertheless, both tended to present higher values during night-time pollution episodes. Simple and multivariate linear regression approaches linked OP_DTT to residential solid fuel burning, and in particular to wood (~87%) and peat (~13%) combustion. The results of the present study show how residential solid fuel burning can have a severe impact on air quality, even in small towns, with potential negative health effects on the exposed population.
Unit Maxwell-Boltzmann Distribution and Its Application to Concentrations Pollutant Data
Authors: Biçer Cenker , Bakouch Hassan S. , Biçer Hayrinisa Demirci , Alomair Gadir , Hussain Tassaddaq , Almohisen Amal
Published: March 2024

In the vast statistical literature, there are numerous probability distribution models that can model data from real-world phenomena. New probability models, nevertheless, are still required in order to represent data with various spread behaviors. It is a known fact that there is a great need for new models with limited support. In this study, a flexible probability model called the unit Maxwell-Boltzmann distribution, which can model data values in the unit interval, is derived by selecting the Maxwell-Boltzmann distribution as a base-line model. The important characteristics of the derived distribution in terms of statistics and mathematics are investigated in detail in this study. Furthermore, the inference problem for the mentioned distribution is addressed from the perspectives of maximum likelihood, method of moments, least squares, and maximum product space, and different estimators are obtained for the unknown parameter of the distribution. The derived distribution outperforms competitive models according to different fit tests and information criteria in the applications performed on four actual air pollutant concentration data sets, indicating that it is an effective model for modeling air pollutant concentration data.
Diphenylphosphoryl Azide as a Multifunctional Flame Retardant Electrolyte Additive for Lithium-Ion Batteries
Authors: Li Zhirui , Han Longfei , Kan Yongchun , Liao Can , Hu Yuan
Published: March 2024

Graphite anode materials and carbonate electrolyte have been the top choices for commercial lithium-ion batteries (LIBS) for a long time. However, the uneven deposition and stripping of lithium cause irreversible damage to the graphite structure, and the low flash point and high flammability of the carbonate electrolyte pose a significant fire safety risk. Here, we proposed a multifunctional electrolyte additive diphenylphosphoryl azide (DPPA), which can construct a solid electrolyte interphase (SEI) with high ionic conductivity lithium nitride (Li3N) to ensure efficient transport of Li+. This not only protects the artificial graphite (AG) electrode but also inhibits lithium dendrites to achieve excellent electrochemical performance. Meanwhile, the LIBS with DPPA offers satisfactory flame retardancy performance. The AG//Li half cells with DPPA-0.5M can still maintain a specific capacity of about 350 mAh/g after 200 cycles at 0.2 C. Its cycle performance and rate performance were better than commercial electrolyte (EC/DMC). After cycling, the microstructure surface of the AG electrode was complete and flat, and the surface of the lithium metal electrode had fewer lithium dendrites. Importantly, we found that the pouch cell with DPPA-0.5M had low peak heat release rate. When exposed to external conditions of continuous heating, DPPA significantly improved the fire safety of the LIBS. The research of DPPA in lithium electrolyte is a step towards the development of safe and efficient lithium batteries.
Aromatic Characterization of Graševina Wines from Slavonia and Podunavlje Sub-Regions
Authors: Martelanc Mitja , Antalick Guillaume , Radovanović Vukajlović Tatjana , Mozetič Vodopivec Branka , Sternad Lemut Melita , Hosseini Ahmad , Obradović Valentina , Mesić Josip , Butinar Lorena
Published: March 2024

This study investigated the chemical aromatic profiles of 60 volatile organic compounds (VOCs), including 21 terpenes, 6 norisoprenoids, 6 volatile phenols, 4 C-6 alcohols, and 23 esters, in Graševina wines originating from Slavonia and Podunavlje sub-regions (Croatia). Headspace solid-phase microextraction gas chromatography coupled with mass spectrometric detection (HS-SPME-GCMS) was used to assess 60 VOCs, and a novel HS-SPME-GCMS method for the determination of terpenes and norisoprenoids was developed and validated. Statistical analysis found no significant differences between VOCs present in Graševina wines and others wines (Chardonnay and Pinot gris); nonetheless, comparison of VOC fingerprints between different wines from the same producer showed that four terpenes (α-terpinene, γ-terpinene, 1,4-cineol, and 4-terpineol) were present in higher amounts in Graševina wines when compared to other wines. By exclusively examining VOCs within Graševina wines through the utilization of a heatmap and hierarchical clustering, a distinct visualization of samples and VOCs emerged, highlighting the vintage effect. Simultaneously, a partial least squares discriminant analysis model was developed with a confidence interval of 95%, demonstrating a noticeable distinction between samples originating from the western and eastern regions. Furthermore, by employing the heatmap using only VOCs from the terpene and norisoprenoidic groups, a clear separation of samples into two groups was observed based on geographical origin; namely, higher concentrations of norisoprenoids and terpenes were observed in the Slavonia sub-region. These results suggest that terpenes and norisoprenoids to a lower extent may be valuable aromatic markers for the regional typicality of Graševina wines.
The Role of Artificial Intelligence in the Identification and Evaluation of Bone Fractures
Authors: Tieu Andrew , Kroen Ezriel , Kadish Yonaton , Liu Zelong , Patel Nikhil , Zhou Alexander , Yilmaz Alara , Lee Stephanie , Deyer Timothy
Published: March 2024

Artificial intelligence (AI), particularly deep learning, has made enormous strides in medical imaging analysis. In the field of musculoskeletal radiology, deep-learning models are actively being developed for the identification and evaluation of bone fractures. These methods provide numerous benefits to radiologists such as increased diagnostic accuracy and efficiency while also achieving standalone performances comparable or superior to clinician readers. Various algorithms are already commercially available for integration into clinical workflows, with the potential to improve healthcare delivery and shape the future practice of radiology. In this systematic review, we explore the performance of current AI methods in the identification and evaluation of fractures, particularly those in the ankle, wrist, hip, and ribs. We also discuss current commercially available products for fracture detection and provide an overview of the current limitations of this technology and future directions of the field.
Surface Modification of Polyetheretherketone (PEEK) Intervertebral Fusion Implant Using Polydopamine Coating for Improved Bioactivity
Authors: Park Suzy , Jung Tae-Gon
Published: March 2024

The occurrence of bone diseases has been increasing rapidly, in line with the aging population. A representative spinal fusion material, polyetheretherketone (PEEK), is advantageous in this regard as it can work in close proximity to the elastic modulus of cancellous bone. However, if it is used without surface modification, the initial osseointegration will be low due to lack of bioactivity, resulting in limitations in surgical treatment. In this study, we aimed to modify the surface of PEEK cages to a hydrophilic surface by coating with polyethylene glycol (PEG), hyaluronic acid (HA), and polydopamine (PDA), and to analyze whether the coated surface exhibits improved bioactivity and changes in mechanical properties for orthopedic applications. Material properties of coated samples were characterized and compared with various PEEK groups, including PEEK, PEEK-PEG, PEEK-HA, and PEEK-PDA. In an in vitro study, cell proliferation was found to be enhanced on PDA-coated PEEK; it was approximately twice as high compared to the control group. In addition, mechanical properties, including static and torsion, were not affected by the presence of the coating. Thus, the results suggest that PEEK-PDA may have the potential for clinical application in fusion surgery for spinal diseases, as it may improve the rate of osseointegration.