bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Turnaround Times for Routine Antimicrobial Sensitivity Testing Following European Committee on Antimicrobial Susceptibility Testing Methodology in Patients with Bacteraemia
Authors: Edmondson Raewyn , Saeed Kordo , Green Steve , O’Dwyer Matthew
Published: November 2024

Background/Objectives: Bacteraemia can be fatal without antibiotic intervention. Antibiotic Susceptibility Testing (AST) provides the necessary information for targeted antibiotic therapy; however, the traditional method using disc diffusion can take over two days from a positive blood culture. Inappropriate empiric therapy is associated with increased mortality and increased antibiotic resistance, highlighting the need for more rapid turnaround times for AST. By making changes to an established method, turnaround times can be reduced. Methods: Eighty-two patient positive blood culture samples were collected from January to April 2022, representing the range of common bacteria causing sepsis. This followed the normal methodology in the laboratory of inoculating agar from positive blood cultures in preparation for European Committee on Antimicrobial Susceptibility Testing (EUCAST) disc diffusion AST method. EUCAST methodology outlines that disc diffusion should be performed on isolates from an overnight culture of 16–24 h. This study looked at comparing disc diffusion results from cultures with 6 h of incubation to those with incubation times of 24 h, after organism identification by MALDI-ToF. Results from 6-h and 24-h cultures were compared by disc zone sizes and by interpreted susceptibility reading following EUCAST guidelines of sensitive, resistant, susceptible with increased exposure, or an area of technical uncertainty. Results: A total of 99.65% interpreted susceptibility readings matched across all organisms to all relevant antibiotics, with an average zone size difference of 1.08 mm between results from 6 h versus 24 h cultures. Conclusions: This method offers a non-automated way of using the traditional disc diffusion method, reducing turnaround times while still producing reliable and accurate results. This would mean validated ASTs can be set up in the same day as a blood culture flags positive rather than waiting for a longer culture. As this method is widely used within the laboratory already, it would mean that additional training is not required, as the process is the same, and only incubation time varies. This would positively impact patient outlook due to the shorter use of empiric therapy, and benefit antimicrobial stewardship (AMS).
Structural Equation Modelling as a Proof-of-Concept Tool for Mediation Mechanisms Between Topical Antibiotic Prophylaxis and Six Types of Blood Stream Infection Among ICU Patients
Authors: Hurley James
Published: November 2024

Whether exposing the microbiome to antibiotics decreases or increases the risk of blood stream infection with Pseudomonas aeruginosa, Staphylococcus aureus, Acinetobacter, and Candida among ICU patients, and how this altered risk might be mediated, are critical research questions. Addressing these questions through the direct study of specific constituents within the microbiome would be difficult. An alternative tool for addressing these research questions is structural equation modelling (SEM). SEM enables competing theoretical causation networks to be tested ‘en bloc’ by confrontation with data derived from the literature. These causation models have three conceptual steps: exposure to specific antimicrobials are the key drivers, clinically relevant infection end points are the measurable observables, and the activity of key microbiome constituents on microbial invasion serve as mediators. These mediators, whether serving to promote, to impede, or neither, are typically unobservable and appear as latent variables in each model. SEM methods enable comparisons through confronting the three competing models, each versus clinically derived data with the various exposures, such as topical or parenteral antibiotic prophylaxis, factorized in each model. Candida colonization, represented as a latent variable, and concurrency are consistent promoters of all types of blood stream infection, and emerge as harmful mediators.
An In Vitro Oxidative Stress Model of the Human Inner Ear Using Human-Induced Pluripotent Stem Cell-Derived Otic Progenitor Cells
Authors: Jeong Minjin , Kurihara Sho , Stankovic Konstantina M.
Published: November 2024

The inner ear organs responsible for hearing (cochlea) and balance (vestibular system) are susceptible to oxidative stress due to the high metabolic demands of their sensorineural cells. Oxidative stress-induced damage to these cells can cause hearing loss or vestibular dysfunction, yet the precise mechanisms remain unclear due to the limitations of animal models and challenges of obtaining living human inner ear tissue. Therefore, we developed an in vitro oxidative stress model of the pre-natal human inner ear using otic progenitor cells (OPCs) derived from human-induced pluripotent stem cells (hiPSCs). OPCs, hiPSCs, and HeLa cells were exposed to hydrogen peroxide or ototoxic drugs (gentamicin and cisplatin) that induce oxidative stress to evaluate subsequent cell viability, cell death, reactive oxygen species (ROS) production, mitochondrial activity, and apoptosis (caspase 3/7 activity). Dose-dependent reductions in OPC cell viability were observed post-exposure, demonstrating their vulnerability to oxidative stress. Notably, gentamicin exposure induced ROS production and cell death in OPCs, but not hiPSCs or HeLa cells. This OPC-based human model effectively simulates oxidative stress conditions in the human inner ear and may be useful for modeling the impact of ototoxicity during early pregnancy or evaluating therapies to prevent cytotoxicity.
N-3 Fatty Acids in Seafood Influence the Association Between the Composite Dietary Antioxidant Index and Depression: A Community-Based Prospective Cohort Study
Authors: Moon Junhwi , Kim Minji , Kim Yangha
Published: November 2024

Accumulating evidence suggests that seafood and its components, such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are associated with mental health. However, little is known regarding whether the status of n-3 polyunsaturated fatty acids (PUFAs) modify the effect of dietary antioxidants on depression. The main purpose of study is to investigate longitudinal associations between seafood consumption and depression among 2564 participants aged 40–69 years using data from the Korean Genome and Epidemiology Study. The composite dietary antioxidant index (CDAI) and dietary intake were measured by a validated 106-item food frequency questionnaire and depression was assessed using the Beck Depression Inventory (BDI). The Cox’s proportional hazard model was used to examine the risk of depression according to seafood consumption. During an 8-year follow-up period, 165 (11.9%) men and 224 (18.9%) women experienced depression. After adjustment for confounders, the risk of depression was inversely associated with seafood consumption, with a 42% lower risk (HR T5 vs. T1 = 0.58, 95% CI: 0.35–0.98, p = 0.040) only being found among women. In a group with a high n-3 PUFA intake, CDAI scores were negatively correlated with BDI scores (r = −0.146, p < 0.001) among women. Seafood consumption might lead to more favorable outcomes against depression if accompanied by an increased intake of foods that are rich in antioxidants.
Radar Echo Extrapolation Based on Translator Coding and Decoding Conditional Generation Adversarial Network
Authors: Mou Xingang , He Yuan , Li Wenfeng , Zhou Xiao
Published: November 2024

In response to the shortcomings of current spatiotemporal prediction models, which frequently encounter difficulties in temporal feature extraction and the forecasting of medium to high echo intensity regions over extended sequences, this study presents a novel model for radar echo extrapolation that combines a translator encoder-decoder architecture with a spatiotemporal dual-discriminator conditional generative adversarial network (STD-TranslatorNet). Initially, an image reconstruction network is established as the generator, employing a combination of a temporal attention unit (TAU) and an encoder–decoder framework. Within this architecture, both intra-frame static attention and inter-frame dynamic attention mechanisms are utilized to derive attention weights across image channels, thereby effectively capturing the temporal evolution of time series images. This approach enhances the network’s capacity to comprehend local spatial features alongside global temporal dynamics. The encoder–decoder configuration further bolsters the network’s proficiency in feature extraction through image reconstruction. Subsequently, the spatiotemporal dual discriminator is crafted to encapsulate both temporal correlations and spatial attributes within the generated image sequences. This design serves to effectively steer the generator’s output, thereby augmenting the realism of the produced images. Lastly, a composite multi-loss function is proposed to enhance the network’s capability to model intricate spatiotemporal evolving radar echo data, facilitating a more comprehensive assessment of the quality of the generated images, which in turn fortifies the network’s robustness. Experimental findings derived from the standard radar echo dataset (SRAD) reveal that the proposed radar echo extrapolation technique exhibits superior performance, with average critical success index (CSI) and probability of detection (POD) metrics per frame increasing by 6.9% and 7.6%, respectively, in comparison to prior methodologies.
Neural and Cardio-Respiratory Responses During Maximal Self-Paced and Controlled-Intensity Protocols at Similar Perceived Exertion Levels: A Pilot Study
Authors: Poinsard Luc , Palacin Florent , Hashemi Iraj Said , Billat Véronique
Published: November 2024

Self-paced exercise protocols have gained attention for their potential to optimize performance and manage fatigue by allowing individuals to regulate their efforts based on perceived exertion. This pilot study aimed to investigate the neural and physiological responses during a self-paced V˙O2max (SPV) and incremental exercise tests (IET). Six trained male cyclists (mean age 39.2 ± 13.3 years; V˙O2max 54.3 ± 8.2 mL·kg−1·min−1) performed both tests while recording their brain activity using electroencephalography (EEG). The IET protocol involved increasing the power every 3 min relative to body weight, while the SPV allowed participants to self-regulate the intensity using ratings of perceived exertion (RPE). Gas exchange, EEG, heart rate (HR), stroke volume (SV), and power output were continuously monitored. Statistical analyses included a two-way repeated measures ANOVA and Wilcoxon signed-rank tests to assess differences in alpha and beta power spectral densities (PSDs) and the EEG/V˙O2 ratio. Our results showed that during the SPV test, the beta PSD initially increased but stabilized at around 80% of the test duration, suggesting effective management of effort without further neural strain. In contrast, the IET showed a continuous increase in beta activity, indicating greater neural demand and potentially leading to an earlier onset of fatigue. Additionally, participants maintained similar cardiorespiratory parameters (V˙O2, HR, SV, respiratory frequency, etc.) across both protocols, reinforcing the reliability of the RPE scale in guiding exercise intensity. These findings suggest that SPV better optimizes neural efficiency and delays fatigue compared to fixed protocols and that individuals can accurately control exercise intensity based on perceived exertion. Despite the small sample size, the results provide valuable insights into the potential benefits of self-paced exercise for improving adherence to exercise programs and optimizing performance across different populations.
A New Image Oversampling Method Based on Influence Functions and Weights
Authors: Ye Jun , Lu Shoulei , Chen Jiawei
Published: November 2024

Although imbalanced data have been studied for many years, the problem of data imbalance is still a major problem in the development of machine learning and artificial intelligence. The development of deep learning and artificial intelligence has further expanded the impact of imbalanced data, so studying imbalanced data classification is of practical significance. We propose an image oversampling algorithm based on the influence function and sample weights. Our scheme not only synthesizes high-quality minority class samples but also preserves the original features and information of minority class images. To address the lack of visually reasonable features in SMOTE when synthesizing images, we improve the pre-training model by removing the pooling layer and the fully connected layer in the model, extracting the important features of the image by convolving the image, executing SMOTE interpolation operation on the extracted important features to derive the synthesized image features, and inputting the features into a DCGAN network generator, which maps these features into the high-dimensional image space to generate a realistic image. To verify that our scheme can synthesize high-quality images and thus improve classification accuracy, we conduct experiments on the processed CIFAR10, CIFAR100, and ImageNet-LT datasets.
The Catastrophic Failure Mechanisms and the Prevention of Dynamic Pressure-Related Hazards During Mining Under an Interval Goaf Through an Isolated Coal Pillar in Shallow and Closely Spaced Coal Seams
Authors: Wang Bin , Zhang Jie , Lin Haifei , Liu Hui , Gao Shoushi , He Yifeng
Published: November 2024

Given the potential for dynamic load-induced support crushing that may occur during mining under an interval goaf through an isolated coal pillar (ICP) in shallow closely spaced coal seams, this paper systematically explored this issue through a case study of the 30,103 working face at the Nanliang Coal Mine. We employed a combined approach of similarity simulations, theoretical analyses, numerical simulations, and field measurements to investigate the catastrophic failure mechanisms and prevention strategies for dynamic pressure-related hazards encountered when mining a lower coal seam that passes through an ICP. The findings indicated that the synchronous cutting instability of the interlayer effective bearing stratum (IEBS) and double-arch bridge structure of the ICP roof were the primary causes of dynamic load-induced support crushing at the working face. A mechanical model was developed to characterize the IEBS instability during mining under an interval goaf. The sources and transmission pathways of dynamic mining pressure during mining passing through the ICP were clarified. The linked instability of the double-arch bridge structure of the ICP roof was induced by IEBS failure. The UDEC numerical model was utilized to elucidate the instability of the IEBS during mining in the lower coal seam and to analyze the vertical stress distribution patterns in the floor rock strata of the interval goaf. A comprehensive prevention and control strategy for roof dynamic pressure, which includes pre-releasing concentrated stress in the ICP, strengthening the support strength of the working face, and accelerating the advancement speed was proposed. The effectiveness of this prevention and control strategy was validated through actually monitoring the characteristics of mining pressure data from the 30,103 working face following pressure relief. The findings provide valuable insights for rock stratum control of shallow and closely spaced coal seam mining under similar conditions.
Multimethod Analysis of Heavy Metal Pollution and Source Apportionment in a Southeastern Chinese Region
Authors: Qi Dingwei , Chen Haiyang , Hu Litang , Sun Jianchong
Published: November 2024

Excessive levels of heavy metals in soil can significantly impact human health and ecological safety. Evaluating heavy metal pollution and identifying its sources are crucial for environmental management. This study investigates the status of heavy metal pollution in a southeastern region of China and aims to identify its sources using data from the first national soil pollution survey, which includes 282 sampling points. Indicators such as the geoaccumulation index (Igeo), the potential ecological risk index (RI), the hazard index (HI), and the total lifetime cancer risk (TLCR) were utilized to assess contamination levels. Geographic information systems (GIS), positive matrix factorization (PMF) receptor modeling, cluster analysis (ClusA) and human health risk assessments were integrated to analyze the sources of heavy metals. The results indicate that agricultural pollution sources have a minor impact on overall heavy metal contamination, with low ecological risk levels in the eastern and western regions. In contrast, the central region exhibited moderate risk, with areas of extremely high risk distributed across the central-west and central-south regions. PMF analysis identified pollution sources including natural origins, coal combustion, industrial emissions, and traffic, with contributions of 17.62%, 18.50%, 28.35%, and 35.56%, respectively. Overall, the carcinogenic risk in the study area is not high. Targeted recommendations were made in response to the pollution situation in the study area. This research enhances our understanding of heavy metal pollution in the soil of the study area and provides a reference for pollution source delineation in other regions.
Emotion Recognition in a Closed-Cabin Environment: An Exploratory Study Using Millimeter-Wave Radar and Respiration Signals
Authors: Wang Hanyu , Chen Dengkai , Gu Sen , Zhou Yao , Xiao Jianghao , Sun Yiwei , Sun Jianhua , Huang Yuexin , Zhang Xian , Fan Hao
Published: November 2024

In the field of psychology and cognition within closed cabins, noncontact vital sign detection holds significant potential as it can enhance the user’s experience by utilizing objective measurements to assess emotions, making the process more sustainable and easier to deploy. To evaluate the capability of noncontact methods for emotion recognition in closed spaces, such as submarines, this study proposes an emotion recognition method that employs a millimeter-wave radar to capture respiration signals and uses a machine-learning framework for emotion classification. Respiration signals were collected while the participants watched videos designed to elicit different emotions. An automatic sparse encoder was used to extract features from respiration signals, and two support vector machines were employed for emotion classification. The proposed method was experimentally validated using the FaceReader software, which is based on audiovisual signals, and achieved an emotion classification accuracy of 68.21%, indicating the feasibility and effectiveness of using respiration signals to recognize and assess the emotional states of individuals in closed cabins.
Design and Verification of Multiphase Multilevel Traction Inverter
Authors: Resutík Patrik , Praženica Michal , Kaščák Slavomír
Published: November 2024

The paper presents the practical design and implementation of a three-level neutral point clamped (TNPC) six-phase inverter rated at 100 kVA. The study initiates with prior work review, whereby most research work done earlier was mainly simulation-based. Based on the simulation results, this paper focuses on the practical aspects of inverter design, such as the development of a power board on an Insulated Metal Substrate, a gate driver board, an interconnect board, and the main control board. An inverter physical prototype has been built and tested at 500 V and 20 kW of output power. The SiC semiconductor technology is the base of the inverter, which represents the main merit of the work. Finally, high power density, compact design, and high efficiency are shown, which are major contributions of the paper. Tests performed proved that the designed converter was operating reliably and efficiently. While a simple Sinusoidal Pulse Width Modulation (SPWM) control algorithm has been implemented, the overall performance of the inverter showed great promise for higher-power applications. Compact and high-efficiency TNPC converters are developed for meeting increasing demands of advanced energy, automotive, and industrial applications.
Accuracy of GNSS-Derived Acceleration Data for Dynamic Team Sport Movements: A Comparative Study of Smoothing Techniques
Authors: Ellens Susanne , Carey David L. , Gastin Paul B. , Varley Matthew C.
Published: November 2024

This study examined the impact of various smoothing techniques on acceleration data obtained from a Global Navigation Satellite System (GNSS) device during accelerating and decelerating movements, resembling those commonly observed in team sports. Eight participants performed six different accelerating and decelerating movements at different intensities and starting speeds for a total of 46 trials each. The movements were collected concurrently at 10 Hz using a GNSS device (Vector S7, Catapult Sports) at 100 Hz using a motion analysis system (Vicon). Acceleration data were smoothed using (I) a fourth-order Butterworth filter (cut-off frequencies ranging from raw to 4.9 Hz), (II) exponential smoothing (smoothing constant ranging from 0.1 to 0.9), and (III) moving average (sliding window ranging from 0.2 s to 2.0 s). To determine the ability of a GNSS to quantify acceleration, a variety of measurement indices of validity were obtained for each movement and each smoothing technique. The fourth-order Butterworth filter with a cut-off frequency of 2 Hz (mean bias 0.00 m·s−2, 95% LoA ± 1.55 m·s−2, RMSE 0.79 m·s−2) showed the strongest relationship with the Vicon data. These results indicate that this smoothing technique is more accurate than those currently used and accepted on GNSS devices in the sports science community.
Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial Data
Authors: Abbeg Coproski Clemir , Liang Bingqing , Dietrich James T. , DeGroote John
Published: November 2024

Thorough investigations into air temperature variation across urban environments are essential to address concerns about city livability. With limited research on smaller cities, especially in the American Midwest, the goal of this research was to examine the spatial patterns of air temperature across multiple small to medium-sized cities in Iowa, a relatively rural US state. Extensive fieldwork was conducted utilizing manually built mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten Iowa urban areas during the afternoon, evening, and night on days exceeding 32 °C from June to September 2022. Using the random forest machine-learning algorithm and estimated urban morphological variables at varying neighborhood distances derived from 1 m2 aerial imagery and derived products from LiDAR data, we created 24 predicted surface temperature models that demonstrated R2 coefficients ranging from 0.879 to 0.997 with the majority exceeding an R2 of 0.95, all with p-values < 0.001. The normalized vegetation index and 800 m neighbor distance were found to be the most significant in explaining the collected air temperature values. This study expanded upon previous research by examining different sized cities to provide a broader understanding of the impact of urban morphology on air temperature distribution while also demonstrating utility of the random forest algorithm across cities ranging from approximately 10,000 to 200,000 inhabitants. These findings can inform policies addressing urban heat island effects and climate resilience.
Hyperspectral Image Classification Method Based on Morphological Features and Hybrid Convolutional Neural Networks
Authors: Ran Tonghuan , Shi Guangfeng , Zhang Zhuo , Pan Yuhao , Zhu Haiyang
Published: November 2024

The exploitation of the spatial and spectral characteristics of hyperspectral remote sensing images (HRSIs) for the high-precision classification of earth observation targets is crucial. Convolutional neural networks (CNNs) have good classification performance and are widely used neural networks. Herein, a morphological processing (MP)-based HRSI classification method and a 3D–2D CNN are proposed to improve HRSI classification accuracy. Principal component analysis is performed to reduce the dimensionality of the HRSI cube, and MP is implemented to extract the spectral–spatial features of the low-dimensional HRSI cube. The extracted features are concatenated with the low-dimensional HRSI cube, and the designed 3D–2D CNN framework completes the classification task. Residual connections and an attention mechanism are added to the CNN structure to prevent gradient vanishing, and the scale of the control parameters of the model structure is optimized to guarantee the model’s feature extraction ability. The CNN structure uses multiscale convolution, involving depthwise separable convolution, which can effectively reduce the amount of parameter calculation. Two classic datasets (Indian Pines and Pavia University) and a self-made dataset (My Dataset) are used to compare the performance of this method with existing classification techniques. The proposed method effectively improved classification accuracy despite its short classification time.
Polarized Three-Dimensional Reconstruction of Maritime Targets Through Zenith Angle Estimation from Specular and Diffuse Reflections
Authors: Zhang Shuolin , Zhang Zhenduo , Ma Rui , Wang Zhen , Jia Qilong
Published: November 2024

Polarized 3D imaging technology reconstructs the three-dimensional (3D) surface shape of an object by analyzing the polarization characteristics of light reflected from its surface. A key challenge in polarized 3D imaging is accurately estimating the zenith angle. Specular light poses a notable challenge in estimating the zenith angle because it conveys limited information regarding the target. To enhance the accuracy and robustness of zenith angle estimation for specular light, this study proposes a novel zenith angle estimation method that utilizes both specular and diffuse reflections. Based on the estimated zenith angle, the target surface shape was reconstructed. The feasibility of the proposed method was validated using polarimetric images of marine targets, offering a new solution for the accurate identification and 3D imaging of distant maritime targets.
Mixed-Strategy Harris Hawk Optimization Algorithm for UAV Path Planning and Engineering Applications
Authors: You Guoping , Hu Yudan , Lian Chao , Yang Zhen
Published: November 2024

This paper introduces the mixed-strategy Harris hawk optimization (MSHHO) algorithm as an enhancement to address the limitations of the conventional Harris hawk optimization (HHO) algorithm in solving complex optimization problems. HHO often faces challenges such as susceptibility to local optima, slow convergence, and inadequate precision in global solution-seeking. MSHHO integrates four innovative strategies to bolster HHO’s effectiveness in both local exploitation and global exploration. These include a positive charge repulsion strategy for diverse population initialization, a nonlinear decreasing parameter to heighten competitiveness, the introduction of Gaussian random walk, and mutual benefit-based position updates to enhance mobility and escape local optima. Empirical validation on 12 benchmark functions from CEC2005 and comparison with 10 established algorithms affirm MSHHO’s superior performance. Applications to three real-world engineering problems and UAV flight trajectory optimization further demonstrate MSHHO’s efficacy in overcoming complex optimization challenges. This study underscores MSHHO as a robust framework with enhanced global exploration capabilities, significantly improving convergence accuracy and speed in engineering applications.
A Case Study on Integrating an Eco-Design Tool into the Construction Decision-Making Process
Authors: Cornely Kétlin , Ascensão Guilherme , Ferreira Victor M.
Published: November 2024

To enhance the sustainability of construction and meet the sector’s environmental agenda, it is essential to comprehensively scrutinize the environmental, social, and economic impacts of construction projects from the project’s design stage. Such assessment is of utmost importance to minimize the impacts of both new construction and rehabilitation projects and is particularly critical during the selection of building materials and construction solutions. This work reports improvements in functionality and user-friendliness of an eco-design tool (UAveiroGreenBuilding) targeting the construction/rehabilitation sector and previously developed within our research group. The optimized version of the eco-design tool underwent validation through the assessment of competitive window frame materials (e.g., wood, PVC, and aluminum) for potential implementation in a rehabilitation project. Windows with PVC frames were identified as the preferred window configuration due to their superior environmental performance and favorable economic profile. Additionally, a digital communication interface was developed to connect the eco-design tool with building information modeling (BIM) projects, achieved through a routine integrated using a Dynamo application. Such successful integration not only streamlined and expedited the data transfer process by obviating the need for manual input but it also enabled the storage of environmental data throughout the life cycle of the project using a simple and reliable data storage protocol.
Multi-Objective Optimization Study on Production of AlSi10Mg Alloy by Laser Powder Bed Fusion
Authors: Toprak İnayet Burcu , Dogdu Nafel
Published: November 2024

In additive manufacturing, production parameters play a critical role in the microstructure, mechanical properties, and surface quality of a product. The correct selection of these parameters is of great importance for the success of the production process. In this study, the aim was to improve product quality in the additive manufacturing of an AlSi10Mg alloy. The experiments were conducted using a full factorial design, with a constant layer thickness of 0.04 mm. The production parameters included two laser powers (200 and 275 W), two scanning speeds (800 and 1400 mm/s), and two hatch distances (0.08 and 0.14 mm). The performance properties of the produced parts were evaluated according to the relative density and surface roughness criteria. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to optimize both relative density and surface roughness performances simultaneously. The results revealed that the most suitable production parameters for the additive manufacturing of the AlSi10Mg alloy were 275 W laser power, 0.14 mm hatch distance, and 800 mm/s scan speed.
Experimental Study of Wind Characteristics at a Bridge Site in Mountain Valley Considering the Effect of Oncoming Wind Speed
Authors: Li Weikang , Cui Shengnan , Zhao Jian , An Luming , Yu Chao , Ding Yan , Jing Hongmiao , Liu Qingkuan
Published: November 2024

The topography of mountainous areas is characterized by large undulations, which lead to a very complex wind field at bridge sites in mountain valleys. The influence of oncoming wind speed on long-span bridges built in mountain valleys is quite pronounced. To investigate the wind characteristics at a bridge site in a mountain valley under different oncoming wind speeds, a wind tunnel test of a terrain model with a scaling ratio of 1:1000, where a long-span bridge would be built in the V-shaped canyon, was conducted. Uniform and atmospheric boundary layer (ABL) inflows were both applied, and the effect of different oncoming wind speeds (basic wind speeds of 6 m/s, 8 m/s, 10 m/s, 12 m/s, and 14 m/s) under three wind directions (0°, 30°, and 180°) on the wind characteristics at the main beam and two bridge towers were studied. The results indicate that increasing oncoming wind speed leads to decreased wind profiles and wind speed amplification factors and increased wind attack angles, while wind yaw angles remain largely unchanged. In addition, compared to ABL inflow, the variation of fluctuating wind characteristics is more pronounced with the oncoming wind speed under uniform inflow. Under uniform inflow conditions, increasing the oncoming wind speed causes decreased turbulence intensity, reduces the peak frequency of the power spectrum, and slows down the high-frequency decay rate. Under ABL inflow conditions, turbulence intensity and the power spectrum remain unchanged with different oncoming wind speeds. Additionally, the turbulent integral scale derived from fitting with the von Kármán wind spectrum is sufficiently accurate, and the variation in the turbulent integral scale is greatly influenced by the terrain. Furthermore, higher wind speeds result in stronger coherence between two points. When two points are at different locations but with the same spacing, the coherence function remains roughly the same. Locations with higher kurtosis and skewness values exhibit steeper probability density functions, with larger kurtosis and skewness coefficients typically found on the leeward side. High wind speeds are more detrimental to bridge safety, and appropriate preventive measures should be implemented in advance to address extreme conditions that may arise at high wind speeds.
FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
Authors: Liu Hang , Huang Detian , Lin Mingxin
Published: November 2024

Visual object tracking is a fundamental task in computer vision, with applications ranging from video surveillance to autonomous driving. Despite recent advances in transformer-based one-stream trackers, unrestricted feature interactions between the template and the search region often introduce background noise into the template, degrading the tracking performance. To address this issue, we propose FETrack, a feature-enhanced transformer-based network for visual object tracking. Specifically, we incorporate an independent template stream in the encoder of the one-stream tracker to acquire the high-quality template features while suppressing the harmful background noise effectively. Then, we employ a sequence-learning-based causal transformer in the decoder to generate the bounding box autoregressively, simplifying the prediction head network. Further, we present a dynamic threshold-based online template-updating strategy and a template-filtering approach to boost tracking robustness and reduce redundant computations. Extensive experiments demonstrate that our FETrack achieves a superior performance over state-of-the-art trackers. Specifically, the proposed FETrack achieves a 75.1% AO on GOT-10k, 81.2% AUC on LaSOT, and 89.3% Pnorm on TrackingNet.
Factors Affecting MARPE Success in Adults: Analysis of Age, Sex, Maxillary Width, and Midpalatal Suture Bone Density
Authors: Javier Echarri-Nicolás , María José González-Olmo , Pablo Echarri-Labiondo , Marta Olmos-Valverde , Martín Romero
Published: November 2024

Microimplant-assisted rapid palatal expansion (MARPE) is a technique widely used to treat transverse discrepancies in adult patients. The present study aims to examine how age, sex, maxillary width, and suture bone density might influence MARPE efficacy. It also analyzes bone density variations across the midpalatal suture regions. Materials and Methods: This retrospective study included 30 adult patients who underwent MARPE treatment. Pre- and post-treatment CT scans were analyzed to quantify the maxillary width and bone density measured in Hounsfield units (HUs) in the anterior, middle, and posterior nasal spine regions. Statistical analyses were carried out and included descriptive statistics, t-tests, and effect size calculations. Results: Younger patients (age 22.13 ± 4.58) had significantly higher success rates compared to older patients (aged 25.66 ± 4.67). No significant differences were found regarding sex or the initial maxillary width. Lower bone density in the middle and posterior nasal spine regions was correlated with higher success rates. Data showed that the anterior nasal spine exhibited higher bone density, but this finding did not affect treatment outcomes significantly. Conclusions: Age seems to be a crucial factor in MARPE success, with younger patients showing better outcomes. Regarding bone density, results showed that its values in the middle and posterior nasal spine regions could be a determinant of treatment success. On the contrary, sex and the initial maxillary width did not appear to affect outcomes. These findings emphasize the importance of preoperative assessments and the consideration of individual anatomical variations for optimal MARPE treatment planning.
Effects of Different Exercise Doses Through an Augmented Reality Exergame in Older Adults: A Pilot Randomized Controlled Trial
Authors: Bravo-Aparicio Javier , Domínguez-López Patricia , Díaz-González Cristina , Martín-Caro Álvarez Diego , Álvarez David Martín-Caro , Beltran-Alacreu Hector
Published: November 2024

(1) Background: Growth in the use of video games has spurred innovations in the health sector, especially through exergames, which promote physical activity using interactive technologies like augmented reality. Exergames are shown to enhance exercise motivation and engagement, yet enjoyment remains inconsistent across studies. This pilot study aims to provide evidence on how different exergaming doses affect exercise heart rate, perceived exertion, adverse effects, and enjoyment in older adults. (2) Methods: A pilot randomized controlled trial was conducted to compare different doses of exercise through video games (13 vs. 28 min) in older adults living in a nursing home. A single bout of exergaming was provided to assess the outcomes: heart rate, rate of perceived exertion, physical activity enjoyment scale score, and adverse effects. (3) Results: Thirty-two older adults were recruited. This study revealed no significant differences in heart rate between groups (p = 0.1). There is a weak correlation between the rate of perceived exertion and the level of enjoyment (rs = −0.193) and between the total time of the intervention and the incidence of adverse effects (rs = 0.295). (4) Conclusions: The use of a higher dose of exergaming is effective in achieving moderate physical intensity. Also, the results suggest the intervention was generally well tolerated and enjoyed by older adults, with no serious adverse effects reported.
Optimizing Fundamental Frequencies in Axially Compressed Rotating Laminated Cylindrical Shells
Authors: Hu Hsuan-Teh , Huang Yi-Wei
Published: November 2024

Optimizing composite materials, particularly in rotating structures, offers several practical benefits in the mechanical engineering and aerospace engineering industries. Improved material configurations, such as optimal fiber orientations, enhance the structural performance by maximizing stiffness-to-weight ratios and reducing vibrations. This study optimized the fundamental frequencies of rotating laminated cylindrical shells using the golden section method with respect to fiber orientations. The investigation explored the impact of various factors such as end conditions, shell length, axial compressive force, rotating speed, and the size of the cutout on the maximum fundamental frequencies. Additionally, the associated vibration modes and optimal fiber orientations were demonstrated in relation to these influencing parameters. Generally, it was observed that the optimal frequency decreased with increasing length-to-radius ratio and compressive force.
Support Patterns of Roadways Under Fractured Surrounding Rocks Based on the Quality Level of Rock Mass
Authors: Ma Chi , Wang Peitao , Li Jiajun , Fan Xingwang , Dong Zhihong , Chen Ziming , Wang Xingwu
Published: November 2024

The roadway project of broken rock mass in the Shilu Iron Deposit was taken as the research object to discuss the stability control of the surrounding rocks using support patterns with broken rock mass. The grade evaluation of rock mass quality was conducted based on the geomechanical classification of rock mass. The roadway support effect in broken rock mass was calculated. Then, the section convergence characteristics of roadway surrounding rocks were analyzed under the current support scheme using displacement monitoring technology. The conclusions were described as follows: (1) The surrounding rock integrity of the No.6 and No.7 transportation roadways in the middle section of the Baoxiu mining area (Level 120) is poor. Based on geological survey data and rock mass classification, the rock mass quality in this region has been rated as Class IV. The joint support pattern of the anchor net and shotcrete was used according to the support guidelines. (2) The parameters for the support structure within Shilu Iron Deposit were designed, according to the requirements, as follows: the use of Portland cement; a shotcrete thickness of 55 mm; resin bolts with a minimum outer diameter of φ 20 mm and a length of 2200 mm; and row and column spacings of 1100 mm each. Meanwhile, a rectangular metal mesh of 80 × 80 mm was used as the anchor mesh. (3) The cross-section displacement reached 20 mm within a week using the support pattern according to displacement monitoring results of the on-site roadway section. Moreover, the convergence deformation rate significantly changed. However, the rate rapidly decreased after the first week, and the cross-section showed no further deformation after one month. The support pattern could control the displacement of surrounding rocks, which was verified by the monitoring results.
Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model
Authors: Cai Binqing , Ye Zhukai , Chen Shiwei , Liang Xun
Published: November 2024

Tower cranes are the most used equipment in construction projects, and the path planning of tower crane operations directly affects the safety performance of construction projects. Traditional tower crane operations rely on only the driving experience and manual path planning of crane operators. Poor judgement and bad path planning may increase safety risks and even cause severe construction safety accidents. To reduce safety risks in construction tower crane operations, this research proposes a dynamic path planning model for tower crane operations based on computer vision technology and dynamic path planning algorithms. The proposed model consists of three modules: first, a path information collection module preprocessing the video data to capture relevant operational path information; second, a path safety risk evaluation module employing You Only Look Once version 8 (YOLOv8) instance segmentation to identify potential risk factors along the operational path, e.g., potential drop zones and the positions of nearby workers; and finally, a path planning module utilizing an improved Dynamic Window Approach for tower cranes (TC-DWA) to avoid risky areas and optimize the operational path for enhanced safety. A prototype based on the theoretical model was constructed and tested on actual construction projects. Through experimental scenarios, it was found that each tower crane operation poses safety risks to 3–4 workers on average, and the proposed prototype can significantly reduce the safety risks of dropped loads from tower crane operations affecting ground workers and important equipment. A comparison between the proposed model and other regular algorithms was also conducted, and the results show that compared with traditional RRT and APF algorithms, the proposed model reduces the average maximum collision times by 50. This research provides a theoretical model and a preliminary prototype to provide dynamic path planning and reduce safety risks in tower crane operations. Future research will be conducted from the aspects of multiple device monitoring and system optimization to increase the analysis speed and accuracy, as well as on human–computer interactions between tower crane operators and the path planning guidance model.
Morpho-Molecular and Genomic Characterization of Penicillium mexicanum Isolates Retrieved from a Forsaken Gold Mine
Authors: Trovão João , Soares Fabiana , Paiva Diana Sofia , Portugal António
Published: November 2024

During the ongoing studies designed to examine the fungal diversity present within the abandoned and flooded Escádia Grande gold mine (Góis, Portugal), we repeatedly isolated several specimens belonging to a Penicillium species. Molecular phylogenetic analysis, coupled with morphological observations, positioned this fungus within subgen. Penicillium sect. Paradoxa, series Atramentosa, pinpointing its identity as Penicillium mexicanum (the first record for mining soils and the country). Given the limited research conducted on Penicillia isolated from similar environments, the species genome was sequenced utilizing the Oxford Nanopore® MinION™ methodology and studied through bioinformatic analysis. The obtained genome has a size of 29.62 Mb, containing a 47.72% GC content, 10,156 genes, with 44 rRNAs and 178 tRNAs/tmRNAs, providing the first genomic resource for this microorganism. Bioinformatic analysis allowed us to identify multiple genomic traits that can contribute towards this species survival in these extreme environments, including the presence of high levels of major facilitator transporters (MFS), Zn (2)-C6 fungal-type DNA-binding domains, P-loop containing nucleoside triphosphate hydrolases, specific fungal transcription factors and sugar transporters. Furthermore, putative advantageous metabolic traits, such as methylotrophy, assimilatory nitrate and sulfate reduction abilities, were also detected. In addition, the results also highlighted a strong genomic and metabolic organization and investment towards arsenic detoxification (transport and oxidation). Lastly, thirty-two putative biosynthetic gene clusters were predicted, including some with high similarity values to monascorubrin, nidulanin A, histidyltryptophanyldiketopiperazine/dehydrohistidyltryptophanyldiketopiperazine/roquefortine D/roquefortine C/glandicoline A/glandicoline B/meleagrine, YWA1 and choline. Overall, this study expands the current Penicillia knowledge from mining environments while also enhancing our understanding regarding fungal arsenic resistance.
Operational Modal Analysis and Safety Assessment of a Historical Masonry Bell Tower
Authors: Chisari Corrado , Zizi Mattia , Lavino Angelo , Freda Salvatore , De Matteis Gianfranco
Published: November 2024

The seismic assessment of historical masonry bell towers is of significant interest, particularly in Italy, due to their widespread presence and inherent vulnerability given by their slenderness. According to technical codes and standard practice, the seismic evaluation of masonry bell towers can be conducted using a range of methodologies that vary in their level of detail. This paper presents a case study of a historical masonry bell tower located in the Caserta Province (Italy). Extensive investigative efforts were undertaken to determine the tower’s key geometric and structural characteristics, as well as to document ongoing damage phenomena. The dynamic behavior of the tower was assessed through ambient vibration testing, which enabled the identification of the principal modal shapes and corresponding frequencies, also highlighting peculiar dynamical characteristics caused by the damage conditions. Subsequently, the seismic assessment was carried out using both Level 1 (simplified mechanical) and Level 2 (kinematic limit analysis) methodologies. This assessment helped identify the most probable collapse mechanisms and laid the foundation for employing more advanced methodologies to design necessary retrofitting interventions. The study emphasizes the importance of Level 2 analyses for structures where out-of-plane failure mechanisms are likely due to pre-existing cracking. Both approaches provide less-than-unity acceleration factors, ranging from 0.45 for Level 1 (assuming non-ductile behavior) to 0.59 for Level 2, in this case specifically using the information available about existing cracking pattern.