bluuebunny/crossref_metadata_2025 huggingface dataset:
Patient Safety Culture in Hospital Settings Across Continents: A Systematic Review
Authors: Alabdullah Hassan , Karwowski Waldemar
Published: September 2024

Patient Safety Culture (PSC) is the cornerstone of quality healthcare. Prior research has highlighted the critical role of a safety culture in healthcare. The Institute of Medicine (IOM) has emphasized its effectiveness in reducing errors and improving patient care. This systematic review investigated variations in how staff at international hospitals perceive PSC. In addition to assessing the status of patient safety, this investigation offers a comprehensive synthesis of research from multiple countries to inform future research endeavors. It focused on studies published between 2010 and 2024 that used the standardized Hospital Survey on Patient Safety Culture (HSOPSC) tool, versions 1.0 and 2.0. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review identified vital strengths and weaknesses in PSC globally. Despite these variations, two key strengths emerged consistently across studies, particularly in Asia: teamwork within units and a focus on continuous learning. However, healthcare professionals also raised concerns about a punitive environment that discourages error reporting and inadequate staffing levels. Interestingly, nurses held less positive views on some PSC aspects compared to mixed staff. Several PSC areas of concern raised in previous studies have continued to appear in recent studies. These findings highlight the need for a holistic approach to strengthen PSC globally. This includes fostering a just culture that encourages error reporting, addressing staffing shortages, and promoting open communication among all healthcare professionals. Additionally, tailoring interventions to address regional variations is crucial for creating a safer and more supportive environment for patients worldwide.
Four Different Build Angles in 3D-Printed Complete Denture Bases: A Comparative In Vitro Study
Authors: Kim Ki Won , Kim Sung Yong , Kim Seong-A , Jang Hee-Won , Lee Keun-Woo , Lee Yong-Sang
Published: September 2024

In this study, we aimed to investigate the differences in tissue surface adaptation and the variations in distances between reference points on the polished surfaces of 3D-printed denture bases produced at different build angles. The build angles were 0°, 30°, 60°, and 90°, with 15 denture bases printed for each angle. Using the Geomagic Control® software, a 3D best-fit alignment was conducted between the denture base tissue surface and the reference shape of the edentulous maxilla model to calculate the root mean square error. The distances between reference points on the polished surface were measured using digital calipers. A one-way analysis of variance was conducted for statistical analysis. The adaptation, as measured by the root mean square error, varied significantly among denture bases with different build angles. The distances between the anterior and posterior reference points of the polished surface were also significantly different. However, within the limitations of this study, the variations in adaptations and dimensional accuracy across different build angles were within clinically acceptable ranges. In clinical practice, the print angle can be adjusted based on factors such as printing time, resin consumption, and the number of denture bases being printed simultaneously.
Deep-Learning-Based Approach for IoT Attack and Malware Detection
Authors: Taşcı Burak
Published: September 2024

The Internet of Things (IoT), introduced by Kevin Ashton in the late 1990s, has transformed technology usage globally, enhancing efficiency and convenience but also posing significant security challenges. With the proliferation of IoT devices expected to exceed 29 billion by 2030, securing these devices is crucial. This study proposes an optimized 1D convolutional neural network (1D CNN) model for effectively classifying IoT security data. The model architecture includes input, convolutional, self-attention, and output layers, utilizing GELU activation, dropout, and normalization techniques to improve performance and prevent overfitting. The model was evaluated using the CIC IoT 2023, CIC-MalMem-2022, and CIC-IDS2017 datasets, achieving impressive results: 98.36% accuracy, 100% precision, 99.96% recall, and 99.95% F1-score for CIC IoT 2023; 99.90% accuracy, 99.98% precision, 99.97% recall, and 99.96% F1-score for CIC-MalMem-2022; and 99.99% accuracy, 99.99% precision, 99.98% recall, and 99.98% F1-score for CIC-IDS2017. These outcomes demonstrate the model’s effectiveness in detecting and classifying various IoT-related attacks and malware. The study highlights the potential of deep-learning techniques to enhance IoT security, with the developed model showing high performance and low computational overhead, making it suitable for real-time applications and resource-constrained devices. Future research should aim at testing the model on larger datasets and incorporating adaptive learning capabilities to further enhance its robustness. This research significantly contributes to IoT security by providing advanced insights into deploying deep-learning models, encouraging further exploration in this dynamic field.
Numerical and Experimental Analysis of SNCR Installation Performance in a Power Stoker Boiler
Authors: Krawczyk Piotr , Kurkus-Gruszecka Michalina , Dzido Aleksandra
Published: September 2024

The correct design of effective SNCR (Selective Non-Catalytic Reduction) requires solving several technological challenges. For this purpose, CFD modeling and bench tests were used. This study investigated various parameters affecting the NOx reduction rate in a WR-25 stoker boiler. The first parameter analyzed was the NSR (normalized stoichiometric ratio), with a constant urea concentration of 12.5% in the solution injected into the boiler. CFD modeling indicated that increasing the NSR significantly enhances reduction efficiency, especially between NSR 1 and 2, where the efficiency more than doubles. Bench tests confirmed this trend across all boiler power levels, showing deeper reagent penetration in the chamber at higher NSR levels. However, further doubling of NSR did not yield significant efficiency improvements, likely due to limitations in chemical mixing under reagent excess conditions. Further, it was revealed that NOx reduction efficiency decreases as boiler power increases, probably due to reduced reagent residence time at the required thermodynamic conditions. Additionally, different nozzle tip designs were tested, with multi-hole nozzles (two-hole and three-hole), showing better NOx reduction than single-hole nozzles due to improved reagent distribution. Finally, a lower urea concentration in the reagent (12%) led to better NOx reduction compared to a 32.5% concentration, likely due to improved droplet penetration and mixing with flue gases.
A Comparative Study of Single-Chain and Multi-Chain MCMC Algorithms for Bayesian Model Updating-Based Structural Damage Detection
Authors: Liu Luling , Chen Hui , Wang Song , Zeng Jice
Published: September 2024

Bayesian model updating has received considerable attention and has been extensively used in structural damage detection. It provides a rigorous statistical framework for realizing structural system identification and characterizing uncertainties associated with modeling and measurements. The Markov Chain Monte Carlo (MCMC) is a promising tool for inferring the posterior distribution of model parameters to avoid the intractable evaluation of multi-dimensional integration. However, the efficacy of most MCMC techniques suffers from the curse of parameter dimension, which restricts the application of Bayesian model updating to the damage detection of large-scale systems. In addition, there are several MCMC techniques that require users to properly choose application-specific models, based on the understanding of algorithm mechanisms and limitations. As seen in the literature, there is a lack of comprehensive work that investigates the performances of various MCMC algorithms in their application of structural damage detection. In this study, the Differential Evolutionary Adaptive Metropolis (DREAM), a multi-chain MCMC, is explored and adapted to Bayesian model updating. This paper illustrates how DREAM is used for model updating with many uncertainty parameters (i.e., 40 parameters). Furthermore, the study provides a tutorial to users who may be less experienced with Bayesian model updating and MCMC. Two advanced single-chain MCMC algorithms, namely, the Delayed Rejection Adaptive Metropolis (DRAM) and Transitional Markov Chain Monte Carlo (TMCMC), and DREAM are elaborately introduced to allow practitioners to understand better the concepts and practical implementations. Their performances in model updating and damage detection are compared through three different engineering applications with increased complexity, e.g., a forty-story shear building, a two-span continuous steel beam, and a large-scale steel pedestrian bridge.
Innovating Statistics Education: The Design of a Novel App Using Design Thinking
Authors: Quiñones Daniela , Ruz Felipe , Díaz-Arancibia Jaime , Paz Freddy , Osega José , Rojas Luis Felipe
Published: September 2024

Statistical education at university level faces significant challenges, particularly with the rapid advancements in technology and evolving teaching methods. Updating teaching methodologies for statistics in higher education is essential. Information technologies can greatly enhance the learning of statistical content, but many existing platforms are not well-suited for university contexts, either due to language barriers or how the content is presented (often confusing, with limited explanations or lacking context). It is crucial to have a student-centered platform that addresses these issues, ensuring an effective and efficient learning experience. This study introduces “EstApp”, an innovative mobile application prototype designed for teaching and learning descriptive statistics in university courses. The application was developed using the Design Thinking methodology, which emphasizes user experience and focuses on the needs of the end users. The design process involved stages of empathizing, defining, ideating, prototyping, and testing, culminating in validation through user tests with university students and professors. This study concludes that EstApp’s functionalities enhance understanding of (1) statistical models through interactive graphs and data visualizers; (2) probability concepts via a probability calculator; and (3) descriptive statistics through real-time data generation and visualizers.
The Use of Experimental Modal Analysis in Modeling the Complex Timber Structure of a Historical Building
Authors: Marciniak Piotr , Pawlak Zdzisław Mikołaj , Wyczałek Ireneusz
Published: September 2024

The subject of this study was the complex structure of a wooden historic church, which consisted of several segments that differed in the material used, the structural system employed, and the degree of wear of the elements. Particularly examined were the multi-branched columns located at the junction of different segments. The main objective of the analyses was to determine the degree of bonding of the column branches and the effect of this bonding on the operation of the entire structure. To evaluate the degree of bonding of the column branches, it was proposed to use experimental modal analysis. Based on the results of the experiment, it was found that the column branches are connected, but their cooperation is limited. Several computational models of the analyzed structure were also created, differing, for example, in the degree of bonding of the column branches. The effect of bonding the column branches on the values of stresses in the nave columns and on the values of extreme wind-induced displacements was studied.
Implementation of PMDL and DRM in OpenSees for Soil-Structure Interaction Analysis
Authors: Uzun Sefa , Ayvaz Yusuf
Published: September 2024

It is widely acknowledged that the effects of soil-structure interaction (SSI) can have substantial implications during periods of intense seismic activity; therefore, accurate quantification of these effects is of paramount importance in the design of earthquake-resistant structures. The analysis of SSI is typically conducted using either direct or substructure methods. Both of these approaches involve the use of numerical models with truncated or reduced-order computational domains. To ensure effective truncation, it is crucial to employ boundary representations that are capable of perfectly absorbing outgoing waves and allowing for the consistent application of input motions. At present, such capabilities are not widely available to researchers and practicing engineers. In order to address this issue, this study implemented the Domain Reduction Method (DRM) and Perfectly Matched Discrete Layers (PMDLs) in OpenSees. The accuracy and stability of these implementations were verified through the use of vertical and inclined incident SV waves in a two-dimensional problem. In terms of computational efficiency, PMDLs require a shorter analysis time (e.g., with PMDLs, the analysis concluded in 35 min as compared to 250 min with extended domain method) and less computational power (one processor for PMDLs against 20 processors for the extended domain method) thus offering a balance between accuracy and efficiency. Furthermore, illustrative examples of the aforementioned implemented features are presented, namely the response analysis of single-cell and double-cell tunnels exposed to plane waves inclined at an angle.
Search for True Ternary Fission in Reaction 40Ar + 208Pb
Authors: Ashaduzzaman Md , Di Nitto Antonio , Vardaci Emanuele , La Rana Giovanni , Setaro Pia Antonella , Banerjee Tathagata , Vanzanella Antonio , Alifano Giuseppe
Published: September 2024

True ternary fission, the fission of a nucleus into three fragments of nearly equal mass, is an elusive and poorly known process influenced by shell effects. An increase in the probability of this process with respect to binary fission, which is very low in spontaneous and neutron-induced fission, has been envisaged. Heavy-ion-induced reactions are adopted due to the possibility of an increase in the fissility parameter and the excitation energy of the compound nuclei. Nuclei with mass number around A = 250, accessible in heavy-ion-induced reactions, are favorable and should be investigated. It is still debated if the process takes place in a single step, direct ternary fission, or in a two step, sequential ternary fission. The purpose of this work is to define experimental conditions and observables that allow the disentangling of the products from the direct and sequential ternary fission, as well as from the usual most probable binary fission. This step is essential for gaining insights into the ternary fission dynamics and the binary to ternary fission competition. The method proposed here is for simulating the kinematics of the ternary and binary fission processes to compute the energy distributions and angular correlations of direct and sequential ternary fission products, as well as those of binary fission. The reaction taken as a benchmark is 40Ar + 208Pb at 230 MeV and is supposed to form the 248Fm* compound nucleus. The simulation results have been filtered by considering the response function of a multi-coincidence detection system virtually constructed using the Geant4 simulation toolkit. The simulations support the possibility of separating the products of different multimodal fission decays with the proposed setup that consequently represents an effective tool to obtain insights into ternary fission from the observables selected.
Effects of Laboratory Ageing on the Chemical Composition and High-Temperature Performance of Warm Mix Asphalt Binders
Authors: Maciejewski Krzysztof , Chomicz-Kowalska Anna , Bartos Joanna
Published: September 2024

Warm asphalt mixtures can suffer from decreased short-term high-temperature performance; therefore, introducing additional modifiers can mitigate this risk. This study investigates the effects of a liquid organosilane warm mix additive (WMAd) and grade-bumping polyethylene-based additive added simultaneously to asphalt binders on their chemical composition and its relationship with performance characteristics. Previous studies found relationships between the formation of certain chemical species during bitumen ageing and the increase in their viscosity, stiffness and other performance characteristics—the present work intended to verify these relationships when the two mentioned additives are used. Two asphalt binders were investigated—a paving-grade 50/70 binder and a 45/80-55 polymer-modified bitumen. The chemical analysis was performed using Fourier-transform infrared (FTIR) spectroscopy in attenuated total reflectance mode and focused on the quantification of carbonyl, sulfoxide, polybutadiene and polystyrene structures in the asphalt binders subjected to laboratory short- and long-term ageing. Additionally, the relationships between asphalt binder performance and selected FTIR indices were evaluated using a dynamic shear rheometer. It was found that the investigated additives significantly affected the apparent contents of all evaluated chemical structures in the asphalt binders; however, these changes were not reflected in their performance evaluation.
Improving Ship Fuel Consumption and Carbon Intensity Prediction Accuracy Based on a Long Short-Term Memory Model with Self-Attention Mechanism
Authors: Wang Zhihuan , Lu Tianye , Han Yi , Zhang Chunchang , Zeng Xiangming , Li Wei
Published: September 2024

The prediction of fuel consumption and Carbon Intensity Index (CII) of ships is crucial for optimizing decarbonization strategies in the maritime industry. This study proposes a ship fuel consumption prediction model based on the Long Short-Term Memory with Self-Attention Mechanism (SA-LSTM). The model is applied to a container ship of 2400 TEU to predict its hourly fuel consumption, hourly CII, and annual CII rating. Four different feature sets are selected from these data sources and are used as inputs for SA-LSTM and another ten models. The results demonstrate that the SA-LSTM model outperforms the other models in prediction accuracy. Specifically, the Mean Absolute Percentage Error (MAPE) for fuel consumption predictions using the SA-LSTM model is reduced by up to 20% compared to the XGBoost and by up to 12% compared to the LSTM model. Additionally, the SA-LSTM model achieves the highest accuracy in annual CII predictions.
Determination of the Relative Profile of Velocity and Acceleration in Semi-Professional Soccer Players: A Cross-Sectional Study
Authors: Cotteret Charles , Prieto-Bermejo Jaime , Almazán Polo Jaime , Jiménez-Saiz Sergio L.
Published: September 2024

The velocity and acceleration of a soccer player varies depending on the specific demands of the field position as well as individual characteristics, establishing the need to determine relative profiles by position. A cross-sectional study was conducted in 18 semi-professional soccer players to determine (i) the specific demands of external load according to playing position, (ii) distances covered at different intensities and the number of sprints, and (iii) the number of accelerations at moderate and high intensity. GPS tracking systems were used to collect data, and the relative acceleration profiles were analyzed based on initial velocity (0–7 km/h; 7.1–14.3 km/h; >14.3 km/h), intensity (moderate 50–75% and high > 75% of maximal acceleration), number of sprints/accelerations, and distance covered. Additionally, relative speed profiles were evaluated through the distance covered at moderate intensity (40–60% Vmax), high intensity (60–75.5% Vmax), very high intensity (>75.5% Vmax), total distance, and number of sprints. Statistically significant differences were observed in the distance covered at moderate and high intensity (midfielders), distance covered sprinting (center backs), and acceleration at moderate and high intensity in all positions (p < 0.05). These findings will enhance the monitoring of external loading strategies and prescription of specific training exercises for soccer players based on their respective playing position, ultimately contributing to optimized performance.
Catastrophe Information Characteristics and Prevention Measures of Water Inrush in Tunnel Approaching Fault with Different Water Pressure
Authors: Gu Jiheng , Guo Jiaqi , Chen Fan , Wu Wentao
Published: September 2024

In order to ensure the safety of the tunnel approaching the fault and prevent water inrush disasters, and then take reasonable protective measures, a fault-tunnel-surrounding rock is established by using a three-dimensional (3D) discrete element numerical analysis method, which takes into account the fluid-structure coupling effect. Based on the method of control variables, the catastrophe information characteristics of displacement and water pressure of the surrounding rock of the tunnel face and the corresponding characteristics of changes before the occurrence of water inrush disasters were studied under different fault water pressures during the excavation of the tunnel approaching the water-rich fault. The results show that, during excavation at the same step, displacement and its magnitude in the surrounding rock escalate as fault water pressure increases. The maximum pressure of the water in the surrounding rock is also constantly increasing. As tunnel excavation progresses, at constant fault water pressure, longer excavation distances result in greater axial displacement of the surrounding rock mass and increased water pressure at corresponding positions within the surrounding rock, leading to higher magnitude increases. As excavation proceeds, the displacement and water pressure in the surrounding rock and the increase of its amplitude continue to increase. Pre-reinforcement grouting techniques and pipe umbrella support systems that are very effective protective measures can be determined by a comprehensive approach integrating advanced geological forecasting methods, real-time water pressure detection, and the analysis of stress-strain and seepage pressure field variations in the surrounding rock mass.
Chromaticity Recognition Technology of Colored Noise and Operational Modal Analysis
Authors: Lu Xiangyu , Chen Huaihai , He Xudong
Published: September 2024

Operational Modal Analysis (OMA) refers to the modal analysis with only output vibration signals of a structure in its operating state. Classic OMA has developed multiple recognition methods in both the time and frequency domains, where when the random excitation is unknown, the excitation chromaticity is usually treated as white color, which can often cause errors and affect the accuracy of identifying frequencies or damping ratios. In this article, the chromaticity recognition function is defined and a method Chromaticity Recognition Technology (CRT) for identifying noise chromaticity based on system response is proposed. Then, a simulation example is presented. The noise chromaticity is identified for the response of the system under four types of colored noise excitation, and the results of the identification of operational mode parameters with and without CRT are compared. Furthermore, the sensitivity of traditional OMA to different colored noise has been investigated. An experiment with a cantilever under base excitation of pink noise has been undertaken and the results demonstrate the feasibility of the proposed CRT in this paper.
Phoneme Recognition in Korean Singing Voices Using Self-Supervised English Speech Representations
Authors: Wu Wenqin , Lee Joonwhoan
Published: September 2024

In general, it is difficult to obtain a huge, labeled dataset for deep learning-based phoneme recognition in singing voices. Studying singing voices also offers inherent challenges, compared to speech, because of the distinct variations in pitch, duration, and intensity. This paper proposes a detouring method to overcome this insufficient dataset, and applies it to the recognition of Korean phonemes in singing voices. The method started with pre-training the HuBERT, a self-supervised speech representation model, on a large-scale English corpus. The model was then adapted to the Korean speech domain with a relatively small-scale Korean corpus, in which the Korean phonemes were interpreted as similar English ones. Finally, the speech-adapted model was again trained with a tiny-scale Korean singing voice corpus for speech–singing adaptation. In the final adaptation, melodic supervision was chosen, which utilizes pitch information to improve the performance. For evaluation, the performance on multi-level error rates based on Word Error Rate (WER) was taken. Using the HuBERT-based transfer learning for adaptation improved the phoneme-level error rate of Korean speech by as much as 31.19%. Again, on singing voices by melodic supervision, it improved the rate by 0.55%. The significant improvement in speech recognition underscores the considerable potential of a model equipped with general human voice representations captured from the English corpus that can improve phoneme recognition on less target speech data. Moreover, the musical variation in singing voices is beneficial for phoneme recognition in singing voices. The proposed method could be applied to the phoneme recognition of other languages that have less speech and singing voice corpora.
Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
Authors: Wahlquist Scott , Ali Amir
Published: September 2024

The integration of LPBF printing technologies in various innovative applications relies on the resilience and reliability of parts and their quality. Reducing or eliminating the factors leading to defects in final parts is crucial to producing satisfactory high-quality parts. Extensive efforts have been made to understand the material properties and printing process parameters of LPBF-printed geometries that trigger defects. Studies of interest include the use of various sensing technologies, numerical modeling, and artificial intelligence (AI) to enable a better understanding of the phenomena under investigation. The primary objectives of this article are to introduce the reader to the most widely read published data on (1) the roles of numerical and analytical models in LPBF defect detection; (2) AI algorithms and models applicable to predict LPBF metal defects and causes; and (3) the integration of modeling, AI, and sensing technology, which is commonly used in material characterization and has been proven efficient and applicable to LPBF metal part defect detection over extended periods.
A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
Authors: Ren Yougui , Zhao Zhibin , Jiang Junjian , Jiao Yuning , Yang Yining , Liu Dawei , Chen Kefu , Yu Ge
Published: September 2024

With the rapid development of remote sensing image data, the efficient retrieval of target images of interest has become an important issue in various applications including computer vision and remote sensing. This research addressed the low-accuracy problem in traditional content-based image retrieval algorithms, which largely rely on comparing entire image features without capturing sufficient semantic information. We proposed a scene graph similarity-based remote sensing image retrieval algorithm. Firstly, a one-shot object detection algorithm was designed for remote sensing images based on Siamese networks and tailored to the objects of an unknown class in the query image. Secondly, a scene graph construction algorithm was developed, based on the objects and their attributes and spatial relationships. Several construction strategies were designed based on different relationships, including full connections, random connections, nearest connections, star connections, or ring connections. Thirdly, by making full use of edge features for scene graph feature extraction, a graph feature extraction network was established based on edge features. Fourthly, a neural tensor network-based similarity calculation algorithm was designed for graph feature vectors to obtain image retrieval results. Fifthly, a dataset named remote sensing images with scene graphs (RSSG) was built for testing, which contained 929 remote sensing images with their corresponding scene graphs generated by the developed construction strategies. Finally, through performance comparison experiments with remote sensing image retrieval algorithms AMFMN, MiLaN, and AHCL, in precision rates, Precision@1 improved by 10%, 7.2%, and 5.2%, Precision@5 improved by 3%, 5%, and 1.7%; and Precision@10 improved by 1.7%, 3%, and 0.6%. In recall rates, Recall@1 improved by 2.5%, 4.3%, and 1.3%; Recall@5 improved by 3.7%, 6.2%, and 2.1%; and Recall@10 improved by 4.4%, 7.7% and 1.6%.
Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM
Authors: Xiao Shuangshuang , Liu Jin , Ma Yajie , Zhang Yonggui
Published: September 2024

Accurate prediction of dust concentration is essential for effectively preventing and controlling mine dust. The environment of opencast mines is intricate, with numerous factors influencing dust concentration, making accurate predictions challenging. To enhance the prediction accuracy of dust concentration in these mines, a combined prediction algorithm utilizing RF-GA-LSSVM is developed. Initially, the random forest (RF) algorithm is employed to identify key features from the meteorological and dust concentration data collected on site, ultimately selecting five indicators—temperature, humidity, stripping amount, wind direction, and wind speed—as the input variables for the prediction model. Next, the data are split into a training set and a test set at a 7:3 ratio, and the genetic algorithm (GA) is applied to optimize the least squares support vector machine (LSSVM) model for predicting dust concentration in opencast mines. Additionally, model evaluation metrics and testing methods are established. Compared with LSSVM, PSO-LSSVM, ISSA-LSSVM, GWO-LSSVM, and other prediction models, the GA-LSSVM model demonstrates a final fitting degree of 0.872 for PM2.5 concentration data and 0.913 for PM10 concentration data. The GA-LSSVM model clearly demonstrates a strong predictive performance with low error and high fitting. The research results can serve as a foundation for developing dust control measures in opencast mines.
Effects of Externally Applied Stress on Multiphase Flow Characteristics in Naturally Fractured Tight Reservoirs
Authors: Hawez Haval Kukha , Asim Taimoor
Published: September 2024

Externally applied stress on the rock matrix plays a crucial role in oil recovery from naturally fractured tight reservoirs, as local variations in pore pressure and in-situ tension are expected. The published literature severely lacks in evaluations of the characteristics of hydrocarbons, displaced by water, in fractured reservoirs under the action of externally applied stress. This study intends to overcome this knowledge gap by resolving complex time- and stress-dependent multiphase flow by employing a coupled Finite Element Method (FEM) and Computational Fluid Dynamics (CFD) solver. Extensive three-dimensional numerical investigations have been carried out to estimate the effects of externally applied stress on the multiphase flow characteristics at the fracture–matrix interface by adding a viscous loss term to the momentum conservation equations. The well-validated numerical predictions show that as the stress loading increases, the porosity and permeability of the rock matrix and capillary pressure at the fracture–matrix interface decrease. Specifically, matrix porosity decreases by 0.13% and permeability reduces by 1.3% as stress increases 1.5-fold. Additionally, stress loading causes a decrease in fracture permeability by up to 29%. The fracture–matrix interface becomes more water-soaked as the stress loading on the rock matrix increases, and thus, the relative permeability curves shift to the right.
Beyond Simple Tapping: Is Timed Body Movement Influenced When Balance Is Threatened?
Authors: Emmanouil Analina , Boudolos Konstantinos , Rousanoglou Elissavet
Published: September 2024

The tapping paradigm offers valuable insights into movement timing; however, it simplifies mechanics by minimizing force, restricting motion, and relying on a clear contact endpoint. Thus, it may not fully capture the complexity of larger-scale multi-segmental (or single-segment) timed body movements. The aim of this study was to extend beyond the tapping paradigm by examining the timing of two large-scale movements commonly performed in physical fitness or rehabilitation modalities, with varying inherent balance threats: two-legged squatting (low balance threat) and standing hip abduction (higher balance threat) paced by a metronome set at the participants’ preferred tempo (N = 39, all physically active). In synchronization with the metronome audio signal, the trunk and shank angular velocities were also recorded to extract the entrainment, synchronization, and pace stability metrics. Paired t-tests indicated similar entrainment in both movements (p > 0.05 for IRI match) but significant differences in timing metrics’ manifestations (p ≤ 0.05, standing hip abduction: 50% greater IRI error, 30% lower synchronization error, 2.6% units lower pace stability). The similar entrainment but different synchronization error and pace stability highlight a complex timing interplay between balance threat/challenges and movement complexity concerning the two large-scale movements employed in physical fitness and rehabilitation modalities.
Increasing the Efficiency of Ecological Solar Panels Combined with the Building’s Roof
Authors: Voznyak Orest , Kasynets Mariana , Shapoval Stepan , Savchenko Olena , Sukholova Iryna , Spodyniuk Nadiia , Dovbush Oleksandr
Published: September 2024

This article is devoted to increasing the efficiency of ecological solar panels with their combination with the house’s roof. A solar panel construction that combines both the solar collector and the building cover is considered. This work investigates the efficiency of solar panels depending on the type of coating, heat-carrying medium mass flow rate, tube diameter, and the distance between the tubes. Among the coatings, Grafplast PDA roofing material is the most effective. Prandelli/Tuborama tubes with a diameter of 16 mm are recommended. The diameter of the tubes significantly affects the efficiency of the solar panel only at low intensity of solar radiation.
A Study on the Spatial Layout of Newly Built Townhouses in Kaohsiung City
Authors: Tseng Cheng-Chi , Huang Long-Sheng , Huang Chung-Fah
Published: September 2024

Due to their independent structure, piping, and access, townhouses offer great flexibility in floor plan changes and high spatial autonomy, making them the mainstream housing type in Taiwan. This study focuses on row houses and examines 2022 completion cases in Kaohsiung City. It collects floor plans from 14 newly constructed buildings, totaling 227 units, and analyzes spatial dimensions including the facade width, depth, habitable room, staircases, bathrooms, and total floor area for each case. The objective of this study is to examine the spatial layout of row houses in Kaohsiung City, with the aim of providing a reference for the future planning and design of such structures. The study results showed that 81.8% of the row houses analyzed have a total floor area per unit ranging from 136 to 192 m2, a facade width between 4.1 and 6.38 m, and a building depth from 7.67 to 12.68 m. In addition, they showed a low negative correlation between the facade width and total floor area, a high positive correlation between the building depth and total floor area, and a moderate negative correlation between the facade width and building depth. The spatial distribution within these houses includes 78% allocated to habitable room, 13.1% to staircases, and 8.7% to bathrooms. The total area of habitable rooms and bathrooms increases with the total floor area of the building. However, the total area of staircases remains almost constant as the area of habitable rooms increases. Furthermore, the most common location for staircase planning is the rear-right (RB) position, while the least common is the rear-left (LB) position. Among various staircase types, the C-shaped staircase has the largest average area per floor and the highest average proportion. Conversely, the I-shaped staircase has the smallest average area and the lowest average proportion. The U-shaped staircase is the most frequently planned, whereas the C-shaped staircase is the least frequently used. This means that planning of staircase location and type significantly affects the size and layout of habitable rooms and bathrooms in townhouses.
Analysis of Granite Deformation and Rupture Law and Evolution of Grain-Based Model Force Chain Network under Anchor Reinforcement
Authors: Guo Jiangfeng , Fan Doudou , Yu Liyuan , Shi Meixia , Su Haijian , Zhang Tao , Hu Bowen
Published: September 2024

In actual underground rock engineering, to prevent the deformation and damage of the rock mass, rock bolt reinforcement technology is commonly employed to maintain the stability of the surrounding rock. Therefore, studying the anchoring and crack-stopping effect of rock bolts on fractured granite rock mass is essential. It can provide significant reference and support for the design of underground engineering, engineering safety assessment, the theory of rock mechanics, and resource development. In this study, indoor experiments are combined with numerical simulations to explore the impact of fracture dip angles on the mechanical behavior of unanchored and anchored granite samples from both macroscopic and microscopic perspectives. It also investigates the evolution of the anchoring and crack-stopping effect of rock bolts on granite containing fractures with different dip angles. The results show that the load-displacement trends, displacement fields, and debris fields from indoor experiments and numerical simulations are highly similar. Additionally, it was discovered that, in comparison to the unanchored samples, the anchored samples with fractures at various angles all exhibited a higher degree of tensile failure rather than shear failure that propagates diagonally across the samples from the regions around the fracture tips. This finding verifies the effectiveness of the numerical model parameter calibration. At the same time, it was observed that the internal force chain value level in the anchored samples is higher than in the unanchored samples, indicating that the anchored samples possess greater load-bearing capacity. Furthermore, as the angle αs increases, the reinforcing and crack-stopping effects of the rock bolts become increasingly less pronounced.
Energy-Efficient Industrial Internet of Things in Green 6G Networks
Authors: Fernando Xavier , Lăzăroiu George
Published: September 2024

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.
Investigating the Effect of Motion Capture Suits on the Test–Retest Reliability of Gait Parameters
Authors: Smith Matt C. , Leveridge Phaedra , Massey Garry , Tyrrell Jessica , Hilton Malcolm , Williams Genevieve K. R.
Published: September 2024

When collecting marker-based motion capture data from clinical populations, speed of collection and comfort for the participant is a priority. This could be achieved by attaching markers to motion capture Velcro suits, as opposed to the skin. This study aimed to ascertain the reliability of sagittal-plane gait parameters estimated using Plug-in Gait (PiG) and Conventional Gait Model 2 (CGM2) marker sets from data collected in Suited and Non-suited (markers placed onto skin) conditions. For ten participants, markers were placed based on PiG and CGM2 models and data captured during a 2-min treadmill walk. Trials were repeated in suited and non-suited conditions. PiG ankle flexion/extension measurements had poor/moderate reliability (Non-suited ICC = 0.531, Suited ICC = 0.435). CGM2 ankle flexion/extension measurements had good/excellent reliability (Non-suited ICC = 0.916, Suited ICC = 0.900). There were significant differences in minimal detectable change (MDC) between conditions at the ankle for PiG (Non-suited MDC = 2.32°, Suited MDC = 18.90°), but not for CGM2 (Non-suited MDC = 0.63°, Suited MDC = 0.95°). When using CGM2, knee (Non-suited ICC = 0.878, Suited ICC = 0.855) and hip (Non-suited ICC = 0.897, Suited ICC = 0.948) showed good/excellent reliability in both conditions. A motion capture suit is not a reliable solution when collecting joint angle data using the PiG model but is reliable enough to consider when using the CGM2 model.
A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting
Authors: Joshua Salaki Reynaldo , Yeon An Na , Park Sanguk , Kwon Kihyeon
Published: September 2024

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings.
Accurate and Reliable Food Nutrition Estimation Based on Uncertainty-Driven Deep Learning Model
Authors: Ahn DaeHan
Published: September 2024

Mobile Near-Infrared Spectroscopy (NIR) devices are increasingly being used to estimate food nutrients, offering substantial benefits to individuals with diabetes and obesity, who are particularly sensitive to food intake. However, most existing solutions prioritize accuracy, often neglecting to ensure reliability. This oversight can endanger individuals sensitive to specific foods, as it may lead to significant errors in nutrient estimation. To address these issues, we propose an accurate and reliable food nutrient prediction model. Our model introduces a loss function designed to minimize prediction errors by leveraging the relationships among food nutrients. Additionally, we developed a method that enables the model to autonomously estimate its own uncertainty based on the loss, reducing the risk to users. Comparative experiments demonstrate that our model achieves superior performance, with an R2 value of 0.98 and an RMSE of 0.40, reflecting a 5–15% improvement over other models. The autonomous result rejection mechanism showing a 40.6% improvement further enhances robustness, particularly in handling uncertain predictions. These findings highlight the potential of our approach for precise and trustworthy nutritional assessments in real-world applications.
LSTM with Short-Term Bias Compensation to Determine Trading Strategy under Black Swan Events of Taiwan ETF50 Stock
Authors: Chang Ray-I , Wang Chia-Hui , Wei Lien-Chen , Lu Ya-Fang
Published: September 2024

This paper uses Long Short-Term Memory (LSTM) networks to predict the stock prices of the Yuanta Taiwan Top 50 ETF (ETF50). To improve the accuracy of the model’s predictions, a calibration procedure called “Short-Term Bias Compensation” (STBC) is proposed to adjust the predicted stock prices. In STBC, the daily prediction error is calculated to estimate the short-term bias (STB) in prediction. Then, the predicted price of its next day will be corrected if this STB has exceeded a certain threshold. In this paper, we apply Genetic Algorithms (GAs) to optimize the parameters used in STBC for providing more confidence in its estimation. Based on these predicted stock prices, we propose a Genetic Fuzzy System (GFS) to determine the trading strategy, with trading points for buying and selling stocks. In GFS, various technical indicators are used to establish the fuzzy rules of the trading strategy, and GAs are used to evolve the best parameters for these fuzzy rules. Our experiments cover over 17 years of data (from 2003 to 2020) for ETF50 to consider black swan events such as the 2020 COVID-19 pandemic, the 2018 US–China trade war, and the 2011 US debt crisis. The first 90% of the data is used as training data, and the last 10% is used as testing data. We use 12 technical indicators of these data as the input of LSTM. The predicted values of LSTM are corrected using STBC and compared to the uncorrected prices. We use Mean Square Error (MSE) to evaluate the prediction accuracy. The results show that STBC can nearly reduce 90% of the prediction error (where MSE drops from 11.5758 to 1.2687). By using GFS with STBC to determine trading points, we achieve a return rate of 32.0%.