bluuebunny/crossref_metadata_2025 huggingface dataset:
GWAS and Meta-QTL Analysis of Yield-Related Ear Traits in Maize
Authors: Qian Fu , Jing Jianguo , Zhang Zhanqin , Chen Shubin , Sang Zhiqin , Li Weihua
Published: November 2023

Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three years of planting, and whole genome sequencing was performed using the maize 40 K breeding chip based on genotyping by targeted sequencing (GBTS) technology. Five models were used to conduct a genome-wide association study (GWAS) on best linear unbiased estimate (BLUE) of ear traits to find the best model. The FarmCPU (Fixed and random model Circulating Probability Unification) model was the best model for this study; a total of 104 significant single nucleotide polymorphisms (SNPs) were detected, and 10 co-location SNPs were detected simultaneously in more than two environments. Through gene function annotation and prediction, a total of nine genes were identified as potentially associated with ear traits. Moreover, a total of 760 quantitative trait loci (QTL) associated with yield-related traits reported in 37 different articles were collected. Using the collected 760 QTL for meta-QTL analysis, a total of 41 MQTL (meta-QTL) associated with yield-related traits were identified, and 19 MQTL detected yield-related ear trait functional genes and candidate genes that have been reported in maize. Five significant SNPs detected by GWAS were located within these MQTL intervals, and another three significant SNPs were close to MQTL (less than 1 Mb). The results provide a theoretical reference for the analysis of the genetic basis of ear-related traits and the improvement of maize yield.
Characterization of the Chemopreventive Properties of Cannabis sativa L. Inflorescences from Monoecious Cultivars Grown in Central Italy
Authors: Di Giacomo Silvia , Percaccio Ester , Vitalone Annabella , Ingallina Cinzia , Mannina Luisa , Macone Alberto , Di Sotto Antonella
Published: November 2023

Hemp bioproducts hold great promise as valuable materials for nutraceutical and pharmaceutical applications due to their diverse bioactive compounds and potential health benefits. In line with this interest and in an attempt to valorize the Lazio Region crops, this present study investigated chemically characterized hydroalcoholic and organic extracts, obtained from the inflorescences of locally cultivated Felina 32, USO 31, Ferimon and Fedora 17 hemp varieties. In order to highlight the possible chemopreventive power of the tested samples, a bioactivity screening was performed, which included studying the antimutagenic activity, radical scavenging power, cytotoxicity in human hepatoma HepG2 cells, leakage of lactate dehydrogenase (LDH) and modulation of the oxidative stress parameters and glucose-6-phosphate dehydrogenase (G6PDH) involved in the regulation of the cell transformation and cancer proliferation. Tolerability studies in noncancerous H69 cholangiocytes were performed, too. The organic extracts showed moderate to strong antimutagenic activities and a marked cytotoxicity in the HepG2 cells, associated with an increased oxidative stress and LDH release, and to a G6PDH modulation. The hydroalcoholic extracts mainly exhibited radical scavenging properties with weak or null activities in the other assays. The extracts were usually well-tolerated in H69 cells, except for the highest concentrations which impaired cell viability, likely due to an increased oxidative stress. The obtained results suggest a possibility in the inflorescences from the Felina 32, USO 31, Ferimon and Fedora 17 hemp varieties as source of bioactive compounds endowed with genoprotective and chemopreventive properties that could be harnessed as preventive or adjuvant healing strategies.
The Development of Advanced Fluorescent Tracers Aimed at Drill Cuttings Labelling and Depth Correlation via Injection with Oil-Based Drilling Mud
Authors: Khmelnitskiy Vladimir , Alqahtani Hassan S. , Kwak Hyung , Solovyeva Vera
Published: November 2023

Fast and precise geo-steering and geo-navigation upon well drilling are the key parameters for improved well targeting, optimal well placement, and maximal hydrocarbon recovery. To advance geo-steering parameters, we propose a new approach to on-site formation evaluation through the use of fluorescent tracers for drill cuttings tagging according to the depth of origin. Cuttings labelling at the drill bit site is followed by near-real-time drilling depth correlation at the well-head via a camera and AI image recognition systems. To suite the drilling process, the engineered tracers should match to the rheology of the utilized drilling mud. This study was performed to comprehensively investigate the effect of fluorescent tracers on the rheological properties of oil-based drilling mud (OBM) and to determine the optimal quantities of the tracers’ addition. We evaluated critical mud characteristics including electrical stability, thixotropic parameters, shear stress, gel strength, plastic viscosity, and yield point as prepared and in the presence of fluorescent tracers at the range of 1 to 20 wt.%. Additionally, the mud’s effects on the long-term stability of the fluorescent tracers were assessed via hot-rolling tests in conditions mimicking downhole conditions, with the aim of determining the tags’ feasibility for drill cuttings labelling applications. The study also examines the recovery potential of the tracers and their reusability in the drilling process. This investigation provides valuable insights into the potential application of fluorescent tracers for downhole drill cuttings depth correlation which will improve geo-steering works.
A Comparative Case Study on Stress Redistribution due to Extraction of Conventional and Split-Level Longwall Panels in Deep Inclined Coal Seams
Authors: Wang Pengfei , Zhao Peng , Cao Yang
Published: November 2023

Through field observations, theoretical analysis, and a calibrated numerical model, a study of stress redistribution due to the extraction of longwall panels at depths ranging from 580 to 660 m with a 30° dip angle at Tangshan coal mine is presented in this paper. Conventional and new split-level longwall layouts are compared regarding their stress redistributions. The height of the caved zone is 21.7 m; angles of break of 55.6° on the left and 54.2° on the right side of the gob are observed using cross-measure boreholes. Structural models as well as numerical models are constructed based on the above field data to make the geometry of the gobs closer to the in situ situation and more realistic. Compared with the conventional layout, the theoretical analysis shows that the overall influence of the elevated split-level longwall gob on the lowest intact stratum increases by more than 5.07%, meaning that the split-level longwall layout is more likely to maintain the stability of the overlying strata. This is also corroborated by numerical modeling. Conventional longwall panels and split-level longwall panels with and without considering the gob are all simulated using FLAC3D. Instead of only backfilling the height of the coal seam or the height of the coal seam and the immediate roof, as in many numerical modeling studies in the past, in this study, the whole caved zone is backfilled with “double-yield” material. It is found that along the floor, the split-level longwall gob assumes 23.4% more load than the conventional longwall gob, and the split-level longwall abutment bears 6.2% less load than the conventional longwall abutment; stress arches are developed within the gob; concave-down stress beddings are more evident at higher locations of the gob; a self-supporting structure develops within the gob and surrounding rock mass around the lower end of the gob, forming a protective localized intact destressed zone around the location where the split-level tailgate is situated; the yield zone in the floor of the curved section tends to extends toward the center of the curved part, where the curvature is the maximum; the upper stress concentration zone is within the coal seam, while the lower one is above the coal seam; the upper one is more concentrated.
Few-Shot High-Resolution Range Profile Ship Target Recognition Based on Task-Specific Meta-Learning with Mixed Training and Meta Embedding
Authors: Kong Yingying , Zhang Yuxuan , Peng Xiangyang , Leung Henry
Published: November 2023

High-resolution range profile (HRRP), characterized by its high availability and rich target structural information, has been extensively studied. However, HRRP-based target recognition methods using closed datasets exhibit limitations when it comes to identifying new classes of targets. The scarcity of samples for new classes leads to overfitting during the deep learning process, and the similarity in the scattering structures of different ships, combined with the significant structural differences among samples of the same ship, contribute to a high level of confusion among targets. To address these challenges, this paper proposed Task-Specific Mate-learning (TSML) for few-shot HRRP. Firstly, a Task-Adaptive Mixed Transfer (TAMT) strategy is proposed, which combines basic learning with meta-learning, to reduce the likelihood of overfitting and enhance adaptability for recognizing new classes of ships. Secondly, a Prototype Network is introduced to enable the recognition of new classes of targets with limited samples. Additionally, a Space-Adjusted Meta Embedding (SAME) is proposed based on the Prototype Network. This embedding function, designed for HRRP data, modifies the distances between samples in meta-tasks by increasing the distances between samples from different ships and decreasing the distances between samples from the same ship. The proposed method is evaluated based on an actual measured HRRP dataset and the experimental results prove that the proposed method can more accurately recognize the unknown ship classes with a small number of labels by learning the known classes of ships. In addition, the method has a degree of robustness to the number of training samples and a certain generalization ability, which can produce improved results when applied to other backbones.
Detection Ground Deformation Characteristics of Reclamation Land with Time-Series Interferometric Synthetic Aperture Radar in Tianjin Binhai New Area, China
Authors: Chen Yanan , Yan Fuli , Chen Jian , Fan Xiangtao
Published: November 2023

In order to alleviate the conflict between populations and land resource, Tianjin adopted multi-phase reclamation projects to the formed large-scale artificial reclamation land. The reclamation areas, however, are prone to subsidence, which poses a significant threat to infrastructure as well as the safety and assets of the residents. The SBAS-InSAR was used to acquire surface deformation of Tianjin Binhai New Area from January 2017 to December 2022, analyze in depth the response relationship between land subsidence, reclamation project time, and land-use type. There is a strong correlation between surface deformation and reclamation time. Severe land subsidence occurred over newly reclaimed areas. In the offshore direction, the deformation values of the Nangang Industrial Zone, the Lingang Industrial Zone, and Hangu Harbor were −98 mm to −890 mm, 45 mm to −580 mm, and −140 mm to −290 mm, respectively. Significant differences in deformation were detected among different land-use types where reclamation projects were completed in the same time. Subsidence was positively correlated with surface load; in areas with higher surface loads, the surface settlement was also more severe. The average surface settlement for the heavy shipyard, with 67 grain storage tanks and 27 grain storage tanks, road, and bare land were −201 mm, −166 mm, −107 mm, −64 mm, and −43 mm, respectively. This study reveals significant differences of surface deformation in the reclamation completed at different times, and determines that the load is the main driving factor of settlement difference in the reclamation land completed at the same time. This has important guiding significance for preventing and controlling geological disasters in the reclamation area and later development planning.
Soil Data Cube and Artificial Intelligence Techniques for Generating National-Scale Topsoil Thematic Maps: A Case Study in Lithuanian Croplands
Authors: Samarinas Nikiforos , Tsakiridis Nikolaos L. , Kokkas Stylianos , Kalopesa Eleni , Zalidis George C.
Published: November 2023

There is a growing realization among policymakers that in order to pave the way for the development of evidence-based conservation recommendations for policy, it is essential to improve the capacity for soil-health monitoring by adopting multidimensional and integrated approaches. However, the existing ready-to-use maps are characterized mainly by a coarse spatial resolution (>200 m) and information that is not up to date, making their use insufficient for the EU’s policy requirements, such as the common agricultural policy. This work, by utilizing the Soil Data Cube, which is a self-hosted custom tool, provides yearly estimations of soil thematic maps (e.g., exposed soil, soil organic carbon, clay content) covering all the agricultural area in Lithuania. The pipeline exploits various Earth observation data such as a time series of Sentinel-2 satellite imagery (2018–2022), the LUCAS (Land Use/Cover Area Frame Statistical Survey) topsoil database, the European Integrated Administration and Control System (IACS) and artificial intelligence (AI) architectures to improve the prediction accuracy as well as the spatial resolution (10 m), enabling discrimination at the parcel level. Five different prediction models were tested with the convolutional neural network (CNN) model to achieve the best accuracy for both targeted indicators (SOC and clay) related to the R2 metric (0.51 for SOC and 0.57 for clay). The model predictions supported by the prediction uncertainties based on the PIR formula (average PIR 0.48 for SOC and 0.61 for clay) provide valuable information on the model’s interpretation and stability. The model application and the final predictions of the soil indicators were carried out based on national bare-soil-reflectance composite layers, generated by employing a pixel-based composite approach to the overlaid annual bare-soil maps and by using a combination of a series of vegetation indices such as NDVI, NBR2, and SCL. The findings of this work provide new insights for the generation of soil thematic maps on a large scale, leading to more efficient and sustainable soil management, supporting policymakers and the agri-food private sector.
Recurrent Neural Network Methods for Extracting Dynamic Balance Variables during Gait from a Single Inertial Measurement Unit
Authors: Yu Cheng-Hao , Yeh Chih-Ching , Lu Yi-Fu , Lu Yi-Ling , Wang Ting-Ming , Lin Frank Yeong-Sung , Lu Tung-Wu
Published: November 2023

Monitoring dynamic balance during gait is critical for fall prevention in the elderly. The current study aimed to develop recurrent neural network models for extracting balance variables from a single inertial measurement unit (IMU) placed on the sacrum during walking. Thirteen healthy young and thirteen healthy older adults wore the IMU during walking and the ground truth of the inclination angles (IA) of the center of pressure to the center of mass vector and their rates of changes (RCIA) were measured simultaneously. The IA, RCIA, and IMU data were used to train four models (uni-LSTM, bi-LSTM, uni-GRU, and bi-GRU), with 10% of the data reserved to evaluate the model errors in terms of the root-mean-squared errors (RMSEs) and percentage relative RMSEs (rRMSEs). Independent t-tests were used for between-group comparisons. The sensitivity, specificity, and Pearson’s r for the effect sizes between the model-predicted data and experimental ground truth were also obtained. The bi-GRU with the weighted MSE model was found to have the highest prediction accuracy, computational efficiency, and the best ability in identifying statistical between-group differences when compared with the ground truth, which would be the best choice for the prolonged real-life monitoring of gait balance for fall risk management in the elderly.
A Novel Hybrid Technique Combining Improved Cepstrum Pre-Whitening and High-Pass Filtering for Effective Bearing Fault Diagnosis Using Vibration Data
Authors: Kiakojouri Amirmasoud , Lu Zudi , Mirring Patrick , Powrie Honor , Wang Ling
Published: November 2023

Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of the most powerful methods for BCF detection in noisy signals is envelope analysis. However, the selection of an effective band-pass filtering region presents significant challenges in moving towards automated bearing fault diagnosis due to the variable nature of the resonant frequencies present in bearing systems and rotating machinery. Cepstrum Pre-Whitening (CPW) is a technique that can effectively eliminate discrete frequency components in the signal whilst detecting the impulsive features related to the bearing defect(s). Nevertheless, CPW is ineffective for detecting incipient bearing defects with weak signatures. In this study, a novel hybrid method based on an improved CPW (ICPW) and high-pass filtering (ICPW-HPF) is developed that shows improved detection of BCFs under a wide range of conditions when compared with existing BCF detection methods, such as Fast Kurtogram (FK). Combined with machine learning techniques, this novel hybrid method provides the capability for automated bearing defect detection and diagnosis without the need for manual selection of the resonant frequencies. The results from this novel hybrid method are compared with a number of established BCF detection methods, including Fast Kurtogram (FK), on vibration signals collected from the project I2BS (An EU Clean Sky 2 project ‘Integrated Intelligent Bearing Systems’ collaboration between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and those from three databases available in the public domain—Case Western Reserve University (CWRU), Intelligent Maintenance Systems (IMS) datasets, and Safran jet engine data—all of which have been widely used in studies of this kind. By calculating the Signal-to-Noise Ratio (SNR) of each case, the new method is shown to be effective for a much lower SNR (with an average of 30.21) compared with that achieved using the FK method (average of 14.4) and thus is much more effective in detecting incipient bearing faults. The results also show that it is effective in detecting a combination of several bearing faults that occur simultaneously under a wide range of bearing configurations and test conditions and without the requirement of further human intervention such as extra screening or manual selection of filters.
EEG Signal Processing Techniques and Applications
Authors: Zhao Yifan , He Fei , Guo Yuzhu
Published: November 2023

Electroencephalography (EEG) is a widely recognised non-invasive method for capturing brain electrophysiological activity [...]
Interval Type-2 Fuzzy PID Controller Using Disassembled Gradational Optimization
Authors: Chu Yongzhi , Han Hasiaoqier , Ma Tianjiao , Zhu Mingchao , Li Zhongcan , Xu Zhenbang , Wu Qingwen
Published: November 2023

This paper presents an interval type-2 fuzzy proportional–integral–derivative (IT2F-PID) controller that is designed using a new disassembled gradational optimization (D-GO) method. A PID controller is first optimized using the D-GO method and then connected to a type-1 fuzzy logic system (T1-FLS). The parameters of the T1-FLS are optimized, and the T1-FLS is blurred into the interval type-2 fuzzy logic system (IT2-FLS). Finally, the IT2F-PID controller is formed. The proposed method is compared with the concurrent and general optimization methods. The simulation results show that the D-GO method reduces the optimization time by over 90% compared with the general method, and decreases the integral-of-time-absolute-error (ITAE) by 30%. Beyond that, compared with the concurrent optimization method, the D-GO method reduces time by over 25%, and the ITAE value by about 95%. In the normal case, model uncertainty, target uncertainty, and external disturbance, the control ability of the IT2F-PID controller designed using the D-GO method is verified via simulations using a nonlinear forced closed-loop system. The results show that the overshoot is reduced by 80% and the fluctuation is reduced by 67% compared with a traditional PID controller and an IT2F-PID controller built using the general method.
Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition
Authors: Islam Muhammad , Aloraini Mohammed , Aladhadh Suliman , Habib Shabana , Khan Asma , Alabdulatif Abduatif , Alanazi Turki M.
Published: November 2023

Sign language recognition, an essential interface between the hearing and deaf-mute communities, faces challenges with high false positive rates and computational costs, even with the use of advanced deep learning techniques. Our proposed solution is a stacked encoded model, combining artificial intelligence (AI) with the Internet of Things (IoT), which refines feature extraction and classification to overcome these challenges. We leverage a lightweight backbone model for preliminary feature extraction and use stacked autoencoders to further refine these features. Our approach harnesses the scalability of big data, showing notable improvement in accuracy, precision, recall, F1-score, and complexity analysis. Our model’s effectiveness is demonstrated through testing on the ArSL2018 benchmark dataset, showcasing superior performance compared to state-of-the-art approaches. Additional validation through an ablation study with pre-trained convolutional neural network (CNN) models affirms our model’s efficacy across all evaluation metrics. Our work paves the way for the sustainable development of high-performing, IoT-based sign-language-recognition applications.
Adaptive DCS-SOMP for Localization Parameter Estimation in 5G Networks
Authors: da Conceição Paulo Francisco , Rocha Flávio Geraldo Coelho
Published: November 2023

In this work, we model a 5G downlink channel using millimeter-wave (mmWave) and massive Multiple-Input Multiple-Output (mMIMO) technologies, considering the following localization parameters: Time of Arrival (TOA), Two-Dimensional Angle of Departure (2D-AoD), and Two-Dimensional Angle of Arrival (2D-AoA), both encompassing azimuth and elevation. Our research focuses on the precise estimation of these parameters within a three-dimensional (3D) environment, which is crucial in Industry 4.0 applications such as smart warehousing. In such scenarios, determining the device localization is paramount, as products must be handled with high precision. To achieve these precise estimations, we employ an adaptive approach built upon the Distributed Compressed Sensing—Subspace Orthogonal Matching Pursuit (DCS-SOMP) algorithm. We obtain better estimations using an adaptive approach that dynamically adapts the sensing matrix during each iteration, effectively constraining the search space. The results demonstrate that our approach outperforms the traditional method in terms of accuracy, speed to convergence, and memory use.
Intelligent Hotel Guidance System via Face Recognition Technology
Authors: Bao Chenlu , Yang Yongjie , Wang Zhiliang , Xu Peng
Published: November 2023

In modern large hotels, due to a large number of rooms and complex layouts, it is difficult for customers to find rooms, which increases a lot of workloads for hotel attendants to guide. In this paper, a hotel intelligent guidance system based on face recognition is designed. After entering the customer’s facial photos, the room guidance and customer management are carried out through face recognition. With this, hotels can move toward card-free management, green environmental protection, and save on resources. With these improvements, hotel management will be card-free and green. Each monitoring device of the system adopts dual STM32 core architecture, in which STM32H7 is responsible for face recognition, while STM32L4 is the main control chip, which is responsible for data exchange, guest room guidance and other work. The monitoring master not only guides, but also uploads customer check-in information to the cloud platform to facilitate the management of the hotel. The system adopts contactless information collection and guidance, which improves the intelligence and humanization of the hotel, and has a good application prospect.
Analysis of Kazakhstan Crude Oil Biomarkers by Gas Chromatography in Combination with Mass Spectrometry
Authors: Alimzhanova Mereke , Abdykarimov Bauyrzhan
Published: November 2023

Kazakhstan ranks as the 12th largest oil producer globally and boasts a diverse range of crude oils. This research introduces a method for distinguishing between the different types of crude oils based on biomarker analysis of 28 crude oils from Western and Southern Kazakhstan using gas chromatography-mass spectrometry. Biomarkers serve as valuable tools, especially in forensic investigations of oil spills. These biomarkers effectively retain a significant portion of the original natural product’s carbon structure, providing crucial evidence regarding the origin and identity of the oils under examination. This study identifies a set of biomarkers, including pristine, phytane, n-C17 and n-C18 alkanes, hopanes, bisnorhopanes, iso-copalanes, pregnane, androstane, allopregnane, homopregnane, cholestane, and stigmastane. By examining ratios such as pristane/phytane, pristane/n-C17 alkane, tricyclic/pentacyclic terpanes, and hopane, as well as the distribution of steranes, it was deduced that crude oils from West Kazakhstan exhibited resilience to biodegradation. These findings showed that gas chromatography-mass spectrometry is an effective method for oil biomarkers determination, especially because it provides efficient separation and identification. Additionally, this study delved into the origin conditions and maturity of these oils, contributing to a deeper understanding of their characteristics and analysis that is simple to use and available worldwide.
Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory
Authors: Yang Fan , Mao Qian
Published: November 2023

As the world grapples with the challenges posed by climate change and depleting energy resources, achieving sustainability in the construction and operation of buildings has become a paramount concern. The construction and operation of buildings account for a substantial portion of global energy consumption and carbon emissions. Hence, the accurate prediction of building energy consumption is indispensable for reducing energy waste, minimizing greenhouse gas emissions, and fostering sustainable urban development. The aspiration to achieve predicted outcomes with remarkable accuracy has emerged as a pivotal objective, coinciding with the burgeoning popularity of deep learning techniques. This paper presents an auto-evaluation model for building energy consumption prediction via Long Short-Term Memory with modified Kalman filtering (LSTM-MKF). Results gleaned from data validation activities evince a notable transformation—a reduction of the maximal prediction error from an initial 83% to a markedly ameliorated 24% through the intervention of the proposed model. The LSTM-MKF model, a pioneering contribution within this paper, clearly exhibits a distinct advantage over the other models in terms of predictive accuracy, as underscored by its superior performance in all three key metrics, including mean absolute error, root mean square error, and mean square error. The model presents excellent potential as a valuable tool for enhancing the precision of predictions of building energy consumption, a pivotal aspect in energy efficiency, smart city development, and the formulation of informed energy policy.
Daily Peer Relationships and Academic Achievement among College Students: A Social Network Analysis Based on Behavioral Big Data
Authors: Zhou Yuan , Meng Xiao , Wang Jiayin , Mo Xu , Jiang Sa , Dai Chengjun , Liu Mengting
Published: November 2023

This study aimed to detect college students’ daily peer networks through a behavioral big-data-driven social network analysis and to explore the relationship between college students’ daily peer relationships and academic achievement. We collected data on the class attendance, eating, and bathing records of 4738 undergraduate students who entered a university in 2018 to infer the daily peer relationship networks of students. The Louvain algorithm and some network indicators such as density and average clustering coefficient were used to investigate social network characteristics of peer relationship networks. The findings show that initially, students in the same dormitory tended to form daily peer relationships, gradually shifting toward relationships centered on classmates as time progressed. These peer networks often aligned with geographical location and living areas. Moreover, the peers of college students who received high-level scholarships were more likely to receive scholarships. The number of peers was positively correlated with the likelihood of receiving a scholarship. The research findings contribute to the application of information technology to promote the sustainable development of higher education and individual students.
Geosynthetic Solutions for Sustainable Transportation Infrastructure Development
Authors: Yoo Chungsik
Published: November 2023

Geosynthetic engineering has made significant advances during the past decade in the areas of manufacturing and practical applications. As a result, geosynthetics have become essential materials that facilitate construction, better improve short- and long-term performance, and reduce long-term maintenance costs in routine civil engineering projects. Geosynthetics are also being recognized as fundamental to sustainable infrastructure development as they reduce the carbon footprint generated by infrastructure development by minimizing the use of natural construction materials. Creative use of geosynthetics in geo-engineering practices is expected to continue to expand as innovative materials and products are becoming available. In this paper, we begin by discussing issues related to climate change. The sustainable benefits of geosynthetics are then presented by demonstrating the potential of geosynthetics to significantly reduce carbon footprints compared to traditional solutions. Finally, recent geosynthetic technologies have been introduced for use in transportation infrastructure. The pathway forward of the geosynthetic technology is also discussed from the view of sustainable infrastructure development.
Surface Subsidence Prediction Method for Backfill Mining in Shallow Coal Seams with Hard Roofs for Building Protection
Authors: Huo Wenqi , Li Huaizhan , Guo Guangli , Wang Yuezong , Yuan Yafei
Published: November 2023

The mining of shallow coal seams with hard roofs poses a threat to surface structures. In order to ensure the protection of these buildings, backfill mining is increasingly used in these types of coal seams. However, due to the lack of appropriate surface subsidence prediction methods, there are concerns about whether backfill mining can meet the requirements of building protection. In this study, through numerical simulation and physical experiments, the movement characteristics of the strata and surface were studied in the backfill mining of a shallow coal seam with a hard roof. Our results indicate that the backfilling ratio significantly influences strata movement and surface subsidence. As the backfilling ratio increases, the surface deformation in the backfill under the hard roof of the shallow coal seam transitions from discontinuous to continuous. When the backfilling ratio exceeds 60%, the deformation characteristics of the overburden and surface align with the probability integral method model. Consequently, a novel surface subsidence prediction method for backfill mining in shallow coal seams under hard roofs is proposed. This method was successfully applied at Yungang Mine, validating its effectiveness. These research findings have significant practical implications for the design of backfill mining in shallow coal seams under hard roofs.
Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit
Authors: Chen Ting , Ma Jianxiao , Li Shuang , Zhu Zhenjun , Guo Xiucheng
Published: November 2023

When urban rail transit is affected by interference, the fluctuation pattern of passenger flow undergoes mutation, which is not conducive to its operational safety and sustainable development. The more intense the mutation in the passenger flow, the greater the impact on the network and operations. Therefore, it is necessary to measure and evaluate the mutation degree of the urban rail transit passenger flow. In this study, we clarify the definition of the mutation degree of urban rail transit passenger flow and construct an evaluation index system for the mutation degree of passenger flow from two dimensions: horizontal mutation amplitude and vertical mutation amplitude. Based on the catastrophe theory, an evaluation model of the mutation degree was constructed. Using this evaluation method, abbreviated as CDCT, the level division of the mutation degree at different time intervals under different interference scenarios can be obtained, achieving a dynamic evaluation of the mutation degree of passenger flow. Finally, taking the passenger flow data of the Suzhou rail transit as an example, the mutational fluctuation of passenger flow affected by interference is analyzed, and the evaluation results of the mutation degree of passenger flow are obtained. The analysis results show that the CDCT evaluation method can better reflect the dynamic changes in the mutation degree throughout the process under the influence of the mutational passenger flow.
Could Globalisation and Renewable Energy Contribute to a Decarbonised Economy in the European Union?
Authors: Neagu Olimpia , Anghelina Andrei Marius , Teodoru Mircea Constantin , Boiță Marius , David Katalin Gabriela
Published: November 2023

This study investigates the impact of globalisation, renewable energy consumption, and economic growth on CO2 emissions in 26 European Union (EU) countries, in the period 1990–2020. Second-generation panel unit root tests are applied, the Westerlund cointegration test is used, and a panel of fully modified least squares (FMOLS) and dynamic ordinary least squares (DOLS) techniques are employed to estimate the long-term relationship between variables. The causality relationship among the considered variables is identified using the heterogeneous Dumitrescu–Hurlin causality test. It was found that globalisation and renewable energy consumption contributed to the carbon emissions’ mitigation, while economic growth induced their increase. The results are robust when control variables (i.e., financial development, foreign direct investment, and urbanisation) are added to the model. Foreign direct investment and urbanisation are contributors to carbon emissions’ increase, whereas financial development induces their decrease. The effect of the considered variables on carbon emissions is differentiated by economic development and level of institutional quality. Unidirectional causality relationships were identified from globalisation to carbon emissions and from carbon emissions to foreign direct investment and bidirectional relationships were found between economic growth, renewable energy consumption, financial development, and carbon emissions. The policy implications of the results are also discussed.
The Impact of Travel and Tourism Sustainability on a Country’s Image and as the Most Important Factor in the Global Competitive Index: Building Brands Based on Fogel, Schultz, and Schumpeter
Authors: Kálmán Botond Géza , Grotte Judit Katalin
Published: November 2023

Two economic metrics demonstrate the actual, practical judgment of a country: on the one hand, the number of arriving tourists and overnight stays and their share within the GDP; on the other hand, the increase in FDI arriving in the country. The impact of macroenvironmental elements is becoming more and more intense in the life of each country. How a country deals with the issue of sustainability greatly influences the perception of a given destination among travelers. In other words, sustainability has become a serious element of a country’s image. Impressions of a country formulated by others build on the image the country authentically reflects about itself. This image, if successful, boosts countries’ competitiveness in all sectors. This study focuses on the liaison between tourism and a country’s brand. Researchers examined the body of networks and the interactions thereof. For this reason, the authors established a model to explain the relationship and coaction of a country’s brand and tourism. To build up research models, the authors used data from publicly available international public databases. The researchers detected areas where the improvement achieved increases the value of the country’s brand in addition to its competitiveness in tourism.
Assessment of Stochastic Numerical Schemes for Stochastic Differential Equations with “White Noise” Using Itô’s Integral
Authors: Bogoi Alina , Dan Cătălina-Ilinca , Strătilă Sergiu , Cican Grigore , Crunteanu Daniel-Eugeniu
Published: November 2023

Stochastic Differential Equations (SDEs) model physical phenomena dominated by stochastic processes. They represent a method for studying the dynamic evolution of a physical phenomenon, like ordinary or partial differential equations, but with an additional term called “noise” that represents a perturbing factor that cannot be attached to a classical mathematical model. In this paper, we study weak and strong convergence for six numerical schemes applied to a multiplicative noise, an additive, and a system of SDEs. The Efficient Runge–Kutta (ERK) technique, however, comes out as the top performer, displaying the best convergence features in all circumstances, including in the difficult setting of multiplicative noise. This result highlights the importance of researching cutting-edge numerical techniques built especially for stochastic systems and we consider to be of good help to the MATLAB function code for the ERK method.
Immersive VR (Virtual Reality) Simulator for Vein Blood Sampling
Authors: Kim Jun-Seong , Kim Kun-Woo , Yang Seong-Won , Chung Joong-Wha , Moon Seong-Yong
Published: November 2023

Vein blood sampling is a method of mass blood sampling that involves drawing blood from a vein for blood type discrimination, confirmation of various physiological indicators, disease diagnosis, etc.; it is the most commonly used blood sampling method. An important aspect of vein blood sampling is the search for the exact location of the vein for insertion of the syringe to draw blood. This is influenced by obesity as well as skin and blood vessel conditions in the patient and the experience of the clinical technologist, nurse, and resident who performs the blood sampling. Frequent practice is required to effectively perform blood sampling techniques. However, due to the many limitations of the practice room or laboratory, there is a problem of using only a limited environment and model for clinical practice. As a result, many medical educational institutions have situations in which only fragmentary clinical practices are performed, and it is difficult to practice many blood sampling skills, so they do not provide enough experience to understand the actual skill field. In this paper, we propose a virtual-reality-based vein blood sampling simulator that allows the practice of blood sampling techniques without limitation. The proposed vein blood sampling simulator can operate a 3D model related to vein blood sampling using an HMD controller and a haptic device in a virtual space for vein blood sampling practice by wearing an HMD (head-mounted display). Vein blood sampling can also be practiced through interaction with the patient 3D model. In addition, the effectiveness of a simulator developed for dental students was verified, and as a result of the verification, the potential of the proposed vein blood sampling simulator was confirmed.
Assessment of the Adverse Health Effects of Aflatoxin Exposure from Unpackaged Peanut Oil in Guangdong, China
Authors: He Zhini , Chen Zihui , Mo Yunying , Lu Xiaodan , Luo Yanheng , Lin Shaoliang , Zhong Yanxu , Deng Junfeng , Zheng Shixiong , Xia Lei , Wu Hang , Routledge Michael N. , Hong Ye , Xian Xiaoyu , Yang Xingfen , Gong Yunyun
Published: November 2023

Aflatoxins are liver carcinogens and are common contaminants in unpackaged peanut (UPP) oil. However, the health risks associated with consuming aflatoxins in UPP oil remain unclear. In this study, aflatoxin contamination in 143 UPP oil samples from Guangdong Province were assessed via liquid chromatography–tandem mass spectrometry (LC-MS). We also recruited 168 human subjects, who consumed this oil, to measure their liver functions and lipid metabolism status. Aflatoxin B1 (AFB1) was detected in 79.72% of the UPP oil samples, with levels ranging from 0.02 to 174.13 μg/kg. The average daily human intake of AFB1 from UPP oil was 3.14 ng/kg·bw/day; therefore, the incidence of liver cancer, caused by intake of 1 ng/kg·bw/day AFB1, was estimated to be 5.32 cases out of every 100,000 persons per year. Meanwhile, Hepatitis B virus (HBV) infection and AFB1 exposure exerted a synergistic effect to cause liver dysfunction. In addition, the triglycerides (TG) abnormal rate was statistically significant when using AFB1 to estimate daily intake (EDI) quartile spacing grouping (p = 0.011). In conclusion, high aflatoxin exposure may exacerbate the harmful effects of HBV infection on liver function. Contamination of UPP oil with aflatoxins in Guangdong urgently requires more attention, and public health management of the consumer population is urgently required.
Variability in measured resonance frequencies and loss factors of a bolted panel structure
Authors: Jerome Trevor
Published: November 2023

Structural bolted joints are not perfectly contiguous or rigid. Instead, the joint stiffness, mass, and damping depend on many parameters including the joint thickness, number and size of bolts, the bolt preload, and the motion of the jointed region. Also, damping can vary with motion
type (stick-slip for friction and impact for joint opening and closing) and amplitude. Finally, all joint dynamic parameters vary from installation to installation. We measured the variability of bolted joint stiffness and damping for four low-order modes of a bolted Aluminum panel structure
- two flexural modes and two twisting/torsional modes. While a large portion of the bolted joint literature features a lap joint or an assembly of two beams, there are few studies dealing with flanged connections of plates. Two flanged rectangular panels were bolted together with two cap screws
threaded into one of the flanges. Modal resonance frequencies and loss factors were tracked throughout a decaying vibration response induced by hammer impacts on the structure. Measurements were made for three bolt torques and two flange thicknesses, three impact force strengths, and three
repeated assemblies (the panel was unbolted and bolted together again). Relative motion at the joint with respect to mode shape determines whether the joint primarily adds stiffness or mass, as well as the amount of added damping. Not surprisingly, reducing bolt preload reduces resonance frequencies.
Only minor variability in resonance frequency and loss factor were observed for most conditions, except for cases with low bolt preload. In these cases mild nonlinearity is evident in the response, where resonance frequency decreases and loss factor increases with increasing vibration amplitude.
Also, variability of resonance frequencies and loss factors between installations often exceeds that due to variability in preload and vibration amplitude.
Road Pattern Classification Using Deep Learning for Noise Data for Autonomous Driving Vehicle
Authors: Lee Sang Kwon
Published: November 2023

Tire-pavement Interaction Noise (TPIN) is noise caused by interactions between rolling tires and road surfaces. After measuring the TPIN using a microphone, transform TPIN to images using continuous wavelet transform (CWT). The transformed images are used to classify the driving road
and tire using convolutional neural network (CNN). The road and tire classification network using CNN is required for braking systems in autonomous vehicle. The CNN in this paper can classify snow road, asphalt road, and two types of tires with over 97% accuracy.
Sound transmission reduction of double walls using porous and viscoelastic materials
Authors: Bakhouche Soraya
Published: November 2023

Double-walled structures separated by an acoustic cavity are widely used in many applications for sound insulation purposes. Their sound transmission loss is estimated by calculating the ratio between the incident sound power on the first wall and the radiated sound power by the second.
The introduction of a melamine foam associated with heavy masses brings a better sound insulation against external noise. In this context, this work deals with the numerical prediction of the sound transmission of a roller shutter box equipped with porous and viscoelastic materials. Such system
can be modeled, at first approximation, as a double-walled structure. The proposed approach is based on the use of the finite element method for the modeling of the different solid and fluid domains of the problem. A mixed displacement pressure formulation of the Biot poroelasticity equations
is used to model the poroelastic domain. The structure is excited by a diffuse field represented by a superposition of plane waves with random phases and directions. The acoustic power radiated by the second wall is calculated by applying the Rayleigh integral method. The results show the
importance of optimizing the position of the porous and the heavy layers to maximize the sound insulation.
Vibro-acoustic optimisation of composites with multiple parameters
Authors: Klaerner Matthias
Published: November 2023

Composites, light and stiff, are sensitive to propagate structure borne sound but simultaneously offer a wide range of adjusting the material behaviour. Thereby, stiffness and damping are contradictory material properties related to the fibre orientation. Commonly, the composite design
is based on FEA simulations requiring special modelling efforts. In contrast, the multi-dimensional optimisation of a laminate with numerous layers requires very fast numerical solutions for numerous repetitions. Using a complex but efficient vibro-acoustic simulation model is essential in
optimising composites. Therefore, the FEA is extended by a strain energy based modal damping approach for the layerwise accumulation of the anisotropic damping. In addition, the radiated sound power is determined by a velocity-based approach directly on steady state structural simulations
avoiding a complex multi-physical modelling. Moreover, the frequency dependent radiation is consolidated to a single scalar optimisation objective with analytical formulations of amplification factors of the modal power contributions. This methodology is applied to design a vibro-acoustically
optimised composite part. The achieved results emerge the NVH potential of thermoplastic composites compared to a steel reference case.