bluuebunny/crossref_metadata_2025 huggingface dataset:
Induction Coil Design Considerations for High-Frequency Domestic Cooktops
Authors: Erken Ahmet , Obdan Atiye Hulya
Published: September 2024

The use of wide band gap (WBG) semiconductor switches in power converters is increasing day by day due to their superior chemical and physical properties, such as electrical field strength, drift speed, and thermal conductivity. These new-generation power switches offer advantages over traditional induction cooker systems, such as fast and environmentally friendly heating. The size of passive components can be reduced, and the decreasing inductance value of induction coils and capacitors with low ESR (equivalent series resistance) values contributes to total efficiency. Other design parameters, such as passive components with lower values, heatsinks with low volumes, cooling fans with low power, and induction coils with fewer turns, can offset the cost of WBG power devices. High-frequency operation can also be effective in heating non-ferromagnetic materials like aluminum and copper, making them suitable for heating these types of pans without complex induction coil and power converter designs. However, the use of these new generation power switches necessitates a re-examination of induction coil design. High switching frequency leads to a high resonance frequency in the power converter, which requires lower-value passive components compared to conventional cookers. The most important component is the induction coil, which requires fewer turns and magnetic cores. This study examines the induction heating equivalent circuit, discusses the general structure and design parameters of the induction coil, and performs FEM (finite element method) analyses using Ansys Maxwell. The results show that the induction coil inductance value in new-generation cookers decreases by 80% compared to traditional cookers, and the number of windings and magnetic cores decreases by 50%. These analyses, performed for high-power applications, are also performed for low-power applications. While the inductance value of the induction coil is 90 μH at low frequencies, it is reduced to the range of 5 μH to 20 μH at high frequencies. The number of windings is reduced by half or a quarter. The new-generation cooker system experimentally verifies the coil design based on the parameters derived from the analysis.
Virtual Tours as Effective Complement to Building Information Models in Computer-Aided Facility Management Using Internet of Things
Authors: Aguacil Moreno Sergi , Loup Matthias , Lebre Morgane , Deschamps Laurent , Bacher Jean-Philippe , Duque Mahecha Sebastian
Published: September 2024

This study investigates the integration of Building Information Models (BIMs) and Virtual Tour (VT) environments in the Architecture, Engineering and Construction (AEC) industry, focusing on Computer-Aided Facility Management (CAFM), Computerized Maintenance Management Systems (CMMSs), and data Life-Cycle Assessment (LCA). The interconnected nature of tasks throughout a building’s life cycle increasingly demands a seamless integration of real-time monitoring, 3D models, and building data technologies. While there are numerous examples of effective links between IoT and BIMs, as well as IoT and VTs, a research gap exists concerning VT-BIM integration. This article presents a technical solution that connects BIMs and IoT data using VTs to enhance workflow efficiency and information transfer. The VT is developed upon a pilot based on the Controlled Environments for Living Lab Studies (CELLS), a unique facility designed for flexible monitoring and remote-control processes that incorporate BIMs and IoT technologies. The findings offer valuable insights into the potential of VTs to complement and connect to BIMs from a life-cycle perspective, improving the usability of digital twins for beginner users and contributing to the advancement of the AEC and CAFM industries. Our technical solution helps complete the connectivity of BIMs-VT-IoT, providing an intuitive interface (VT) for rapid data visualisation and access to dashboards, models and building databases. The practical field of application is facility management, enhancing monitoring and asset management tasks. This includes (a) sensor data monitoring, (b) remote control of connected equipment, and (c) centralised access to asset-space information bridging BIM and visual (photographic/video) data.
Fabric Defect Detection Based on Improved Lightweight YOLOv8n
Authors: Ma Shuangbao , Liu Yuna , Zhang Yapeng
Published: September 2024

In response to the challenges posed by complex background textures and limited hardware resources in fabric defect detection, this study proposes a lightweight fabric defect detection algorithm based on an improved GSL-YOLOv8n model. Firstly, to reduce the parameter count and complexity of the YOLOv8n network, the GhostNet concept is used to construct the C2fGhost module, replacing the conventional convolution layers in the YOLOv8n structure with Ghost convolutions. Secondly, the SimAM parameter-free attention mechanism is embedded at the end of the backbone network to eliminate redundant background, enhance semantic information for small targets, and improve the network’s feature extraction capability. Lastly, a lightweight shared convolution detection head is designed, employing the scale layer to adjust features, ensuring the lightweight nature of the model while minimizing precision loss. Compared to the original YOLOv8n model, the improved GSL-YOLOv8n algorithm increases the mAP@0.5 by 0.60% to 98.29% and reduces model size, computational load, and parameter count by 66.7%, 58.0%, and 67.4%, respectively, meeting the application requirements for fabric defect detection in textile industry production.
Effect of Dietary Enrichment with Hempseed (Cannabis sativa L.) on Blood Pressure Changes in Growing Mice between Ages of 5 and 30 Weeks
Authors: Blanton Cynthia A. , Streff Hailey M. , Gabaldón Annette M.
Published: September 2024

Dietary hempseed (Cannabis sativa L.) reduces blood pressure in adults and animal models of hypertension; however, whether hempseed consumption throughout early life reduces adult blood pressure is not known. This study tested the hypothesis that hempseed enrichment versus a control diet modifies the age-dependent pattern of blood pressure changes in growing female C57BL/6 mice and results in lower adult blood pressure. From ages 5 to 30 weeks, early post-weaning to mid-adulthood, mice were fed either a control AIN-93G (0%), 50 g/kg (5%), or 150 g/kg (15%) hempseed-supplemented diet (n = 8 per group). Biweekly measurements of systolic, diastolic, and mean arterial pressure were collected using the tail-cuff method. Mice fed 5% or 15% hempseed versus the control diet exhibited no significant differences in systolic, diastolic, or mean arterial blood pressure (repeated measures ANOVA main effect of diet, p > 0.05). Blood pressure did not differ significantly between diet groups in adulthood (p > 0.05). However, mice fed a control or 5% hempseed, but not 15% hempseed, diet exhibited blood pressure changes across age marked by significant increases during early adulthood (weeks 11–17) versus early post-weaning (week 5) (p < 0.05). In conclusion, long-term dietary hempseed enrichment at 5% and 15% concentrations during development does not reduce adult blood pressure, but a 15% dose blunts the temporary increase in blood pressure during early adulthood seen in mice fed a control diet.
Simulation Analysis and Experimental Investigation on the Fluid–Structure Interaction Vibration Characteristics of Aircraft Liquid-Filled Pipelines under the Superimposed Impact of External Random Vibration and Internal Pulsating Pressure
Authors: Zhu Lei , Chen Chang , Jiang Yu
Published: September 2024

This paper investigated the fluid–structure interaction vibration response of an aircraft liquid-filled pipeline under external random vibration and internal pulsating pressure. First, the fluid–structure interaction solution is theoretically analyzed, and the advantages and disadvantages of the direct coupling method and the separation coupling method are compared, with the latter chosen as the simulation analysis method in this study. Second, taking the U-shaped oil pipeline of an aircraft engine as an example, simulation modeling was performed to compare and analyze the fluid–structure interaction vibration response of aircraft liquid-filled pipelines under different working conditions, obtaining the vibration response characteristics of stress danger points under various conditions. Finally, a test bench for an aircraft liquid-filling pipeline was built to explore the influence of external random vibrations with different kurtoses, different pipe wall thicknesses and different working conditions on the vibration response danger points of aircraft liquid-filling pipelines, verifying the simulation conclusions and providing a basis for aircraft liquid-filling pipeline design.
Venus Magnetotail Long-Term Sensing Using Solar Sails
Authors: Quarta Alessandro A.
Published: September 2024

Propellantless propulsion systems, such as the well-known photonic solar sails that provide thrust by exploiting the solar radiation pressure, theoretically allow for extremely complex space missions that require a high value of velocity variation to be carried out. Such challenging space missions typically need the application of continuous thrust for a very long period of time, compared to the classic operational life of a space vehicle equipped with a more conventional propulsion system as, for example, an electric thruster. In this context, an interesting application of this propellantless thruster consists of using the solar sail-induced acceleration to artificially precess the apse line of a planetocentric elliptic orbit. This specific mission application was thoroughly investigated about twenty years ago in the context of the GeoSail Technology Reference Study, which analyzed the potential use of a spacecraft equipped with a small solar sail to perform an in situ study of the Earth’s upper magnetosphere. Taking inspiration from the GeoSail concept, this study analyzes the performance of a solar sail-based spacecraft in (artificially) precessing the apse line of a high elliptic orbit around Venus with the aim of exploring the planet’s induced magnetotail. In particular, during flight, the solar sail orientation is assumed to be Sun-facing, and the required thruster’s performance is evaluated as a function of the elliptic orbit’s characteristics by using both a simplified mathematical model of the spacecraft’s planetocentric dynamics and an approximate analytical approach. Numerical results show that a medium–low-performance sail is able to artificially precess the apse line of a Venus-centered orbit in order to ensure the long-term sensing of the planet’s induced magnetotail.
Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
Authors: Elbasi Ersin , Mostafa Nour , Zaki Chamseddine , AlArnaout Zakwan , Topcu Ahmet E. , Saker Louai
Published: September 2024

The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively.
Efficient Implementation of Multilayer Perceptrons: Reducing Execution Time and Memory Consumption
Authors: Cedron Francisco , Alvarez-Gonzalez Sara , Ribas-Rodriguez Ana , Rodriguez-Yañez Santiago , Porto-Pazos Ana Belen
Published: September 2024

A technique is presented that reduces the required memory of neural networks through improving weight storage. In contrast to traditional methods, which have an exponential memory overhead with the increase in network size, the proposed method stores only the number of connections between neurons. The proposed method is evaluated on feedforward networks and demonstrates memory saving capabilities of up to almost 80% while also being more efficient, especially with larger architectures.
Electrical Bioimpedance in Milk Adulterated with Water: Measurement Methodology for Quantification, Influence of Temperature, and Mathematical Modelling
Authors: Oliveira Wemerson de Castro , Geller Ana Maria , Neuenfeld Renato Hartwig , Carvalho Claudia Wollmann , Húngaro Humberto Moreira , Ayres Luciano Carvalho , Oliveira Maria Beatriz Prior Pinto , Porto Rodrigo Wolff
Published: September 2024

Milk has a high nutritional value and is also one of the most versatile products in the food industry. Unfortunately, fraud involving the product is constant and needs to be combated. This work presents a measurement methodology to perform the electrical impedance analysis in food. The influence of temperature on the measurements has been evaluated through mathematical models specially developed for identifying the fraudulent activity of adding water to milk. The bioimpedance measurements have been performed in a sample container with a four-electrode measurement system and a digital oscilloscope. Samples of 150 mL were analysed, prepared using whole UHT milk adulterated with distilled water in different % by volume (V/V), at temperatures of 16, 28, and 37 °C. The proposed measurement methodology has been validated and showed sensitivity for analysis in milk, revealing the influence of temperature on the impedance values; from the results mathematical models were proposed for investigating adulteration in milk. This work deals with the design of a device capable of measuring bioimpedance in milk with a view to its application in physicochemical analyses. The influence of temperature on the impedance values was also evaluated to propose mathematical models to facilitate analysis with complex factors and matrices.
Multi-Objective Route Planning Model for Ocean-Going Ships Based on Bidirectional A-Star Algorithm Considering Meteorological Risk and IMO Guidelines
Authors: Wang Yingying , Qian Longxia , Hong Mei , Luo Yaoshuai , Li Dongyv
Published: September 2024

In this study, a new route planning model is proposed to help ocean-going ships avoid dangerous weather conditions and ensure safe ship navigation. First, we integrate ocean-going ship vulnerability into the study of the influence of meteorological and oceanic factors on navigational risk. A multi-layer fuzzy comprehensive evaluation model for weather risk assessment is established. A multi-objective nonlinear route planning model is then constructed by comprehensively considering the challenges of fuel consumption, risk, and time during ship navigation. The International Maritime Organization (IMO) guidelines are highlighted as constraints in the calculations, and wind, wave, and calm water resistance to ships in the latest ITTC method is added to the fuel consumption and sailing time in the objective function. Finally, considering the large amount of data required for ocean voyages, the bidirectional A* algorithm is applied to solve the model and reduce the planning time. Furthermore, our model is applied to the case of an accident reported in the Singapore Maritime Investigation Report, and the results show that the model-planned route is very close to the original planned route using the Towing Manual, with an average fit of 98.22%, and the overall meteorological risk of the model-planned route is 11.19% smaller than the original route; our model can therefore be used to plan a safer route for the vessel. In addition, the importance of risk assessments and the IMO guidelines as well as the efficiency of the bidirectional A* algorithm were analyzed and discussed. The results show that the model effectively lowers the meteorological risk, is more efficient than the traditional route planning algorithm, and is 86.82% faster than the Dijkstra algorithm and 49.16% faster than the A* algorithm.
Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation
Authors: Amorim Fernando Rodrigues de , Guimarães Camila Carla , Afonso Paulo , Tobias Maisa Sales Gama
Published: September 2024

Considering that investing in the production of corn and soybeans is conditioned by production costs and several risks, the objective of this research work was to develop a simulation model for the prediction of the production costs of these commodities, considering the variability and correlation of key variables. The descriptive analysis of the data focused on measures such as mean, standard deviation, and coefficient of variation. To evaluate the relationship between commodity and input prices, Spearman’s demonstration coefficient and the coefficient of determination (R2) were used. A Monte Carlo simulation (MCS) was used to evaluate the variation in production costs and net revenues. The Predictor tool was used to make predictions based on historical data and time series models. This study was made for the period between 2018 and 2022 based on data provided by fifty companies from the state of São Paulo, Brazil. The results showed that the production cost/ha of corn faces a high-cost risk, particularly when production and market conditions are characterized by high levels of volatility, uncertainty, complexity, and ambiguity. The model proposed forecasts prices more accurately, as it considers the variation in the costs of inputs that most significantly influence the costs of corn and soybean crops.
A Simple Procedure to Obtain the Grounding Resistance Measurement of Very Large and Urban Electrodes by a Modified Fall-of-Potential Method
Authors: Moreno Jorge , Simón Pascual , Faleiro Eduardo , García Daniel , Denche Gregorio , Asensio Gabriel
Published: September 2024

The measurement of the grounding resistance of grounding grids in large installations as well as grounding electrodes in urban areas is addressed in this article. The resistance value is obtained using a three-pin array by measuring the fall-of-potential on the ground surface. The resistance measured by this method is adjusted to its true value using a correction factor that aligns the measured resistance with the actual value. The proposed measurement method obtains correct values of the grounding resistance even when the auxiliary and potential electrodes of the tree-pin array are close to the electrode to be measured. Thus, it can be applied to large electrodes as well as electrodes in urban areas. Several simulated examples are used to illustrate the method, and some real cases with field measurements are presented for a final validation of the method.
Modeling the Color Characteristics of Sapphires through the Statistical Method and Function Simulation Method
Authors: Liu Fukang , Ying Guo , Zhao Bei , Liu Meiying
Published: September 2024

The aim of this study was to explore the feasibility of quantitatively evaluating a sapphire’s color by analyzing gemstone images. The color characteristics of gemstones through photography and color extraction were accurately captured, and the key color parameters for different color regions of gemstones were extracted. Then, the color of sapphires was simulated by statistical method and function simulation method. The results indicate that the brightness value of the bright area was the highest and the chroma value of the “fire” area was the highest. The chroma (C) of the sapphires was linearly positively correlated with the absolute value of its b value, and the brightness (L) showed a quadratic correlation with its chromaticity parameters (b or C). The function simulation method with an average of b as the main independent variable had a larger standard deviation, which proves that this method has a stronger ability to distinguish the differences in a sapphire’s color. This method can be extended to the color evaluation of other colored gemstones, and it can be used to form a gemstone color evaluation system.
A Self-Adaptive Neighborhood Search Differential Evolution Algorithm for Planning Sustainable Sequential Cyber–Physical Production Systems
Authors: Hsieh Fu-Shiung
Published: September 2024

Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers’ order requirements and mitigate negative impacts on the environment. The planning of processes to achieve sustainable CPSs becomes an important issue to meet demand timely in a dynamic environment. The problem with planning processes in sustainable CPSs is the determination of the configuration of workflows/resources to compose processes with desirable properties, taking into account time and energy consumption factors. The planning problem in sustainable CPSs can be formulated as an integer programming problem with constraints, and this poses a challenge due to computational complexity. Furthermore, the ever-shrinking life cycle of technologies leads to frequent changes in processes and makes the planning of processes a challenging task. To plan processes in a changing environment, an effective planning method must be developed to automate the planning task. To tackle computational complexity, evolutionary computation approaches such as bio-inspired computing and metaheuristics have been adopted extensively in solving complex optimization problems. This paper aims to propose a solution methodology and an effective evolutionary algorithm with a local search mechanism to support the planning of processes in sustainable CPSs based on an auction mechanism. To achieve this goal, we focus on developing a self-adaptive neighborhood search-based Differential Evolution method. An effective planning method should be robust in terms of performance with respect to algorithmic parameters. We assess the performance and robustness of this approach by performing experiments for several cases. By comparing the results of these experiments, it shows that the proposed method outperforms several other algorithms in the literature. To illustrate the robustness of the proposed self-adaptive algorithm, experiments with different settings of algorithmic parameters were conducted. The results show that the proposed self-adaptive algorithm is robust with respect to algorithmic parameters.
Green Scalar Function Method for Analyzing Dielectric Media
Authors: Bravo J. C. , Colomina-Martínez J. , Sirvent-Verdú J. J. , Mena E. J. , Álvarez M. L. , Francés J. , Neipp C. , Gallego Sergi
Published: September 2024

In this work we present a formalism based on scalar Green’s functions to deal with electromagnetic scattering problems. Although the formulations of the Mie theory and Born approximations in terms of electromagnetic scattering are well known and relevant, they have certain disadvantages: complexity, computational time, few symmetries, etc. Therefore, the study with scalar Green’s functions allows dealing with these problems with greater simplicity and efficiency. However, the information provided by the vector formulation is sacrificed. Nevertheless, different cases of electromagnetic scattering of dielectric media with different dimensions, geometries and refractive indices will be presented. Thus, we will be able to verify the capacity of this scalar method in predicting light-scattering problems.
Exploring the Cutting Process of Coaxial Phase Change Fibers under Optical Characterization Tests
Authors: Hammes Nathalia , Monteiro José , Pinheiro Claver , Felgueiras Helena P. , Soares Graça M. B. , Segundo Iran Rocha , Costa Manuel F. M. , Carneiro Joaquim
Published: September 2024

Urban heat islands (UHI) are a growing issue due to urbanization, causing citizens to suffer from the inadequate thermal properties of building materials. Therefore, the need for climate-resistant infrastructure is crucial for quality of life. Phase change materials (PCMs) offer a solution by being incorporated into construction materials for thermoregulation. PCMs store and release heat as latent heat, adjusting temperatures through phase changes. Polymeric phase change fibers (PCFs) are an innovative technology for encapsulating PCMs and preventing leaks. This study produced PCFs via wet-spinning, using commercial cellulose acetate (CA, Mn 50,000) as the sheath and polyethylene glycol (PEG 2000) as the core. The PCFs were cut using a hot-cutting method at three different temperatures and washed with distilled water. Morphological analysis was conducted with a bright-field microscope, and chemical analysis was performed using Fourier transform infrared spectroscopy (FTIR) before and after controlled washing. Additionally, the washing baths were analyzed by UV-visible spectroscopy to detect PEG. The PCFs displayed a well-defined core-shell structure. Although some PEG 2000 leakage occurred in unsuccessful cuts, cuts at 50 °C showed sealed ends and less material in the baths, making it viable for civil engineering materials.
Optimization Design of Redundant Parallel Posture Adjustment Mechanism for Solar Wing Docking Based on Response Surface Methodology
Authors: Wang Rui , Xiong Xiaoyan , Liang Haoshuo , Zhang Jinzhu
Published: September 2024

The parallel mechanism exhibits high stiffness and excellent dynamic response, making it ideal for high-precision applications. In our early work, a novel 6-DOF redundant parallel posture mechanism with four limbs for solar wing docking has been proposed; each limb consists of three links and four joints. This paper primarily focuses on optimization design of the mechanism. The calculation of workspace volume reveals that factors influencing the range of posture adjustment include dynamic platform parameters, static platform parameters, the drive trajectory of each kinematic pair, and the angles between each kinematic pair. A sensitivity analysis was conducted to examine the impact of each parameter on the range of posture adjustment. To reduce computational complexity and improve analysis efficiency, a combined approach of single-factor analysis and response surface methodology (RSM) is used in the paper. Single-factor analysis is utilized to evaluate the effect of each parameter on the posture adjustment range. Based on these results, RSM is used to establish a regression model for parameters; thereby, the optimal parameter combination for the mechanism is determined. The regression coefficient R2 = 0.9374 attests to the validity of the proposed model. Finally, a comparison of the posture adjustment range before and after optimization is presented, providing a foundation for the practical application of the redundant parallel mechanism. This paper introduces a novel structural design concept aimed at resolving the conflict between heavy loads and compact sizes in redundant parallel mechanisms while providing valuable insights for miniaturized design.
Influence of Temperature on Brushless Synchronous Machine Field Winding Interturn Fault Severity Estimation
Authors: Pascual Rubén , Rivero Eduardo , Guerrero José M. , Mahtani Kumar , Platero Carlos A.
Published: September 2024

There are numerous methods for detecting interturn faults (ITFs) in the field winding of synchronous machines (SMs). One effective approach is based on comparing theoretical and measured excitation currents. This method is unaffected by rotor temperature in static excitation SMs. However, this paper investigates the influence of rotor temperature in brushless synchronous machines (BSMs), where rotor temperature significantly impacts the exciter excitation current. Extensive experimental tests were conducted on a special BSM with measurable rotor temperature. Given the challenges of measuring rotor temperature in industrial machines, this paper explores the feasibility of using stator temperature in the exciter field current estimation model. The theoretical exciter field current is calculated using a deep neural network (DNN), which incorporates electrical brushless synchronous generator output values and stator temperature, and it is subsequently compared with the measured exciter field current. This method achieves an error rate below 0.5% under healthy conditions, demonstrating its potential for simple implementation in industrial BSMs for ITF detection.
Low-Level Laser Therapy for Tooth Sensitivity after Tooth Bleaching: A Systematic Review
Authors: Giannakopoulou Paraskevi , Neophytou Chariklia , Karakostas Panagiotis , Papadimitriou Konstantinos , Dionysopoulos Dimitrios , Tolidis Kosmas , Davidopoulou Sotiria
Published: September 2024

Tooth bleaching is a popular cosmetic procedure known for its effective whitening results. However, it may cause tooth sensitivity. Various desensitizing therapies, including laser treatments, are used to alleviate pain and improve patient comfort. This study aims to conduct a systematic review to evaluate the effectiveness of low-level laser therapy for treating tooth sensitivity following bleaching therapy. A comprehensive search was conducted across 13 electronic databases (PubMed, Scopus, ScienceDirect, Google Scholar, Web of Science, Ovid, BMJ evidence-based medicine, proQuest, Greylit.org, Ethos, Livivo, Clinical trials gov, and Meta register of controlled trials) to identify relevant studies according to specific eligibility criteria, following the PRISMA guidelines. Two independent reviewers screened and selected the studies, performed data extraction, and assessed the risk of bias using the revised Cochrane risk-of-bias tool for randomized clinical trials (RCTs). The initial search yielded 2875 articles, which were subsequently screened to remove duplicates. After evaluating 1532 articles based on title and 136 by abstract, 21 studies remained for full-text assessment. Ultimately, only six RCTs met all of the eligibility criteria. The application of low-level laser therapy appears to reduce tooth sensitivity following tooth bleaching. Despite the positive reported effects, further research is necessary to determine the optimal use of low-level laser therapy for effective pain relief.
Numerical Study of Internal Flow Field in a Disc Stack Centrifuge Based on Mixture-PBM Model
Authors: Dong Hefeng , Wan Ran , Huang Changan , Liu Shoulie , Luo Shamiao , Chen Liangbin , Li Shaobin , Song Xizhen
Published: September 2024

Disc stack centrifuge belongs to one kind of sedimentation centrifuge, widely used in the environmental protection, pharmacy, and chemical industries, etc. The flow process inside the disc stack centrifuge seriously affects the separation efficiency. However, the flow process inside the disc stack centrifuge and its influence on the separation efficiency have not yet been detailed. We plan to study the flow process of oil and water phases inside the disc stack centrifuge and to explore the process of fragmentation and accumulation of water droplets. In this study, the Mixture-PBM (Population Balance Model) model is used to numerically simulate the two-phase flow of oil and water in the disc stack centrifuge and compare it with the tests. The research found that with the increase in rotational speed, the separation efficiency rises in both the test and numerical simulation results, and the difference between the test and simulation results is below 1%. The effect of ribs on the flow is considered, and the results show that the hysteresis of the liquid flow in the disc stack centrifuge is significantly reduced after considering the ribs, and the numerical simulation results can reach 98% of the theoretical results. Fluid entering the separation channel from the neutral pore creates a vortex, and as the dimensionless number λ increases, the degree of deviation of the fluid’s trajectory from the generatrix increases. The circumferential and radial velocities in the separation channel appear large in the center and small near the wall. The water content in the rising channel gradually decreases, and 90% of the water finishes settling in the distributor. The processing volume of the separation channel in each layer shows a small bottom layer, a large top layer, and a uniform law in the middle. The coalescence of water droplets occurs mainly in the separation channel, as found by analyzing the laws of the internal flow of the disc stack centrifuge, which provides the basis for improving the structure of the disc stack centrifuge, increasing the separation efficiency and reducing the floor space.
A Domain-Specific Lexicon for Improving Emergency Management in Gas Pipeline Networks through Knowledge Fusing
Authors: Zhao Xinghao , Hu Yanzhu , Qin Tingxin , Wan Wang , Wang Yudi
Published: September 2024

Emergencies in gas pipeline networks can lead to significant loss of life and property, necessitating extensive professional knowledge for effective response and management. Effective emergency response depends on specialized knowledge, which can be captured efficiently through domain-specific lexicons. The goal of this research is to develop a specialized lexicon that integrates domain-specific knowledge to improve emergency management in gas pipeline networks. The process starts with an enhanced version of Term Frequency–Inverse Document Frequency (TF-IDF), a statistical method used in information retrieval, combined with filtering logic to extract candidate words from investigation reports. Simultaneously, we fine tune the Chinese Bidirectional Encoder Representations from Transformers (BERT) model, a state-of-the-art language model, with domain-specific data to enhance semantic capture and integrate domain knowledge. Next, words with similar meanings are identified through word similarity analysis based on standard terminology and risk inventories, facilitating lexicon expansion. Finally, the domain-specific lexicon is formed by amalgamating these words. Validation shows that this method, which integrates domain knowledge, outperforms models that lack such integration. The resulting lexicon not only assigns domain-specific weights to terms but also deeply embeds domain knowledge, offering robust support for cause analysis and emergency management in gas pipeline networks.
Intercomparison of Machine Learning Models for Spatial Downscaling of Daily Mean Temperature in Complex Terrain
Authors: Bhakare Sudheer , Dal Gesso Sara , Venturini Marco , Zardi Dino , Trentini Laura , Matiu Michael , Petitta Marcello
Published: September 2024

We compare three machine learning models—artificial neural network (ANN), random forest (RF), and convolutional neural network (CNN)—for spatial downscaling of temperature at 2 m above ground (T2M) from a 9 km ERA5-Land reanalysis to 1 km in a complex terrain area, including the Non Valley and the Adige Valley in the Italian Alps. The results suggest that CNN performs better than the other methods across all seasons. RF performs similar to CNN, particularly in spring and summer, but its performance is reduced in winter and autumn. The best performance was observed in summer for CNN (R2 = 0.94, RMSE = 1 °C, MAE = 0.78 °C) and the lowest in winter for ANN (R2 = 0.79, RMSE = 1.6 °C, MAE = 1.3 °C). Elevation is an important predictor for ANN and RF, whereas it does not play a significant role for CNN. Additionally, CNN outperforms others even without elevation as an additional feature. Furthermore, MAE increases with higher elevation for ANN across all seasons. Conversely, MAE decreases with increased elevation for RF and CNN, particularly for summer, and remains mostly stable for other seasons.
Impact of Chronic Beryllium Exposure on Liver and Lung Function and Hematologic Parameters
Authors: Dai Jing , Bi Xinlin , Yuan Hui , Meng Qingyu , Yang Yina , Wang Xueqin , Ma Xiaoying , Ding Chunguang , Wang Fen
Published: September 2024

Beryllium is a lightweight metal that is toxic to humans. The critical health effects related to beryllium exposure are liver toxicity, immune system toxicity, and chronic beryllium disease (CBD). This study investigated the effects of occupational beryllium exposure on liver and lung function and hematologic parameters among beryllium smelter workers. A cross-sectional study was performed by comparing 65 exposed workers and 34 non-exposed workers. Health information was collected through questionnaire surveys and biochemical tests. The concentration of urinary beryllium was determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The findings indicated that the urinary beryllium levels of the exposed workers and the controls were 0.48 (0.115, 1.19) μg/mL and 0.0125 (0.005, 0.005) μg/mL, respectively (p < 0.001). Compared with the controls, the exposed workers showed a significant increase in serum alanine aminotransferase (ALT) level, hemoglobin (HGB) concentration, white blood cell (WBC) count, red blood cell (RBC) count, and systolic and diastolic blood pressure (SBP, DBP) (p < 0.05). Furthermore, the HGB concentration and ALT level were significantly correlated with the concentration of beryllium in urine (p < 0.05). The exposed workers had increased urinary concentrations of beryllium, in contrast to the control subjects. Moreover, the urinary beryllium levels among the exposed workers are much higher than that in the Chinese general population. Beryllium-exposed workers may be at risk of liver and hematologic impairments.
Study on Downscaling Correction of Near-Surface Wind Speed Grid Forecasts in Complex Terrain
Authors: Liu Xin , Li Zhimin , Shen Yanbo
Published: September 2024

Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and October 2022, employing the random forest algorithm to establish and evaluate a downscaling correction model for near-surface 1 km resolution wind-speed grid forecast in the complex terrain area of northwestern Hebei Province. The results indicate that after downscaling correction, the spatial distribution of grid forecast wind speeds in the entire complex terrain study area becomes more refined, with spatial resolution improving from 3 km to 1 km, reflecting fine-scale terrain effects. The accuracy of the corrected wind speed forecast significantly improves compared to the original model, with forecast errors showing stability in both time and space. The mean bias decreases from 2.25 m/s to 0.02 m/s, and the root mean square error (RMSE) decreases from 3.26 m/s to 0.52 m/s. Forecast errors caused by complex terrain, forecast lead time, and seasonal factors are significantly reduced. In terms of wind speed categories, the correction significantly improves forecasts for wind speeds below 8 m/s, with RMSE decreasing from 2.02 m/s to 0.59 m/s. For wind speeds above 8 m/s, there is also a good correction effect, with RMSE decreasing from 2.20 m/s to 1.65 m/s. Selecting the analysis of the Zhangjiakou strong wind process on 26 April 2022, it was found that the downscaled corrected forecast wind speed is very close to the observed wind speed at the station and the ground truth grid points. The correction effect is particularly significant in areas affected by strong winds, such as the Bashang Plateau and valleys, which has significant reference value.
Abnormal Vestibulo–Ocular Reflex Function Correlates with Balance and Gait Impairment in People with Multiple Sclerosis
Authors: Tramontano Marco , Casagrande Conti Laura , Orejel Bustos Amaranta Soledad , Ferri Nicola , Lelli Tommaso , Nocentini Ugo , Grasso Maria Grazia , Turolla Andrea , Pillastrini Paolo , Manzari Leonardo
Published: September 2024

Background: Multiple Sclerosis (MS) is the most prevalent autoimmune neurological condition in the world, leading to a wide variety of symptoms, including balance disorders. Objective: To evaluate the angular vestibulo–ocular reflex (aVOR) of all six semicircular canals (SCCs) through Head Impulse (HIMP) and Suppression HIMP (SHIMP) paradigms and any correlations with clinical balance scales. Methods: All participants were assessed using the Expanded Disability Status Scale (EDSS), Berg Balance Scale (BBS), and Mini-BESTest (MBT). Vestibular function was measured by video Head Impulse Test (vHIT), obtaining aVOR gain for each SSC. Results: Twenty-seven PwMS (mean age 47.93 ± 8.51 years old, 18 females) were recruited. Most of the patients (81.48%) presented abnormal aVOR gains for at least one SSC. A moderate to strong correlation between aVOR gains of the left anterior SSC and, respectively, the MBT and the BBS was found. The subgroup analysis, based on the EDSS class, confirmed the correlation with the BBS in the patients with the most significant disability. Conclusions: People with MS may present impairments of the aVOR in one or more semicircular canals. The aVOR gain impairment of the vertical semicircular canals correlates with balance and gait disorders identified through clinical scales in PwMS.
F-Bipolar Metric Spaces: Fixed Point Results and Their Applications
Authors: Alamri Badriah
Published: September 2024

The aim of this research article is to broaden the scope of fixed point theory in F-bipolar metric spaces by introducing the concept of rational (⋏,⋎,ψ)-contractions. These new contractions allow for the formulation of fixed point theorems specifically designed for contravariant mappings. The validity of our approach is substantiated by a meticulously crafted example. Moreover, we explore the practical implications of these theorems beyond the realm of fixed point theory. Notably, we demonstrate their effectiveness in establishing the existence and uniqueness of solutions to integral equations. Additionally, we investigate homotopy problems, focusing on the conditions for the existence of a unique solution within this framework.
An End-to-End Scene Text Recognition for Bilingual Text
Authors: Albalawi Bayan M. , Jamal Amani T. , Al Khuzayem Lama A. , Alsaedi Olaa A.
Published: September 2024

Text localization and recognition from natural scene images has gained a lot of attention recently due to its crucial role in various applications, such as autonomous driving and intelligent navigation. However, two significant gaps exist in this area: (1) prior research has primarily focused on recognizing English text, whereas Arabic text has been underrepresented, and (2) most prior research has adopted separate approaches for scene text localization and recognition, as opposed to one integrated framework. To address these gaps, we propose a novel bilingual end-to-end approach that localizes and recognizes both Arabic and English text within a single natural scene image. Specifically, our approach utilizes pre-trained CNN models (ResNet and EfficientNetV2) with kernel representation for localization text and RNN models (LSTM and BiLSTM) with an attention mechanism for text recognition. In addition, the AraElectra Arabic language model was incorporated to enhance Arabic text recognition. Experimental results on the EvArest, ICDAR2017, and ICDAR2019 datasets demonstrated that our model not only achieves superior performance in recognizing horizontally oriented text but also in recognizing multi-oriented and curved Arabic and English text in natural scene images.
Chemometric Approach Application in Modern Wine Studies
Authors: Fikselová Martina , Benešová Lucia , Jakabová Silvia , Mezey Ján , Čapla Jozef , Golian Jozef
Published: September 2024

The wine market is affected by the origin of wines, but the current wine traceability system has some limitations. The idea of geographical authenticity and quality has increased as one of the most important parameters influencing consumers’ preferences. Chemical parameters such as total polyphenolic content (TPC), alcohol and organic acid content (total acids, malic and tartaric acids), and antioxidant activity were observed in this work at 15 dry white wines of varieties Pinot blanc, Riesling, Sauvignon blanc, and Chardonnay, which originated from three different countries. FTIR and spectrophotometry methods (TPC and DPPH) were used and chemometric approaches such as ANOVA and PCA were selected as the most important for evaluation. In general, high levels of malic acid, TPC, and antioxidant activity were detected in Austrian wines. Pinot blanc, Chardonnay, and Sauvignon from the same producing region in Austria showed higher results. The higher overall acid concentration was a distinguishing feature of the Slovakian Sauvignon and Riesling varieties, particularly in the case of tartaric acid. Austrian samples showed significantly higher antioxidant activity compared to Hungarian Chardonnay (p = 0.002) and Sauvignon (p = 0.007), comparable to TPC. The application of statistical analysis was useful in demonstrating many discrepancies, particularly by application of the PCA method.
Axonal Regeneration after Spinal Cord Injury: Molecular Mechanisms, Regulatory Pathways, and Novel Strategies
Authors: Elmalky Mohammed Ibrahim , Alvarez-Bolado Gonzalo , Younsi Alexander , Skutella Thomas
Published: September 2024

Axonal regeneration in the spinal cord after traumatic injuries presents a challenge for researchers, primarily due to the nature of adult neurons and the inhibitory environment that obstructs neuronal regrowth. Here, we review current knowledge of the intricate network of molecular and cellular mechanisms that hinder axonal regeneration, with a focus on myelin-associated inhibitors (MAIs) and other inhibitory guidance molecules, as well as the pivotal pathways implicated in both inhibiting and facilitating axonal regrowth, such as PKA/AMP, PI3K/Akt/mTOR, and Trk, alongside the regulatory roles of neurotrophins and axonal guidance cues. We also examine current insights into gene therapy, tissue engineering, and pharmacological interventions that show promise in overcoming barriers to axonal regrowth.