bluuebunny/crossref_metadata_2025 huggingface dataset:
Phytochemical and Functional Diversity of Enzyme-Assisted Extracts from Hippophae rhamnoides L., Aralia cordata Thunb., and Cannabis sativa L.
Authors: Januskevice Viktorija , Gomes Ana Maria , Sousa Sérgio , Barbosa Joana Cristina , Vedor Rita , Martusevice Paulina , Liaudanskas Mindaugas , Zvikas Vaidotas , Viskelis Pranas , Cesoniene Laima , Balciunaitiene Aiste , Viskelis Jonas , Szonn Sonata , Urbonaviciene Dalia
Published: August 2024

Plant leaves are a source of essential phenolic compounds, which have numerous health benefits and can be used in multiple applications. While various techniques are available for recovering bioactive compounds from by-products, more data are needed on enzyme-assisted extraction (EAE). The aim of this study was to compare EAE and solid–liquid extraction (SLE), to evaluate the impact on bioactive compounds’ extraction yield, phytochemical composition, and the antioxidant, antimicrobial, and antidiabetic properties of Aralia cordata leaves and roots, sea buckthorn Hippophae rhamnoides, and hemp Cannabis sativa leaves. The results indicate that EAE with Viscozyme L enzyme (EAE_Visc) extracts of the tested plant leaves possess the highest yield, antioxidant activity, and total phenolic content. Moreover, the EAE_Visc extract increased by 40% the total sugar content compared to the control extract of A. cordata root. Interestingly, the sea buckthorn leaf extracts exhibited α-glucosidase inhibitory activity, which reached an almost 99% inhibition in all extracts. Furthermore, the sea buckthorn leaves SLE and EAE_Visc extracts possess antibacterial activity against Staphylococcus aureus. Additionally, scanning electron microscopy was used to examine changes in cell wall morphology after EAE. Overall, this study shows that EAE can be a promising method for increasing the yield and improving the functional properties of the resulting extracts in a fast and sustainable way compared to SLE.
Ultimate Support Pressure Determination for Shield Tunnel Faces in Saturated Strata Based on Seepage Flow Control
Authors: Hu Ziwei , Lei Mingfeng , Zhang Hu , Jia Chaojun , Shi Chenghua
Published: August 2024

Existing methods for calculating the ultimate support pressure of tunnel faces do not consider the control of seepage flow. Therefore, a model for calculating the ultimate support pressure under seepage conditions was established based on a two-dimensional water head distribution model and the upper bound theorem of limit analysis. The reliability of this method was verified through comparisons with other studies. Subsequently, the influence of water level and tunnel face water pressure coefficient on stability was analyzed. The results indicate that the ultimate support pressure is linearly positively correlated with the water level and tunnel face water pressure coefficient; as the water level increases and the water pressure coefficient decreases, the failure area extends and enlarges. Finally, an existing seepage flow calculation formula was introduced, and a method for calculating the ultimate support pressure based on seepage control was proposed. The appropriate tunnel face water pressure coefficient is determined through the seepage flow calculation formula, and the corresponding ultimate support pressure is then calculated. The results demonstrate that this method can provide better theoretical guidance for seepage control in tunnel faces in practical engineering.
Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
Authors: Bai Yujing , Wang Jun , Chen Penghui , Gong Ziwei , Xiong Qingxu
Published: August 2024

Gesture plays an important role in human–machine interaction. However, the insufficient accuracy and high complexity of gesture recognition have blocked its widespread application. A gesture recognition method that combines state machine and bidirectional long short-term memory (Bi-LSTM) fusion neural network is proposed to improve the accuracy and efficiency. Firstly, gestures with large movements are categorized into simple trajectory gestures and complex trajectory gestures in advance. Afterwards, different recognition methods are applied for the two categories of gestures, and the final result of gesture recognition is obtained by combining the outputs of the two methods. The specific method used is a state machine that recognizes six simple trajectory gestures and a bidirectional LSTM fusion neural network that recognizes four complex trajectory gestures. Finally, the experimental results show that the proposed simple trajectory gesture recognition method has an average accuracy of 99.58%, and the bidirectional LSTM fusion neural network has an average accuracy of 99.47%, which can efficiently and accurately recognize 10 gestures with large movements. In addition, by collecting more gesture data from untrained participants, it was verified that the proposed neural network has good generalization performance and can adapt to the various operating habits of different users.
Numerical Simulation Study of Cavity Formation in Soil under Blast Load
Authors: Dobrilović Mario , Dobrilović Ivana , Sućeska Muhamed , Težak Denis
Published: August 2024

The main applications of civil explosives in soils are soil compaction, mass excavation, and in situ pile creation. The suitability of explosives for each of these applications strongly depends upon the explosive properties and the soil properties. For those reasons, a reliable estimation or process simulation regarding cost efficiency and explosive work ability in the soil with known soil parameters is relevant. This paper presents a numerical simulation study of different types of soil (different amounts of gravel, sand, silt, and clay) under a blast load modeled using Ansys 2020 R1 Autodyn 2D hydrocode, with different types of explosives. The calculated results from the Ansys 2020 R1 Autodyn 2D and the experimental results obtained from the in situ cavity formation caused by blasting are presented. The Jones–Wilkins–Lee (JWL) equation of state parameters was calculated using EXPLO5 V7.01.01 supported by experimental data, while the soil and explosive properties were measured in laboratory and in situ.
A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
Authors: Ganesan Praveena , Ramesh G. P. , Puttamdappa C. , Anuradha Yarlagadda
Published: August 2024

Recently, Alzheimer’s disease (AD) is one of the common neurodegenerative disorders, which primarily occurs in old age. Structural magnetic resonance imaging (sMRI) is an effective imaging technique used in clinical practice for determining the period of AD patients. An efficient deep learning framework is proposed in this paper for AD detection, which is inspired from clinical practice. The proposed deep learning framework significantly enhances the performance of AD classification by requiring less processing time. Initially, in the proposed framework, the sMRI images are acquired from a real-time dataset and two online datasets including Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL), and Alzheimer’s Disease Neuroimaging Initiative (ADNI). Next, a fuzzy-based superpixel-clustering algorithm is introduced to segment the region of interest (RoI) in sMRI images. Then, the informative deep features are extracted in segmented RoI images by integrating the probabilistic local ternary pattern (PLTP), ResNet-50, and Visual Geometry Group (VGG)-16. Furthermore, the dimensionality reduction is accomplished by through the modified gorilla troops optimizer (MGTO). This process not only enhances the classification performance but also diminishes the processing time of the capsule network (CapsNet), which is employed to classify the classes of AD. In the MGTO algorithm, a quasi-reflection-based learning (QRBL) process is introduced for generating silverback’s quasi-refraction position for further improving the optimal position’s quality. The proposed fuzzy based superpixel-clustering algorithm and MGTO-CapsNet model obtained a pixel accuracy of 0.96, 0.94, and 0.98 and a classification accuracy of 99.88%, 96.38%, and 99.94% on the ADNI, real-time, and AIBL datasets, respectively.
Dynamic Analysis of Cortisol Hormone, Alpha-Amylase Enzyme, and Blood Lactate Levels during a Rowing Ergometer 6 km Race
Authors: Kuko Mate , Veršić Šime , Modrić Toni , Pavlinović Vladimir , Škomrlj Jakša , Perić Mia , Marić Dora , Foretić Nikola , Nikolovski Zoran
Published: August 2024

Rowing races require extreme physical and psychological effort from every athlete. This study aimed to determine the dynamics of the salivary cortisol and alpha-amylase, as well as blood lactate throughout the specific load represented by a 6 km rowing race, conducted on a rowing ergometer. The sample consisted of 11 junior and senior rowers from HVK Gusar in Split (n = 11) who actively competed at club and international levels. Variables consisted of three repeated oral samples of the hormone cortisol and the enzyme alpha-amylase determined in saliva and three repeated blood lactate samples. Potential differences in the levels of the studied variables at different time points were determined using a repeated-measures ANOVA test. The results showed different dynamics of hormonal (cortisol) and metabolic (alpha-amylase and lactates) variables. All variables experienced a significant post-race increase, while other changes were not significant. The results highlighted that high-intensity rowing causes an increase in the body’s cortisol, alpha-amylase, and lactate levels. This should be implemented in rowing training to find the right balance between high and low-intensity rowing, enabling athletes’ progression while reducing the risk of overtraining.
Is Mild Really Mild?: Generating Longitudinal Profiles of Stroke Survivor Impairment and Impact Using Unsupervised Machine Learning
Authors: Adikari Achini , Nawaratne Rashmika , De Silva Daswin , Carey David L. , Walsh Alistair , Baum Carolyn , Davis Stephen , Donnan Geoffrey A. , Alahakoon Damminda , Carey Leeanne M.
Published: August 2024

The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as ‘mild’, ‘moderate’, or ‘severe’ based on neurological impairment. Yet, stroke survivors argue that the classification of ‘mild’ does not represent the holistic experience and impact of stroke on their daily lives. In this observational cohort study, we aimed to identify different types of impairment profiles among stroke survivors classified as ‘mild’. We used survivors of mild stroke’ data from the START longitudinal stroke cohort (n = 73), with measures related to sensorimotor, cognition, depression, functional disability, physical activity, work, and social adjustment over 12 months. Given the multisource, multigranular, and unlabeled nature of the data, we utilized a structure-adapting, unsupervised machine learning approach, the growing self-organizing map (GSOM) algorithm, to generate distinct clinical profiles. These diverse impairment profiles revealed that survivors of mild stroke experience varying degrees of impairment and impact (cognitive, depression, physical activity, work/social adjustment) at different time points, despite the uniformity implied by their NIHSS-classified ‘mild’ stroke. This emphasizes the necessity of creating a holistic and more comprehensive representation of survivors of mild stroke’ needs over the first year after stroke to improve rehabilitation and poststroke care.
Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis
Authors: Liu Xiao-Yang , Zhang Kang-Qi , Fiumara Giacomo , Meo Pasquale De , Ficara Annamaria
Published: August 2024

Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used to represent the texts, and in the case of short messages, it is often difficult to obtain high-quality features. Conversely, methods based on deep learning can achieve better expressiveness, but these methods are computationally demanding and often suffer from over-fitting. This paper proposes a new adaptive evolutionary computational integrated learning model (AdaECELM) to overcome the problems encountered by traditional machine learning and deep learning models in sentiment analysis for short texts. AdaECELM consists of three phases: feature selection, sub classifier training, and global integration learning. First, a grid search is used for feature extraction and selection of term frequency-inverse document frequency (TF-IDF). Second, cuckoo search (CS) is introduced to optimize the combined hyperparameters in the sub-classifier support vector machine (SVM). Finally, the training set is divided into different feature subsets for sub-classifier training, and then the trained sub-classifiers are integrated and learned using the AdaBoost integrated soft voting method. Extensive experiments were conducted on six real polar sentiment analysis data sets. The results show that the AdaECELM model outperforms the traditional ML comparison methods according to evaluation metrics such as accuracy, precision, recall, and F1-score in all cases, and we report an improvement in accuracy exceeding 4.5%, the second-best competitor.
Structural Design and Analysis of Multi-Directional Foot Mobile Robot
Authors: Yang Hui , Shi Wen , Long Zhongjie , Jiang Zhouxiang
Published: August 2024

Traditional mobile robots have limited mobility in complex terrain environments. Generally, the closed-chain leg structure of a foot-type robot relies on the speed difference to turn, but it is difficult to complete the turning action in narrow spaces. Therefore, this study proposes a closed-chain foot-type robot that can move in multiple directions, inspired by the WATT-I leg structure. Firstly, the closed-chain single-leg structure is designed, and the leg structure is analyzed in terms of the degrees of freedom, kinematics, and singularity. A simulation is also carried out. Secondly, based on the present trajectory, a heuristic algorithm is used to solve the inverse trajectory problem, and the size of the mechanism is optimized. Finally, the steering mechanism of the leg with a zero turning radius is designed and analyzed, which achieves the steering function of the whole robot and satisfies the goal of enabling the foot robot to walk in all directions. This study provides theoretical guidance for the structural dimension optimization of the proposed foot mobile robot and its application in engineering fields such as rescue, exploration, and the military.
How Do Location-Based AR Games Enhance Value Co-Creation Experiences at Cultural Heritage Sites? A Process Perspective Analysis
Authors: Guo Jiahui , Xu Jiayi , Pan Younghwan
Published: August 2024

The sustainable development of tourism in cultural heritage sites benefits from the active participation of tourists in the co-creation process. Location-based AR games show great potential in tourists’ participation in creation and positive experiences. This study explores the relationship between the stage factors of the co-creation experience and the overall co-creation. Combining the service-dominant logic and process perspective of value co-creation theory, this research proposes a conceptual framework for co-creating experiences in cultural heritage tourism using augmented reality technology through two studies. In the first phase of the study, quantitative research was conducted on 256 visitors to measure the impact of factors in the three processes of pre-co-creation experience, on-site experience, and post-co-creation experience on the overall co-creation experience. In the study’s second phase, follow-up qualitative interviews were conducted based on multiple linear regression analysis results to expand the interpretation of the relationship and importance of factors affecting the co-creation experience process. The results show that psychological engagement, awareness, knowledge, and social relations during pre-visitation help enhance the overall co-creation experience. In contrast, the overall co-creation experience is enriched by real-time storytelling, interaction, and emotional resonance in both on-site and post-experience processes. The proposal of this framework model advances the discussion of augmented reality technology and co-creation experience to the empirical level. It provides a basis for further tourism co-creation experience design practice.
Augmented Reality Improved Knowledge and Efficiency of Root Canal Anatomy Learning: A Comparative Study
Authors: Alsalleeh Fahd , Okazaki Katsushi , Alkahtany Sarah , Alrwais Fatemah , Bendahmash Mohammad , Al Sadhan Ra’ed
Published: August 2024

Teaching root canal anatomy has traditionally been reliant on static methods, but recent studies have explored the potential of advanced technologies like augmented reality (AR) to enhance learning and address the limitations of traditional training methods, such as the requirement for spatial imagination and the inability to simulate clinical scenarios fully. This study evaluated the potential of AR as a tool for teaching root canal anatomy in preclinical training in endodontics for predoctoral dental students. Six cone beam computed tomography (CBCT) images of teeth were selected. Board-certified endodontist and radiologist recorded the tooth type and classification of root canals. Then, STereoLithography (STL) files of the same images were imported into a virtual reality (VR) application and viewed through a VR head-mounted display. Forty-three third-year dental students were asked questions about root canal anatomy based on the CBCT images, and then, after the AR model. The time to respond to each question and feedback was recorded. Student responses were paired, and the difference between CBCT and AR scores was examined using a paired-sample t-test and set to p = 0.05. Students demonstrated a significant improvement in their ability to answer questions about root canal anatomy after utilizing the AR model (p < 0.05). Female participants demonstrated significantly higher AR scores compared to male participants. However, gender did not significantly influence overall test scores. Furthermore, students required significantly less time to answer questions after using the AR model (M = 4.09, SD = 3.55) compared to the CBCT method (M = 15.21, SD = 8.01) (p < 0.05). This indicates that AR may improve learning efficiency alongside comprehension. In a positive feedback survey, 93% of students reported that the AR simulation led to a better understanding of root canal anatomy than traditional CBCT interpretation. While this study highlights the potential of AR in learning root canal anatomy, further research is needed to explore its long-term impact and efficacy in clinical settings.
Effect of Wettability and Permeability on Pore-Scale of CH4–Water Two-Phase Displacement Behavior in the Phase Field Model
Authors: Wang Zedong , Guo Chang , Liu Nan , Fan Kai , Zhang Xiangliang , Liu Ting
Published: August 2024

Hydraulic measures such as hydraulic slotting and hydraulic fracturing are commonly used in coal seam pressure relief and permeability enhancement. Two-phase flow patterns of CH4–water in pore-sized coal seams after hydraulic measures are critical to improve gas extraction efficiency. The phase field module in COMSOL Multiphysics™ 5.4 and the classical ordered porous media model were used in this paper. The characteristics of CH4–water two-phase immiscible displacement in coal seams under different capillary numbers (Ca) and viscosity ratios (M) were simulated and quantitatively analyzed. By changing the contact angle of the porous media, the flow patterns of CH4–water two-phase in coal with different wettability were simulated. Results show that wettability significantly affects the displacement efficiency of CH4. Additionally, by constructing a dual-permeability model to simulate the varying local permeability of the coal, the flow patterns of different Ca and M in dual-permeability media were further investigated. It is found that CH4 preferentially invades high-permeability regions, and the displacement efficiency in low-permeability regions increases with higher Ca and M, providing a reference for gas extraction from coal seams after hydraulic measures.
Frequency Coordination Control Strategy for Large-Scale Wind Power Transmission Systems Based on Hybrid DC Transmission Technology with Deep Q Network Assistance
Authors: Hui Jianfeng , Tai Keqiang , Yan Ruitao , Wang Yuhong , Yuan Meng , Zheng Zongsheng , Gao Shilin , Liao Jianquan
Published: August 2024

Wind power is currently the most mature representative of sustainable energy generation technology, which has been developed and utilized on a large scale worldwide. The random and fluctuating nature of wind power output poses a threat to the secure and stable operation of the system. Consequently, the transmission of wind power has garnered considerable attention as a crucial factor in mitigating the challenges associated with wind power integration. In this paper, an artificial-intelligence-aided frequency coordination control strategy applicable to wind power transmission systems based on hybrid DC transmission technology is proposed. The line commutated converter (LCC) station at the sending end implements the strategy of auxiliary frequency control (AFC) and automatic generation control (AGC) to cooperate with each other in order to assist the system frequency regulation. The AFC controller is designed based on the variable forgetting factor recursive least squares (VFF-RLS) algorithm for system identification. First, the VFF-RLS algorithm is used to identify the open-loop transfer function of the system. Then, the AFC controller is designed based on the root locus method to achieve precise control of the system frequency. The DC line power modulation quantity is introduced in the AGC to automatically track the active power fluctuation and frequency deviation of the system. The AGC utilizes the classical proportional-integral (PI) control. By selecting the integrated time absolute error (ITAE) performance index to construct the reward function, and using a deep Q-network (DQN) for controller parameter optimization, it achieves improved regulation performance for the AGC. The voltage source converter (VSC) station at the receiving end implements an adaptive DC voltage droop control (ADC)strategy. Finally, the effectiveness and robustness of the proposed frequency control strategy are verified through simulation experiments.
A Comprehensive Review: Multisensory and Cross-Cultural Approaches to Driver Emotion Modulation in Vehicle Systems
Authors: Zhang Jieshu , Raja Ghazilla Raja Ariffin Bin , Yap Hwa Jen , Gan Woun Yoong
Published: August 2024

Road accidents are caused by multiple factors. Aggressive driving and traffic violations account for 74% of road traffic accidents. In total, 92% of fatalities occur in low- and middle-income countries. Drivers’ emotions significantly influence driving performance, making emotional modulation critical during vehicle interaction. With the rise of smart vehicles, in-vehicle affective computing and human-centered design have gained importance. This review analyzes 802 studies related to driver emotional regulation, focusing on 74 studies regarding sensory stimuli and cultural contexts. The results show that single-sensory methods dominate, yet multisensory approaches using auditory and visual elements are more effective. Most studies overlook cultural factors, particularly the differences in East–West cultural values, indicating a need to tailor modulation methods based on cultural preferences. Designs must emphasize adaptability and cultural consistency. This review aims to analyze driver emotional modulation thoroughly, providing key insights for developing vehicle systems that meet the diverse emotional and cultural needs of global drivers. Future research should focus on creating multisensory emotional modulation systems that offer positive reinforcement without causing excessive relaxation or aggression, accommodating subtle cultural and individual differences, thus enhancing the safety of autonomous driving.
CFD Analysis of Aerodynamic Characteristics in a Square-Shaped Swarm Formation of Four Quadcopter UAVs
Authors: İnan Ahmet Talat , Çetin Berkay
Published: August 2024

The aerodynamic behavior of a square-shaped formation of four quadcopter UAVs flying in a swarm is investigated in detail through three-dimensional computer simulations utilizing Computational Fluid Dynamics (CFD) methodology. The swarm configuration comprises four UAVs positioned with two in the upper row and two in the lower row along the same propeller axes. The flow profile generated by the UAV propellers rotating at 10,000 revolutions per minute is analyzed parametrically using the Multiple Reference Frame (MRF) technique. UAVs within the swarm are positioned at 75 cm from the motion centers of adjacent propellers. This distance, the effects of horizontally and vertically positioned UAVs on each other, and the collective behavior of the swarm are thoroughly examined. Pressure, velocity, and turbulent kinetic energy values are meticulously analyzed. This research represents a milestone in understanding the aerodynamic characteristics of UAV swarms and the optimization of swarm performance. The findings highlight effective factors in swarm flights and their consequences for UAVs. Additionally, the article describes the “near-UAV phenomenon”. Furthermore, the methodology developed for CFD simulations provides an approach to analyzing close flight scenarios and evaluating their performance in various swarm configurations. These achievements contribute to the future development of UAV technology.
The Long-Term Performance of a High-Density Polyethylene Geomembrane with Non-Parametric Statistic Analysis and Its Contribution to the Sustainable Development Goals
Authors: Urashima Beatriz M. C. , Santos Renato , Ferreira Lucas D. , Inui Toru , Urashima Denise C. , Duarte Anderson R.
Published: August 2024

The tailings from gold beneficiation can cause various negative impacts, necessitating measures to prevent their transport and environmental contamination. Geomembranes serve as hydraulic barriers in mining tailings reservoirs, thereby supporting the Sustainable Development Goals (SDGs). To ensure that the geomembrane effectively mitigates environmental impact, it is essential to study its durability when applied in the field. This article examines the long-term performance of an HDPE geomembrane exposed for 7 and 11 years at a gold mining tailing site in Brazil. Samples were exhumed from different locations at the dam, and their properties were evaluated. Non-parametric statistics were employed using the Kernel Density Estimator (KDE). For the 11-year-old geomembranes, the probability of the geomembrane reaching nominal failure in terms of tensile strength was 0.4%. The peel separation values exceeded the maximum allowable by the GRI GM13 standard. Although the geomembranes showed significant antioxidant depletion, suggesting they were close to or had already reached their residual stages, they approached nominal failure based on their stress crack resistance but did not rupture. The environmental analysis indicated no significant contamination in the area, corroborating that the geomembrane is fulfilling its function. The non-parametric methodology proved promising for durability analysis and could be applied to other engineering projects with geosynthetics, thereby adding reliability to decision-making in alignment with sustainable development.
Heart Murmur Quality Detection Using Deep Neural Networks with Attention Mechanism
Authors: Wu Tingwei , Huang Zhaohan , Li Shilong , Zhao Qijun , Pan Fan
Published: August 2024

Heart murmurs play a critical role in assessing the condition of the heart. Murmur quality reflects the subjective human perception of heart murmurs and is an important characteristic strongly linked to cardiovascular diseases (CVDs). This study aims to use deep neural networks to classify the patients’ murmur quality (i.e., harsh and blowing) from phonocardiogram (PCG) signals. The phonocardiogram recordings with murmurs used for this task are from the CirCor DigiScope Phonocardiogram dataset, which provides the murmur quality labels. The recordings were segmented, and a dataset of 1266 segments with average lengths of 4.1 s from 164 patients’ recordings was obtained. Each patient usually has multiple segments. A deep neural network model based on convolutional neural networks (CNNs) with channel attention and gated recurrent unit (GRU) networks was first used to extract features from the log-Mel spectrograms of segments. Then, the features of different segments from one patient were weighted by the proposed “Feature Attention” module based on the attention mechanism. The “Feature Attention” module contains a layer of global pooling and two fully connected layers. Through it, the different features can learn their weight, which can help the deep learning model distinguish the importance of different features of one patient. Finally, the detection results were produced. The cross-entropy loss function was used to train the model, and five-fold cross-validation was employed to evaluate the performance of the proposed methods. The accuracy of detecting the quality of patients’ murmurs is 73.6%. The F1-scores (precision and recall) for the murmurs of harsh and blowing are 76.8% (73.0%, 83.0%) and 67.8% (76.0%, 63.3%), respectively. The proposed methods have been thoroughly evaluated and have the potential to assist physicians with the diagnosis of cardiovascular diseases as well as explore the relationship between murmur quality and cardiovascular diseases in depth.
Adaptive Imputation of Irregular Truncated Signals with Machine Learning
Authors: Ward Tyler , Jenab Kouroush , Ortega-Moody Jorge
Published: August 2024

In modern advanced manufacturing systems, the use of smart sensors and other Internet of Things (IoT) technology to provide real-time feedback to operators about the condition of various machinery or other equipment is prevalent. A notable issue in such IoT-based advanced manufacturing systems is the problem of connectivity, where a dropped Internet connection can lead to the loss of important condition data from a machine. Such gaps in the data, which we call irregular truncated signals, can lead to incorrect assumptions about the status of a machine and other flawed decision-making processes. This paper presents an adaptive data imputation framework based on machine learning (ML) algorithms to assess whether the missing data in a signal is missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR) and automatically select an appropriate ML-based data imputation model to deal with the missing data. Our results demonstrate the potential for applying ML algorithms to the challenge of irregularly truncated signals, as well as the capability of our adaptive framework to intelligently solve this issue.
Experimental Study on Shear Creep Characteristics of Residual Soil with Different Stone Content
Authors: Dong Jinyu , Wang Tanyu , Zhao Yawen
Published: August 2024

The residual soil on a slope can slowly move downward under the influence of gravity, forming a creep landslide. These types of landslides are known for their extensive coverage, significant magnitude, and prolonged duration of hazard. A systematic study of the creep properties of creep landslide geotechnical bodies is essential for the analysis of the deformation process and long-term safety evaluation of landslides. This paper focuses on studying a creep landslide involving residual soil in western Henan Province. The creep characteristics of residual soil with different stone content are investigated through direct shear creep experiments. The findings reveal that stone content has a profound impact on the creep behavior of residual soil. As the stone content of the soil increased, the structure of the test soil changed significantly, resulting in a gradual decrease in its shear creep. The Burgers model can effectively fit the deceleration creep and steady-state creep stages of the residual soil. With the increase in stone content, the four parameters of the Burgers model show a significant increase, with the instantaneous elasticity coefficient G1 and the viscosity coefficient η1 experiencing more noticeable changes. The average long-term strength of specimens with different stone content is only 54% of their instantaneous strength. Additionally, as the stone content increases, the ratio of long-term strength to instantaneous strength also increases. Notably, the long-term strength of specimens with 10–30% stone content is significantly lower than that of specimens with 50–70% stone content.
Integration of Relation Filtering and Multi-Task Learning in GlobalPointer for Entity and Relation Extraction
Authors: Liu Bin , Tao Jialin , Chen Wanyuan , Zhang Yijie , Chen Min , He Lei , Tang Dan
Published: August 2024

The rise of knowledge graphs has been instrumental in advancing artificial intelligence (AI) research. Extracting entity and relation triples from unstructured text is crucial for the construction of knowledge graphs. However, Chinese text has a complex grammatical structure, which may lead to the problem of overlapping entities. Previous pipeline models have struggled to address such overlap problems effectively, while joint models require entity annotations for each predefined relation in the set, which results in redundant relations. In addition, the traditional models often lead to task imbalance by overlooking the differences between tasks. To tackle these challenges, this research proposes a global pointer network based on relation prediction and loss function improvement (GPRL) for joint extraction of entities and relations. Experimental evaluations on the publicly available Chinese datasets DuIE2.0 and CMeIE demonstrate that the GPRL model achieves a 1.2–26.1% improvement in F1 score compared with baseline models. Further, experiments of overlapping classification conducted on CMeIE have also verified the effectiveness of overlapping triad extraction and ablation experiments. The model is helpful in identifying entities and relations accurately and can reduce redundancy by leveraging relation filtering and the global pointer network. In addition, the incorporation of a multi-task learning framework balances the loss functions of multiple tasks and enhances task interactions.
Optimizing the Influence of Fly Ash as an Anti-Sagging Additive in Highly Deviated Geothermal Well Drilling Fluids Using Surface Response Method
Authors: Oni Opeyemi , Fadairo Adesina
Published: August 2024

Weighting materials such as barite and ilmenite are crucial for controlling fluid density during deep or ultra-deep drilling operations. However, sagging poses significant challenges, especially in highly deviated high-pressure and high-temperature (HP/HT) wells. This leads to inadequate well control, wellbore instability, and variations in hydrostatic pressure in extended-reach wells. Given the challenges of experimental research, reliable prediction models are imperative for evaluating the interaction between the ratio of anti-sagging additives, temperature, and wellbore inclination on sag factor (SF). This research presents statistical-based empirical models for predicting the SF at various wellbore inclinations (0°, 30°, 45°, 60°, 70°, 80°, and 90°) and assessing the influence of fly ash on the SF. The regression equations, developed using the Response Surface Methodology in Minitab 18 software, show high reliability, with R2 values approaching unity. Contour and surface response plots provide a clear understanding of the variable interactions. The analysis reveals that sagging is most severe at 60° to 65° inclination. At 400 °F and 60° inclination, adding 4 lb/bbl of fly ash reduces sagging in barite and ilmenite-densified fluid by 63.9% and 63.1%, respectively. Model validation shows high accuracy, with percentage errors below 3%. This study offers valuable insights for optimizing drilling fluid formulations in HP/HT well environments.
Tibetan Speech Synthesis Based on Pre-Traind Mixture Alignment FastSpeech2
Authors: Zhou Qing , Xu Xiaona , Zhao Yue
Published: August 2024

Most current research in Tibetan speech synthesis relies primarily on autoregressive models in deep learning. However, these models face challenges such as slow inference, skipped readings, and repetitions. To overcome these issues, we propose an enhanced non-autoregressive acoustic model combined with a vocoder for Tibetan speech synthesis. Specifically, we introduce the mixture alignment FastSpeech2 method to correct errors caused by hard alignment in the original FastSpeech2 method. This new method employs soft alignment at the level of Latin letters and hard alignment at the level of Tibetan characters, thereby improving alignment accuracy between text and speech and enhancing the naturalness and intelligibility of the synthesized speech. Additionally, we integrate pitch and energy information into the model, further enhancing overall synthesis quality. Furthermore, Tibetan has relatively smaller text-to-audio datasets compared to widely studied languages. To address these limited resources, we employ a transfer learning approach to pre-train the model with data from resource-rich languages. Subsequently, this pre-trained mixture alignment FastSpeech2 model is fine-tuned for Tibetan speech synthesis. Experimental results demonstrate that the mixture alignment FastSpeech2 model produces higher-quality speech compared to the original FastSpeech2 model, particularly when pre-trained on an English dataset, resulting in further improvements in clarity and naturalness.
Designing Digital Twin with IoT and AI in Warehouse to Support Optimization and Safety in Engineer-to-Order Manufacturing Process for Prefabricated Building Products
Authors: Pracucci Alessandro
Published: August 2024

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. This paper outlines a methodology encompassing users’ requirements and the design to support the development of a digital twin that integrates Internet of Things devices, Building Information Modeling, and artificial intelligence capabilities. It delves into the specific challenges of outdoor warehouse optimization and worker safety within the context of engineer-to-order manufacturing, and how the digital twin aims to address these issues through data collection, analysis, and visualization. The research is conducted through an in-depth analysis of the warehouse of Focchi S.p.A., a leading manufacturer of high-tech prefabricated building envelopes. Focchi’s production processes and stakeholder interactions are investigated, and the paper identifies key user groups and their multiple requirements for warehouse improvement. It also examines the potential of the digital twin to streamline communication, improve decision-making, and enhance safety protocols. While preliminary testing results are not yet available, the paper concludes by underlining the significant opportunities offered by a BIM-, IoT-, and AI-powered digital twin for engineer-to-order manufacturers. This research, developed within the IRIS project, serves as a promising model for integrating digital technologies into complex warehouse environments, paving the way for increased efficiency, safety, and ultimately, a competitive edge in the market of manufacturing companies working in the construction industry.
Experimental and Numerical Study of Air Flow Reversal Induced by Fire in an Inclined Mine Working
Authors: Levin Lev , Popov Maksim , Semin Mikhail , Zhikharev Sergey
Published: August 2024

Effective fire prevention in mine workings and tunnels requires a thorough theoretical analysis of the heat and mass transfer processes within these structures. This involves using established models to calculate non-isothermal air flow dynamics in long tunnels and mine workings. While the ventilation of tunnels has been extensively studied, significant challenges persist regarding mine ventilation systems, particularly due to their complex and branched topology. This study aimed to address these challenges and gaps in mine ventilation. We designed a laboratory bench to simulate an inclined mine working with a heat source (fire) and validated a mathematical model of heat and mass transfer in such settings. Using experimental measurements, we verified the model’s accuracy. It is important to note that our experimental and theoretical analyses focused solely on the thermal effects of a fire, without considering the release of harmful impurities. Using the validated model, we conducted multiparameter simulations to identify the conditions leading to the formation of a thermal slug in an inclined mine working and the subsequent reversal of air flow. The simulation data enabled us to determine the dependency of the critical heat release rate on the aerodynamic parameters of the mine working. Additionally, we evaluated the changes in average air density within a mine working at the critical heat release rate. These findings are crucial for the further development of a network-based method to analyze air flow stability in mine ventilation networks during fires.
A Prediction Method for the Average Winding Temperature of a Transformer Based on the Fully Connected Neural Network
Authors: Feng Junjie , Feng Ziyu , Jiang Guojun , Zhang Guangyong , Jin Wei , Zhu Huijun
Published: August 2024

The average winding temperature of a transformer (AWTT), serving as a key indicator for assessing the running state of the transformer, is of utmost importance in determining a transformer’s electrical properties and the insulation longevity of the transformer. An accurate prediction of AWTT is essential for ensuring the safe operation of the transformer. A novel method for predicting AWTT is introduced based on the analysis of field monitoring data. Firstly, the thermal characteristics and operational mechanisms of oil-immersed transformers are examined. Secondly, a factor analysis model is developed to streamline the network structure, accounting for the strong correlations among ambient temperature, load current, and top oil temperature. Thirdly, the independent temperature factor and load factor are extracted as pivotal features, and then input into the fully connected neural network to predict AWTT. Through a case study involving a 110 kV/10 kV oil-immersed transformer, the results show that the proposed method reduces redundant correlation information compared to traditional methods and improves the prediction accuracy of AWTT, establishing a foundation for further transformer state assessments.
New Web-Based Ventilator Monitoring System Consisting of Central and Remote Mobile Applications in Intensive Care Units
Authors: Kim Kyuseok , Kim Yeonkyeong , Kim Young Sam , Kim Kyu Bom , Lee Su Hwan
Published: August 2024

A ventilator central monitoring system (VCMS) that can efficiently respond to and treat patients’ respiratory issues in intensive care units (ICUs) is critical. Using Internet of Things (IoT) technology without loss or delay in patient monitoring data, clinical staff can overcome spatial constraints in patient respiratory management by integrated monitoring of multiple ventilators and providing real-time information through remote mobile applications. This study aimed to establish a VCMS and assess its effectiveness in an ICU setting. A VCMS comprises central monitoring and mobile applications, with significant real-time information from multiple patient monitors and ventilator devices stored and managed through the VCMS server, establishing an integrated monitoring environment on a web-based platform. The developed VCMS was analyzed in terms of real-time display and data transmission. Twenty-one respiratory physicians and staff members participated in usability and satisfaction surveys on the developed VCMS. The data transfer capacity derived an error of approximately 10−7, and the difference in data transmission capacity was approximately 1.99×10−7±9.97×10−6 with a 95% confidence interval of −1.16×10−7 to 5.13×10−7 among 18 ventilators and patient monitors. The proposed VCMS could transmit data from various devices without loss of information within the ICU. The medical software validation, consisting of 37 tasks and 9 scenarios, showed a task completion rate of approximately 92%, with a 95% confidence interval of 88.81–90.43. The satisfaction survey consisted of 23 items and showed results of approximately 4.66 points out of 5. These results demonstrated that the VCMS can be readily used by clinical ICU staff, confirming its clinical utility and applicability. The proposed VCMS can help clinical staff quickly respond to the alarm of abnormal events and diagnose and treat based on longitudinal patient data. The mobile applications overcame space constraints, such as isolation to prevent respiratory infection transmission of clinical staff for continuous monitoring of respiratory patients and enabled rapid consultation, ensuring consistent care.
Capturing Snowmelt Runoff Onset Date under Different Land Cover Types Using Synthetic Aperture Radar: Case Study of Sierra Nevada Mountains, USA
Authors: Gao Bing , Ma Wei
Published: August 2024

Snow plays a crucial role in the global water and energy cycles, and its melting process can have a series of impacts on hydrological or climatic systems. Accurately capturing the timing of snowmelt runoff is essential for the utilization of snow resources and the early warning of snow-related disasters. A synthetic aperture radar (SAR) offers an effective means for capturing snowmelt runoff onset dates (RODs) over large areas, but its accuracy under different land cover types remains unclear. This study focuses on the Sierra Nevada Mountains and surrounding areas in the western United States. Using a total of 3117 Sentinel-1 images from 2017 to 2023, we extracted the annual ROD based on the Google Earth Engine (GEE) platform. The satellite extraction results were validated using the ROD derived from the snow water equivalent (SWE) data from 125 stations within the study area. The mean absolute errors (MAEs) for the four land cover types—tree cover, shrubland, grassland, and bare land—are 24, 18, 18, and 16 d, respectively. It indicates that vegetation significantly influences the accuracy of the ROD captured from Sentinel-1 data. Furthermore, we analyze the variation trends in the ROD from 2017 to 2023. The average ROD captured by the stations shows an advancing trend under different land cover types, while that derived from Sentinel-1 data only exhibits an advancing trend in bare land areas. It indicates that vegetation leads to a delayed trend in the ROD captured by using Sentinel-1 data, opposite to the results from the stations. Meanwhile, the variation trends of the average ROD captured by both methods are not significant (p > 0.05) due to the impact of the extreme snowfall in 2023. Finally, we analyze the influence of the SWE on RODs under different land cover types. A significant correlation (p < 0.05) is observed between the SWE and ROD captured from both stations and Sentinel-1 data. An increase in the SWE causes a delay in the ROD, with a greater delay rate in vegetated areas. These findings will provide vital reference for the accurate acquisition of the ROD and water resources management in the study area.