bluuebunny/crossref_metadata_2025 huggingface dataset:
Pedestrian Trajectory Prediction in Crowded Environments Using Social Attention Graph Neural Networks
Authors: Zong Mengya , Chang Yuchen , Dang Yutian , Wang Kaiping
Published: October 2024

Trajectory prediction is a key component in the development of applications such as mixed urban traffic management and public safety. Traditional models have struggled with the complexity of modeling dynamic crowd interactions, the intricacies of spatiotemporal dependencies, and environmental constraints. Addressing these challenges, this paper introduces the innovative Social Attention Graph Neural Network (SA-GAT) framework. Utilizing Long Short-Term Memory (LSTM) networks, SA-GAT encodes pedestrian trajectory data to extract temporal correlations, while Graph Attention Networks (GAT) are employed to precisely capture the subtle interactions among pedestrians. The SA-GAT framework boosts its predictive accuracy with two key innovations. First, it features a Scene Potential Module that utilizes a Scene Tensor to dynamically capture the interplay between crowds and their environment. Second, it incorporates a Transition Intention Module with a Transition Tensor, which interprets latent transfer probabilities from trajectory data to reveal pedestrians’ implicit intentions at specific locations. Based on AnyLogic modeling of the metro station on Line 10 of Chengdu Shuangliu Airport, China, numerical studies reveal that the SA-GAT model achieves a substantial reduction in ADE and FDE metrics by 34.22% and 38.04% compared to baseline models.
Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
Authors: Zhao Xi , Zhang Yuekun , Zhou Yaqing
Published: October 2024

For the reconstruction of objects during hand–object interactions, accurate pose estimation is indispensable. By improving the precision of pose estimation, the accuracy of the 3D reconstruction results can be enhanced. Recently, pose tracking techniques are no longer limited to individual objects, leading to advancements in the reconstruction of objects interacting with other objects. However, most methods struggle to handle incomplete target information in complex scenes and mutual interference between objects in the environment, leading to a decrease in pose estimation accuracy. We proposed an improved algorithm building upon the existing BundleSDF framework, which enables more robust and accurate tracking by considering the occlusion relationships between objects. First of all, for detecting changes in occlusion relationships, we segment the target and compute dual-layer masks. Secondly, rough pose estimation is performed through feature matching, and a keyframe pool is introduced for pose optimization, which is maintained based on occlusion relationships. Lastly, the estimated results of historical frames are used to train an object neural field to assist in the subsequent pose-tracking process. Experimental verification shows that on the HO-3D dataset, our method can significantly improve the accuracy and robustness of object tracking in frequent interactions, providing new ideas for object pose-tracking tasks in complex scenes.
Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
Authors: Park Jinwan
Published: October 2024

Accurate forecasting of ship encounter positions is crucial for preventing collisions at sea. This paper presents a framework for predicting a ship’s trajectory using a sparse Gaussian process. The proposed method effectively addresses the limitations of existing full Gaussian processes, specifically the significant storage requirements and time complexity associated with data training. The model is trained using Automatic Identification System (AIS) data on trajectories, with hyperparameters optimized through a genetic algorithm. Experimental analysis demonstrates that the proposed model reduces average time complexity by 61.3 s and improves average prediction error to 9.2 m compared to full Gaussian-process-based models.
Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
Authors: Themelis Nikos , Nikolaidis George , Zagkas Vasilios
Published: October 2024

A hull and propeller biofouling assessment framework is presented and demonstrated using a bulk carrier as a case study corresponding to an operational period of two and a half years. The aim is to support the decision-making process for optimizing maintenance related to hull and propeller cleaning actions. For the degradation assessment, an appropriate key performance indicator is defined comparing the expected shaft power required with the measured power under the same operational conditions. The power prediction models are data-driven based on machine learning algorithms. The process includes feature engineering, filtering, and data smoothing, while an evaluation of regression algorithms of the decision tree family is performed. The extra trees algorithm was selected, presenting a mean absolute percentage error of 1.1%. The analysis incorporates two prediction models corresponding to two different approaches. In the first, the model is employed as a reference performance baseline representing the clean vessel. When applied to a dataset reflecting advanced stages of biofouling, an average power increase of 11.3% is predicted. In the second approach, the model entails a temporal feature enabling the examination of scenarios at different points in time. Considering synthetic data corresponding to 300 days since hull cleaning, it was derived that the fouled vessel required an average 20.5% increase in power.
Impact of Bioactivity on Push-Out Bond Strength of AH Plus Bioceramic versus BC Bioceramic Root Canal Sealers
Authors: Abu Zeid Sawsan T. , Alnoury Arwa S.
Published: October 2024

This study compared the push-out bond strength and adaptation of the recently developed AH Plus bioceramic (AHP-Bio) root canal sealer with Bioceramic Endosequence (BC) and AH Plus (AHP) sealers when exposed to simulated body fluid for inducing bioactivity. Cross-section discs of 1 mm thick slices from obturated root canals were prepared and either kept dry or immersed in serum for 30 days. All discs were evaluated using scanning electron microscopy (SEM) and then subjected to a push-out test. The failure modes were also determined. The data were statistically analyzed using an ANOVA test at p < 0.05. In both environments, the BC sealer recorded the greatest bond strength, while the AHP-Bio sealer recorded the lowest mean values. However, bond strength was significantly improved after immersion in serum (p < 0.001). The chi-square test and Fisher’s exact test revealed a significant difference in failure mode among the tested groups at p < 0.001. The predominant failure mode was cohesive failure in both bioceramic sealers, with the greatest value for AHP-Bio (70%), and adhesive failure for AHP/gutta-percha (60%, 80%) in both environments. SEM revealed good dentin adaptation of the three sealers, with marked decreases in gaps at the bioceramic/dentin interface after immersion in serum. In conclusion, although BC exhibited greater push-out bond strength than AHP-Bio, the latter achieved good displacement resistance that increased when the sealer was exposed to simulated body fluid (serum).
Could Radiomic Signature on Chest CT Predict Epidermal Growth Factor Receptor Mutation in Non-Small-Cell Lung Cancer?
Authors: Kayi Cangir Ayten , Köksoy Elif Berna , Orhan Kaan , Özakinci Hilal , Gürsoy Çoruh Ayşegül , Gümüştepe Esra , Kahya Yusuf , İbrahimov Farrukh , Büyükceran Emre Utkan , Akyürek Serap , Sak Serpil Dizbay
Published: October 2024

Background: Detecting molecular drivers is crucial in the management of non-small-cell lung cancer (NSCLC). This study aimed to evaluate the use of pretreatment chest computed tomography (CT) radiomics features for predicting epidermal growth factor receptor (EGFR) mutation status in NSCLC. Materials and Methods: CT images were used to develop a radiomics-based model for predicting EGFR mutation status. Two different groups were formed from the dataset, namely groups for training (n = 380) and testing (n = 86). Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm on a radiomics platform. Machine learning methods were then applied to construct the radiomics models. Receiver operating characteristic curve analysis was conducted to assess the performance of the radiomics signature across different datasets and methods. Results: The frequency of EGFR mutation was 13.5% (58/430). A total of 1409 quantitative imaging features were extracted from CT images using the Radcloud platform. Among the six radiomics-based classifiers (k-Nearest Neighbor, Support Vector Machine (SVM), eXtreme Gradient Boosting, Random Forest, Logistic Regression, and Decision Tree), SVM demonstrated the highest area under the curve values in both the testing and training groups, reaching 0.87 and 0.98, respectively. Our model, which incorporated both clinical and radiomics data, successfully predicted EGFR mutation status with an accuracy rate of 86.9%. Conclusion: Our findings highlight the potential of radiomics features as a non-invasive predictive imaging biomarker for EGFR mutation status, which could enhance personalized treatment in NSCLC. Radiomics emerges as a valuable tool for identifying driver mutations, although further studies are necessary to validate its clinical utility in NSCLC.
Spatiotemporal Analysis of Urban Expansion in Beijing, China
Authors: Zhang Jing , Han Jichang , Li Yanan , Lei Na
Published: October 2024

Using Landsat TM/OLI remote sensing images and social statistical data from 1995, 2000, 2005, 2010, 2015, and 2020, construction land information in Beijing’s main urban area was extracted with ArcGIS 10.4.1 and other software. Based on calculations of the expansion speed, expansion intensity, fractal dimension, and elasticity coefficient, the spatiotemporal expansion characteristics of the urban area of Beijing were analyzed to reveal the laws and driving forces of urban expansion in Beijing. The results showed that the urban construction land area in Beijing expanded by a factor of 0.53 from 1995 to 2020, and its expansion speed and intensity gradually slowed. The overall expansion trend is that the central urban area remains basically unchanged, while the peripheral areas are rapidly expanding, showing a trend of rapid growth first and then stable growth, and the urban layout is basically stable. The urban expansion of Beijing has led to increasingly complex, tortuous, and unstable boundaries. Overall, the center of gravity of Beijing is moving toward the northeast, and the elasticity coefficient of urban expansion is 1.67 times that of a reasonable coefficient. The intensity and direction of urban expansion in Beijing are most significantly related to population mobility. Research on the expansion of Beijing lies the foundation for the integration and coordinated planning of resources in the various districts of Beijing and provides a basis for its sustainable development.
Research on Lithofacies Paleogeography and Caprock Evaluation of the Middle Cambrian in the Tarim Basin, NW China
Authors: Wu Xueqiong , Yang Wei , Bo Dongmei , Ji Tianyu , Dong Caiyuan , Luan Tiansi , Qu Junya
Published: October 2024

Cambrian subsalt dolomite is an important strategic area for natural gas exploration in the Tarim Basin. The gypsum-salt rocks, argillaceous mudstone and argillaceous dolomite strata developed in large areas of the Middle Cambrian can be used as good caprocks. The sealing ability and favorable area distribution of the Middle Cambrian caprock in the Tarim Basin are studied through the lithofacies paleogeography and microscopic evaluation of the Middle Cambrian strata in this paper. Based on the 2D seismic interpretation covering the entire basin, combined with data from drilling, outcrops, well logging, core samples and thin sections, the sedimentary characteristics and lithofacies paleogeography of the Middle Cambrian were studied and then the thickness of the Middle Cambrian gypsum-salt rocks, gypsiferous mudstone and gypsiferous dolomite was analyzed in the Tarim Basin. Studies suggest that the Middle Cambrian is primarily characterized by the development of restricted-platform facies. In the Awati Depression, the northern part of the Tazhong Uplift, the southern part of the Manxi Low Uplift, and the central and northern parts of the Bachu Uplift, the thickness of the gypsum-salt rock strata is relatively large. Moreover, centered on the northern part of the Bachu Uplift, the thickness of the gypsum-salt rocks decreases irregularly towards the periphery, forming a circumferential distribution. To investigate the sealing ability of caprocks, 64 core samples from four wells were examined under a microscope, and physical parameters as well as breakthrough-pressure tests were conducted. By establishing correlations between various parameters, the sealing ability of different rock types in the Cambrian formation within the study area was quantitatively assessed. The research suggests that gypsum-salt rocks exhibit superior sealing ability compared to gypsiferous mudstone and gypsiferous dolomite, but factors such as faults and geological conditions of gypsum can influence the sealing performance of caprocks. According to both micro- and macro-scale evaluations of the Cambrian strata in the study area, along with constraints imposed by actual drilling exploration results, a comprehensive evaluation method for assessing caprock sealing ability has been established. The results suggest that the Awat Depression, the western and southern parts of the Manxi Low Uplift, the northern and western parts of the Tazhong Uplift, and the central part of the Tabei Uplift are favorable areas for the development of caprocks.
Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention
Authors: Fu Sijia , Zhu Rui , Yu Feiyang
Published: October 2024

Building energy consumption prediction has always played a significant role in assessing building energy efficiency, building commissioning, and detecting and diagnosing building system faults. With the progress of society and economic development, building energy consumption is growing rapidly. Therefore, accurate and effective building energy consumption prediction is the basis of energy conservation. Although there are currently a large number of energy consumption research methods, each method has different applicability and advantages and disadvantages. This study proposes a Time Convolution Network model based on an attention mechanism, which combines the ability of the Time Convolution Network model to capture ultra-long time series information with the ability of the BiGRU model to integrate contextual information, improve model parallelism, and reduce the risk of overfitting. In order to tune the hyperparameters in the structure of this prediction model, such as the learning rate, the size of the convolutional kernel, and the number of recurrent units, this study chooses to use the Golden Jackal Optimization Algorithm for optimization. The study shows that this optimized model has better accuracy than models such as LSTM, SVM, and CNN.
A Multi-Scale Covariance Matrix Descriptor and an Accurate Transformation Estimation for Robust Point Cloud Registration
Authors: Xiong Fengguang , Kong Yu , Kuang Xinhe , Hu Mingyue , Zhang Zhiqiang , Shen Chaofan , Han Xie
Published: October 2024

This paper presents a robust point cloud registration method based on a multi-scale covariance matrix descriptor and an accurate transformation estimation. Compared with state-of-the-art feature descriptors, such as FPH, 3DSC, spin image, etc., our proposed multi-scale covariance matrix descriptor is superior for dealing with registration problems in a higher noise environment since the mean operation in generating the covariance matrix can filter out most of the noise-damaged samples or outliers and also make itself robust to noise. Compared with transformation estimation, such as feature matching, clustering, ICP, RANSAC, etc., our transformation estimation is able to find a better optimal transformation between a pair of point clouds since our transformation estimation is a multi-level point cloud transformation estimator including feature matching, coarse transformation estimation based on clustering, and a fine transformation estimation based on ICP. Experiment findings reveal that our proposed feature descriptor and transformation estimation outperforms state-of-the-art feature descriptors and transformation estimation, and registration effectiveness based on our registration framework of point cloud is extremely successful in the Stanford 3D Scanning Repository, the SpaceTime dataset, and the Kinect dataset, where the Stanford 3D Scanning Repository is known for its comprehensive collection of high-quality 3D scans, and the SpaceTime dataset and the Kinect dataset are captured by a SpaceTime Stereo scanner and a low-cost Microsoft Kinect scanner, respectively.
Microscopic Analysis of Structure and Wear for Metallic Materials Using SEM
Authors: Marček Ľuboš , Vavro Ján , Vavro Ján
Published: October 2024

The introduced work deals with the microscopic analysis of metallographically prepared selected metal materials structures, using a scanning electron microscope (SEM). Prepared samples of seamless steel pipes were subjected to a thorough microscopic examination from the outer surface to the inner regions in order to interpret the specific structure, including the change in the inner surfaces due to wear. The experiment demonstrated that the microstructure and character of the surfaces play a key role in the behavior of metallic materials in real conditions of hot water heating. Four pipe samples were monitored according to their use. The unused steel pipe (designated as sample No. 1) exhibited a rough outer surface with identified inclusions, while the used pipe (designated as sample No. 2) showed marks of intergranular corrosion and significant wear after long-term use. The older pipe (designated as sample No. 3) showed a decarburized area and inclusions containing sulfides and aluminum. The steel pipe with corrosion layers (designated as sample No. 4) exhibited a continuous corrosion layer with cavitation and cracks. The results of this study offer a comprehensive view of the influence of the nature of microstructure and wear on water flow in metal pipes, with an emphasis on the identification of possible risks associated with geometry change, corrosion, and wear. The findings form the basis for predicting degradation and appropriate maintenance in order to ensure their long and reliable service life under real conditions of use. They offer the possibility of continuing and expanding research and analysis of the use of metallic materials in comparison with polymers and composites.
Development of a Deep Learning Model for Predicting Speech Audiometry Using Pure-Tone Audiometry Data
Authors: Shin Jae Sung , Ma Jun , Choi Seong Jun , Kim Sungyeup , Hong Min
Published: October 2024

Speech audiometry is a vital tool in assessing an individual’s ability to perceive and comprehend speech, traditionally requiring specialized testing that can be time-consuming and resource -intensive. This paper approaches a novel use of deep learning to predict speech audiometry using pure-tone audiometry (PTA) data. By utilizing PTA data, which measure hearing sensitivity at specific frequencies, we aim to develop a model that can bypass the need for direct speech testing. This study investigates two neural network architectures: a multi-layer perceptron (MLP) and a one-dimensional convolutional neural network (1D-CNN). These models are trained to predict key speech audiometry outcomes, including speech recognition thresholds and speech discrimination scores. To evaluate the effectiveness of these models, we employed two key performance metrics: the coefficient of determination (R2) and mean absolute error (MAE). The MLP model demonstrated predictive solid power with an R2 score of 88.79% and an average MAE of 7.26, while the 1D-CNN model achieved a slightly higher level of accuracy with an MAE score of 88.35% and an MAE of 6.90. The superior performance of the 1D-CNN model suggests that it captures relevant features from PTA data more effectively than the MLP. These results show that both models hold promise for predicting speech audiometry, potentially simplifying the audiological evaluation process. This approach is applied in clinical settings for hearing loss assessment, the selection of hearing aids, and the development of personalized auditory rehabilitation programs.
Variation in Relative Abundance of Small Mammal Species Caught in Two Different Ecosystems and Implicated in the Spread of Emerging Pathogens in Mali
Authors: Koné Abdoulaye Kassoum , Diatta Georges , Niare Doumbo Safiatou , Ag Atteynine Solimane , Coulibaly Maïmouna , Diarra Adama Zan , Sagara Issaka , Djimdé Abdoulaye , Doumbo Ogobara K. , Thera Mahamadou Ali
Published: October 2024

Background: Small rodents and insectivores are potential reservoirs of many pathogens transmissible to humans, such as bacteria, parasites and viruses responsible for epidemics in sub-Saharan Africa, particularly in West Africa. Few studies on small mammal species in West Africa are available. Our previous findings from a study investigating emerging pathogens in two localities in Mali has determined the prevalence of pathogens in small mammals (rodents and insectivores). We used the data collected from this small mammal population with different eco-climatic characteristics to test hypothesis that small mammal distribution in different eco-climatic settings could explain the diversity and frequency of pathogens they carry. Methods: Sessions of trapping were carried out in December 2016 in Faladjè and Bougouni with “Besançon tous services” (BTS) wire mesh traps baited with peanut butter and/or onion. All animals captured were identified morphologically. Results: Out of 123 small rodents and insectivores captured over 674 trap-nights, 75 (60.97%) were from Faladjè and 48 (39.02%) from Bougouni. Of these, six species of small rodents belonged to the family Muridae (Mastomys erythroleucus, Mastomys natalensis, Rattus rattus, Praomys daltoni, Gerbilliscus gambianus, Taterillus gracilis) and two species of insectivores associated with the genus Crocidura spp. belonged to the family Soricidae and Erinaceidae (Crocidura cf olivieri and Atelerix cf albiventris), respectively. There is low species diversity within these two areas, but the variation in relative abundance is significant (binomial test, p ˂ 0.05) between Faladjè and Bougouni. Mastomys erythroleucus was the most dominant species (57.33%, 43/75) in Faladjè, while R. rattus dominated (37.5%,18/48) in Bougouni. Conclusions: These two species of small mammals potentially involved in the transmission of bacteria, parasites and pathogenic viruses to humans are differently present in two distinct eco-climatic areas in Mali.
MSMP-Net: A Multi-Scale Neural Network for End-to-End Monkeypox Virus Skin Lesion Classification
Authors: Huan Eryang , Dun Hui
Published: October 2024

Monkeypox is a zoonotic disease caused by monkeypox virus infection. It is easily transmitted among people and poses a major threat to human health, making it of great significance in public health. Therefore, this paper proposes MSMP-Net, a multi-scale neural network for end-to-end monkeypox virus skin lesion classification ConvNeXt is used as the backbone network, and designs such as inverse bottleneck layers and large convolution kernels are used to enhance the network’s feature extraction capabilities. In order to effectively utilize the multi-level feature maps generated by the backbone network, a multi-scale feature fusion structure was designed. By fusing the deepest feature maps of multi-scale features, the model’s ability to represent monkeypox image features is enhanced. Experimental results show that the accuracy, precision, recall, and F1-score of this method on the MSLD v2.0 dataset are 87.03 ± 3.43%, 87.59 ± 3.37%, 87.03 ± 3.43%, and 86.58 ± 3.66%, respectively.
Ground Ivy (Glechoma hederacea L.) as an Innovative Additive for Enriching Wheat Bread: Study on Flour Fermentation Properties, Dough Rheological Properties and Bread Quality
Authors: Pycia Karolina , Pawłowska Agata Maria , Posadzka Zuzanna , Kaszuba Joanna
Published: October 2024

The aim of the study was to assess the effect of water infusion of dried and crushed ground ivy (GH) on the fermentation properties of wheat flour (WF), farinographic properties of flour and dough (WD) and the quality of the obtained bread. In the tested systems, tap water was replaced with water infusion with GH (m/v) at a concentration of 1% (A), 3% (B) and 5% (C). As part of the research methodology, the fermentation properties of flour and rheological properties of dough were assessed using a farinograph, and bread was obtained using a single-phase method using yeast and its quality was assessed. As part of this, the antioxidant potential and the profile and level of polyphenol content were determined. It was shown that replacing water with GH infusions shortened the total fermentation time of the dough and reduced the fermentation capacity of the dough. In the farinographic evaluation, an increase in flour water absorption (54.0–57.0%), dough development time (2.3–7.6 min), dough stability and softening were observed with an increase in the concentration of the added GH infusion. In turn, the volume of the loaf and the specific volume of the bread decreased with increasing the concentration of the GH infusion. The bread crumb darkened, and the elasticity and chewiness of the crumb decreased in relation to the control sample. In turn, the presence of GH infusion did not significantly affect the hardness of the crumb. As the concentration of the added GH infusion increased, an increase in the antioxidant potential of bread and the content of polyphenols and flavonoids was observed, and the UPLC-PDA-MS/MS analysis allowed the identification of 11 polyphenols in the bread.
Accelerated Creep of Asphalt Concrete at Medium Temperatures
Authors: Iskakbayev Alibay , Teltayev Bagdat , Aitbayev Yerbol , Zhaisanbayev Azamat
Published: October 2024

In this paper, the accelerated creep under uniaxial tension of hot fine-grained dense asphalt concrete at temperatures of 22–24 °C and stresses from 0.084 MPa to 0.596 MPa is experimentally investigated. The names and brief descriptions of the creep curve and accelerated creep characteristics are given. A mathematical model is proposed that has physical meaning and satisfactorily approximates the asphalt concrete accelerated creep. The model parameters are determined by the Levenberg–Marquardt method. The deformation and time characteristics of the accelerated creep of asphalt concrete are established and analyzed.
Method for Enhancing AI Accuracy in Pressure Injury Detection Using Real and Synthetic Datasets
Authors: Kim Jaeseung , Kim Mujung , Youn Heejun , Lee Seunghyun , Kwon Soonchul , Park Kyung Hee
Published: October 2024

Pressure injuries pose significant health risks, especially for the elderly, immobile individuals, and those with sensory impairments. These injuries can rapidly become chronic, making initial diagnosis important. Due to the difficulty of transporting patients from local health facilities to higher-level general hospitals for treatment, it is essential to utilize telemedicine tools, such as chatbots, to ensure rapid initial diagnosis. Recent advances in artificial intelligence have demonstrated potential for medical imaging and disease classification. Ongoing research in the field of dermatological diseases focuses on disease classification. However, the assessment accuracy of artificial intelligence is often limited by unequal class distributions and insufficient dataset quantities. In this study, we aim to enhance the accuracy of artificial intelligence models by generating synthetic datasets. Specifically, we focused on training models for Pressure Injury assessment using both real and synthetic datasets. We used PI data at a domestic medical university. As part of our supplementary research, we established a chatbot system to facilitate the assessment of pressure injuries. Using both constructed and synthetic data, we achieved a top-1 accuracy of 92.03%. The experimental results demonstrate that combining real and synthetic data significantly improves model accuracy. These findings suggest that synthetic datasets can be effectively utilized to address the limitations of small-scale datasets in medical applications. Future research should explore the use of diverse synthetic data generation methods and validate model performance on a variety of datasets to enhance the generalization and robustness of AI models for Pressure Injury assessment.
Design of a Spin Test System for Burst Phenomen Considering Nonlinear Rotordynamic Effects
Authors: Uğur Mert , Kopmaz Osman
Published: October 2024

This study presents the development and evaluation of a spin test rig for high-speed rotating components. The innovation of this study is using a discrete model to determine dynamic positioning during the preliminary design phase based on vibration characteristics. The primary aim is to manage vibrations and alternating stress effects from rotor dynamics to prevent issues such as crack propagation or bursts before achieving desired speeds. MATLAB 2023b Simulink-based tool was developed, which played a role in providing the design of bearing support regions and stiffness values. The system underwent rigorous validation through structural, modal, and harmonic finite element analyses. Testing showed a maximum deviation of 4.2% for 10 kg specimen and 4.85% for 4 kg specimen between Simulink predictions and actual data, due to factors such as contact and bearing damping. For the 10 kg specimen, the maximum equivalent stress was 314.92 MPa with an alternating stress of 64.87 MPa at 0.5% damping ratio, representing 37.27% of the yield limit. For the 4 kg specimen, the maximum equivalent stress was 513 MPa with alternating stress below 40 MPa at 3% damping ratio, corresponding to 54.32% of the yield limit. This research enhances the reliability and performance of high-speed rotating machinery.
Evaluating Energy Retrofit and Indoor Environmental Quality in a Serbian Sports Facility: A Comprehensive Case Study
Authors: Miletić Mirjana , Komatina Dragan , Babić Lidija , Lukić Jasmina
Published: October 2024

This research addresses the challenge of enhancing energy efficiency in public buildings while maintaining or improving occupant comfort. With stricter modern energy regulations, many older facilities, such as sports halls built between 1960 and 1980, face the need for renovation to meet current standards. The central research question investigates what measures can be implemented to improve the energy efficiency of sports halls without compromising comfort for the occupants. This study examines strategies, techniques, and possibilities for optimizing energy performance during the rehabilitation of universal sports halls within sports centers. It includes a theoretical and analytical evaluation of various measures in line with existing regulations and thermal comfort requirements. This research uses simulation software, the Integrated Environmental Solutions Virtual Environment, to model different Passive House measures applied to a case study of a sports center built in 1976 in Belgrade. This study provides practical guidelines for enhancing thermal insulation on the building’s envelope to achieve energy savings. The application of these measures demonstrates that significant energy savings can be realized by focusing on specific sections of the building, such as the administrative areas, rather than the entire facility. The findings offer valuable insights into energy-optimization strategies for existing sports facilities, highlighting the practical application of measures to improve energy performance in a real-world context. The results contribute to the development of effective renovation practices for older sports buildings, ensuring they meet modern energy efficiency standards while maintaining optimal comfort for users.
Advanced Thermal Management for High-Power ICs: Optimizing Heatsink and Airflow Design
Authors: Jebelli Ali , Lotfi Nafiseh , Zare Mohammad Saeid , Yagoub Mustapha C. E.
Published: October 2024

In the rapidly advancing field of 5G technology, efficient thermal management is essential for enhancing the performance and reliability of high-power-density integrated circuits (ICs). This paper introduces an innovative approach to cooling these critical components, significantly surpassing traditional methods. Our design optimizes heatsink and fan configurations through systematic experimentation, varying fin shapes, heatsink dimensions, and fan speeds. The results demonstrate that fan velocity is the most critical factor in reducing IC temperatures, as increased airflow dramatically lowers thermal output. Expanding the heatsink surface area further improves heat dissipation by enhancing airflow interaction, while a larger copper heatsink boosts thermal conduction, effectively reducing the final IC temperature. These optimizations streamline the cooling process, minimizing the need for more complex and expensive equipment. This research sets a new benchmark in thermal management, fostering the development of more efficient and reliable electronic systems in the era of advanced wireless communications. Our approach brings a new dimension to existing research by focusing on the optimization of heatsink and airflow designs specifically for ICs. While previous studies have explored broader thermal management strategies, our work addresses specific challenges in heat dissipation by refining geometric configurations and fan speed adjustments. These optimizations result in measurable improvements in both efficiency and scalability, particularly within the context of high-power 5G systems.
Development of a New Image Reconstruction Method Using Bayesian Estimation with Limited View-Angle Projection Data for BNCT-SPECT
Authors: Lu Fan , Inamoto Haruka , Takeishi Shuto , Tamaki Shingo , Kusaka Sachie , Murata Isao
Published: October 2024

Boron Neutron Capture Therapy (BNCT) is an emerging radiation treatment for cancer, and its challenges are being explored. Systems capable of capturing real-time observations of this treatment’s effectiveness, particularly BNCT-SPECT methods that measure gamma rays emitted instantaneously from outside the body during nuclear reactions and that reconstruct images using Single Photon Emission Computed Tomography (SPECT) techniques, remain unavailable. BNCT-SPECT development is hindered by two main factors, the first being the projection angle. Unlike conventional SPECT, the projection angle range which is achievable by rotating a detector array cannot exceed approximately 90 degrees. Consequently, Fourier-based image reconstruction methods, requiring projections from at least 180 degrees, do not apply to BNCT-SPECT. The second limitation is the measurement time. Given these challenges, we developed a new sequential approximation image reconstruction method using Bayesian estimation, which is effective under the stringent BNCT-SPECT conditions. We also compared the proposed method with the existing Maximum Likelihood-Expectation Maximization (ML-EM) image reconstruction method. Numerical experiments were conducted by obtaining BNCT-SPECT projection data from true images and reconstructing images using both the proposed and ML-EM methods from the resulting sinograms. Performance comparisons were conducted using a dedicated program applying Bayesian estimation and this showed promise as a new image reconstruction method useful under BNCT-SPECT conditions.
Sustainable Transformation: A Case Study of an Engineering-to-Order Additive Manufacturing Firm
Authors: Larsson Daria , Ratnayake R. M. Chandima
Published: October 2024

Purpose: Addressing sustainability challenges in additive manufacturing (AM) is critical due to high energy consumption and waste. Optimising AM operations is vital for sustainability. However, the existing literature lacks practical examples for AM companies on how to enhance sustainability. To address this gap, a case study within an AM firm was conducted. Design approach: Using the analytical hierarchy process (AHP), sustainability key performance indicators (KPIs) were identified, aligned with the firm’s strategic objectives. Sustainable value stream mapping (Sus-VSM) analysed the production process for sustainability performance. A sustainability strategy map (SSM) was created based on the five perspectives of the 4th generation balanced scorecard (BSC) approach and the results from the case study. Findings: The research identified optimisation areas in the multi-jet-fusion (MJF) process, crafted tailored sustainability KPIs, and developed a SSM to seamlessly integrate sustainability initiatives into the company’s vision. Value: This contribution provides a structured methodology for selecting KPIs and offers a holistic approach to implementing Sus-VSM within the AM industry. The manuscript emphasises the need to incorporate a fifth perspective into the BSC and proposes a framework for developing a SSM. This study aims to guide AM companies toward a more environmentally responsible and socially conscious approach to manufacturing.
20 kHz CH2O- and SO2-PLIF/OH*-Chemiluminescence Measurements on Blowoff in a Non-Premixed Swirling Flame under Fuel Mass Flow Rate Fluctuations
Authors: Fu Chen , Wang Xiaoyang , Wu Yunhui , Gao Yi
Published: October 2024

Blowoff limits are essential in establishing the combustor operating envelope. Hence, there is a great demand for practical aero-engines to extend the blowoff limits further. In this work, the behavior of non-premixed swirling flames under fuel flow rate oscillations was investigated experimentally close to its blowoff limits. The methane flame was stabilized on the axisymmetric bluff body and confined in a square quartz enclosure. External acoustic forcing at 400 Hz was applied to the fuel flow to induce a fuel mass flow rate fluctuation (FMFRF) with varying amplitudes. A high-speed burst-mode laser and cameras ran at 20 kHz for OH*-chemiluminescence (CL), CH2O-, and SO2-PLIF measurements, offering the visualization of the two-dimensional flame structure and heat release distribution, temporally and spatially. The results show that the effect of FMFRF is predominantly along the central axis without altering the time-averaged flame structure and blowoff transient. However, the blowoff limits are extended due to the enhanced temperature and longer residence time induced by FMFRF. This work allows us to explore the mechanism of flame instability further.
Energy-Saving Extractive Distillation Process for Isopropanol Dehydration with Propylene Glycol as Novel Extractive Solvent
Authors: Nicolae Marilena , Neagu Mihaela , Cursaru Diana Luciana
Published: October 2024

The extractive distillation process using propylene glycol (IUPAC name: 1,2 propanediol) as an extractive agent for the separation of the isopropanol–water system was investigated in this work. A systematic procedure was set out to obtain the optimal design and process conditions for extractive distillation and solvent recovery columns using the PRO/II process simulator. Four thermally integrated flowsheets were proposed, implying the recovery of the sensible heat and latent heat from the hot streams in the process. To establish the economic feasibility of the proposed process, we calculated the total annual cost for all the simulated versions, and the proposed fully thermally integrated flowsheets could save up to 43.13% in terms of the utility costs and up to 15.57% in terms of the TAC compared to the conventional design. Thus, propylene glycol (PG) is found to be suitable as a new solvent for isopropanol dehydration, being comparable with other classical solvents used for the dehydration of alcohols.
Parametric Selection of Optimized Epicyclic Gearbox Layouts for Wind Power Plant Applications
Authors: Vrcan Željko , Troha Sanjin , Marković Kristina , Marinković Dragan
Published: October 2024

The mechanical multiplier gearbox is one of the most important parts for wind power generation efficiency. Modern epicyclic gearboxes are compact, lightweight builds capable of high power ratings with coaxial input and output shafts. To achieve this, it is very important to select the proper internal gearbox layout and other relevant parameters in the early design stages as the wrong choices will result in a suboptimal solution. Parametric optimization was applied to select the optimal gearbox solution for a wind turbine application, while taking into account both two-carrier and three-carrier solutions. The large number of possible solutions has resulted in the development of the 2-SPEED software to conduct systematic analysis and comparison. The best five two-carrier solutions and the one best three-carrier solution have been selected from the solution pool, with the selection being based on the criteria of maximum efficiency, minimum weight, and minimal greater-ring diameter size. One optimal two-carrier solution was then selected from the five and compared to the three-carrier solution. Recommendations for the selection of either two-carrier and three-carrier gear train solutions according to the application demands have been deducted and provided. This will result in lighter, more efficient designs with smaller radial dimensions.
An Enhanced Particle Filtering Method Leveraging Particle Swarm Optimization for Simultaneous Localization and Mapping in Mobile Robots Navigating Unknown Environments
Authors: Bian Xu , Zhao Wanqiu , Tang Ling , Zhao Hong , Mei Xuesong
Published: October 2024

With the rapid advancement of mobile robotics technology, Simultaneous Localization and Mapping (SLAM) has become indispensable for enabling robots to autonomously navigate and construct maps of unknown environments in real time. Traditional SLAM algorithms, such as the Extended Kalman Filter (EKF) and FastSLAM, have shown commendable performance in certain applications. However, they encounter significant limitations when dealing with nonlinear systems and non-Gaussian noise distributions, especially in dynamic and complex environments coupled with high computational complexity. To address these challenges, this study proposes an enhanced particle filtering method leveraging particle swarm optimization (PSO) to improve the accuracy of pose estimation and the efficacy of map construction in SLAM algorithms. We begin by elucidating the foundational principles of FastSLAM and its critical role in empowering robots with the ability to autonomously explore and map unknown territories. Subsequently, we delve into the innovative integration of PSO with FastSLAM, highlighting our novel approach of designing a bespoke fitness function tailored to enhance the distribution of particles. This innovation is pivotal in mitigating the degradation issues associated with particle filtering, thereby significantly improving the estimation accuracy and robustness of the SLAM solution in various operational scenarios. A series of simulation experiments and tests were conducted to substantiate the efficacy of the proposed method across diverse environments. The experimental outcomes demonstrate that, compared to the standard particle filtering algorithm, the PSO-enhanced particle filtering effectively mitigates the issue of particle degeneration, ensuring reliable and accurate SLAM performance even in challenging, unknown environments.
Is the Endurance Standardized ACWRHMLD or the Underlying Acute and Chronic Components Related to Injuries?
Authors: Marshall Robert Percy , Schulze Stephan , Droste Jan-Niklas , Riepenhof Helge , Delank Karl-Stefan , Kurz Eduard , Schwesig René
Published: October 2024

Acute (AW) and chronic (CW) workload imbalances, including their ratio (ACWR), are largely associated with increased injury risk. However, the inclusion of personal endurance performance (EP) in this calculation as a means of improving accuracy has been neglected in previous studies. The aim of this longitudinal observational study was to evaluate the relevance of the high metabolic load distance (ACWRHMLD) to EP in relation to non-contact injuries. Twenty-three German male first division soccer players (age: 24.5 ± 3.5 years; VO2max: 53.7 ± 4.9 mL/min/kg; v4: 15.2 ± 0.9 km/h) were analyzed. Eleven players with non-contact injuries were identified and matched with players without any injuries within the same time interval. Players were monitored using GPS and LPS tracking to calculate ACWRHMLD on a daily basis over the course of one competitive season. Relationships between different endurance performance parameters (v2, v4, vLT, VO2max) and the ACWRHMLD, AW, CW were established for statistical analysis. An area under the curve analysis (AUC) was performed. Based on the four weeks preceding the non-contact injuries, the CW, especially for the last two weeks before the injury, proved to be the most suitable parameter to estimate the risk of injury. The highest significant AUC value (0.81, 95% CI: 0.59–1.00) was calculated for the CW (last week before injury) in relation to the vLT (suitable cut-off: 0.04 km; sensitivity: 78%, specificity: 80%). With regard to the injury rate, the ACWRHMLD seems to be the most appropriate method of calculation, especially for CW related to EP (vLT). The sole use of ACWR, AW, and CW is not recommended.
Path Planning Strategy for Implementing a Machine Control System in Grader Operations
Authors: Kim Jae-Yoon , Seo Jong-Won , Na Wongi S. , Kim Sung-Keun
Published: October 2024

The construction industry faces challenges of labor shortages and safety concerns. Machine control (MC) technology offers a solution, particularly for graders in earthmoving operations. This study introduces a path-planning algorithm using initial site data, 3D target models, and equipment specifications to create optimal work paths for graders. The algorithm minimizes data exchange and adjusts for varying road widths and curves, enhancing grading efficiency and accuracy. A case study on a road construction site in South Korea validated the algorithm’s practical application. The proposed system aims to improve construction efficiency, safety, and cost-effectiveness, contributing to the advancement of construction automation technology.
The Stability and Failure of Deep Underground Structures at Potash Mining Deposits
Authors: Zhang Yiqiang , Lapatsin Siarhei , Zhuravkov Michael , Yu Guangbin , Karpovich Ivan
Published: October 2024

The article describes the peculiarities of strength and stability evaluation for deep geotechnical structures located in salt rock masses at great depths. A number of numerical studies are presented for the deep mining excavations of various cross-sections. The numerical simulations are conducted using a specific coupled algorithm of the finite element method (FEM) and distinct element method (DEM), which allows not only the prediction of dangerous zones in the undermined rock mass but also to simulation of the block fracture of the rock mass directly. Potential critical zones in the rock mass are established using an original complex limit state criterion for rock masses and FEM simulation results. Mentioned original criterion is a specific multicriterial method, which considers potential tensile, compressive and shear failure as well as crack propagation. To define the block-structure formulation in the rock mass it is proposed to use the Lade criterion in the complex limit state zones. Furthermore, block-structured rock mass behavior is simulated using DEM to predict its block-like fracture. The results of numerical studies clearly show that the mechanical behavior of potash salt rock masses significantly differ at moderate and great mining depths. Namely, the volume of the limit state zones nonlinearly increases with the increase in the mining depths up to double the size of the excavation cross-section. However, the exact amount of potentially failed rock mass has to be established using the direct DEM simulation in the limit state zones.